TW200303275A - Automatic train operation device and train operation assisting device - Google Patents

Abstract

The objective of this invention to reduce the energy loss generated during train operation, so as to conserve energy for train operation. In this invention, an automatic train operation device is able to generate an operation mode causing the train to stop at a designated location at a designated time, and providing a thrust force command to a driving brake device of an electromotive machine to carrying the operation mode. This invention comprises: a loss indication calculating means (16) for calculating the loss indicator representing the energy loss generated during operation of train; and an operation mode compensating measure (19) for compensating mode based on the loss indicator to reach the special value causing the train to stop at a designated location until a designated time.

Description

200303275 (1) Description of the invention [Technical field to which the invention belongs] The present invention relates to an automatic train operating device for automatically operating a tram without stopping it at a specific position at a specific time by a driver, and to instruct a driver to suggest a thrust Or the train operation support device of the main control level. [Prior art] An automatic train operating device (hereinafter referred to as "ΑΤΟ") is a system that automatically implements train-to-station operation and stops the train at a specific parking position at the next station at a specific time. Fig. 47 shows an example of a system configuration of a tram having such an ATTO. The automatic train control device (ATC) not shown in the figure will input the speed limit signal to the automatic train operation device 1, and the database 3 will input the route conditions such as the slope and curvature, the vehicle conditions, and the operation schedule to the automatic train operation device 1. , And storage information such as driving resistance. In addition, the automatic train operating device 1 estimates the current vehicle position based on the vehicle position detected by the ground sub-detector 10 and the vehicle speed detected by the speed detector 9, and inputs a thrust command F cmd to the driving brake device 2 to indicate The thrust should be provided at that point. At this time, the thrust command F cmd in this manual is defined as both including the traction command when the vehicle is accelerating and the braking force command when the vehicle is decelerating. Thrust command is F cmd > 0 for traction and thrust command F cmd < 0 for braking force. The drive braking device 2 is composed of a VVVF (variable voltage, variable frequency) variable frequency and variable voltage inverter 4, a main motor 5, a brake control device 6, and a mechanical brake (2) (2) 200303275 car 8. The main motor 5 is mechanically connected to the wheels 7 running on the track 11 and the mechanical brake 8 is configured so that the wheels 7 can be mechanically braked. The action from the thrust command F cmd until the actual thrust is obtained is different when the traction is obtained and when the braking force is obtained. When traction is obtained, the thrust command F cmd (> 0) is input to the frequency conversion transformer inverter 4. The frequency conversion transformer inverter 4 will control the torque of the main motor 5 so as to obtain the traction force corresponding to the thrust command F c m d. At this time, the brake control device 6 and the mechanical brake 8 will not perform an operation. When the braking force is obtained, the thrust command F cmd (< 0) is input to the braking control device 6 instead of the inverter transformer 4. First, the brake control device 6 outputs a thrust command, that is, a braking force command, to the frequency conversion transformer inverter 4. The frequency conversion transformer inverter 4 feeds back the electric braking force F elec outputted from the main motor 5 to the brake control device 6. In order to obtain the thrust command F cmd, which is the braking force of the braking force command, the brake control device 6 first makes the electric braking force F elec effective, and uses the mechanical braking force F mech of the mechanical brake 8 to make up for the lack of electric braking force. Partial control of mechanical brakes 8. Therefore, the mechanical braking force F mech is shown below. F mech = F cmd-F elec (1) As shown in FIG. 4 8, the automatic train operating device 1 has a tentative driving planning unit 1 2, an optimal driving planning unit 1 3, and a thrust command generating unit i 4 . The tentative driving planning section 12 generates a tentative driving mode (F0 (X), V0 (X)) as an initial target for the purpose of generating an optimal driving mode. At this time, the driving mode represents the position on the route in a manner corresponding to a series of positions. -8- (3) (3) 200303275 x Thrust Fn (x) and speed Vn (x). The optimal driving planning department 13 will plan the optimal driving mode FI (X) of the train according to the tentative driving mode (F0 (X), V0 (X)) and the storage information of the database 3. Under the generated optimal driving mode FI (X), the thrust command generating unit 14 will output a thrust command F cmd to the variable frequency transformer inverter 4 according to the train's detection position, detection speed, and AT C's speed limit signal. , Indicates the thrust that should be output at that time. When planning the best driving mode of a train ’Generally speaking, there are countless possible driving modes. In particular, unlike the morning and evening dense schedules, during the day, morning, or late night when the number of trains running is small, there is a large margin in the plan due to the long interval between trains. There are also fewer restrictions. Japanese Patent Application Publication No. 8-2 1 6 8 8 5 and Japanese Patent Application Publication No. 5- 1 93 5 02 describe the best driving plan based on energy conservation. However, the energy saving of these known examples is not considered from the standpoint of energy loss caused by the drive / brake control of trains and brakes. On the other hand, "Basic Review of the Effective Utilization of Regenerative Energy by Changing the Brake Mode" (The 7th meeting of the Japan Railway Technology Joint Conference), "Review of the Practical Application of Pure Electric Brake" (National Conference of the Japanese Electrical Society 5 _ 244), review the brake control of the train, especially the driving mode of the energy loss caused by mechanical braking when braking. However, the energy loss caused by the driving brake control of a train will also occur during the driving control. In addition to the mechanical braking, other factors will cause the energy loss during the braking control. Therefore, it is not possible to achieve a minimum of -9- (4) (4) 200303275. [Problems to be Solved by the Invention] The object of the present invention is to comprehensively evaluate the energy loss caused during the braking control of the train driving, and to reduce the energy loss of inter-station driving as much as possible 'to realize energy-saving driving. Therefore, a brief description of the energy loss focused on the practice of the present invention will be described below. The losses caused by train operation will change due to the mode of operation, and the machines that may cause losses can be divided into the following two categories. One of them is the energy loss of electric equipment such as the inverter transformer 4 of the driving device and the main motor 5. These losses can be expressed as a function of thrust and speed. The second is the energy loss caused by mechanical braking. When observing the acceleration and deceleration of the train from the perspective of energy flow, and ignoring the aforementioned energy loss and driving resistance of the electric machine, during the acceleration of the operation, the frequency conversion inverter 4 and the electric motor are driven by the unmarked cables. The electric energy provided by the 5th class driving device will be converted into the motion energy of the vehicle, and in the deceleration using the electric brake, the motion energy of the vehicle will be converted into electric energy and the power will be regenerated. In this ideal state ', no energy loss is caused. However, when using electric brakes to decelerate, if AT or the driver's braking force command exceeds the braking force output by the electric machine, the insufficient braking force will be compensated by the mechanical brake 8 to maintain the deceleration at a certain level. When the mechanical brake 8 performs this action, the motion energy of the vehicle will be consumed thermally, and this is the energy loss. In the present invention, the part of the loss caused by the mechanical braking operation is defined as the braking loss. -10-(5) (5) 200303275 This braking loss occurs when the braking force command exceeds the allowable amount of the electric machine, that is, the driving device, and there is no load corresponding to the regenerative power on the power supply side. In the latter aspect, if the driving device obtains the braking force command, it will control the inverter transformer 4 so that the main motor 5 outputs the braking force corresponding to it. At this time, the vehicle's motion energy will be converted into the regenerative energy of the power supply. However, when there is no load corresponding to this regenerative power on the power supply side, that is, if there is no accelerating train, excess regenerative power will be generated, which will lead to wiring. The voltage rises. Therefore, in order to suppress the increase of the overhead voltage, the driving device performs a control to suppress the braking force. This is called light load regeneration control. During this light load regeneration control operation, the main motor 5 outputs a braking force that is less than the braking force command. At this time, the insufficient braking force is compensated by the braking force of the mechanical brake 8. When implementing energy-saving operation, it is important to plan a suitable driving mode to calculate the day and actually execute driving based on the driving mode. Means for achieving operation consistent with the driving mode, such as an automatic train operating device (ATO) and an automatic train stopping device (TASC), which are capable of automatically generating a thrust instruction without passing through the driver, are well known. With these devices, it is possible to smoothly supply the actual thrust and achieve the best driving mode. However, the system is very complicated and costly because it directly targets the vehicle's driving and braking device and requires ground equipment for the purpose of position detection. On the other hand, “Using the thrust of instructing the driver to plan the best plan, and using the skills of driving tribute”, one can expect to achieve a train close to the planned driving mode. This is the operation support device. When this kind of operation support device is used, although its energy saving effect is better than when using the driver ’s response delay, etc.-11-(6) (6) 200303275 ATTO and TASC, however, it is only necessary to instruct the driver There is no direct relationship with the driving and braking device of the vehicle, so it has the advantage of simplifying the system. In addition, since it relies on the operation of the driver, the above-ground equipment for the purpose of position detection can be removed or simplified. In this way, you can reduce the cost and get better cost efficiency. In addition, in recent years, it is feared that the driving skills of the driver will be reduced due to AT AT. Therefore, when using the operation support device, the thrust must be adjusted according to the driver's judgment at any time, so there is no problem in reducing the driving skills. ® In addition, the automatic train operating device has been put into practical use to track the speed limit of the train and the speed limit with a certain degree of margin to the speed limit. However, since the error-following control such as pi control is the main body, there are many places that rely on the characteristics of trains and routes. In the current situation, the operation of adjusting the characteristics or control parameters of each train and route requires a huge amount of work. Time and labor. In addition, it is also possible to prepare a driving plan and an automatic train operating device for performing train driving based on the plan. When planning a driving plan, ® Easy Train Model is sometimes used. The simplest is the method of expressing the train operation of its target by the following simple physical formula. F— Fr = M · a ... (7) At this time, the F series is running traction or braking force, the driving resistance of the Fr series cars, the weight of the M series cars, and the α series acceleration (including negative acceleration and deceleration) ). Train running resistance The resistance generated by the Fr series cars during driving. For the convenience of calculation, usually only the following resistances are considered. Departure resistance: Resistance at departure -12- (7) (7) 200303275 Air resistance: Slope resistance of air resistance when the train is running: Slope resistance curve resistance of the route: Curve resistance of the route Tunnel resistance: Generated when traveling in a tunnel The air resistance is usually a quadratic formula of speed if the resistance between the wheel tread and the track surface is considered. Generally speaking, the train running resistance Ft * is usually considered for the resistance composed of slope resistance, air resistance, curve resistance, tunnel resistance, and starting resistance. Here, it is considered when the train is traveling outside the tunnel, so only slope resistance, air resistance, and curve resistance are considered. At this time, the slope resistance, air resistance, and curve resistance can be obtained by the following formulas (8), (9), and (10), respectively (for example, refer to the document "Operation Theory (DC AC Electric Vehicle)" Dating Agency Ed.). (a) Slope resistance

Frg = s ... (8)

Frg: slope resistance [kg weight / ton] s: slope [% G] (positive when going uphill, negative when going downhill) (b) air resistance

Fra = A + Bv + Cv2 (9)

Fra: air resistance [kg weight / ton] A, B, C: coefficient v: speed [km / h] (c) curve resistance formula

Frc = 800 / r… (10) (8) (8) 200303275

Frc: Curve resistance [kg weight / ton] r: Curvature radius [m] If the model shown in formula (7) is used for automatic train operation, even if it is the automatic train operation mode based on the traffic schedule, train characteristics and route characteristics, etc. Features also have a significant impact on ride comfort and stopping accuracy. [Summary of the Invention] [Means to Solve the Problem] The present invention is based on the premise that a train stops at a specific position at a specific time when traveling between stations, and its purpose is to provide an automatic train operation device and a train operation support device, which can reduce The energy loss caused by driving can realize energy-saving operation. In addition, the object of the present invention is to provide an automatic train operating device, which can reduce the time and labor necessary for adjustment, and can also automatically perform the learning of characteristics after the business operation, which can further improve the riding comfort and improve the situation. Stop accuracy. Furthermore, the object of the present invention is to provide a device that performs necessary data collection operations for the purpose of operating the device only when a train travels to and from a specific route. In addition, the purpose of the present invention is to provide an automatic train operating device, which can realize: firstly, the influence of chasing during automatic operation of the train is eliminated as much as possible to improve the energy saving effect; secondly, the delay time can be obtained, Improving the stopping accuracy of the target position. Thirdly, it can improve the poor riding comfort caused by the phase change of the speed control instruction when performing the level operation. -14-(9) (9) 200303275 Also, the object of the present invention is to provide an automatic control device for fixed-position stop of trains, which can ensure stop accuracy without frequent switching of levels, and does not require a long adjustment period. In order to achieve the above object, the present invention generates a driving mode for the purpose of stopping a train at a specific position at a specific time, and provides a driving brake device for an electric device including a frequency conversion transformer inverter and a main motor. The thrust instruction for the purpose of implementing the current vehicle mode has the following characteristics: a calculation method of a loss index that calculates a loss index that represents the energy loss caused by the driving brake device in the train yad car; and based on the foregoing loss index, it is low in the future. The first driving mode compensation means for compensating the aforementioned driving modes for the purpose of energy loss. In addition, in order to achieve the above object, the automatic train operating device of the present invention is characterized by having data processing means for online processing of acquired train driving data, based on the train driving data acquired by using this data processing means, and previously acquired data, Automatically learns the control parameters of the train while it is running, and the automatic characteristics learning means of train characteristics and route characteristics; and uses the train characteristics and route characteristics learned by this automatic feature learning method to perform the automatic operation of the train. Means of operation. In addition, in order to achieve the above-mentioned object, the automatic train operating device of the present invention is characterized by having a train characteristic learning means for collecting information on train characteristics and route characteristics during train operation, and a train-related information collected based on the aforementioned train characteristic learning means, Automatic train operation means that calculates the optimal operation mode of the train and performs the automatic operation of the train according to this mode. In addition, in order to achieve the above object, the automatic train operating device of the present invention -15- (10) (10) 200303275 is based on the train detection position, the train detection speed, the operating characteristic data stored in the database, and the automatic train control device. The input of operating conditions to control the driving device or braking device of the train to perform automatic operation is characterized in that it has: the aforementioned calculation circuit is implemented when the train stops and performs specific calculations; and the aforementioned train is operated between stations Implementing calculation circuits when driving between stations that implement specific calculations or controls; and, the implementation of the calculation circuits when stopping at a stop has the best driving plan formulation method for planning the best driving plan. When the train stops at a station, the aforementioned The train stops at the target position of the next parking station at the target time. The operation circuit implemented when the train is running between the stations has the following: The train starts from the previous station and executes the best driving plan prepared according to the aforementioned best driving plan formulation method. During this period, if the error between the best driving plan and the actual driving result is specified At the time, the driving plan recalculation means of recalculating the driving plan will be implemented; the control instruction precipitating means of the driving plan recalculation from the foregoing driving plan recalculating means; and the aforementioned control instruction precipitating means The control command is output to the control command output means of the aforementioned driving device or braking device. In addition, in order to achieve the above-mentioned object, the train fixed-position stop automatic control device of the present invention automatically stops the train at a specific position, which is characterized by storing deceleration of each brake level of the train, a delay time of brake level switching, and "Brake characteristic data storage unit" for response characteristics such as response delay time; obtain current train speed, current position, current brake level, etc. "train current data acquisition means"; based on the information stored in "brake characteristic data storage unit" The brake characteristics data and the current data of the train obtained by "train now -16- (11) (11) 200303275 data acquisition means", a "deceleration control plan for the purpose of stopping the train at a specific position by a plurality of brake levels" is formulated. "Deceleration control plan preparation means"; "Deceleration control instruction extraction means" which extracts deceleration control instructions at various points from the deceleration control plan prepared by "Deceleration control plan preparation means"; and The deceleration control command is output to the "deceleration control Output means. " [Embodiment] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Fig. 1 is a block diagram showing a schematic configuration of the automatic train operating device of the first embodiment. Therefore, the implementation system is particularly relevant to the optimal travel planning department of the automatic train operating device, and the illustration of other parts is omitted. The best driving planning unit 13 shown in FIG. 1 is composed of a driving mode compensation index computing unit 15, a driving mode compensation unit 19, a driving distance compensation unit 20, and a timing judgment unit 21. The driving mode compensation index calculation section 15 is composed of a loss index calculation section 16, an overload index calculation section 17, and an adder 18. The loss index calculation unit 16 calculates a loss index CPL (X) of the train position X based on the tentative driving mode (F0 (X), V0 (X)). At this time, CPL is Cost of Power Loss. At this time, the driving mode is expressed by the thrust Fn (x) and the speed Vn (x) at a certain position X. Figures 2 and 3 are examples of various loss indicators. Figure 2 shows the loss index during operation, and Figure 3 shows the loss index during braking deceleration. In more detail, Figure 2 (a) is the machine loss index, Figure 2 (b) is the total -17- (12) (12) 200303275 meter loss index, and Figure 3 (a) is the machine loss index Figure 3 (b) is the braking loss index, and Figure 3 (c) is the total loss index. Here, the machine loss index refers to the loss index of the electric equipment, specifically, the loss index of the converter (frequency conversion transformer inverter) and the loss index of the motor (main motor) are added. These indicators are expressed as a function of speed v and thrust F, and are calculated by multiplying the loss [W] of a certain operating point (v, F) by the inverse of speed [m / s]. Multiplying by the reciprocal of the speed, a formal assessment of the loss caused by the speed vl [m / s] at a certain operating point △ v [m / s] can be carried out. The calculation of the total loss index CPL (X) is calculated by multiplying the total of the machine loss index and the brake loss index by the weighting factor W 1. The weighting factor W 1 is set from the viewpoint of the degree of loss reduction effect that can be obtained, or is set in a way that balances with other indicators. That is, the loss index CPL (X) = Wlx (machine loss index + brake loss index) ... (2) The overload index calculation unit I7 will calculate the train based on the tentative driving mode (F0 (X), V0 (X)) Overload indicator COL (X) at position X. COL is Cost of Over Load 〇 Machine loss is the sum of converter loss and motor loss. Fig. 4 (a) and (b) are examples of converter loss [W] and motor loss [W] at each operating point. According to the tentative driving mode (F0 (X), VO (X)), the corresponding converter loss [W] and motor loss [W] are respectively integrated, and can be calculated (13) (13) 200303275 plus inter-station driving Converter loss [J] and motor loss [J]. If it is an overload exceeding the specification 値 [W], the corresponding overload index is calculated. For example, if the weighting factor is W2, the converter loss index COLC (X) can be C Ο LC (X) = W2x {converter loss [J] / (travel time + stop time)-a converter specification [W]} x converter loss index (Figure 5 (a)) ... (3). COLC is Cost of Over Load in Converter. Similarly, the motor loss index COLM (X) can also be obtained using the motor loss index shown in Figure 5 (b) (however, the weighting factor is W3, which can be set independently). The overload index C 0 L (X) can be added to these indexes, and it can be obtained by COL (x) × COLC (x) + COLM (x) ... (4). COLM is Cost of Over Loss in Motor 〇Additional benefit 18 will add the CPL (x) and overload index COL (x), and C (x) = CPL (x) + COL (x) (14) (14) 200303275 to find the total index C (x) of the train position x. The driving mode compensation unit 19 adds the total index C (X) to the thrust mode F 0 (X) of the tentative driving mode, and outputs the first compensated driving mode f 0 1 (x) (at this stage, the speed mode VO ( X) will not change). The first compensated driving mode F 0 1 (X) is a compensator 'that implements only the thrust mode, so the driving distance and the specific distance do not match. In order to make the driving distance x consistent with the specific distance, the driving distance compensation unit 20 will implement the first compensation driving mode (F01 (X), V0 (x)) based on the route conditions, vehicle conditions, and driving resistance stored in the database 3. Compensation, and output the second compensation driving mode (F02 (X), V02 (X)) and driving time T run. Distance compensation can be achieved by, for example, adjusting the taxi time. However, the distance compensation method is not limited to this. The timing judgment unit 21 judges whether or not the travel time T run is within the allowable error for a specific condition. When the running time T run is outside the tolerance, the second compensation driving mode (F〇2 (X), V〇2 (X)) will be regarded as the new tentative driving mode (F0 '(X), V0' (X )), Re-execute the calculation. When the travel time T run is within the allowable error, it is output as the optimal travel mode (Fl (x), Vl (x)). With the above configuration, the tentative driving mode (F 0 (X), VO (X)) can make use of the loss index CPL (X) and the overload index COL (X) to obtain significant compensation for the thrust of position X. For example, Fig. 6 is a result of a driving mode according to an embodiment of the present invention. At this time, it is assumed that the overload state has not been reached, so the overload index has no effect. The "Original Mode (A)" shown is temporarily -20-(15) (15) 200303275 fixed mode. The "applicable index (B)" indicates the first compensation driving mode (F01 (X), V01 (X)). The higher the loss index is, the higher the thrust compensation is, the weaker the braking force is. On the other hand, although the acceleration side of the operation is small, it will also implement traction compensation according to the loss index. "Level quantization (C)" Although the embodiment of the present invention does not appear, the level is only 6 levels, which corresponds to the time when continuous thrust cannot be output. For the thrust F0 1 (X) of the first compensation driving mode, the corresponding selection is made. Thrust with minimum thrust error. The "distance adjustment (D)" is the second compensation driving mode (F02 (X), V02 (X)) that compensates for the "range quantization (C)" mode to make the driving distance to a specific range of 1300m. Compared to the loss of the driving mode before compensation of 2070 [kJ], the loss of the second compensation driving mode is 1650 [kJ], which reduces considerable energy loss. In terms of travel time, compared with the former of 84.5 [seC], the latter is an increase of 8 4.9 [sec]. By repeating the calculation until the travel time becomes a certain threshold, the optimal driving mode F 1 (X) that minimizes the energy loss in the driving brake control can be generated while ensuring the timing and fixed stop. With this method, the best energy saving effect can be achieved while ensuring regularity and fixed stop. In the driving mode that only seeks to minimize the total energy loss, it may increase the energy loss caused by driving electric equipment such as the frequency conversion transformer inverter 4 (converter) and main motor 5 (motor) included in the braking device 2. . The operating range of electric machines will be limited by the specifications. When the operating conditions exceed the specifications, that is, overload conditions, the temperature will rise due to heat generation, and the protection operation will start, or failure or burnout will occur. The overload index calculation unit 17 will judge the overload degree of each machine against the -21-(16) (16) 200303275 tentative driving mode. When the judgment result is overload, the purpose is to suppress the energy loss of electric equipment, and the thrust compensation is implemented in accordance with the overload index. Because thrust compensation is implemented from areas with large energy losses, overload conditions can be effectively avoided. Using this method, it is possible to avoid the operation stop and failure of electric equipment due to overload, and improve the reliability of the system. ○ Because the optimal driving plan is also implemented during the train operation, the position and speed at each instant can be used as the initial conditions. And, in the case of ensuring the timing and stop at the fixed position until the next stop, the best energy-saving driving mode is generated. In other words, when the driving mode deviates from the original driving mode due to the speed limit of ATC, etc., the best energy-saving driving mode can be obtained from this state. Reluctantly following the original driving mode may lead to increased losses, which is inconsistent with the viewpoint of energy loss. Therefore, even if an unexpected situation deviates from the original driving mode, the best energy-saving driving can be realized from that point of time. This embodiment uses the position and speed as the initial conditions. When the timing and stop at the fixed position are ensured, the optimal energy-saving driving mode is generated. Therefore, it can not only be applied to the implementation of automatic trains between stations. The automatic train operation device (AT Ο) that is in operation can also be applied to a train automatic stop control device (TASC) that implements fixed-position parking control only in the braking section. In addition, the present embodiment is based on the premise that the driving distance is consistent with the specific train, and its structure is to perform a calculation of the driving mode compensation until the driving time reaches a specific train. On the contrary, the configuration can also make the driving time The premise is the same as that of the specific train, and until the driving distance reaches the specific train, the calculation of -22- (17) (17) 200303275 driving mode is implemented. FIG. 7 is a block diagram of a schematic configuration example of the automatic train operating device of the second embodiment. The same parts as those in FIG. 1 are given the same symbols and their descriptions are omitted. Here, the parts different from those in FIG. For explanation. The database 3 inputs the operation schedule to the loss index calculation unit 16, and the database 36 inputs the operation load amount to the loss index calculation unit 16. The running load stored in the database 36 is the running load of the train during acceleration at a certain time. The loss index calculation unit 16 will extract the corresponding operating load from the database of the operating schedule and the operating load. As mentioned earlier, because the magnitude of the brake loss varies with the operating load, a loss index corresponding to the amount of operating load is calculated. Others are the same as in Figure 1. From the above, the following actions and effects can be obtained. Correspond to the predicted operating load, adjust the loss index CPL (X), especially the brake loss index. For example, Figure 3 (b) shows the braking loss index when the load is fully running. Because of the limitation of the capacitance of the inverter transformer 4, the higher the high-speed and high braking force, the larger the loss index will be. Figure 8 shows the braking loss index when there is no full running load (125kW / main motor). At this time, due to insufficient running load, it is an area where electric braking force equal to the thrust command F cmd cannot be output. That is, the loss index starts to increase from lower speeds. Therefore, the energy loss caused by the load condition can be accurately predicted, and more efficient energy-saving driving can be realized. Fig. 9 is a block diagram of a schematic configuration example of the automatic train operating device of the third embodiment. The same parts as those in Fig. 16 are attached with the same symbols, and descriptions of -23- (18) (18) 200303275 are omitted. , Only the different parts are described here. The apparatus of FIG. 9 is provided with a database 34 and a driving mode precipitation section 35 'to replace the tentative driving planning section 12 and the optimal driving planning section 13 of FIG. The database 34 stores the driving mode of each train at each station. The driving mode analysis unit 35 will generate a driving mode F 1 (X) corresponding to the current driving between stations from the database 3 in which the operating timetable is stored. The driving mode stored in the database 34 can be realized by the following method, that is, the optimal driving mode shown in the first embodiment is implemented in advance, and the optimal driving mode of the result is stored. With the above structure, the following effects and effects can be achieved. The optimal driving mode is generated because the convergence calculation is repeatedly performed to execute the optimal plan, so it takes some time to calculate. For this reason, when the plan for the next stop is implemented while parking at the starting station, the optimal time may not be sufficient because the calculation time is limited. Implementing these plans in advance can avoid the limitation of calculation time and obtain the best driving mode. In this way, the effect of energy saving can be further improved. In addition, the driving mode is calculated in advance, so that the driving mode can be accurately confirmed. With this method, abnormal patterns can be eliminated and the reliability of the system can be improved. Fig. 10 is a block diagram of a schematic configuration of a tram system having a train operation support device of a fourth embodiment. The same parts as those in Figs. Be explained. Here, a train operation support device 22 is provided instead of the automatic train operation device 1 of the first embodiment. The train operation support device 22 performs the same processing as the automatic train operation device 1 of the first embodiment, and generates and outputs -24- (19) (19) 200303275 Thrust recommendation 値 Free. That is, the train operation support device 22 outputs a thrust suggestion 値 Ffec instead of the thrust command F cmd of the automatic train operation device 1. This thrust suggestion 値 Free is input to a thrust indicating device 24 provided in the main controller 23. The main controller 23 outputs a thrust command F cmd corresponding to the angle or position of the main controller to the driving brake device 2. A configuration example of the thrust indicating device 24 is shown in FIG. 11. The thrust instruction device 24 is composed of an angle command calculation unit 25, an impedance controller 26, a servo amplifier 27, a servo motor 28, and an encoder 29. The servo motor 28 and the main controller 23 are mechanically connected. The thrust recommendation 値 Free output from the train operation support device 22 is input to the angle command calculation unit 25. The angle command calculation unit 25 calculates the main controller angle of the thrust recommendation 値 Free corresponding to the input, and outputs it as the angle command θ cmd. The impedance controller 26 will input the angle command Θ cmd and the actual main controller angle Θ detected by the encoder 29, and output it to the servo amplifier 27 to make the latter (angle Θ) and the former (angle command Θ cmd) match Torque command T cmd for the purpose. The servo amplifier 27 drives the servo motor 28 in such a way that the output torque of the servo motor 28 and the torque command T cmd are consistent. The impedance controller 26 will respond to the driver's torque Tope to the main controller 23 in order to The desired impedance (moment of inertia J, damping D, stiffness K) is formed to control the servo motor 28. The block diagram of the control system is shown in Fig. 12. J0 is the equivalent equivalent moment of the rotor of the servo motor 28 and the main controller 23, and g1 and g2 are equivalent to the cutoff frequency of the filter for the purpose of removing interference. When the angle command Θ cmd is zero, the torque of -25- (20) 200303275 applied from the outside to the main controller 23 means that the torque T ope applied by the driver to the main controller 23 reaches the main controller angle Θ The transfer function θ (8) can be expressed by the following formula if the interference cut-off filter is ignored, so it is known that the desired impedance (J, D, K) can be obtained.

1 J.s2 + D.S + K

Tope (6) The above structure has the following functions and effects. The thrust instruction device 24 will control the angle Θ of the main controller 23 with the servo motor 28 in order to obtain the thrust recommendation calculated with the train operation support device 22 値 Free—to the thrust command F cmd. In this way, when the driver operates the main controller 23, the driver will be controlled by the impedance of the impedance controller 26 to make the driver feel that the desired impedance (J, D, K) has been reached. That is, the driver can obtain and the thrust recommendation 値 Frec-resulting in the thrust command F cmd without touching the main controller 23. On the other hand, when the driver operates the main controller 23, although it can withstand the force from the servo motor 28 in the direction of the thrust recommendation 値 Free, it can be set at an arbitrary angle, that is, the thrust command F cmd. That is, the driver can also entrust the driver to the train operation support device 22, and when necessary, the driver operates the main controller 23 and controls the thrust command in accordance with the consciousness. The angle Θ of the main controller 23 for the purpose of realizing energy-saving operation can be detected by the reaction force from the main controller 23, and the driving can be performed while realizing the energy-saving fixed-position stop mode. Therefore, in addition to the use of the driver's operation to achieve energy-saving driving and stop driving at a fixed position, in the event of an unexpected situation, you can quickly take countermeasures. -26-(21) (21) 200303275 The thrust command F cmd for the driving brake device 2 is not directly controlled by the train operation support device 22, but is provided by the angle Θ of the existing main controller 23, which can realize the system simplify. In addition, when the train operation support device needs to pass through the driver after all, the train operation support device 22 does not need to have strict fixed-position stopping accuracy, so the device can be simplified. Using this method can improve the reliability of the system and reduce costs. In addition, the train operation support device requires the driver after all, so the operator's operation skills are required at any time. With this embodiment, the following problems can be avoided. That is, when a system having an automatic train operating device is used, the driver's operation skills may be reduced and he may not know how to deal with unexpected problems. Fig. 13 is a block diagram showing a schematic configuration example of a train operation support device of a fifth embodiment. This embodiment is different from the fourth embodiment in that the configuration of the thrust indicating device 24 is different. Therefore, the different parts will be described here. However, in this embodiment, the thrust command uses 6 steps of acceleration (P1 ~ P6), 6 steps of braking deceleration (B1 ~ B6), and neutral (N). The emergency brake (EB) adopts the main controller level. the way. The grade here refers to the person who modeled the speed versus the thrust, and it is the thing used in the current electric car drive control. The number of levels can range from several to more than 30, and there are various forms depending on the system. The main controller 23 in Fig. 13 is a schematic configuration when viewed from above. The thrust instruction device 24 is composed of a recommended level display control unit 30 and a lamp group 31. In the illustrated embodiment, the light group 31 is composed of 6 lights corresponding to the running acceleration levels P 1 to P 6, 6 lights corresponding to the braking deceleration levels B 1 to B 6, lights corresponding to the neutral level N, And corresponding to the emergency brake class EB lamp (22) (22) 200303275, here is composed of 14 lights. The suggestion level indication control unit 30 executes control for lighting a lamp corresponding to the suggestion level command N rec ′ when the train operation support device 22 receives the suggestion level command N rec ′. With the above configuration, the following actions and effects can be obtained. The driver can check whether the setting is set to the level for the purpose of achieving energy-saving driving while ensuring the timing and stopping at a fixed position by using the light. For example, the content of the recommended level command N rec is the running acceleration level P6, and its corresponding light is turned on, and when it is the braking deceleration level B 3, the corresponding light is turned on. The driver observes the state of the lighting, and implements the corresponding level operation of the main controller 23, thereby realizing energy-saving driving while suppressing energy loss. There is no direct electrical / mechanical connection between the thrust indicating device 24 and the drive braking control system, and the driver's operation is required. Therefore, when an unexpected situation occurs, the driver can quickly respond to the situation and improve the system. Faithfulness. Lamps and display devices using LEDs (light-emitting diodes) are easier to implement and improve the reliability of the system than the servo mechanism of the main controller 23 of the fourth embodiment, and can further reduce the cost of the device. Fig. M is a block diagram showing a schematic configuration example of the train operation support device of the sixth embodiment. Compared with the fifth embodiment, this embodiment differs only in the structure of the thrust indicating device 24, so only the different parts will be described here. The thrust indicating device 24 of this embodiment is a recommended level indicating control unit 32 and a sound. The output unit 33 is configured. When the recommended level indication control unit 32 receives the recommended level command N rec from the train operation support device 22, it will control the sound output unit 33 to output a corresponding voice. For example, when the recommended level is B 3, a voice such as "Brake 3 level" will be issued. With the above configuration, the following actions and effects can be obtained. The driver can know by voice the level for the purpose of achieving energy-saving driving while ensuring regularity and fixed stop. In this way, the same functions and effects as those of the fifth embodiment can be achieved. When the suggestion level is indicated by a lamp in the fifth embodiment, the driver's attention will be focused on the indication. As a result, an accident may occur due to failure to pay attention to the front. In contrast, the use of voice instructions can avoid this problem and improve the reliability of the system. 15 and 16 show an embodiment of the automatic train operating device of the present invention. The automatic train operating device (ATO) mounted on the illustrated train 0 is obtained from the automatic train control device (AT C) 102 of the ground system, and the speed limit data is obtained from the database (DB) 1 in train 0. 03 Obtain information on route conditions (inclination angle, curve curvature radius, etc.), vehicle conditions (number of trains and weights, etc.), and operating conditions, etc. It will also obtain the departure signal from the bridge 104 and the load-response device 105 The load response signal is obtained, the train speed signal is obtained from the speed detector 106, and the ground position detector 107 which responds to the ground position appropriately arranged on the route is obtained from the train position signal. The above-ground sub-system that is moderately arranged on the route is used to confirm the train position. Here, DB103 refers to a person placed on train 0, and it may be an above-ground system located outside train 0, or it may be distributed in train 0 and on the ground. AT 0 1 0 8 has data processing means (24) (24) 200303275 1 8 0 and automatic train operation means 8 1 for online data processing. It also has pre-operation characteristic estimation means 124 and post-operation that will be described later. The characteristic learning method 134 is a representative estimation method and a learning method. The data processing means 180 will process the train speed signal. In addition to the processing of train speed, the train position (speed time integration 値), train acceleration (speed differential 値), and train travel distance (speed absolute time) Integral 値) Perform continuous operations. From the train position to the distance traveled by the train, moderate compensation will be implemented according to the train position signal of the ground sub-detector 107. The data processing means 180 performs specific calculations based on each input signal, and provides measurement data necessary for learning and automatic train operation described later. The necessary measurement data for the automatic operation of the train will be provided to the automatic train operation means. The train automatic operation means 1 8 1 outputs a running command to the driving device 9 or a deceleration command to the decelerating device 1 1 0 according to a result of performing calculations using each input data. The driving device 109 includes a main motor for the purpose of towing a train, and a power converter controlling the main motor. In addition, the reduction gear 110 usually includes both mechanical brakes and electric brakes. ΑΤ0 108 is placed on train 0. The part of the pre-business characteristic estimation means 124 and the post-business characteristic learning means 134 related to the learning of the present invention is shown in detail in FIG. 1 20. Pre-opening characteristic initial setting method 1 2 1. Pre-opening test automatic train operation means 1 22, driving result storage means 1 23, pre-opening characteristic estimation means 124, estimation result compensation means 125, characteristic estimation 値Storage means 126, learning characteristic database (learning characteristic DB) 1 3 0, characteristic initial setting means 1 3 1, automatic train operation means 1 32, driving result storage hand after operation -30- (25) (25) 200303275 1 3 3. It consists of post-business characteristic learning means 1 3 4 and learning result compensation means 1 3 5. Means 1 2 1 to 1 2 6 are the processing means for the purpose of test driving before business operation, means 1 3 1 to 1 3 5 are the treatment means for the purpose of after business operation, driving judgment method 120 before learning and learning The DB130 series has nothing to do with before and after operation, but it is set in a way that they are shared. · In Figure 16, the data processing method 180 and automatic train operation method 1 8 1 originally used by AT01 08 used as automatic train operation devices are omitted. Appeal Next, the functions of the devices in Figs. 15 and 16 will be described. In Figure I5, AT01〇8 will obtain the speed limit data from ATCl02, the route conditions, the vehicle conditions, and the running conditions from DB103 in advance, and obtain the speed at the same time, and then perform specific calculations. Generate control commands composed of running commands or deceleration commands, and realize the automatic operation of train 0 as described above. AT 0 1 0 8 received the departure signal from the driver's station 104 and started to use the automatic train operation method to execute the automatic operation. After departure, the load information obtained from the load response device 105, the speed data obtained from the speed detector 106, and the ground detection information obtained from the ground detection device 107 are used. The load information is used as the weight-related information of the train, and the ground detection information is used to compensate for the position information. Using this information, AT 〇 108 can formulate train control commands (running command / deceleration command). When the prepared running command is used as the control command, the running command is output and the train is driven by the driving device 109. In addition to the running torque (running traction) command, the running command does not include a running level command. And -31-(26) (26) 200303275, when a deceleration command is prepared as a control command, a deceleration command is output ', and the train is decelerated using a deceleration device 1 10. The deceleration command is the braking force command 'level, and when driving, the braking level command is used. Next, a detailed description will be given of the effect of AT0 108 with reference to FIG. 16. When receiving the departure signal from the driver's station 104, first, the driving test before operation or the driving test after operation will be implemented by the driving test method before operation 120. The judgment method at this time may be a method of using a flexible flag "test vehicle without flag", "a vehicle operating with flag", and a method using a setting result of a hard switch. Pre-business driving judgment method 1 2 0 If it is judged that it is a test driving before business, the pre-business characteristic initial setting means 1 2 1 will set the initial characteristic parameters during the pre-business test driving. For the setting method, you can consider using the man-machine interface to manually implement the setting before driving. As for the content of setting 値, you can extract the characteristic parameters from information that can be obtained in advance such as the specifications of the train and route characteristics, and enter them. Next, using the characteristic parameters set by the pre-opening characteristic initial setting means 1 21, the pre-opening test automatic train running means 1 22 is used to implement the test running of the train using automatic operation. In terms of the method of automatic train operation, for example, when the optimal driving plan is drawn up when stopping at a stop, the automatic operation is implemented according to it, and when there is a great deviation from the optimal driving plan, the driving plan is re-planned or the control instruction is implemented. Method of compensation for error feedback. Here, because the vehicle is operated in advance before the business is opened, for example, when the train is running on a level, a test run using a level for the purpose of characteristic estimation is performed, and the service is performed for the purpose of characteristic estimation. -32- (27) (27) 200303275 Next, the results of the automatic operation performed by the automatic operation means 1 22 of the test driving train before operation are stored using the operation result storage means 1 23. When saving, the target's driving plan, speed data and position data measured during driving are regarded as electronic files stored in media such as hard disk (HD). Secondly, using the test driving results stored by the driving result storage means 1 23, the pre-opening characteristic estimation means 1 24 is used to estimate the characteristic parameters. The estimated characteristic parameters such as weight, acceleration characteristics, and deceleration characteristics should be implemented before business. In terms of the weight of the train as a whole, because it is a trial run before business, there are no passengers on board. You can use the acceleration or deceleration during taxiing and the driving resistance of the train to calculate it. Here, consider the case where the target train is represented by the simple physical formula of equation (7). In terms of train running resistance, calculations can be performed using formulas that take into account the characteristics of the route, such as slope and curvature, air resistance, and friction resistance. For the calculation of the running resistance of a train, please refer to the document "Operation Theory (DC AC Electric Vehicle)" by Diaoyousha. In general, the driving resistance Fr of a train can be expressed by the following formula.

Fr = Frg + Fra + Frc s + (A + Bv + Cv2) + 8 00 / r ... (11) However, Fr is the train resistance [kg weight / ton], and Frg is the slope resistance [kg weight / ton] (Uphill is positive, downhill is negative), Fra is driving resistance [kg weight / ton] (28) 200303275, Frc is curve resistance [kg weight / ton], s is slope [% 〇], A, B, C is the coefficient, v is the train speed, and r is the radius of curvature. If the above items are considered, the weight can be calculated by the following formula (7). Μ Ⅱ (F — Fr) / α ... (12) In the formula (I2), it is only necessary to make the running traction force F 0 (zero) when the vehicle is coasting. The acceleration (or deceleration) α can be calculated using the least square method and the like based on the measurement results (train speed). In the above process, the weight M can be calculated. After the calculation of the weight M is finished, the weight estimation 値 can be used to estimate the running characteristics and braking characteristics. First, use the weight to estimate 重量 Mest, the acceleration a acc during operation, and the train running resistance Fr to estimate the operating characteristics (the relationship between the operation level and the traction force, etc.). The acceleration a a c c and the running resistance Fr of the train can be obtained by the same processing as the aforementioned weight calculation. Using this and weight estimation 値, the running traction force F can be estimated by the following formula.

13 F = Mest a acc + Fr When a train is operated by class, the running traction of each class can be estimated by formula (1 3). It can also be used to estimate the relationship between operation level and operation traction. -34- (29) 200303275 In addition, using the weight estimation 値, deceleration during deceleration, and train running resistance, the braking force characteristics can be estimated. The deceleration and train resistance during deceleration can be obtained by the same processing as the aforementioned weight calculation. Using this and weight estimation 値, the braking force F can be estimated by the following formula. … (14) F = Mest a dec + Fr However, adec is deceleration (negative acceleration). For trains that perform braking operation by class, the braking force of each class can be estimated by formula (1 4). And this result can be used to estimate the relationship between braking level and braking force. Forcing some calculations, it is best to perform calculations after driving between stations or when stopping. However, calculations can also be performed during train operations, and the results of the calculations can be confirmed during train operations. By using this method to estimate the weight, running characteristics, and braking characteristics, the deviation of the number of trains can be adjusted in a shorter time than before. Secondly, the property estimation based on the pre-operating characteristic estimation means 1 24 is used to perform the compensation, and the estimation result compensation means 1 2 5 is used to compensate. When implementing compensation, it should be set within the allowable range of theoretically achievable characteristic parameters, and it must be corrected to the allowable range. For example, if the characteristic estimation (if it exceeds the allowable range), you can consider using a setting that performs calculations in advance, or use a limit within the allowable range. If it deviates too much from this allowable range, it is necessary to perform operations such as test driving again. Next, the characteristics of the compensation implemented by the estimation result compensation means 1 2 5 are calculated -35- (30) (30) 200303275, and the characteristic estimation storage means 126 is stored in the learning characteristic DB 130. For the storage method, the same method as the aforementioned driving result storage means 123 can be used. The learning characteristic DB 130 can store the characteristic estimation results obtained from the test driving before the business trip, and can not store the characteristic learning results obtained after the driving test after the business trip described below. The following is a description of the situation when the vehicle is determined to be driving after business using the driving judgment means 1 20 before business. When operating a business, the characteristic initial parameter setting means 1 3 1 is used to set the initial parameter of the characteristic parameter. In the first operation, the characteristic parameters (characteristic estimation results) stored in the utilization characteristic estimation / storage means 126 obtained from the learning characteristic DB13 0 are used. When learning at the same time as the business vehicle passes, the characteristic parameters (characteristic learning results) obtained from the learning results can be used. Second, the characteristic parameters set by the characteristic initial setting method 1 3 1 and the automatic train operation method 1 3 can be used. 2 will perform the automatic operation of the train. The automatic operation of the train is basically the same as the automatic operation means 122 of the test train for pre-operation test. After the operation, the weight will change due to an unspecified number of passengers. Therefore, in the initial operation after departure from the station, the weight during driving between stations must be estimated. Method of weight estimation 'If the load can be obtained, the load can also be used. When the load cannot be used, the weight can be estimated by performing the same function as the pre-business characteristic estimation means 1 2 4 and the estimation result compensation means 1 2 5 during the initial operation after the departure of the station. If the result of the calculation is different from the initial setting method of the characteristic, the setting method 1 3 1 must be carried out again. Figure 17 is a summary of the weight estimation during the initial operation after departure from the station -36- (31) (31) 200303275. In Fig. 17, the distance from the starting station to the next station on the horizontal axis is the position, and the vertical axis indicates the speed of each position in the speed mode. The optimal driving mode 1 3 1 (thin dashed line) is calculated based on the characteristics when parking at the departure station. After starting the driving, the actual driving results in the initial operating zone 1 3 0 will be used as the actual driving mode 1 3 2 (rough (Solid line) Carry out weight estimation, and based on the weight estimation, calculate the driving mode 1 3 2 (thick dashed line) by recalculating and implementing compensation, and implement the actual driving operation accordingly. Next, the results of the automatic operation performed by the train automatic operation means 32 are stored using the post-operation driving result storage means 33. The method of storage 'can be the same as the driving result storage means 23 described above. Next, use the driving results stored by the post-operating driving result storage means 1 3 3 and use the post-operating characteristic learning means 134 to implement characteristic learning. The regular learning aspect of this feature will be implemented as follows. (1) Learning based on driving results between stations (2) Learning based on driving results of the entire route (3) Learning based on driving results of 1 day (4) Learning based on driving results of several days (5) Driving based on months The learning of the results is described below with respect to (1) to (5) above. (1) Learning based on inter-station driving results Perform learning based on inter-station driving results obtained after inter-station driving, and reflect the learning results to the next inter-station driving. For example, at the beginning of rain, -37- (32) (32) 200303275 learns the correspondence when the braking force decreases. An example of judging that it is necessary to learn the results of driving between stations. For example, the response to a reduction in braking force in rainy days. On rainy days, if the train uses air brakes, rain will reduce the friction of the brake pads and reduce the braking force (deceleration performance). At this point, it should be noticed that the deceleration performance decreases after the rain starts. Just learn the characteristics of the braking force based on this result. The learning results at this time are usually temporary, so they can be stored separately and used as temporary characteristic parameters. (2) Learning based on the driving results of all routes The learning is performed based on the driving results of the first to last route, and the learning results are reflected on the driving of the next route. For example, when driving on a route, if there are almost enough stops (deviations) at the target stop position at each station, in order to eliminate the deviations, you only need to learn the braking force characteristics corresponding to the deviations. For example, when the target stop position is exceeded, the setting of the braking force characteristic should be slightly larger than the actual value. That is, the assumed deceleration cannot be obtained because it is larger than the actual braking force. In this case, it is only necessary to perform learning to make the setting of the braking force characteristics slightly smaller. (3) Learning based on the driving results on the 1st day Perform learning based on the driving results on the 1st day and reflect the learning results on the driving on the next day. For example, when observing the driving results for one day (for example, driving results for several times for the entire route 1), you can almost say that you will find that parking at a certain station will exceed the target stop position by more than the same degree. It is likely that there is an error in the setting of the route characteristics such as the slope and curve between the stations. (38) (33) (33) 200303275 At this time, as long as the learning of the route characteristic parameters such as slope and curve is slightly adjusted according to the driving results, it is sufficient. (4) Learning based on driving results of several days. Saving driving results of several days and performing learning based on the stored results. For example, when observing the driving results for several days, if the deviation of the driving plan occurs only in the same time zone, it should be affected by some factors, and only the running traction characteristics or braking force characteristics of the time zone deviate. Actual situation. If there is no deviation in other time zones, the characteristic parameter itself should not deviate from the actual, so only the characteristics of the target time zone should be compensated, and then the compensation can be corrected by learning. (5) Learning based on driving results of several months When storing driving results of several months, learning is performed based on the stored results. For example, learning is performed based on the driving results stored during the maintenance check. For example, observing the driving results for 3 months, we can find that the braking force of 3 months ago, 2 months ago, and 1 month ago will gradually decrease over time. This situation is difficult to judge by studying the driving results for several days. When using air brakes, friction is likely to cause the brake pads to wear out. Therefore, it is necessary to take measures such as changing (learning) characteristic parameters based on the results, or implementing replacement of the brake block according to the degree. In addition, ’countermeasures such as changing wheel diameter may be adopted. The above learning can be carried out selectively using the examples shown in the flow chart in Fig. 18. In Figure 18, the pre-business driving judgment method 1 2 0 is used to implement -39- (34) (34) 200303275 is a test driving before business or a business driving after business (step 15 1) 'The judgment result is In the former case (pre-opening test run), the pre-opening test run is performed (step 15 2), the initial parameter estimation is performed (step 1 5 3), and the process ends. If the judgment result of step 15 is business driving, then one of the five types of learning corresponding to the driving content is implemented. That is, to determine the form of the end-of-traffic operation (step 1 5 4), if it is to end the inter-station driving, implement "(1) learning based on the inter-station driving results" (step 1 5 5) 'If it is to end the entire route The driving is carried out "(2) Learning based on the driving results of the whole route" (step 156). In step 154, if it is the end of the i-day driving, it will further determine how many days of data are stored (step i 57). According to the judgment result, if the data of the 1-day is stored, implement "(3) based on 1 day "Learning of driving results" (step 丨 5 8), if it is stored several copies of data, then implement "(4) Learning based on driving results of several days" (step 1 5 9), if it has been stored for several months For the data, "(5) Learning based on driving results of several months" is implemented (step 160). However, the learning steps 1 5 5, 1 5 6, 1 5 8 '1 5 9, 1 60 shown by thick lines in Fig. 18 will only be shown when the driving results show the tendency to learn as shown below. Implement learning. That is, a) when deviations that consistently show the same tendency (for example, when the target stop position of the same degree is exceeded between all stations in the results of the full route driving; etc.); and b) when there is a significant deviation. In study, you can consider the method of increasing or decreasing the relevant characteristic parameter by a certain percentage. For example, as mentioned above, in the results of all-route driving, when the target stop position of the same degree is exceeded at all stations -40- (35) (35) 200303275, the braking force should be set 値 slightly larger than the actual braking force Therefore, the learning of reducing the setting of the braking force characteristic by a certain ratio is implemented. Especially in terms of learning based on the results of driving between stations, there are few deviations that show the same tendency. Therefore, at this time, the following learning should be implemented. That is, • Target automatic train operation method: When there is a considerable deviation between the driving plan and the actual measurement, the automatic train operation method that compensates for the control command (operation level command, brake level command, etc.) according to the deviation. • Learning method: When there is a deviation between the driving plan and the actual measurement, the learning will be carried out according to the situation of compensation of control instructions. Take the braking force characteristics as an example. For example, when braking, if there is compensation for a control command that will make the braking level greater than the plan, it should be the assumed deceleration. At this time, the setting of the braking force characteristic should be too large, so it is only necessary to carry out the learning of reducing the setting of the braking force characteristic by a certain ratio. If there is compensation for the control command that will make the brake level lower than the plan, the opposite is only to learn the setting 値 that increases the braking force characteristics by a certain percentage. The estimated characteristics are different from the actual ones, based on the acceleration and deceleration obtained in the form of measured data, using the assumed characteristics of the train's driving-related characteristics, 'route shape-related characteristics (slope, curve, etc.), weight, and operation -41-( 36) (36) 200303275 Traction or braking force can be used to determine whether the formula (7) is satisfied. As shown above, the results of the learning using the post-business characteristic learning means 1 3 4 will be compensated by the learning result compensation means [35]. The method of compensation 'can be treated in the same way as the compensation means 1 2 5 of the aforementioned calculation result. This compensation result is regarded as the characteristic learning result and stored in the learning characteristics. DB130 〇 As shown above, even during the business operation, learning will be carried out, and business driving will be performed while adjusting the characteristic parameters. · Most of the above learning is automatic on the train while the train is waiting. However, the estimation of the weight during running is automatically calculated on the line during driving. In this way, the automatic operation of the train can be implemented through continuous implementation of learning and estimation, and the automatic operation can be implemented under the circumstances that have a good response to the differences in the number of trains and changes in time. As described above, using the automatic train operating device of the seventh embodiment, calculation of weight, running traction, and braking force can be performed before operation. For different trains, the number of trains can be adjusted in a shorter time than before, and the learning of the characteristic parameters can be implemented after business, so even when the characteristic parameters change, it can still achieve a good ride comfort and stopping accuracy Automatic operation. In addition, in terms of learning after business, you can divide the learning between the station driving section and the route driving section according to the period of use of the data. Therefore, you can obtain more realistic learning. In addition, in the estimation before business and in the study after business, the compensation of the estimation and learning results will be implemented. In the event of an impossible result, it can also be compensated. Without using -42-(37) ( 37) 200303275 In the case of the characteristic parameter of energy, estimation and learning are performed. By adopting the above method, as the characteristic learning progresses, an effective optimal driving plan can be formulated. Also, if a large learning occurs during train driving, the learning will be triggered and the driving plan will be re-drafted to realize the automatic train operation that can satisfy the riding comfort, the target stop position stop accuracy, and the driving time. In Embodiment 7, most of the learning is online automatic learning while the train is stopped, such as when arriving at the station, and the weight estimation during running is automatically calculated online while the train is running. However, if there is a human-machine interface that can confirm the learning progress during the train operation, it is also possible to implement online automatic learning in the train and implement the system using the learning results at the judgment of the driver. At this time, it is also possible to make the learning means only a separate device and use it as a support device for automatic train operation. Fig. 19 is a diagram showing the configuration of important parts of the automatic train operating device of the ninth embodiment. In this embodiment, the post-business characteristic learning means includes automatic characteristic learning means of each request item 1 3 4 1. Automatic characteristic learning means 1 3 4 2. Automatic characteristic learning means 1 343, automatic characteristic learning means 1 344, and automatic characteristics Learning means 1 3 4 5 In addition, 尙 has learning result comparison means 1 3 6 for inputting learning results obtained by these automatic characteristic learning means, and learning to perform compensation based on the comparison results of learning result comparison means 1 3 6 Result compensation means 1 3 7. The automatic characteristic learning means 1 3 4 1 to 1 3 4 5 respectively perform characteristic learning as described in the description of the seventh embodiment. The learning result comparison means 1 3 6 will accept the learning results of the automatic feature learning means 1341 to 1 45, and compare each learning result with (38) (38) 200303275 to check whether there is a major contradiction between them. In the automatic characteristic learning means 1 3 4 1 to 1 3 4 5, there is a considerable difference in the learning period, that is, the learning interval. Basically, the result of the shorter one is used to check the longer one. The result is just fine. For example, when the learning result of the automatic feature learning means 1 3 4 5 is obviously η times to, for example, 10 times the learning result of the automatic feature learning means 1 3 44 of the same time zone, it is judged to be obviously abnormal, and the automatic The learning result of the characteristic learning means 1 3 45 can be regarded as a result with major contradiction. In addition, using the automatic result learning means 1 3 4 1 to 1 3 4 5 to perform the inspection can further improve the inspection accuracy. Secondly, the learning result compensation means 1 3 7 will target the learning result comparison means 1 3 6 Compensation will be performed if there are major contradictions in the comparison results. In terms of compensation, the simplest method is to directly use the learning result of the automatic feature learning means with a short learning period (learning interval). However, when using the complex learning results of automatic learning methods 1 3 4 1 to 1 3 4 5, the average value of these learning results can also be considered. Also, if most of the learning results of the automatic feature learning means 1 3 4 1 to 1 3 4 5 show contradictory results, or the learning results of the automatic feature learning means 1341 to 1 3 45 have a large error with each other. You can also consider using its average 値. The automatic characteristic learning means i 3 4 can perform characteristic learning using an adaptive observer. If the target device has been modeled by the formula (7), the applicable observer uses the observable (detected) element to identify the parameter. The system identification can also be implemented by type. The automatic train operation means 1 8 1 can use the identification result of the adaptive observer at any time to form an adaptive control system. When (7) -44- (39) (39) 200303275, the adaptive observer can be used to observe the acceleration and deceleration (can be calculated from the detection speed of the speed detector 106), and the traction or braking of the control command Force, at any time to identify the weight, train running resistance. In the calculation of adaptive observer, the extended least square method, expanded Kalman observer, and adaptive observer can be used. Chapter 2 "Estimation and Adaptation Observer of Unknown Equipment" P.47 ~ 87, or "Best Filtering of System Control Series 6" (by Xishan Jing, Pei Fengxi) Section 3.3 "Adaptation Observer" P.50 ~ 57). As shown above, a comparison of several automatic feature learning methods that are different during the learning period (learning interval) is performed to eliminate contradictory learning results, and more accurate feature learning results can be obtained. In the eleventh embodiment, the automatic characteristic learning means 134 may also perform characteristic learning by using an interference observer. The interference observer metropolis uses motion control, etc., to identify the interference (for details, please refer to "Design with the control system using Matlab" (edited by Kenichi Noto, co-authored by Hideo Nishimura, co-authored by Mitsuo Hirada, and published by Tokyo Denki University) "Motion Control Interference Observer" P. 99 ~ 102). Considering the driving resistance of the formula (1) as the force interference of motion control, the interference observer can be used to estimate the driving resistance of the train at any time. Using this estimation result for learning, you can perform higher-precision learning. A detailed description will be given of a twelfth embodiment of the present invention with reference to the drawings. Fig. 20 is a configuration diagram of the automatic train operating device 1 and the data storage unit 201. The automatic train operation device 1 is composed of a train characteristic learning device 207 of a train characteristic learning means, and an automatic operation control section (40) (40) 200303275 of the automatic train operation means. The train characteristic learning device 2007 will obtain train characteristics data (train resistance, delay time, etc. (described later)) and route data while the train is running. The data obtained by using the train characteristic learning device 207 will be stored in the data storage unit 201. The data acquired by the train characteristic learning device 20 7 and stored in the data storage section 201 are output to the automatic operation control section 208. The automatic operation control unit 2008 will draw up a driving plan based on the data obtained by using the train characteristic learning device 2007 and stored in the data storage unit 201. The train will run automatically according to this traffic plan. The train characteristic learning device 207 is composed of data storage means and data storage section 20 1. Train weight calculation means and running traction deviation detection means of train weight calculation part 209, train resistance calculation means of train resistance calculation part 2 1 0, braking force calculation means And the braking force calculation unit 2 1 of the braking force deviation detection means, the delay time calculation unit 2 1 2 of the delay time calculation means, and the riding rate calculation unit 2 1 3 of the ride rate calculation means, which are composed of detecting the speed of the train. The output of the data storage section 201 is input to the train weight calculation section 209, the train resistance calculation section 2 10, the braking force calculation section 21, the ride factor calculation section 213, and the automatic operation control section 208. The output of the train weight calculation unit 209 is input to the data storage unit 201. The output of the train resistance calculation section 2 1 0 is input to the data storage section 20 1. The output of the braking force calculation section 2 1 1 is input to the data storage section 201. The output of the delay time calculation unit 212 is input to the data storage unit 201. The output of the boarding rate calculation section 213 is input to the data storage section 201. The output of the operation control unit 8 is input to the train weight calculation unit 2 0 9, the braking force calculation unit (41) 200303275 2 1 1, the delay time calculation unit 2 1 2, and the load factor calculation unit 2 1 3. When the train is accelerated for train acceleration, the data storage unit 201 will input the train resistance 値, and the automatic operation control unit 20 8 will input the running traction 値 F and the current train speed V to the train weight calculation unit 209. The train weight calculation unit 209 calculates the train weight M using Equation 15 using the train resistance 値 Fr, the running traction force 、 F, and the train speed V. The train weight M obtained by the train weight calculation unit 209 stores the data storage unit. In Formula 15, M is the train weight, F is the running traction force, and Fr is the train resistance,. The train acceleration α can be obtained from the train speed V. 15 M = (F — Fr) / a The train weight calculation unit 209 can also be used as a running traction force deviation detection method for the operation traction force 値 F. The train weight Calculated by the train weight calculation unit 209 can be used. When it is different from 値 V 1 used to calculate the train weight M, it can be substituted into Formula 15 to obtain the correct running traction force 値 F. The train weight calculation unit 20 9 can also detect deviations in the running traction force 値 F and the running traction force command 値 Fk instructed by the automatic operation control unit 20 8. The deviation between the running traction command 値 Fk and the running traction force 値 F is output to the data storage section 201 for storage. Because the deviation of the running traction command 値 Fk and the running traction 値 F can be detected, the running traction command 値 Fk can be added to the running traction command 値 Fk and the running traction 値 F to determine the new running traction. Command 値 Fk, using this process, can realize more accurate automatic train operation. -47- (42) (42) 200303275 When the train is taxiing, the data storage unit 201 inputs the train weight M and the speed V to the train resistance calculation unit 2 10. Using the weight M and speed V of the train input from the data storage unit 201, the formula 16 can be used to calculate the train resistance 値 Fr. When the train is taxiing, there is no running traction, so the running traction 値 F is 0. Since the running tractive force 値 F is 0, Equation 15 can be transformed to derive Equation 16. The train resistance 値 Fr calculated using formula 16 will be output to the data storage section and stored. In Formula I6, M is the train weight, F is the running traction force, Fr is the train resistance, and α is the train acceleration. The train acceleration α can be obtained by using the train speed V.

Fr = F— Μα = 0— Μα (16) Train resistance 値 F r is shown in "Operation Theory (DC AC Electric Power Vehicle) Diaoyousha", etc. For general trains (there will be some differences in high-speed vehicles), the slope The sum of the resistance 値 Frg, curve resistance 値 Frc, and driving resistance 値 Fra can be expressed by Equation 17. It can also be seen that the slope resistance 値 Frg, the driving resistance 値 Fra, and the curve resistance 値 Frc can be expressed by Equation 18, Equation 19, and Equation 20, respectively. Since the train resistance 値 Fr during taxiing can be calculated using the train weight M and speed V, the train resistance calculation unit 210 can also calculate the slope resistance 値 Frg and the running resistance 値 Fra. Driving resistance 値 Fra can be calculated using speed V. In addition, the curve resistance 値 F r c uses the data stored in the data storage unit 1 in advance. Since the train resistance 値 F r, driving resistance 値 F r, and curve resistance 値 frc can be used as data, the train resistance calculation section 2 1 0 can use the formula; ι 7 -48- (43) (43) 200303275 Deformation calculation slope resistance 値 F rg. The slope resistance 値 Frg calculated using the train resistance 2 10 is output to the data storage unit 201 and stored. In Equation 18, s is the slope [%] (positive when going uphill and negative when going downhill). In Formula 19, 'A, B, and C system coefficients and V system speed [km / h]. In Equation 20, r is the radius of the curve [m]. The train resistance calculation department can detect the slope resistance 値 and train resistance 时 while the train is running, so it can detect the correct data. In addition, the data can be detected as long as one round trip is performed on the scheduled route, which has a considerable effect of shortening the time. In Equation 17, Equation 18, Equation 19, and Equation 20, the train resistance is Fr, the slope resistance is Frg, the driving resistance is Fra, and the curve resistance is Frc. A, B, C system coefficients, r system curve radius.

Fr = Frg + Fra + Frc (17) F r g = s (18)

Fra = A + B v + Cv2 (19)

Frc = 800 / r (20) For the braking force calculation unit 2 1 1, the automatic operation control unit 208 will input the train speed V and the braking command 値 Fs, and the data storage unit 201 will input the train weight M and the train resistance 値 Fr. The braking force calculation unit 2 1 1 calculates the braking force bFb by using formula 2 1 using the train speed V, the train weight M, and the train resistance 値 Fr *. The braking force 値 Fb calculated by the braking force calculation unit 21 1 is output to the data storage unit 2 0 1 and stored. The description is implemented again using the aforementioned formula. In Formula 21, 'braking force 値 is Fb, weight is M, acceleration is α, and train resistance 値 is Fr. -49- (44) 200303275

Fb = Μ α + Fr (21) The braking force calculation unit 2 1 1 can calculate the braking force calculation unit 2 1 1 as the braking force deviation detection means and calculate the braking force 値 Fb and the deviation Fh of the braking command 値 Fs (see Equation 22). The brake force 値 Fb calculated by the brake calculation unit 2 1 and the deviation Fh of the brake command 値 Fs are output to the storage unit 201 and stored in the storage unit 201. A new braking force command 値 Fs can be obtained by adding the braking force 値 Fb calculated by the braking force calculation section 2 1 1 and the braking command 値 F s at the time of detecting the deviation F h to the braking force 部 Fs. This calculation method can provide a more accurate braking force 値 Fb for the train. In Equation 22, the braking force is Fb, the braking command is Fs, and the deviation is Fh. (twenty two )

Fh = Fs — Fb When braking, the delay time calculation unit is input to the automatic operation control unit 208 to output the data of the time T1 of the brake command 値 Fs and the time T2 of the deceleration of the train speed. The delay time calculation unit 21 calculates the deviation Th between the data of the time T1 at which the brake command 値 Fs is output and the data of the time T2 at which the train speed is decelerating (refer to Equation 23). The deviation Th calculated by the delay time calculation unit 2 1 1 is output to the data storage unit 201 and stored. The delay time Th is the time from when the actual braking command is received from the automatic operation control section 208 until the braking command reaches the driving device 205 and the braking device 206 and executes the operation. By detecting the delay time Th, a driving plan can be formulated with consideration of the delay time Th, and a more accurate and safer driving plan can be obtained. -50- (45) 200303275 In formula 23, the time when the automatic operation control unit 20 8 outputs the brake command 値 F is Ti, the time when the train speed is decelerated is Τ2, and the delay time is Th. (twenty three )

Th 2 T2-T 1 The data storage unit 20 1 will input the train weight Mk when the vehicle is empty, the current train weight M at the current point, the number of passengers N when the vehicle is full, and the average human weight Me . The occupancy calculation unit 213 uses the train weight Mk when the vehicle is empty, the current train weight M, the number of passengers N when the vehicle is full, and the average human body weight Me, and calculates the occupancy using formula 24 to calculate Mr ate. The ride rate calculation 値 Mrate calculated by the ride rate calculation unit 213 is input to the data storage unit 201 and stored in the data storage unit 201. In Formula 24, the train weight is Mk when empty, the train weight at current point is M, the number of passengers when full is N, the average human weight is Me, and the occupancy rate is calculated as Mrate. M -Mk

Mrate II Mc (24)

N In the train characteristic learning device 2 07 having this structure, the train weight calculation unit 209 can calculate the train weight M while the train is running, and output the current train weight to the load factor calculation unit via the data storage unit 201. μ. Therefore, the ride rate Mrate between stations can be calculated. Since the rate Mrate can be estimated between stations, it is possible to analyze the change in the rate of each station and the change in the rate of time. In addition, since the train weight calculation unit 209 can calculate the train weight at the current point (46) (46) 200303275 M, the correct data of the train resistance 値 F r and the slope resistance 値 F rg are also calculated. As for the automatic operation control unit 208, as shown in Japanese Unexamined Patent Publication No. 5-193502 and Japanese Unexamined Patent Publication No. 6-2 84 5 1 9, the current position of the train is detected using the ground surface, the speed of the train, and the elapsed time, and the automatic train is operated according to The mode (refer to Figure 21 (the vertical axis is speed and the horizontal axis is distance (position))) determines the target speed. The train implements automatic train control at this target speed. In addition, it is also possible to use the method of detecting the position by the driving distance and the ground, so the control method of the automatic operation control section is not particularly limited. The operation control unit 208 of this embodiment has a delay time compensation means, a running traction deviation compensation means, and a braking force deviation compensation means not available in the conventional automatic operation control unit. The delay time calculation unit 2 1 2 will input the delay time to the delay time compensation unit of the delay time compensation means (not shown in the figure). The delay time compensation unit (not shown in the figure) calculates the braking force or running tractive start time and controls the running tractive start time in consideration of the delay time. The train weight calculation unit 209 of the running traction deviation detection means will input the running traction deviation to the running traction deviation compensation means (not marked in the figure). The running traction deviation compensation method (not shown in the figure) will calculate the new running traction command 値 to control the running traction under the circumstances of taking the running traction deviation into consideration. The braking force calculation unit inputs the braking force deviation compensation 値 into the braking force deviation compensation means (not shown in the figure). The braking force deviation compensation method (not shown in the figure) will consider the braking force deviation compensation 値, calculate a new braking force command 値, and control the braking force. The automatic train operating device according to the twelfth embodiment of the present invention, because the train special -52- (47) (47) 200303275 sex learning device 2 0 7 can collect the riding rate, train weight, train resistance, and braking force during driving. Such data will not only be collected before the implementation of safe automatic operation, but also can be applied to vehicles that use the data collected during driving to further modify the driving plan when actual passengers are driving. In this embodiment, the train characteristic learning device 207 adopts a method of processing data while the train is running, and the data processing may also be processed after the train is running. In this embodiment, although only the braking force is indicated, there is of course no limitation on the method of braking, including the braking level. In addition, the train characteristic learning device of the present embodiment can also collect data of rainy days, data of each season, data of each route, and data of each station, etc. Therefore, it is not limited to only collecting data once for the route. Fig. 22 is a block diagram showing the structure of a train on which an automatic train operating device according to each embodiment of the present invention is mounted. The train 0 has a speed detector 302 composed of a pulse generator (PG) installed on a rotating shaft of a wheel, and an above-ground sub-detector 303 for detecting an above-ground sub-unit (answering machine) installed on a track, Furthermore, the automatic train operating device 1 which inputs these train detection speed signals and train detection position signals, and a driving device 305 and a braking device 306 which perform control by the automatic train operating device 1 are further provided. The automatic train control device (ATC), which is not shown in the figure, inputs the relevant AT C signals such as speed limit and operating conditions to the automatic train operating device 4. The automatic train operation device 1 includes a database 300, an arithmetic circuit 3 04A when stopping at a station, and an arithmetic circuit 3 04B when driving between stations. The above-mentioned train detection speed signal and train detection position signal are input to this station and implemented. Operation circuit 3 0 4 B. Implement the calculation circuit when stopping at the station (48) (48) 200303275 3 0 4 A When the train 0 stops, it will perform the specific calculation described below. When the train runs between stations, the arithmetic circuit 3 04B will be used when the train 0 runs between stations. Perform specific operations or controls described later. Secondly, the database 300 stores characteristic data during operation such as route conditions (slope, curvature, etc.), vehicle conditions (speed limit, vehicle weight, train characteristics such as acceleration and deceleration performance, etc.), and timetables (operation tables) All kinds of information. This database 3 00 can be a hard disk, such as that installed in the automatic train operating device 1, or a recently developed 1C card that can be carried by drivers. Fig. 23 is a block diagram showing the configuration of an automatic train operating device 1 according to the 13th embodiment of the present invention. The implementation of the calculation circuit 304A when stopping at the station has the best driving plan drawing means 3 07, and the implementation of the operation circuit 3 between stations when running the 04 04B has the calculation plan recalculation means 3 0 8, the control instruction extraction means 3 0 9, and the control The command output means 3 1 0. Secondly, the data stored in the database 3 0 0 will be input to both the calculation circuit 3 04 A when parking at the station and the calculation circuit 3 04B when driving between stations, and from the speed detector 302 and the ground detection. Each detection signal of the device 303 and the a TC signal are only input to the inter-station driving circuit to implement the arithmetic circuit 3 04B. The best driving plan planning method 3 0 7 will be based on various data stored in the database 3 0 0, to make train 0 run from one station to the next stop, and stop at the target position at the target time. Driving plan. The "optimal" conditions at this time can be set in various ways. For example, driving time is the best priority, energy saving efficiency is the highest priority, or ride comfort to avoid rapid acceleration and deceleration is the highest priority. In addition, in the case of the method of holding the best driving plan related information of the best driving plan drawing method 7, for example, the speed target corresponding to the time -54- (49) (49) 200303275 or the distance is regarded as a control instruction. . Means of Optimizing the Driving Plan 307 The method of drawing the optimal driving plan is, for example, a method of predicting the behavior of a train by using a train model in mechanics (for example, Japanese Patent Laid-Open No. 5-1 93 5 02). As shown in FIG. 37, the running curve, the coasting curve, and the reverse braking curve are predicted, and the intersection of the coasting curve and the reverse braking curve is used as the starting point of braking. The driving plan recalculation means 3 08 will not only enter the best driving plan formulation means 3 07, but also input the train detection speed and train detection from the speed detector 3 02 and the ground sub-detector 3 03. The location and the ATC signal from ATC will recalculate the driving plan when the error between the planned driving plan and the actual driving result exceeds a certain threshold. The control instruction extraction means 3 09 will calculate the acceleration plan and deceleration order for the current point of the driving device 3 05 and braking device 3 0 according to the driving plan recalculated by the driving plan 3 0 8 and output it to Control instruction output means 3 1 0. The control instruction output means 3 1 0 outputs the acceleration instruction and deceleration instruction input by the control instruction extraction means 9 to the driving device 305 and the braking device 306. Next, the operation of FIG. 22 having the above configuration will be described. Assuming that train 0 stops at a certain station, the best driving plan drawing method 3 07 will refer to the data stored in the database 3 00 to formulate the best driving plan to the next parking station. Secondly, when the train 0 starts to run, the driving plan recalculation means 3 08 will implement the optimal driving plan day preparation method 3 0 7 and the optimal driving plan drawn up, and based on the speed detector 302 and the ground sub-detector. (50) (50) 200303275 3 0 3 The comparison between the actual speed of the train and the actual speed of the train. The difference between the two (for example, the speed target of the best driving day and the actual speed) When the differential speed error is greater than a predetermined threshold, a recalculation of the traffic plan will be performed. If the difference between the two is greater than the critical threshold, in addition to the chase phenomenon mentioned above, it may also occur because other trains are parked in front of the direction of travel, so AT C enters a speed limit change command. In addition, the recalculation performed by the driving plan recalculation means 3 08 can be performed only by considering the actual speed at the time of the recalculation, the actual distance (train position), or the remaining time allowed for driving between stations. Secondly, the control instruction extraction means 9 will re-calculate the control plan from the driving plan recalculation means 3 08, and the control plan output means 3 1 0 will output the isolated control order to the drive. Device 3 05 or brake device 3 06. With such calculation and control of the automatic train operating device 304, train 0 can be stopped at the target position of the next stop at the target time. Thereafter, during the period when the train 0 stops at the next stop, the best driving plan preparation means 3 07 will further develop the best driving plan until the next stop, the execution is the same as the means 3 0 8 ~ 3 10 Action. In addition, when the error between the optimal driving plan and actual driving results drawn by 07 is not more than a certain time, the driving plan recalculation means 308 will not perform the recalculation and will directly calculate the optimal driving plan. The best driving plan of the vehicle driving day preparation method 7 is output to the control instruction precipitation method 3 09. In the thirteenth embodiment of FIG. 23 above, after the train 0 starts to operate according to the best driving plan prepared by the best driving plan planning means 307, if the actual -56- (51) (51) 200303275 driving results and this driving plan When the drawing deviates to a certain degree or more, the driving plan recalculation means 3 08 will immediately recalculate the driving plan, which can greatly suppress the chasing phenomenon that has occurred in the past, so the energy saving effect can be improved. Figure 24 is the present invention A block diagram showing the configuration of an automatic train operating device 1 according to a fourteenth embodiment. The difference between Fig. 24 and Fig. 23 is the recalculation method of the driving plan in Fig. 23, which is the recalculation method of the cumulative error reference type driving plan. The recalculation means 3 0 8 of the driving plan in Fig. 23, because at each recalculation point, it will be judged whether the error at that time exceeds the critical threshold. Therefore, a recalculation with a sense of chase is sometimes performed due to the influence of interference. . Therefore, in this embodiment, the cumulative error reference type driving plan recalculation means 3 1 1 performs judgment on errors accumulated to a certain degree (for example, errors accumulated within 5 minutes). In this way, it is possible to prevent the above-mentioned recalculation with chasing feeling due to the influence caused by interference. Fig. 25 is a block diagram showing the configuration of the automatic train operating device 1 according to the 15th embodiment of the present invention. The difference between Fig. 25 and Fig. 24 is that the control instruction compensation means 3 09 and the control instruction output means 3 10 have control instruction compensation means 3 12. This control command compensation means 3 12 has the function of judging whether the driving plan recalculation means 3 0 8 and the error between the actual driving result and the actual driving result exceed the threshold. If it is judged to be critical or higher, it will control the control instruction. 9 The isolated control instructions are compensated. The control command compensation means 3 1 2 is provided to enable the automatic train operating device 1 to have a supporting function. That is, if the train 0 performs the actual driving according to the optimal driving plan planning means 3 07 or the driving (52) (52) 200303275 plan recalculation means 3 0 8's actual driving plan, there is no problem, however, there are There may be situations where traffic deviates significantly from the traffic plan. For example, when one of the plural brakes is abnormal. However, in this state, the control command compensation means 3 1 2 can also play a supporting function to perform appropriate compensation for the control command, and prevent the stop position of train 0 from deviating too much from the target position. The structure of FIG. 25 is an example in which the control instruction compensation means 3 1 2 is provided between the control instruction extraction means 3 09 and the control instruction output means 3 10 in FIG. 23. Of course, this control instruction compensation means 3 1 2 may also be provided between the control instruction precipitation means 3 09 and the control instruction output means 310 in FIG. 24. Fig. 26 is a block diagram showing the configuration of an automatic train operating device 1 according to a sixteenth embodiment of the present invention. The difference between Fig. 26 and Fig. 25 is the control command compensation means 3 1 2 of Fig. 25, which uses the cumulative error reference control command compensation means 3 1 3. The control command compensation means 3 1 2 in FIG. 25, even if the judgment of the driving plan and the actual driving result is greater than the threshold value, the control command compensation means 3 1 2 will immediately control the control command precipitation means 3 09 The instruction executes compensation, and is easily affected by interference, and executes control with chasing feeling. Therefore, in this embodiment, the cumulative error reference-type control instruction compensation means 3 1 3 performs judgment on errors that have accumulated to a certain degree (for example, errors accumulated within 5 minutes). With this method, the above-mentioned recalculation with a sense of chase can be prevented due to the influence caused by the interference. Fig. 27 is a block diagram showing the configuration of an automatic train operating device 1 according to the seventh embodiment of the present invention. The difference between Figure 27 and Figure 26 is that the driving plan is recalculated. The new calculation method 3 0 8 is the recalculation method for the cumulative error reference driving plan. -58- (53) (53) 200303275 3 1 1 Since other structures are the same as those in FIG. 26, detailed description is omitted. In this embodiment, two means 3 1 1 and 3 1 3 are used to determine the cumulative error of the driving plan and the actual driving result. However, the critical threshold used by these means when performing the cumulative error judgment can be set. In order to respond to different conditions. Fig. 28 is a block diagram showing the configuration of an automatic train operating device 1 according to an eighteenth embodiment of the present invention. The difference between Fig. 28 and Fig. 27 is the implementation of the calculation circuit 3 04 A when the vehicle is stopped at the station. The optimal driving plan preparation method 3 07 A is the optimal driving plan preparation method 3 1 for delay time consideration, and the storage The "delay time" data is included in the train characteristic data of database 3 00. During the calculation of the travel plan, the delay time for the train to respond to the control instruction, that is, the time from the output of the control instruction to the time when the control instruction affects the actual train operation, requires a large computational load to obtain the foregoing period. There are difficulties in practical operation speed. Therefore, in this embodiment, in addition to the pre-determined delay time contained in the train characteristic data stored in the database 3 00, the best driving plan preparation method is also the "delayed time consideration" type of optimal driving plan preparation. Means 3 1 4 This delay time will also be considered when drawing up the best driving plan. In this way, the target position stop accuracy of the next stop can be improved. Fig. 29 is a block diagram showing the configuration of an automatic train operating device 1 according to a nineteenth embodiment of the present invention. The difference between Fig. 29 and Fig. 28 is the cumulative error of Fig. 28 with reference to the recalculation method of the driving plan 3 1 1 for the delay time consideration. This delay time-considerable driving plan recalculation means 3 1 5 and the delay time-considered optimal travel plan are prepared. -59- (54) (54) 200303275 Means 3 I4 are the same. Refer to the train characteristics data in the reference database 3⑽. The delay time data will be recalculated for the traffic plan. Using this method 'can further improve the stopping accuracy of the target position of the next stop. In addition, the structure of this nineteenth embodiment is a combination of the driving plan recalculation method 3 1 5 of the "delayed time consideration type" and the optimal driving plan preparation means 3 1 4 of the "delayed time consideration type". 'However, it is also possible to construct a combination of means 3 and 7 for ordinary optimal driving plan formulations other than the "delayed time consideration type", that is, recalculation means 3 of the driving schedules of Figs. 23 to 27 08 and 311 are replaced with the structure of the recalculation means 3 1 5 of this delay time consideration type driving plan. Fig. 30 is a block diagram showing the configuration of an automatic train operating device 1 according to a twentieth embodiment of the present invention. The difference between Fig. 30 and Fig. 29 is the delay time-considered best driving plan preparation method 314 of Fig. 29 is a forward-predicting best driving plan preparation method 3 1 6. Previously, the prediction method 3 16 for predicting the optimal driving plan was also a type of "delayed time consideration", which was based on the prediction of the direction of travel of the train 0 to make the train 0 stop at the target position of the next stop Driving plan for the purpose. That is, as shown in Fig. 38, calculating the behavior of the train in the direction of travel of the train and performing a convergence operation (or a gradual convergence operation from the point of deceleration) at the target speed at the target speed can be performed without using a retrograde curve. Develop the best driving plan under these circumstances. If you don't need to consider the delay time, you only need to refer to the braking characteristics of the target position and use the reversed position as the starting point of the brake. The calculation will be easier. However, if the delay time must be considered, then this reverse method is used to obtain The operation can be very complicated. Therefore, it takes a lot of calculation time to find the starting point of the brake opening -60- (55) (55) 200303275. When the calculation result of the brake start point is obtained, it may have passed the target position. In addition, the method shown in FIG. 38 uses a plurality of prediction operations to determine the starting point of the brake. Even if this operation is performed multiple times, it can be performed in each specific sampling cycle. Short time. Fig. 31 is a block diagram showing the configuration of an automatic train operating device i according to a 21st embodiment of the present invention. The difference between Figure 31 and Figure 29 is the delay calculation method 3 1 5 of Figure 29, which is the forward calculation method 3 1 7. The previous recalculation method 3 1 7 for the predictive driving plan is the same as the 3 1 6 for the forward prediction best driving plan. The recalculation of the driving plan is based on the prediction of the direction of travel of the train 0. The calculation is performed to stop train 0 at the target position of the next stop. Therefore, the recalculation of the traffic plan taking into account the delay time can be implemented in a short time. In addition, the previous calculation method 3 17 for predictive driving plan can not only replace the delay time consideration type driving plan recalculation method 315 of FIG. 29, but also replace the driving of FIGS. 23 to 27 and 30. Plan recalculation means 3 0 8, 3 1 1, 3 1 5. Fig. 32 is a block diagram showing the configuration of an automatic train operating device 1 according to a twenty-second embodiment of the present invention. The difference between Fig. 32 and Fig. 31 is the recalculation means 3 1 7 for the forward-looking driving plan before the 31st drawing. Fig. 31 Recalculation method of forward-predictive traffic calculation day 3 3 7 Recalculates the traffic plan by performing forward prediction calculations in accordance with specific control cycles set in advance. However, the implementation of this embodiment Recalculation means for predictive driving plan 3 1 8 Not necessary -61-(56) (56) 200303275 Recalculation is performed in each control cycle. For example, when the sampling control period is 0.3 second, it may be implemented only every 1 second, or even every 10 seconds. In this way, changing the recalculation cycle can reduce the calculation load. In addition, the calculation period may be appropriately determined by considering a place where the slope of the line changes rapidly and a place where the speed change is restricted. Fig. 33 is a block diagram showing the configuration of an automatic train operating device 1 according to a twenty-third embodiment of the present invention. The difference between Fig. 33 and Fig. 32 is the recalculation means of the successive forward prediction type driving plan of Fig. 32 3 1 8 is the recalculation means of the speed measurement driven successive forward prediction type driving plan 3 1 9. That is, if the detection sampling period of the speed detector 3 02 is 1 [msec], the operation circuit 3 04B is not directly used for the speed detection signal input according to this period when driving between stations, but for 5 to 10 [msec]. The speed detection signal input during the period is processed by filtering, etc., and then the data is updated. Secondly, the recalculation means 3 1 9 of the speed measurement driving type forward predictive driving plan is based on the renewal cycle of this data to implement the recalculation of forward predictive driving plan. By using this method, the influence of interference and the like can be suppressed, and the calculation accuracy during recalculation can be improved. Fig. 34 is a block diagram showing a configuration of an automatic train operating device 10 according to a twenty-fourth embodiment of the present invention. In this embodiment mode, in addition to implementing the inter-station driving result storage means 3 20 on the inter-station driving circuit 3 04B when driving between stations in FIG. 31, 实施 implementing the arithmetic circuit 3 04A and adding the delay time estimation means 2 when stopping at the stop 1, and the delay time can be calculated based on the latest driving results. Therefore, the database 300 of this implementation mode may not store the delay time data. That is, after train 0 departs from a certain station, the inter-station driving result data of the train position, train speed, (57) (57) 200303275 AT C signal, etc. until the next stop arrives will be stored in the station Interim driving results storage means 3 2 0. Secondly, after train 0 arrives at the next station and stops, during this stop, the delay time estimation means 3 2 1 will calculate the delay time based on the data stored in the inter-station driving result storage means 3 2 0 and output the calculation result to Delay-thinking optimal driving plan preparation method 3 14 and forward-predictive driving plan recalculation method 3 1 7. Delay time consideration best vehicle planning method 3 1 4 and forward prediction type vehicle planning recalculation method 3 1 7 will be further implemented to the next stop in the case of considering the estimated delay time The formulation and recalculation of the traffic plan. If the method for estimating the delay time using the delay time estimation method 3 2 1 is described, this method does not use complicated calculations, but is a simple method of estimating based on the signal level change of the measured data. For example, when braking, after outputting the brake control command and performing the level operation, the speed of the train will decrease after a certain period of time. At this time, the delay time until it reaches the preset threshold 设定 can be estimated. In addition, the delay time stored in the database of FIG. 28 to FIG. 33 described above for the delay time of 3, in particular, because it is not necessary to obtain it in a time-limited state, complex calculations can be used and the results of the calculation can be stored. Implement the trial running of the train 〇 and use the delay time estimation method of this embodiment 3 2 1 'You can obtain more information easily. This implementation mode can obtain the delay time that reflects the latest train characteristics.' Plan formulation means 3 1 4 and forward-predictive driving plan recalculation means 3 1 7 Developed and recalculated driving -63- (58) (58) 200303275 Plan can further improve reliability. Fig. 35 is a block diagram showing the configuration of an automatic train operating device i according to a 25th embodiment of the present invention. The difference between Figure 35 and Figure 34 is the implementation of an on-line delay time estimation method 3 22 on the calculation circuit 3 04B when driving between stations. The forward calculation type recalculation method 3 1 7 can be considered. In the case of the delay time estimated by the online delay time estimation means 22, recalculation is performed. That is to say, the structure of Figure 34 is to calculate the delay time based on the results of driving between stations in a certain section, and apply this calculation result to the recalculation of the driving plan of the next section. In the implementation form, even if the traffic is in the same section, the delay time can be estimated based on a few traffic results between stations, so it can also be applied to recalculation. Therefore, the results of the recalculation to the predictive driving plan recalculation means 3 1 7 in this embodiment can reflect the latest train characteristics more than those shown in Fig. 34. Fig. 36 is a block diagram showing the configuration of an automatic train operating device 1 according to a 26th embodiment of the present invention. In this embodiment, the calculation circuit 3 04B is implemented during the driving between stations in Fig. 35, and the temporary predictive parking time calculation means 3 23 for forward-looking parking and the driving means 324 are used. Secondly, in this embodiment, the driving plan is divided into three types of P1, P2, and P3 according to the driving time of the train. When the train arrives at a specific point in front of the target position, the driving daytime adopting means 3 24 will use the forward direction. Temporary driving day calculation means for predictive parking 3 23 Calculated driving plan P3. Hereinafter, this 26th embodiment will be described in detail. First, the driving plan PI, P2, and P3 are defined as follows. -64- (59) (59) 200303275 p 1: When train 1 stops at the station, the best traffic plan prepared by the traffic plan recalculation means 3 i 4 (or 3007, 3 16 may also be used). P2: During the inter-station driving of Train 1, the recalculation of the recalculation of the travel plan will be carried out by using the recalculation method of the travel plan 3 17 (or 3 0 8, 311, 315, 318, 3 19). P 3: The calculation method of the temporary driving plan for the forward-looking parking after the time point when the train 0 is running and the train 0 reaches the position N meters (for example, 'N 2300 [m]) before the target position. 3 23 Temporary traffic plan for parking. When train 0 reaches N meters ahead of the target position, the temporary driving plan calculation means 3 23 will use a specific period (for example, the detection sampling period of the speed detector 2) to formulate a subsequent temporary driving plan p 3 for parking. In the planning of the temporary traffic meter P3 for parking, using the train detection speed and train detection position at that time point, the train's parking behavior will be predicted by taking into consideration the delay time of the direction of travel of the train. This parking behavior is, for example, a predetermined basic parking behavior when a train is stopped at a specific braking class position immediately at the current point to stop the train, and the parking behavior is used. Secondly, in terms of the prediction of train driving behavior, a method using the physical model of the following formula (2 5) can also be considered. F— Fr = M · a (25) F: running traction or braking force

Fr: Train resistance (driving resistance, slope resistance, curve resistance, tunnel resistance, etc.) Μ: Train mass -65- (60) (60) 200303275 α: Acceleration or deceleration train resistance It is convenient to calculate g. As mentioned above, the components of driving resistance, slope resistance, curve resistance, and tunnel resistance are usually considered. Therefore, the train resistance Fr can be obtained by the formula (26).

Fr = Frg + Fra + Frc + Frt (26) Each resistance 値 in formula (26) is obtained by using the data stored in the database 300 using the following resistance formulas (2 7) to (3 0) (refer to "Operation Theory (DC AC Electric Vehicles)", Diaoyousha). • Slope resistance

Frg = s (21)

Frg: slope resistance (kg weight / ton) s: slope (% 〇) (positive when going uphill, negative when going downhill) • Driving resistance type

Fra = A + B · v + C · v2 (square of v) (28)

Fra: Driving resistance (kg weight / ton) A, B, C: Coefficient v: Speed (km / h) • Curve resistance type

Frc = 8 00 / r ... (29)

Frc: Curvature resistance (kg weight / ton) r: Curve radius [m] -66-(61) (61) 200303275 Changes, so for convenience, the following 値 is sometimes used)

Frt 2 (for single line tunnel) or = 1 (for double line tunnel) (30)

Frt: Tunnel resistance (kg weight / ton) Calculating method for temporary driving plan 3 23 Because the physical model of the above formula (25) is used, the temporary driving for parking will be repeated after reaching the location N meters before the target position. Plan P3. This plan is repeated to make the parking position of the temporary driving plan P3 for parking gradually approach the target position. As shown in Figure 3-9. In addition, the distance N from the target position to the start position of the temporary driving plan calculation for parking can be determined by a formula such as "travel distance" ± "ample distance". Next, referring to the flowchart of FIG. 40, the operation of the driving plan employing means 3 to 24 of FIG. 36 will be described. When a driving plan of one of PI, P2, and P3 is prepared or recalculated and set according to a specific cycle, this flowchart is a processing step of one cycle. First, the driving plan adopts the means 324 to judge whether the current train 0 driving status or driving time is at the stop, or immediately after the train leaves the station, when driving between stations, and whether it is located near the target parking position (step 1). Secondly, when it is judged as “while stopping at the station or immediately after leaving the station”, the optimal driving plan P1 prepared by the delay-thinking optimal driving schedule day-making method 3 1 4 is used (step 2). Thereafter, the driving plan adopting means 324 will output this best (62) (62) 200303275 driving plan P 1 to the control instruction precipitation means 3 09. In addition, since the operation of the control instruction extraction means 309 after the driving plan is input has been described in the foregoing embodiment, repeated description is omitted. In step 1, it is judged as "driving between stations", and the driving plan adopting means 3 2 4 will judge whether the driving plan recalculation of this cycle has been implemented (step 3). Secondly, if recalculation has been implemented, the forward-calculated driving plan recalculation means 3 1 7 is used to recalculate the recalculated driving plan P2 (step 4). On the other hand, if it is judged in step 3 that the recalculation of the traffic plan of the current cycle has not been implemented, it will be judged whether or not the best traffic plan P 1 has been adopted in the previous period (step 5). If the best driving plan P1 has been adopted at the previous hour, the driving plan adopting means 324 will adopt the best driving plan P 1 (step 2). However, when the best driving plan P 1 is not used at the previous time point, the performance time point is the time point when the best driving plan P 1 has been adopted and recalculation has been implemented since then. The user who adopted the previous time point is recalculated. Driving plan. Therefore, the driving plan adoption means 324 uses the driving plan adopted at 1 o'clock (step 6). Furthermore, the judgment in step 1 is "near the target parking position", that is, when the driving position is within N meters of the target parking position. The plan adopting means 3 24 will input the temporary driving plan P 3 for parking, which has been prepared by the temporary driving plan calculation means 3 23, and determine whether the parking position is within the range of "target parking position" and "allowable error" (step 7). Secondly, if the parking position is within this range, the temporary driving time for parking P3 is used (step 8). However, if it is not within this range, then go back to step 5 and use the driving plan for recalculation at the previous 1-68- (63) (63) 200303275 time point (or earlier time point). , Repeat the judgment of step 7 until it is within the range. As described above, this 26th embodiment uses the temporary driving plan for parking within the "target parking position" ± "allowable error" planned to stop the train near the target parking position, so that the train can stop at a good accuracy Target parking position. In addition, it is easy to obtain an automatic train operation device that considers delay time and calculates very simply because it is easy to obtain a temporary driving plan for parking when the train is moving in the direction of the train. In this embodiment, an example is described in which the temporary driving plan calculation means 3 2 3 for parking is a “forward prediction type”. However, the temporary driving plan calculation means 3 23 for parking is not necessarily required It is "forward-looking." The automatic train operating devices of the various embodiments described so far have been adopted to change the control command in stages by operating levels and braking levels for current general trains. However, in the near future, it should be possible to continuously control the command signals to drive the driving device and the braking device. Therefore, as long as the control command during acceleration becomes a continuous traction command or running torque command, the best driving plan is prepared or the driving plan is recalculated to achieve better riding comfort and higher energy saving effects. It runs automatically. In addition, the control command during deceleration can also be used as a continuous braking force command method. The best driving plan can be prepared or the driving time can be recalculated. It can also achieve automatic driving with better riding comfort and higher energy saving effects. Operational. Or, both the accelerating and decelerating sides adopt the above-mentioned continuous control instructions, which can further realize the automatic operation with better riding comfort and higher -69- (64) (64) 200303275 energy-saving effect. Next, the 27th embodiment will be described with reference to the drawings. Fig. 41 is a schematic configuration diagram of an embodiment of the present invention. The speed position calculation unit 405 calculates the speed and position of train 0 on the road based on the information from the speed detection unit 403 such as a tachometer and the information from the ground detection unit 404 that detects the ground signal from the answering machine. And input it to the train fixed position stop automatic control device 4 1 0 through the train current data acquisition means 4 1 2. In addition, it is not marked on the map, and information such as the current brake level and stop target position will also be input to the train's fixed position stop automatic control device 4 1 0 through the train's current data acquisition means 4 1 2. The train fixed position stop automatic control device 4 1 0 will be based on the current speed, current position, current brake level and other data obtained through the train current data acquisition means 4 1 2 and each stored in the brake characteristic data storage unit 4 1 1 Deceleration control of brake level, delay time of brake level switching, and response delay time, etc., using deceleration control plan formulation methods 4 1 3 Deceleration control to make the train stop at the stop target position in a combination of plural levels plan. For example, when a train is stopped at a specific position with a combination of two levels, the deceleration control plan calculates the time distribution of each brake level. First, the first brake level is maintained at the specific time obtained by the foregoing time distribution calculation, and then switched Until the second brake level and maintained until the train stops. Figure 42 is the simplest example of a deceleration control plan. This example is a deceleration control plan for a point with a remaining distance of 10m. The level is switched around the remaining distance of 6m to stop the train at the target stop position. In terms of time allocation, for example, the fake design drawing uses 2 levels. For the current speed and remaining distance, consider the dimension of the first braking level -70- (65) (65) 200303275 as a variable, and decelerate at the first braking level. The sum of the driving distance at the time and the driving distance when the second braking level is decelerated is equal to the remaining distance method. The maintenance time of the first braking level can be obtained, and then the time allocation can be obtained. If there is no solution that satisfies the conditions, you can change the combination of the two levels and repeat the same calculation. When driving distance is calculated, 'it assumes that the level switching delay time after the brake level output command is applied, and deceleration will be performed at the deceleration of the braking level before switching.' During the response delay time after the delay time elapses, it will be changed from before the switching. The deceleration of the braking level is gradually transformed into the deceleration of the braking level after the switch. After the response delay time elapses, the deceleration of the braking level after the switch will be implemented. The calculation of the temporary driving distance will be implemented under the aforementioned assumptions. Consider a plan for braking response characteristics when switching levels. The deceleration of each brake level is maintained at a constant time. According to the plan switching level formulated in this way, the train can be stopped at a specific position without frequent level switching. In the planning, the first braking level is a level with a larger deceleration, and the second braking level is a level with a lower deceleration. When the vehicle is parked at a lower level, ride comfort is improved. When the deceleration of each brake level is changed, for example, when the maintenance time of the first brake level (the level of greater deceleration) elapses (the time of switching the plan), the predicted speed at the time of deceleration at the deceleration adopted by the plan will be used Compare with the actual train speed. If the actual speed is small, that is, if the deceleration is smaller than the assumption, do not immediately switch to the second brake level (the level with a smaller deceleration), and use the extended maintenance time of the first brake level. To prevent the train from exceeding the target stop position. Fig. 43 is an example of adjusting the stop position by changing the switching plan time. In this example, the actual deceleration is less than the hypothesis, and the minus -71-(66) (66) 200303275 is slower, so the original plan is to switch to a level with a slower deceleration near 5 m to 3.2 m before switching. , Adjust the stop position. Fig. 44 is a flowchart for adjusting the stop position by changing the switching time. The extension of the maintenance time is calculated, for example, the actual deceleration is calculated based on the actual train speed at the time of switching day and time, and the deceleration control plan starting from the time point of the first brake level command is recalculated based on the estimated deceleration, or based on the calculated Decelerate and recalculate the plan starting at the moment of the switching plan. In addition, when formulating the initial deceleration control plan, the maximum preset deceleration is used. Regardless of whether the actual deceleration is small or large, the level switching time can be extended to adjust the stop position. Fig. 45 is a schematic configuration diagram of a 28th embodiment of the present invention. The structure is the same as that of the twenty-seventh embodiment except that the deceleration estimating means 416 is used to calculate the deceleration based on the time series data of the train speed during deceleration, and the basic functions are also the same. The deceleration estimation using the deceleration estimation means 4 1 6 can be obtained by the following method. For example, after the delay time of the level switching and the response delay time have elapsed, the specific deceleration corresponding to the level is used for a specific time. The internal speed should be slowed down to calculate its deceleration. When there is a large error in the train speed data, the moving average of the speed should be taken, and the deceleration should be calculated based on the data after the interference is removed by appropriate filtering. Use the deceleration estimation method 4 1 6 to estimate the deceleration at that point in time, and use the deceleration obtained to correct the successive deceleration control plan. In this way, the deceleration at each brake level is caused by the time or speed during one trip. Correspondence can also be obtained when changing, and stop accuracy is ensured. -72- (67) (67) 200303275 Fig. 46 is a schematic configuration diagram of the 29th embodiment of the present invention. Except for the planned deceleration correction means 4 1 7, the rest of the structure is the same as the 27th embodiment, and the basic functions are the same. Compare the predicted speed of the position with the actual train speed, and correct the deceleration used in the deceleration control plan corresponding to the difference. For the calculation of the predicted speed at each time point or position when the deceleration is implemented according to the deceleration control plan, for example, after calculating the braking level used by the plan and the respective time allocation, based on the current train speed and the braking level used by the plan Deceleration, level switching delay time, and response delay time. The predicted speed can be stored as an array method from the beginning to the end of the plan, or it can be referenced one by one. If the memory capacity of the control computer is limited, the train speed at the previous time step, and The deceleration of the braking level at that time is calculated successively. The comparison between the predicted speed at this time point and the actual train speed. When the train speed is small, the actual deceleration should be greater than the deceleration used in the plan. Therefore, the deceleration should be increased and the deceleration control plan should be recalculated. Conversely, when the train speed is large, the actual deceleration should be less than the deceleration used in the plan, so the deceleration should be reduced and the deceleration control plan should be recalculated. When the deceleration is changed, for example, the error tolerance of the predicted speed and the actual train speed is set, and the amount of change in the deceleration is determined according to the time until the error tolerance is reached. The planned deceleration correction method 4 1 7 is used to implement a sequential comparison of the predicted speed and the actual train speed and modify the deceleration. The deceleration control plan can be appropriately updated at any time in response to the time change of the deceleration. Due to errors in the actual train speed data, it is best to use measures such as -73- (68) (68) 200303275 to filter the data, or set the upper and lower limit of the deceleration change to prevent divergence. [Effects of the Invention] In the present invention, when traveling between stations of a train, in addition to ensuring the condition that the train is stopped at a specific time, it can also realize energy-saving operation that reduces energy loss caused by the train. In addition, the present invention can implement automatic learning of train characteristics, route characteristics, and control parameters on the line while driving, and use the learning results to realize efficient automatic train operation. In addition, the present invention can provide a device that can collect necessary data for the purpose of operating the device when the train is traveling to and from the scheduled route. In addition, the present invention achieves an energy saving effect by ruling out the influence of chasing when the train is running automatically. In addition, according to the specific embodiment, the delay time can be used to improve the stopping accuracy of the train stopping at the target position, and other embodiments can also improve the bad riding feeling caused by the step change of the speed control command during the level operation. In addition, the present invention is based on the deceleration of each brake level of the train, the brake characteristic switching delay time and response delay time, etc., the current speed of the train, the current position, the current brake level, etc. A deceleration control plan with the purpose of stopping the train at a specific position, and even if the deceleration can only be set with discrete 値, it can also be formulated to stop the train at a time without frequent switching of levels. -74- (69) (69) 200303275 A plan for specific positions, and based on this plan to improve ride comfort and ensure stopping accuracy during deceleration control. In addition, the present invention uses a combination of plural brake levels to implement time allocation calculations for each brake level for the purpose of stopping the train at a specific position, and constitutes a deceleration control permission based on the used brake level and the switching time of the brake level. Using this method, when the deceleration changes, its time allocation can also be changed, and the stop position can be adjusted without changing the level, thereby improving ride comfort and ensuring stop accuracy. In addition, the deceleration control plan of the present invention will first perform deceleration at a brake level with a larger deceleration, and then switch to a brake level with a lower deceleration, and execute a stop at a brake level with a lower deceleration, which can improve the ride quality. Comfortability: In addition, the present invention implements a comparison of the predicted speed at the switching time when the deceleration is performed according to the deceleration control plan and the actual train speed at the switching time. When the two are different, the deceleration control plan is changed. It is easy to evaluate the actual deceleration of the train, and recalculate the deceleration control plan corresponding to the deceleration change to improve the stopping accuracy. In addition, after the deceleration control plan is formulated in the present invention, if the deceleration is different from the one used in the planning, the deceleration control plan can be changed. This method can improve the ROUBUST property of the deceleration fluctuation interference control. And ensure stopping accuracy. In addition, the present invention estimates the deceleration based on the time-series data of the train speed during deceleration, and formulates a deceleration control plan based on the estimated deceleration. Using this method, the -75- (70 ) (70) 200303275 ROUBU ST and ensure stop accuracy without complicated adjustments. In addition, the present invention implements a comparison of the predicted speed at each time point or position when the deceleration is performed according to the deceleration control plan and the actual train speed, and corrects the deceleration used in the deceleration control plan according to the difference, and according to the modified deceleration By changing the deceleration control plan, this method can improve the ROUBUST of the control against the deceleration fluctuation interference, and ensure the stop accuracy without complicated adjustments. In addition, the present invention will sequentially calculate the predictions at each time point or position when the deceleration is implemented according to the deceleration control plan, based on the speed of the previous time step, the deceleration used when planning the plan, the delay time for level switching, and the response delay time. Speed. In this way, when the memory capacity of the control computer is limited, the ROUBUST property of the control against the deceleration fluctuation interference can be improved, and the stopping accuracy can be ensured without complicated adjustments. [Brief description of the drawings] Fig. 1 is a block diagram of an automatic train operating device according to a first embodiment of the present invention. Figure 2 is an example of the machine loss index and total loss index during operation. Figure 3 is an example of the machine loss index, brake loss index, and total loss index during braking. Figure 4 is an example of converter loss index and motor loss index during operation. Figure 5 is an example of converter loss and motor loss during operation. -76- (71) (71) 200303275 Fig. 6 is a diagram showing an example of the driving mode of the first embodiment. Fig. 7 is a block diagram of an automatic train operating device according to a second embodiment of the present invention. Fig. 8 is a diagram showing an example of braking loss when the running load is restricted. Fig. 9 is a block diagram of an automatic train operating device according to a third embodiment of the invention. Fig. 10 is a block diagram of a train operation support device according to a fourth embodiment of the present invention. Fig. 11 is a block diagram showing a configuration example of a thrust indicating device according to a fourth embodiment. Fig. 12 is a block diagram of the control system of the thrust indicating device of Fig. 11; Fig. 13 is a block diagram showing a configuration example of a thrust indicating device of a train operation support device according to a fifth embodiment of the present invention. Fig. 14 is a block diagram showing a configuration example of a thrust finger unit of a train operation support device according to a sixth embodiment of the present invention. Fig. 15 is an overall block diagram of a train having the automatic train operating device of the present invention. Fig. 16 is a block diagram illustrating the internal structure of the automatic train operating device of Fig. 15; Fig. 17 is a driving mode compensation concept estimated based on the weight during initial operation. Figure 18 is a flowchart of the steps in consideration of pre-business and post-business learning. Fig. 19 is a block diagram of a compensation means for the purpose of compensating for the automatic characteristic learning result of an embodiment of the present invention. (77) (72) (72) 200303275. Figure 20 is a block diagram of an automatic train operating device and a data storage unit. Figure 21 is an example of an automatic train operation mode. Fig. 22 is a block diagram showing a configuration of a train in which an automatic train operating device according to each embodiment of the present invention is arranged. Fig. 23 is a block diagram showing the configuration of the automatic train operation device #j according to the 13th embodiment of the present invention. Fig. 24 is a block diagram showing the configuration of an automatic train operating device j according to a fourteenth embodiment of the present invention. Fig. 25 is a block diagram showing the configuration of an automatic train operating device j according to a fifteenth embodiment of the present invention. Fig. 26 is a block diagram showing a configuration of an automatic train operating device j according to a sixteenth embodiment of the present invention. Fig. 2 is a block diagram showing a configuration of an automatic train operating device i according to an n-th embodiment of the present invention. Fig. 28 is a block diagram showing the configuration of an automatic train operating device j according to an eighteenth embodiment of the present invention. Fig. 29 is a block diagram showing the configuration of an automatic train operating device 1 according to a nineteenth embodiment of the present invention. Fig. 30 is a block diagram showing the configuration of an automatic train operating device 1 according to a twentieth embodiment of the present invention. Fig. 31 is a block diagram showing a configuration of an automatic train operating device 1 according to a second embodiment of the present invention. Fig. 32 is a block diagram showing a configuration of an automatic train operating device 1 -78- (73) (73) 200303275 according to a twenty-second embodiment of the present invention. Fig. 33 is a block diagram showing the configuration of an automatic train operating device 1 according to a twenty-third embodiment of the present invention. Fig. 34 is a block diagram showing the configuration of an automatic train operating device 1 according to a 24th embodiment of the present invention. Fig. 35 is a block diagram showing the configuration of an automatic train operating device 1 according to a 25th embodiment of the present invention. Fig. 36 is a block diagram showing the configuration of an automatic train operating device 1 according to a 26th embodiment of the present invention. Fig. 37 is a diagram illustrating an example of the characteristics of the optimal driving plan prepared in the embodiment of the present invention. Fig. 38 is a diagram illustrating an example of the characteristics of the vehicle meter day prepared or recalculated according to the embodiment of the present invention. Figs. 39 and 9 are explanatory diagrams of an example of characteristics of a temporary driving plan prepared according to an embodiment of the present invention. Fig. 40 is a flowchart for explaining the operation of the driving plan using means 24 in Fig. 36. Fig. 41 is a schematic configuration diagram of a 27th embodiment of the automatic control device for stopping a fixed position of a train according to the present invention. Fig. 42 is a schematic diagram of an example of a deceleration control plan adopted by the automatic control device for fixed-position stopping of a train according to the present invention. Fig. 43 is a schematic diagram showing an example of adjusting the stop position by changing the time of day when the automatic control device for stopping the fixed position of the train according to the present invention is switched. Fig. 44 is a schematic diagram showing an example of a stop position adjustment procedure for changing the stop position automatic adjustment device for the fixed position stop of the train according to the present invention at the time of switching plan (79) (74) (74) 200303275. Fig. 45 is a schematic configuration diagram of a 28th embodiment of the automatic train stop control device according to the present invention. Fig. 46 is a schematic configuration diagram of a 29th embodiment of the automatic train stop control device according to the present invention. Fig. 47 is a block diagram showing a configuration example of a general tram system having an automatic train operating device. Fig. 48 is a block diagram of the automatic train operating device of Fig. 47. [Explanation of component symbols] 0 歹 (1 car 1 automatic train running device (ΑΤΟ) 2 drive brake device 3 database 4 VVVF inverter transformer 5 main motor 6 brake control device 7 wheels 8 mechanical brake 9 speed detector 10 Ground detector 11 Track 12 Tentative driving planning section -80- (75) 200303275 13 Best driving planning section 14 Thrust command generating section 15 Driving mode compensation index calculation section 16 Loss index calculation section 17 Overload index calculation section 18 Addition section 19 Driving mode compensation section 20 Driving distance compensation section 2 1 Timing determination section 22 Train operation support device 23 Main controller 24 Thrust instruction section 25 Angle command calculation section 26 Impedance control section 27 Servo amplifier 28 Servo motor 29 Encoder 30 Suggested level indication control unit 3 1 Light 32 Suggested level indication control unit 33 Sound 'Output unit 34 Library 3 5 Driving model Formulation Department 3 6 Database

-81-(76) 200303275 1 02 White Train Control Device (ATC) 1 03 Database (DB) 1 04 Driving platform 105 Load-receiving device 1 06 Speed detector 1 07 Above ground detector 109 Drive device 110 Reducer 120 Business / driving judgment means 12 1 Business > Eight-characteristics initial setting means 122 Business / trial driving white-running means 123 Driving result storage means 124 Business \ y '刖 Estimation means 125 Estimation result compensation means 126 Features Prediction and storage means 130 Learning characteristics database (learning characteristic DB) 13 1 Initial characteristics setting means 132 Train white-moving means 13 3 After-business driving result storage means 134 After-sales characteristic learning means 13 5 Learning result compensation means 13 6 Learning Result comparison means 13 7 Learning result compensation means 1 80 Material handling means of

-82- (77) 200303275 18 1 Train white-moving operation means 1 34 1 to 1345 Automatic characteristic learning hand 1 20 1 Data storage section 203 ... Ground detector 204 ... Speed detector 20 5 ... Drive 206 ... Braking device 207 … Train characteristics learning device 2 0 8… White-motion operation control unit 209… Train weight calculation unit 2 1 0… Train resistance calculation unit 2 1 1… Braking force calculation unit 2 12 ··· Delay time calculation unit 2 1 3 .. . Ride Rate Calculation Department 3 00 Database 3 02 Speed Detector 3 03 Ground Detector 3 04 A Operation Circuit When Sin Station Is Stopped 3 04B Operation Circuit When Station Is Driving 305 i Drive Device 3 06 Brake Device 3 07 Best driving plan drafting method 308 Driving plan recalculation method 3 09 Control instruction precipitation method

-83- (2003) 200303275 3 10 Control command output means 3 11 Cumulative error reference type driving meter day recalculation means 3 12 Control command compensation means 313 Cumulative error reference type control command compensation means 3 14 Delay time consideration type optimal driving meter Drawing method · 3 1 5 Delay time consideration type traffic calculation day recalculation method 3 16 Forward prediction type best driving day calculation method 3 17 Forward prediction type driving calculation recalculation method 0 3 1 8 Successive forward prediction Re-calculation method for daytime driving model 3 19 Recalculation method for speed measurement-driven progressive forward prediction type travel plan 3 20 Inter-station driving result storage means 3 2 1 Delay time estimation means 3 22 Online delay time estimation means 3 23 Forward Temporary driving plan calculation means for predictive parking 3 24 Driving plan adopting means ^ 402 Brake device 4 0 Speed detection unit 4 04 Ground detection unit 405 Speed position calculation unit. 410 Train fixed position stop automatic control device 4 11 Brake characteristic data storage unit 4 1 2 Means for obtaining current train data 4 13 Means for determining deceleration control day-84-(79) (79) 20 0303275 4 14 Deceleration control instruction precipitation means 4 15 Deceleration control instruction output means 416 Deceleration estimation means 417 Plan deceleration correction means

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Claims (1)

(1) (1) 200303275 Patent application scope 1. An automatic train operating device, which generates a driving mode for the purpose of stopping a train at a specific position at a specific time, and has a driving mode including a frequency conversion transformer inverter and The driving and braking device of an electric machine including a main motor provides a thrust instruction for the purpose of realizing the aforementioned driving mode, and is characterized by having: Calculating a loss index representing the energy loss caused by the aforementioned driving and braking device during train operation Loss index calculation means; and Lu's first driving mode compensation means for compensating the foregoing driving mode based on the aforementioned loss index and for the purpose of reducing energy loss. 2. For the automatic train operating device in the scope of the patent application, the aforementioned first driving mode compensation means is to implement the aforementioned driving mode compensation in such a way that the time until the train stops at a specific position becomes a specific time. 3. For the automatic train operating device in the scope of patent application No. 1 or 2, in which _ the aforementioned loss index calculation means includes a brake loss index calculation means, which calculates a brake loss index representing the energy loss caused by mechanical braking during the braking action. 4. For the automatic train operating device of the scope of application for patents No. 1 or 2, the above-mentioned loss index calculation means includes the machine loss index calculation means, and the calculation is representative of the electrical equipment including the aforementioned frequency conversion transformer inverter and the main motor. Machine loss index for energy loss. -86- (2) (2) 200303275 5. If the automatic train operating device of item 1 or 2 of the scope of patent application, it has: The calculation represents the overload of the electric equipment including the aforementioned variable frequency transformer inverter and main motor A means for calculating the overload index of the overload index of the state; and a second driving mode compensation means for compensating the driving mode in a manner that prevents the aforementioned electric machine from forming an overload according to the aforementioned overload index. 6. For the automatic train operating device in the scope of application for patents No. 1 or 2, in which the compensation means of the aforementioned first driving mode will also implement the compensation of the aforementioned driving mode during train operation. 7. For the automatic train operating device in the third scope of the patent application, wherein the aforementioned braking loss index calculation means is based on the predicted or actual measurement of the running load in the same feeding interval, and the aforementioned braking loss index is calculated. The automatic train operation device of the scope of application for patent item 1 or 2, wherein the aforementioned first driving mode compensation means includes a means for calculating the aforementioned driving mode in advance and storing it in a storage device, and sequentially reading the corresponding train driving time from the aforementioned storage device. Means of driving mode. 9. A train operation support device, which calculates a driving mode for stopping a train at a specific position at a specific time, and provides a driving brake device for an electric machine including a frequency conversion transformer inverter and a main motor. 87- (3) (3) 200303275 The thrust instruction for the purpose of realizing the aforementioned driving mode is characterized by a loss index calculation method for calculating a loss index representing the energy loss caused by the train operation; based on the aforementioned loss index, The driving mode compensation means for compensating the aforementioned driving modes for the purpose of reducing energy loss; and the recommended thrust indicating means for instructing the driver to indicate the thrust recommendation 値. 10. The train operation support device according to item 9 of the scope of patent application, wherein the aforementioned driving mode compensation means is to implement the aforementioned driving mode compensation in such a way that the time until the train stops at a specific position becomes a specific threshold. 11 1. If the train operation support device of item 9 or 10 of the patent application scope, wherein the main controller has a servo mechanism, the aforementioned recommended thrust indication means is to control the position, speed, acceleration, and force of the aforementioned main controller. 1 2. If the train operation support device of item 9 or 10 of the scope of patent application, the aforementioned suggested thrust indication means includes a means for visually displaying the suggested thrust near the main controller. 13. For the train operation support device of the scope of application for patent No. 9 or 10, wherein the above-mentioned suggested thrust indicating means includes a means for conveying the aforementioned suggested thrust by sound. 1 4. An automatic train operating device, characterized by having: -88- (4) (4) 200303275 data processing means for online processing to obtain data while the train is running; based on the use of this data processing means to obtain data while the train is running Data, and pre-obtained data, automatically learn control parameters while the train is running, and train characteristics and route characteristics of automatic feature learning methods; and train characteristics and route characteristics learned using this automatic feature learning method ， Automatic train operation means to execute the automatic operation of the train. 15. If the automatic train operating device of the scope of application for patent No. 14 has a business in which the train characteristics and route characteristics necessary for the automatic operation of the train are estimated in advance using the pre-opening test runs, the initial sales of the control parameters As the former characteristic estimation method, the aforementioned automatic characteristic learning method is based on the initial estimation of the pre-business characteristic estimation method, and the driving learning after business is performed. 16. If the automatic train operating device of item No. 14 or 15 of the scope of patent application, the aforementioned automatic characteristic learning means is to judge the assumed characteristics 时 and actual 値 when the train is running, learning will be performed when there is a clear difference, and The learning content is reflected in subsequent trains. 17. If the automatic train operating device of item No. 14 or 15 of the scope of patent application is applied, the aforementioned automatic characteristic learning means performs learning based on the driving results between stations and reflects the learning content to the bottom On the train to the station. -89- (5) (5) 200303275 1 8. For the automatic train operation device of the scope of application for patent No. 14 or 15, in which the aforementioned automatic characteristic learning means is based on the driving results of 1 route, and Reflect the learning content when driving on the next route. 19. If the automatic train operating device according to item 14 or 15 of the scope of patent application, the aforementioned automatic characteristic learning means is based on the driving results on the first day, and the learning content is reflected on the next day's train When driving. · 20. If the automatic train operating device of the scope of patent application No. 14 or 15, the aforementioned automatic characteristic learning means is based on the driving results of at least a few days, and the learning content is reflected in the train operation after the next day . 2 1. If the automatic train operating device of item No. 14 or 15 of the scope of application for a patent has at least two H means among the aforementioned automatic characteristic learning means at the same time, and further has the learning result of implementing each automatic characteristic learning means Comparative learning result comparison means; and a learning result compensation means that implements each learning result compensation based on the comparison result of the learning result comparison means. 22. If the automatic train operating device of item 15 of the scope of application for patents, it also has a method for estimating the result of recruitment, and it uses the pre-business characteristics to estimate -90- (6) (6) 200303275 The result of the method is actually not When the characteristics may occur, or when the deviation from the limit characteristics that may actually occur, the aforementioned calculation result is compensated so that it is located within the foregoing limit characteristics. 23. If the automatic train operating device in the scope of application for patent No. 14 or 15 has a second learning result compensation means, the learning result using the aforementioned automatic feature learning means is a feature that is unlikely to occur in real time, or deviates from the actual When the limit characteristic 値 occurs, the compensation of the learning result will be implemented so that it is within the limit characteristic 値. 24. If the automatic train operation device of the scope of application for patent No. 14 or 15, wherein the automatic train operation device is implemented based on the error from the target travel plan and the compensation of the control instruction, the aforementioned automatic learning characteristic means is When performing the characteristic learning during the business operation, the characteristic learning is performed corresponding to the compensation amount of the control instruction according to the error between the target driving plan and the target vehicle. 25, such as the automatic train operating device of the patent application scope No. 14 or 15, An automatic feature learning method uses adaptive observers to perform feature learning. 26. For example, the automatic train operating device of the scope of application for patent No. I4 or 15, wherein the aforementioned automatic characteristic learning means uses a disturbance observer to perform characteristic learning. -91-(7) (7) 200303275 27. An automatic train operating device, characterized by having: train characteristic learning means for collecting information on train characteristics and route characteristics during train operation; and according to the aforementioned means for collecting train characteristics Train-related information. Calculates the optimal operating mode of the train. Based on this mode, it is an automatic train operation method that performs the automatic operation of the train. 28. The automatic train operating device as described in item 27 of the scope of patent application, in which the aforementioned means for learning train characteristics and a series of vehicle weight calculation means. 29. The automatic train operating device according to item 27 of the patent application scope, wherein the aforementioned means for learning the train characteristics and a series of means for calculating the resistance of the train. 30. The automatic train operating device according to item 27 of the scope of patent application, wherein the aforementioned means for learning the train characteristics are means for calculating the braking force. 3 1. If the automatic train operating device in the scope of patent application No. 27, the above-mentioned train characteristic learning means in β is a delay time calculation means. 32. The automatic train operating device according to item 27 of the scope of patent application, wherein the aforementioned means for learning the train characteristics is a means for calculating a ride rate. 3 3. The automatic train operating device according to item 27 of the scope of patent application, in which the aforementioned means for learning the train characteristics are means for calculating the shape of the route. -92- (8) (8) 200303275 3 4. The automatic train operating device such as the 27th in the scope of patent application, in which the aforementioned means for learning the train characteristics are means for calculating the slope resistance. 3 5. The automatic train operating device as described in item 27 of the scope of patent application, wherein the aforementioned means for learning the train characteristics is an operating traction force deviation detection means for detecting the deviation of the operating traction command and the deviation of the operating traction force. 36. The automatic train operating device according to item 27 of the scope of patent application, wherein the aforementioned means for learning the train characteristics is a braking force deviation detecting means for detecting the braking force command and the deviation of the braking force. 37. If the automatic train operation device according to item 27 of the patent application scope, wherein the automatic train operation control unit calculates the delay time by using the aforementioned train characteristic learning means, it is a delay time compensation means for compensating the delay time. 3 8. If the automatic train operating device of item 27 of the scope of patent application, wherein the aforementioned automatic train operation control unit detects the deviation of the running traction command and the running traction force by the aforementioned train characteristic learning means, in order to compensate the running traction command and operation Traction deviation compensation means for traction deviation compensation. 3 9. If the automatic train operation device of item 27 of the scope of application for the patent, -93-200303275 〇) When the aforementioned automatic train operation control unit detects the braking force command and the deviation of the braking force by the aforementioned train characteristic learning means, Braking force deviation compensation means for compensating the braking force command and the deviation of the braking force. 40. An automatic train operating device that controls a train's driving device or braking device based on the input of train detection position, train detection speed, operating characteristic data stored in a database, and operating conditions of an automatic train control device to perform automatic The operation is characterized by having: the aforementioned implementation circuit for performing a specific calculation when the train is stopped at the station; and the aforementioned implementation circuit for the inter-station operation when the train performs specific calculation or control when the train is traveling between stations; and When the stop is implemented, the calculation circuit has the best driving plan drafting method for formulating the best driving plan. When the aforementioned train stops at one station, the aforementioned train can be stopped at the target position of the next stop at the target time. The operational calculation circuit during driving has the following: during the execution of the optimal driving plan according to the aforementioned best driving plan formulation method when the aforementioned train departs from the previous station, if the error between the optimal driving day and the actual driving result is Re-calculation of the traffic plan will be carried out when it is more than specified Means for recalculating driving plans; means for extracting control instructions for driving plans recalculated from the means for recalculating driving plans; and outputting control instructions from the means for driving control or braking to the aforementioned driving device or braking device Means of controlling instruction output. 4 1. If the automatic train operating device of item 40 of the scope of patent application, the recalculation means of the aforementioned traffic plan in -94-(10) (10) 200303275, the cumulative error is used as the cumulative error reference type of the aforementioned error Driving plan recalculation means. 4 2. If the automatic train operating device of the scope of patent application No. 40 or 41, wherein the operation circuit implemented during the operation between stations has a control instruction compensation means, and is installed in the aforementioned control instruction precipitation means and the aforementioned control instruction output When the error between the driving plan and the actual driving result is greater than or equal to the specified method, the control instruction from the aforementioned control instruction extraction method may be compensated for the error, and the compensated control instruction may be output to the foregoing control instruction. Output means. 43. For example, the automatic train operating device in the scope of application for patent No. 42, wherein the aforementioned control instruction compensation means is a cumulative error reference type control instruction compensation means using the accumulated error as the aforementioned error. 44. For example, the automatic train operating device in the scope of patent application No. 40, in which the above-mentioned optimal driving plan formulation means is a delay time after considering the output of the aforementioned control instruction from the aforementioned control instruction output means until the control instruction starts to have an influence. Next, we will formulate the above-mentioned considerations for the delay time of the best driving schedule. 4 5. If the automatic train operating device of item 40 in the scope of patent application, wherein the aforementioned driving plan recalculation means' is considering considering the control means -95- (11) (11) 200303275 to output the aforementioned control Under the delay time between the time when the command is issued and the time when the control command starts to have an impact, the recalculation of the delay time consideration type driving plan recalculation method described above is implemented. 46. For example, the automatic train operating device of the scope of application for patent No. 44 in which the above-mentioned delay time consideration type optimal driving plan drawing method is based on the driving prediction of the aforementioned travel direction of the train to make the aforementioned train stop at the aforementioned target position as Aiming at the above-mentioned driving plan, the forward-predicting optimal driving g-ten drawing drawing method. 47. For example, the automatic train operating device in the scope of patent application No. 45, in which the aforementioned delay time consideration type driving plan recalculation means is implemented based on the driving prediction of the direction of travel of the aforementioned train so as to stop the aforementioned train at the aforementioned target position The above-mentioned recalculated forward-predictive driving plan is re-calculated. 48. For example, the automatic train operating device in the scope of patent application No. 47, in which the aforementioned forward-predicted traffic-counting day-to-day recalculation means' is a method of successively predictive traffic-counting day-to-day recalculation means that implements the aforementioned recalculation in a specific cycle. 49. For the automatic train operation device of the 48th scope of the application for a patent, in which the above-mentioned forward-forward predictive traffic counting day recalculation method is to measure the train speed according to the aforementioned specific cycle, and implement the aforementioned re-96- (12) (12) 200303275 Calculated speed measurement driving type forward predictive driving plan recalculation means. 50. The automatic train operating device according to item 45 of the scope of the patent application, in which the aforementioned calculation circuit is implemented during inter-station driving, and the inter-station has the purpose of storing driving result data including train detection position and train detection speed. Means of storing driving results, and the implementation of an arithmetic circuit when the vehicle is stopped at the stop, based on the input of the driving result data stored in the means of storing driving results between stations, the aforementioned delay time is estimated, and the estimated results are output to the aforementioned delayed time consideration Delay time estimation method for the best driving plan formulation method and delay time consideration type recalculation method. 5 1. If the automatic train operating device of item 50 in the scope of the application for patents, wherein the calculation circuit is implemented when driving between stations, the input of the vehicle result data is based on the fT vehicle storage method stored in the aforementioned stations. The aforementioned delay time is estimated, and the result of the estimation is output to the online delay time estimation means of the aforementioned delay time consideration driving plan recalculation means. 5 2. If the automatic train operating device according to item 51 of the scope of the patent application, wherein the operation circuit is implemented when driving between stations, the system has: When the aforementioned train is within a specific distance of the aforementioned target position, it will predict the parking position of the parking position. Temporary driving plan calculation means; and input from the aforementioned delay time consideration type optimal driving plan preparation means -97- (13) (13) 200303275, the aforementioned delay time consideration driving plan recalculation means, and the aforementioned parking temporary provisional means The calculation result of the driving plan calculation means adopts one of these input calculation results corresponding to the current train position, and the used driving plan is output to the driving plan adoption means of the aforementioned control instruction extraction means. The automatic train operating device according to item 52 of the patent, wherein the temporary parking plan calculation means for parking is based on the travel prediction of the travel direction of the train, and implements the previous prediction for the purpose of stopping the train at the target position. Calculation method for temporary parking plans for predictive parking. 54. For example, the automatic train operating device in the scope of application for patent No. 40, in which the aforementioned best driving plan preparation means and the aforementioned driving plan recalculation means will continuously output the aforementioned control instruction output means to the aforementioned driving device during operation. For the purpose of traction instruction, the aforementioned driving plan is formulated and recalculated. 5 5. If the automatic train operating device of the scope of application for patent No. 40, wherein the aforementioned best driving plan preparation means and the aforementioned driving plan recalculation means are used for braking, the aforementioned control instruction output means is continuously applied to the aforementioned braking device. For the purpose of outputting the braking force command, the aforementioned driving schedule calculation and recalculation are implemented. 5 6. An automatic control device for stopping the train at a fixed position, which stops the train from a specific position from -98- (14) (14) 200303275, which is characterized by: deceleration of each brake level of the stored train and brake level switching "Brake characteristic data storage unit" of brake characteristic data such as delay time, response delay time, etc .; obtain the current speed, current position, current brake level, etc. of the train "train current data acquisition means"; The data of the braking characteristics of the "Data Storage Department" and the current data of the trains obtained by "the train current data acquisition means" are drafted. The "Deceleration Control Plan" is formulated for the deceleration control plan with a plurality of brake levels to stop the train at a specific position. Means ";" deceleration control instruction extraction means "that decelerates deceleration control instructions at each point in time from the deceleration control plan prepared by" deceleration control plan formulation means "; and outputs the deceleration control instruction that is deduced by" deceleration control instruction precipitation means " To the "deceleration control command output means" of the brake device. 5 7. If the automatic position control of the fixed position of the train is applied for the item 56 of the scope of the patent application, the purpose is to use a combination of multiple brake levels to stop the train at a specific position, calculate the time allocation of each brake level, and use the brakes. The switching timing of the level and the braking level constitutes a deceleration control plan. 5 8. If the automatic control device for fixed-position stop of the train is applied for item No. 57 in the patent application scope, the deceleration control plan is to decelerate at a higher deceleration braking level, and then switch to a lower deceleration braking level. -99- (15) (15) 200303275 5 9. The automatic control device for fixed-position stop of the train, such as the scope of application for patent No. 57, which implements the predicted speed and switching time of the switching time when deceleration is implemented according to the deceleration control plan. The comparison of actual train speed will not change the deceleration control plan at the same time. 6 〇 If the automatic positioning device for the fixed position stop of the train is applied for item 56 of the patent scope, after the deceleration control plan is planned, the deceleration control plan will be changed if the deceleration has changed from the one used in the planned plan. . 6 1. If the automatic positioning device for stopping the fixed position of the train is applied according to item 5 of the patent scope, it also has a "deceleration estimation method" to calculate the deceleration based on the time-series data of the speed of the decelerating train. Deceleration control plan. 62. For example, the automatic control device for stopping the fixed position of the train under the scope of application patent No. 56 has a "planned deceleration correction means", which will predict the speed at each time point or position when deceleration is implemented according to the deceleration control plan, The actual deceleration is compared with the actual train speed, and the deceleration used in the deceleration control plan is corrected corresponding to the difference, and the deceleration control plan is changed according to the corrected deceleration calculated by the "plan deceleration correction means". 63. For example, the automatic stop device for the fixed-position stop of the train under the scope of application patent No. 59, which is based on the speed of the previous time step, the deceleration used in the planning, -100- (16) 200303275 delay time for level switching, and The response delay time successively calculates the predicted speed at each time point or position when the deceleration is implemented according to the deceleration control plan. -101-