TWI276560B - 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

1276560 (1) Field of the Invention The present invention relates to an automatic train running device that does not require a driver to stop an automatic operation of a train at a specific time at a specific time, and instructs the driver to recommend a thrust or Train operation support device with main control level. [Prior Art] An automatic train running device (hereinafter referred to as "ΑΤΟ") automatically performs the inter-station operation of the train, and the train is stopped at a specific parking position at the specific time at a specific time. Fig. 47 is a diagram showing an example of the system configuration of the electric train having such a crucible. The automatic train control device (ATC) not shown on the figure inputs a speed limit signal to the automatic train running device 1, and the data bank 3 inputs the route conditions such as the slope and curvature, the vehicle condition, and the running time table to the automatic train running device 1. And stored information such as driving resistance. Further, the automatic train running device 1 calculates 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 drive brake device 2, indicating The thrust should be provided at this point in time. At this time, the thrust command F cmd in this manual is defined as both the traction command for vehicle acceleration and the braking force command for vehicle deceleration. The traction command is the thrust command F cm d>0, and the thrust force is the thrust command F cmd<0. The drive brake device 2 is composed of a VVVF (variable voltage, variable frequency) variable frequency transformer inverter 4, a main motor 5, a brake control device 6, and a mechanical 煞 1276560 (2) vehicle 8. The main motor 5 is mechanically coupled to the wheel 7 traveling on the track 11, and the mechanical brake 8 is configured to mechanically brake the wheel 7. The effect from the thrust command F cmd to the actual thrust is different when the traction force is obtained and the braking force is obtained. When the traction is obtained, the thrust command F cmd ( > 0 ) is input to the variable frequency inverter 4 . The variable frequency inverter inverter 4 controls the torque of the main motor 5 to obtain the traction force corresponding to the thrust command F cmd . At this time, the brake control device 6 and the mechanical brake 8 do not perform an operation. When the braking force is obtained, the thrust command F cmd ( < 0 ) is input to the brake control device 6 instead of the inverter variable voltage inverter 4. First, the brake control device 6 outputs a thrust command, that is, a brake force command, to the variable frequency inverter inverter 4. The inverter variable voltage inverter 4 feeds back the electric vehicle force F elec outputted via the main motor 5 to the brake control device 6. In order to obtain the thrust command F cmd, that is, the braking force of the braking force command, the brake control device 6 will first act on the electric vehicle force F elec, and compensate the electric braking force by the mechanical braking force F mech of the mechanical brake 8 . Part of the way to control the mechanical brakes 8. Therefore, the mechanical braking force F mech is as follows. F mech = F cmd ~ F elec (1) As shown in Fig. 48, the automatic train running device 1 includes a tentative driving plan unit 1, an optimal driving plan unit 13 and a thrust command generating unit 14. The tentative driving plan 12 will generate a tentative driving mode (F0 (X), V0 (X)) as the initial 为 for the purpose of producing the best driving mode. At this time, the driving mode indicates the position of the route -8-(3) 1276560 x thrust Fn (x) and speed Vn (x) in a manner corresponding to a series of positions. The Best Driving Plan 13 will plan the best driving mode FI (X) for the train based on the provisional driving mode (F0 (X), V0 (X)) and the storage information of the database 3. In the optimal driving mode FI (X) generated, the thrust command generating unit 14 outputs a thrust command F cmd to the variable frequency variable voltage inverter 4 according to the detected position of the train, the detected speed, and the speed limit signal of the ATC. Indicates the thrust that should be output at that point in time. When planning the best driving mode for a train, there are generally a number of possible driving modes. In particular, unlike the morning and evening when the timetable is too long, the number of trains running on the train is small during the day, morning, or late at night, because the train runs at a longer interval, so the plan has a larger margin, and the driving plan is There are fewer restrictions. Japanese Laid-Open Patent Publication No. Hei 8-216885 and Japanese Patent Laid-Open No. Hei 5-193502 disclose the best driving plan for energy saving as an evaluation item. However, the energy savings of these known examples are not taken into consideration from the standpoint of energy loss caused by the driving/braking control of trains such as the drive unit and the brake unit. In contrast, the "Basic Review of the Effective Use of Recycling Energy by Changing the Brake Mode" (The 7th Annual Meeting of the Japan Railway Technology Joint Conference) and the "Review of the Practicalization of Pure Electric Vehicles" (National Electrical Society National Convention 5 - In 244), the braking mode of the train, especially the driving mode of the energy loss caused by the mechanical braking caused by the braking, is reviewed. However, the energy loss caused by the drive brake control of the train is also generated during the drive control. In addition, when the brake is controlled, there are other factors that cause energy loss in addition to the mechanical brake. Therefore, it is impossible to achieve the minimum of the comprehensive energy loss -9- (4) 1276560 [The problem to be solved by the invention] The object of the present invention is to comprehensively evaluate the energy loss caused by the train driving brake control, and to minimize the station space. The energy loss of driving, to achieve energy-saving driving. Therefore, the following is a brief description of the energy loss of the present invention. The damage caused by train driving will change due to the driving mode, and the machines that may cause losses can be divided into the following two categories. The first is the energy loss of the power inverter such as the variable frequency inverter 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 the mechanical brakes. Observing the acceleration and deceleration of the train from the point of view of energy flow, and ignoring the energy loss and driving resistance of the above-mentioned electric machine, during the running acceleration, the inverter variable frequency inverter 4 and the generator motor are transmitted via the unillustrated overhead line. The electric energy provided by the 5th drive device is converted into the kinetic energy of the vehicle, and in the deceleration of the electric vehicle, the kinetic energy of the vehicle is converted into electric energy and the power is generated again. In this ideal state, there is no energy loss. However, in the deceleration of the electric vehicle, when the braking force command of the electric vehicle or the driver exceeds the braking force that can be output by the electric machine, the mechanical braking device 8 compensates for the insufficient braking force, and the deceleration is maintained at a specific speed. When the mechanical brake 8 performs this action, the kinetic energy of the vehicle is consumed in a thermal manner, which is energy loss. In the present invention, the portion of the loss caused by the execution of the mechanical brake is defined as the vehicle loss. (5) 1276560 This brake loss occurs when the brake force command exceeds the allowable amount of the power unit, that is, the drive unit, and the load on the power supply side does not match the regenerative power. In the latter case, if the driving device obtains the braking force command, the inverter variable voltage inverter 4 is controlled so that the main motor 5 outputs the braking force corresponding thereto. At this time, the kinetic energy of the vehicle is converted into the regenerative energy of the power source. However, if there is no load corresponding to the regenerative power on the power supply side, that is, if there is no train in the acceleration, excess regenerative power is generated, thus causing the wiring. The voltage rises. Therefore, in order to suppress the rise of the overhead voltage, the drive device performs control for suppressing the braking force. This is called light load regeneration control. In this light load regeneration control operation, the main motor 5 outputs a braking force smaller than the braking force command. At this time, the lack of power will be compensated by the power of the mechanical brakes. When implementing energy-saving operation, it is important to plan an appropriate driving mode plan and actually implement driving according to the driving mode. A means for realizing the operation in accordance with the driving mode, such as an automatic train running device (ΑΤΟ) and an automatic train stopping device (TASC), which can automatically generate a thrust command without a driver, is well known. With these devices, it is possible to smoothly push the actual thrust to achieve the best driving mode. However, the system is complicated and costly because it directly targets the driving brake device of the vehicle and requires the ground device for the purpose of position detection. On the other hand, it is desirable to use a driver's skill to indicate the best thrust of the driver's skill to achieve a train approaching the planned driving mode. This is the operation support device. When such a running support device is used, the energy saving effect is better than the use of -11 - 1276560 (6) TA and TASC because of the driver's reaction delay, etc. However, it is only necessary to perform an instruction to the driver, and There is no direct relationship between the driving brakes of the vehicle, so it has the advantage of simplifying the system. Further, since the operation of the driver is relied on in the end, the ground device or the like for the purpose of position detection can be removed or simplified. Using this method can reduce costs and better cost-effectiveness. In addition, in recent years, there has been a concern that the driving skill of the driver is reduced due to degeneration. Therefore, when the operation support device is used, the thrust must be adjusted at any time according to the judgment of the driver, so that there is no problem that the driving technique is lowered. Further, the automatic train running device has been put into practical use as a speed limit that can follow the speed limit of the train and a certain degree of margin with the speed limit. However, because of the error tracking control such as ΡΙ control, there are quite a lot of places that depend on the characteristics of trains and routes. In the current situation, it is necessary to adjust the characteristics or control parameters of each train and each route. Time and labor. In addition, it is also conceivable to formulate a driving plan and to implement an automatic train running device for train driving. When planning a driving plan, the simple train driving model is sometimes used. The simplest is the way in which the trains of its objects can be represented in the following simple physical form. F— Fr = M· a ... ( 7 ) At this time, F system runs traction or braking force, Fr series vehicle driving resistance, Μ series vehicle weight, α system acceleration (including negative acceleration, ie deceleration) ). The resistance generated by the train driving resistance Fr series vehicles is usually only considered for the convenience of calculation. Starting resistance: resistance at the start of the car -12- (7) 1276560 air resistance · air resistance during train driving slope resistance: slope of the line resistance curve resistance: curve of the curve resistance resistance of the tunnel: resistance generated when driving in the tunnel If the air resistance considers the resistance between the wheel tread and the track surface, the speed is usually used twice. In general, the train running resistance Fr is usually considered for the resistance caused by the slope resistance, the air resistance, the curve resistance, the tunnel resistance, the starting resistance, and the like. Here, it is considered for the trains other than the tunnels, so only the slope resistance, the air resistance, and the curve resistance are considered. At this time, the slope resistance, the air resistance, and the curve resistance can be obtained by the following equations (8), (9), and (1 〇) (for example, refer to the document "Operation Theory (DC AC Power Vehicle)" Edit). (a) slope resistance

Frg = s ... ( 8 )

Frg: slope resistance [kg weight / ton] s: slope [%G] (positive on uphill, negative on 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

Frc = 800/r “.(ΙΟ) 1276560 (8)

Frc: curve resistance [kg weight / ton] r: radius of curvature [m] When using the model shown in equation (7) for automatic train operation, even for the automatic train operation mode based on the driving plan, train characteristics and route characteristics, etc. The characteristics also have a great impact on ride comfort and stopping accuracy. [Means for Solving the Problem] The present invention is based on the premise that a train stops at a specific time at a specific time when the train is traveling between stations, and an object of the present invention is to provide an automatic train running device and a train running support device, which can be reduced. Energy-saving operation caused by energy loss caused by driving. Moreover, the object of the present invention is to provide an automatic train running device, which can reduce the time and labor required for adjustment, and can automatically implement the characteristics after the driving, thereby further improving the ride comfort and the promotion. Stop accuracy. Further, it is an object of the present invention to provide a device for performing a necessary data collecting operation for the operation of a running device only when the train travels back and forth on a specific route. Further, an object of the present invention is to provide an automatic train running device which can realize: first, to eliminate the influence of chasing when the train is automatically operated, and to improve the effect of saving energy; and secondly, to obtain the delay time, Improve the stop accuracy of the target position; third, it can improve the poor ride comfort caused by the change of the speed control command during the execution level operation. Further, the object of the present invention is to provide a train position stop automatic control device which can ensure the stop accuracy without frequently switching the level, 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 time at a specific time, and provides a driving brake device for an electric machine having a variable frequency variable voltage inverter and a main motor. A thrust command for realizing a driving mode, which is characterized in that: a loss index calculation means for calculating a loss index representing an energy loss caused by the aforementioned driving brake device in a train driving; and a low energy loss according to the aforementioned loss index The first driving mode compensation means for compensating the aforementioned driving mode. [Embodiment] Hereinafter, embodiments 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 an automatic train operating device according to a first embodiment. Therefore, the embodiment is particularly relevant to the optimal driving plan of the automatic train running device, and the other parts are omitted. The optimal driving plan unit 13 shown in Fig. 1 is composed of a driving mode compensation index calculating unit 15 , a driving mode compensating unit 19 , a driving distance compensating unit 20 , and a timing determining unit 21 . The driving mode compensation index calculation unit i 5 ' is composed of a loss index calculation unit 16 , an overload indicator calculation unit 17 , and an adder 18 . The loss index calculation unit 16 calculates the loss index CPL ( χ ) of the train position x based on the tentative driving mode (F 〇 (X ), V0 ( X )). At this time, the CPL is Cost of Power Loss. At this time, the driving mode is expressed by the thrust Fn(x) and the speed Vn(x) of a position X of -15-(10) 1276560. Figures 2 and 3 are examples of various loss indicators. Figure 2 is the loss indicator for the operation, and Figure 3 is the loss indicator for the vehicle deceleration. In more detail, Fig. 2(a) is a machine loss indicator, Fig. 2(b) is a total loss indicator, Fig. 3(a) is a machine loss indicator, and Fig. 3(b) is a brake loss indicator. Figure 3 (c) is the total loss indicator. Here, the machine loss indicator refers to the loss index of the electric machine. Specifically, it is the addition indicator (frequency conversion transformer) loss indicator and the motor (main motor) loss indicator. These indicators are expressed as a function of velocity v and thrust F and are calculated by multiplying the loss [W] of a certain operating point (v, F) by the reciprocal of velocity [m/s]. Multiplying by the reciprocal of the speed, a formal evaluation can be performed on the loss caused by a slight change in the speed vl [m/s] of an operating point Δ v [m/s]. The total loss indicator CPL (X) is calculated by multiplying the weight loss factor W1 by the total of the machine loss indicator and the braking loss indicator. The weighting factor w 1 is set from the viewpoint of the degree of loss reduction effect that can be obtained, or is set to be balanced with other indexes. That is, the loss index CPL (X) = Wlx (machine loss index + brake loss indicator) ... (2) The overload indicator calculation unit 17 calculates the train according to the tentative driving mode (f〇(x), V0 (x) ) g The overload indicator COL (x) of position x. COL Department Cost of Over Load 〇 -16 - (11) 1276560 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), V0 (X)), the corresponding converter loss [W] and motor loss [W] are integrated respectively, and the converter loss plus the time between stations can be calculated [J ] and motor loss [J]. If it is overloaded beyond the specification 値 [W], the corresponding overload indicator is calculated. For example, if the weighting factor is W2, the converter loss indicator COLC (X) can be COLC (X) = W2x {converter loss [J] / (travel time + stop time) a converter specification [W]} x conversion The loss indicator (Fig. 5(a)) is calculated. COLC system Cost of Over Load in Converter 〇 Similarly, the motor loss indicator COLM ( X ) can be obtained using the motor loss indicator shown in Figure 5 (b) (however, the weighting factor is W3, which can be set separately). The overload indicator COL ( X ) can be added to C0L(x)= COLC(x)+ COLM(x) ... ( 4 ). COLM Department Cost of Over Loss in Motor 〇-17- (12) 1276560 Adder 1 8 will add loss indicator CPL ( χ ) and overload indicator COL (x), and C (x) = CPL ( X ) + COL ( x ) to find the total indicator C ( x ) of the train position x. The driving mode compensating unit 19 adds the total index C ( X ) to the thrust mode of the tentative driving mode; p 〇 ( X ), and outputs the first compensating driving mode F 〇 ! (X ) (in this stage, the speed mode VO ( X) will not change). The first compensating driving mode F0 1 ( X ) does not match the specific enthalpy because the thrust mode is only compensated. In order to make the driving distance X and the specific 値 coincide, the driving distance compensation unit 20 performs the first compensation driving mode (F 0 1 (X), V 0 (based on the route condition, the vehicle condition, and the driving resistance stored in the database 3). X)) compensation, and output the second compensation driving mode (F02 (X), V02 (X)) and the driving time τ run. The distance compensation can be implemented by, for example, adjusting the taxi time. However, the distance compensation method is not limited by this. The timing determination unit 21 determines whether or not the travel time τ run is within the allowable error for the specific 値. When the travel time T run is outside the allowable error, the second compensated driving mode (F02 (X), V02 (X)) will be regarded as the new tentative driving mode (F0'(X), V0, (X)), Perform calculations. When the travel time T run is within the tolerance, it is then used as the best driving mode (FI (X), V1 (X)) output. With the above configuration, the tentative driving mode (F0 (X), V0 (X) -18-1276560 (13)) can use the loss index CPL (x) and the overload indicator COL (x) to make the thrust of the position x effective. make up. For example, Fig. 6 is a result of the driving mode of the embodiment of the present invention. At this time, it is assumed that the overload condition has not been reached, so the overload indicator has no effect. The "original mode (A)" is a tentative mode. The "Applicable indicator (B)" is the first compensation driving mode (F01 (X), V01 (X)). The higher the loss index is, the more the thrust compensation is implemented, and the weaker the braking force will be. On the other hand, although the running acceleration side is small, it also compensates for the traction force corresponding to the loss indicator. Although the "level quantization (C)" does not appear in the embodiment of the present invention, the level is only 6 stages, and if the continuous thrust cannot be output, the thrust F0 1 ( X ) of the first compensation driving mode is selected. The thrust with the lowest thrust error. "Distance adjustment (D)" is the second compensation driving mode (F02 (X), V02 (X)) that compensates for the "Distance Quantization (C)" mode and makes the driving distance a specific 値1 300m. Compared with the loss of 2070 [kJ] in the driving mode before compensation, the loss of the second compensation driving mode is 1 650 [kJ], which reduces considerable energy loss. In terms of driving time, the latter is 84.5 [sec] compared to the former, and the latter is increased by 84. 9 [sec]. By repeating the calculation until the travel time becomes a specific time, it is possible to generate the optimum driving mode F 1 ( X ) that minimizes the energy loss in the drive brake control while ensuring the timing and the fixed position stop. In this way, the best energy saving effect can be achieved while ensuring timing and positional stoppage. When only the driving mode in which the total energy loss is minimized is performed, the power of the variable frequency inverter 4 (converter) and the main power -19-1276560 (14) motive 5 (motor) included in the driving brake device 2 may be generated. The energy loss caused by the machine increases. The operating range of the power machine is limited by the specifications. When the operating condition exceeds the specification, that is, when the overload condition occurs, the temperature rises due to heat generation, and the protective action or malfunction or burnout occurs. The overload indicator calculation unit 17 determines the degree of overload of each machine for the tentative driving mode. When the result of the judgment is overloaded, the purpose of suppressing the energy loss of the electric machine is to compensate the thrust of the overload indicator. Since the thrust compensation is performed from a region where the energy loss is large, the overload state can be effectively avoided. In this way, it is possible to avoid the stoppage and malfunction of the electric machine due to overloading, and improve the reliability of the system. Since the optimal driving plan is also implemented in the train, the position and speed of each moment can be used as the initial condition. And in the case of ensuring the timing and stop position of the stop to the next station, the optimal energy saving driving mode is generated. That is, when the current driving mode is deviated due to the speed limit of the ATC or the like, the optimal energy saving driving mode can also be obtained from this state. If you follow the original driving mode, you may increase the loss and not the energy loss. Therefore, even in the event of an unexpected situation that deviates from the original driving mode, the best energy saving can be achieved from this point in time. In the present embodiment, the position and speed are used as the initial conditions, and when the timing and the stop position are ensured until the next station, the optimal energy saving driving mode is generated, so that it can be applied not only to the automatic train between the stations. The automatic train running device (ΑΤΟ) that is operated can also be applied to the automatic train stop control device (TASC) that performs fixed-position parking control only in the braking section. -20- 1276560 (15) In addition, this embodiment is based on the premise that the driving distance and the specific 値 are the same, and the configuration is based on the calculation of the compensation of the driving mode until the driving time reaches a certain level. The above can also make the driving time and the specific 为 consistent with the premise, and the calculation of the compensation of the driving mode is implemented until the driving distance reaches a certain level. Figure 7 is a block diagram showing a schematic configuration of an automatic train running device according to a second embodiment, and the same portions as those in the first embodiment are denoted by the same reference numerals, and the description thereof will be omitted. Instructions are given. 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 in the acceleration of each feeding section at a certain time. The loss index calculation unit 16 extracts the corresponding operational load from the database information of the operation schedule and the operation load. As mentioned above, since the loss of the brake will change due to the running load, the loss index corresponding to the running load is calculated. Others are the same as in Figure 1. From the above, the following effects and effects can be obtained. Corresponding to the predicted operational load, adjust the loss indicator CPL ( X ), especially the brake loss indicator. For example, Fig. 3(b) shows the vehicle loss index when the load is fully operated. Because of the limitation of the capacitance of the inverter transformer 4, the higher the speed of the vehicle, the higher the loss index will be. Fig. 8 shows the vehicle loss index when the load is fully operated (125 kW / main motor). At this time, because the running load is insufficient, it is impossible to output the area of the electric vehicle force equal to the thrust command F cmd . That is, the loss indicator from the lower speed will increase from -21 - (16) 1276560. Therefore, the energy loss caused by the load state can be reliably predicted, and more efficient energy saving driving can be realized. Fig. 9 is a block diagram showing a schematic configuration of an automatic train running device according to a third embodiment, and the same portions as those in Fig. 16 are attached with the same reference numerals and the description thereof is omitted, and only the different portions are used here. Description. The apparatus of Fig. 9 is provided with a database 34 and a driving mode separating unit 35' in place of the tentative driving plan unit 12 and the optimal driving plan unit 13 of Fig. 48. The data bank 34 stores the driving mode at the time of driving between the stations of each train. The driving mode precipitation unit 35 extracts the driving mode F 1 ( X ) corresponding to the current inter-station driving from the database 3 storing the running time table. The driving mode stored in the database 34 can be realized by the following method, that is, the optimal driving pattern shown in the first embodiment is stored in advance, and the optimal driving mode of the result is stored. According to the above configuration, the following effects and effects can be obtained. In the generation of the optimal driving mode, since the convergence calculation is repeatedly performed to execute the optimal plan, it takes some time to calculate. Therefore, when the driving plan of the next station is implemented in the parking at the departure station, the calculation time is sometimes limited and the optimum is not sufficient. Implementing these plans in advance avoids the limitation of computing time and gets the best driving mode. This way, you can further improve the energy saving effect. In addition, the driving mode is calculated in advance, and the driving mode can be accurately confirmed. In this way, the abnormal mode can be eliminated and the reliability of the system can be improved. Fig. 1 is a block diagram showing a schematic configuration of a train system having a train operation support device according to a fourth embodiment, and the same portion as in Fig. 47 is attached with the same -22-1276560 (17) symbol, and the description thereof is omitted. Only the different parts are explained here. Here, there is provided a train operation support device 22 in place 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 a thrust recommendation 値 Free. That is, the train operation support device 22 outputs a thrust recommendation 値 Free for replacing the thrust command F cmd of the automatic train running device 1. This thrust recommendation 値Free is input to the thrust indicating device 24 provided to 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 drive brake device 2. The configuration of the thrust indicating device 24 is as shown in Fig. 1 for example. The thrust indicating device 24 is composed of an angle command calculating 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 coupled. The thrust recommendation 値Free outputted by the train operation support device 22 is input to the angle command computing unit 25. The angle command computing unit 25 calculates the angle of the main controller corresponding to the thrust recommendation 値Free of the input, and outputs it as an angle command Θ cmd. The impedance controller 26 inputs the angle command Θ cmd and the actual main controller angle 检测 detected by the encoder 29, and outputs the servo amplifier 27 so that the latter (angle Θ) and the former (angle command Θ cmd) Torque command T cmd for the purpose. The servo amplifier 27 drives the servo motor 28 in such a manner that the output torque of the servo motor 28 and the torque command T cmd are generated, and the impedance controller 26 applies a torque Tope applied to the main controller 23 for the driver to form The expected impedance (inertia moment J, damping D, stiffness K) -23-1276560 (18) way to control the servo motor 2 8, the block diagram of the control system is shown in Figure 12. The total coherence moment of the rotor of the servo motor 28 and the main controller 23 is hereinafter, and gl and g2 correspond to the cutoff frequency of the filter for the purpose of removing interference. When the angle command Θ cmd is zero, the torque applied from the outside to the main controller 23, that is, the torque T ope applied by the driver to the main controller 23 reaches the transmission function θ ( s ) of the main controller angle ,, If the interference cutoff filter is ignored, it can be expressed by the following equation, so it is known that the desired impedance (J, D, K) can be obtained. 〇0{s)=J.s2JD.s^KmT〇Pe (6) The above configuration has the following The role and effect. The thrust indicating device 24 controls the angle of the main controller 23 with the servo motor 28 to obtain a thrust command F cmd which is calculated by the train operation support device 22 and which is free. In this manner, when the driver operates the main controller 23, the impedance of the impedance controller 26 is controlled to make the driver feel that the desired impedance (J, D, K) has been reached. That is, the driver can obtain the thrust command F cmd corresponding to the thrust recommendation 値Free without the main controller 23 being touched. On the other hand, when the driver operates the main controller 23, it can withstand the force from the servo motor 28 to the thrust in the direction of the thrust, and can be set at any angle, that is, the thrust command F cmd. That is, the driver can also delegate the driving to the train operation support device 2 2, and if necessary, the driver can operate the main controller 23 and control the thrust command according to the consciousness. The angle of the main controller 23 for the purpose of energy-saving operation can be -24 - (19) 1276560 using the reaction force detection from the main controller 23, and in the case of realizing the energy saving position stop mode Perform driving. Therefore, in addition to the use of the driver's operation to achieve energy-saving driving and positioning to stop driving, in the event of an unexpected situation, you can quickly take countermeasures. The thrust command F cmd for driving the 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, and the system can be simplified. Further, in the case of the train operation support device, it is necessary to pass the driver afterwards, and it is not necessary to require the train operation support device 22 to have strict positional stop accuracy, so that the device can be simplified. In this way, the reliability and cost of the system can be improved. Moreover, the train operation support device needs the driver after all, so the driver's operation technique needs to be required at any time. According to this embodiment, it is possible to avoid the problem that, in the case of a system having an automatic train running device, it is possible to cope with an unexpected problem due to a decrease in the operating technique of the driver. Fig. 3 is a block diagram showing a schematic configuration of a train operation support device according to a fifth embodiment. Since the configuration of the thrust indicating device 24 is different from that of the fourth embodiment in the present embodiment, the different portions will be described here. However, in the present embodiment, the thrust command is accelerated in six stages (P 1 to P6 ), the brake is decelerated in six stages (B1 to B6), and the neutral position (N), and the emergency brake (EB) is adopted as the main controller level. the way. The grade here refers to the model used to control the speed and thrust, and is used in the current tram drive control. The number of segments can range from several segments to more than 30 segments, depending on the system. Further, the main controller 23 of Fig. 3 is a schematic configuration when viewed from above. -25- (20) 1276560 The thrust indicating device 24 is composed of a recommended level indicating the control unit 3 and the lamp group 3! In the illustrated embodiment, the lamp group 31 is composed of six lamps corresponding to the operation acceleration levels Ρ 1 to Ρ 6 , six lamps corresponding to the brake deceleration levels β i to β 6 , and lamps corresponding to the neutral level N, And the light bulb corresponding to the emergency brake class EB, which is composed of 14 lamps. The recommended level indicating control unit 30 receives the recommended level command N r e c of the train operation support device 2 2 and performs control to illuminate the corresponding lamp. According to the above configuration, the following effects and effects can be obtained. The driver can use the lighting to confirm whether or not the level is set to achieve the goal of saving energy in the case of ensuring the timing and the stop position. For example, the content of the recommended level command N rec is the running acceleration level P6, and its corresponding lamp will illuminate, and for the braking deceleration level B3, the corresponding lamp will illuminate. The driver can observe the status of the lighting and implement the level operation of the corresponding main controller 23, thereby realizing energy saving driving that suppresses energy loss. There is no direct electrical/mechanical connection between the thrust indicating device 24 and the driving brake control system, and the driver's operation is required. Therefore, in the event of an unexpected situation, the driver can quickly respond according to the judgment of the driver, and the system is improved. Trustworthiness. The lamp and the display device using the LED (Light Emitting Diode) are easier to implement than the servo mechanism of the main controller 23 of the fourth embodiment, and the reliability of the system can be improved, and the cost of the device can be further reduced. Fig. 14 is a block diagram showing a schematic configuration of a train operation support device according to a sixth embodiment. The present embodiment differs from the fifth embodiment only in the configuration of the thrust indicating device 24. Therefore, only the different portions will be described here. (21) 1276560 The thrust indicating device 24 of the present embodiment indicates the control unit 32 by the recommended level. And the sound output unit 33 is configured. When the recommended level command N rec is received from the train operation support device 22, the recommended level display control unit 32 controls the sound output unit 33 to output the corresponding voice. For example, when the recommended level is B3, a voice such as "Block 3" will be issued. With the above configuration, the following effects and effects can be obtained. The driver can know by voice the level of energy saving driving in the case of ensuring timing and positional cessation. According to this aspect, the same effects and effects as those of the fifth embodiment can be achieved. When the recommended level is indicated by a lamp in the fifth embodiment, the driver's attention will be focused on the expression, and as a result, an accident may occur due to failure to pay attention to the front. In contrast, using the instructions of the voice, this problem can be avoided and the reliability of the system can be improved. Fig. 15 and Fig. 16 show an embodiment of the automatic train running device of the present invention. The automatic train running device (ΑΤΟ) 108 placed on the train shown in the figure is obtained from the automatic train control device (ATC) 102 of the above-ground system, and is obtained from the database (DB) 103 of the train. Route conditions (inclination angle and radius of curvature of the curve, etc.), vehicle conditions (number of trains, weight, etc.), and operating conditions, etc., will also obtain a departure signal from the driver's station 104, and obtain a load signal from the load-carrying device 105. The train speed signal is obtained from the speed detector 106, and the train position signal is obtained from the above-ground sub-detector 107 that responds to the ground position that is appropriately placed on the route. A sub-segment that is moderately placed on the route is used to confirm the position of the train -27- (22) 1276560. Here, the DB 103 indicates that it is placed in the train 0, and may be a ground system located outside the train, or may be distributed in the train 0 or on the ground. In addition to the data processing means 180 for performing on-line data processing and the automatic train operation means 181, the AT0 108 has a calculation means and a learning means represented by the pre-employment characteristic estimating means 124 and the post-business characteristic learning means 134 which will be described later. The data processing means 1 80 will process the train speed signal. In addition to the processing of the train speed, the train position (time integral of speed), train acceleration (differential speed of speed), and train travel distance (absolute speed) Time integration 値) Implement continuous operations. From the train position to the train travel distance, moderate compensation is performed according to the train position signal of the ground sub-detector 107. The data processing means 180 performs a specific calculation based on each input signal, and provides the measurement data necessary for the learning and the automatic operation of the train to be described later. The necessary measurement data for the automatic operation of the train will be provided to the train automatic operation means 81. The train automatic operation means 1 8 1 outputs an operation command to the drive unit 9 or a deceleration command to the speed reduction device 110 in accordance with the result of calculation using each input data. The drive unit 109 includes a main motor for the purpose of towing the train, and a power converter for controlling the same. Moreover, the reduction gear unit U0 usually has both a mechanical brake and an electric brake. The AT0108 is placed on the train, and 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 illustrated in detail in Fig. 16, which is determined by the pre-business driving means 1 20. Pre-business characteristics initial setting means 1 2 1. Pre-business test train automatic train -28- (23) 1276560 Operation means 122, driving result storage means 123, pre-service characteristic estimating 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, train automatic operation means 1 32, post-business driving result storage means 133, post-business characteristic learning means 134 and learning result compensation means 135 are formed. The means 121 to 126 are processing means for the purpose of driving the vehicle before the driving test, and the means 1 3 1 to 1 3 5 are the processing means for the purpose of driving, the pre-business driving determination means 120 and the learning characteristic DB 13 0 system. It has nothing to do with the business before and after the driving, but it is set by the two. In Fig. 16, the data processing means 180 and the automatic train operation means 1 8 1 which are originally used as the automatic train running device are omitted. Next, the operation of the devices of Figs. 15 and 16 will be described. In Fig. 15, ΑΤ0108 will obtain the speed-restricted data from the ATC 102 in advance, obtain the pre-acquired information such as the route condition, the vehicle condition, and the operating conditions from the DB 103, and simultaneously obtain the speed, and then perform a specific operation to generate the operation. The control command formed by the command or the deceleration command realizes the automatic operation of the train 如 as described above. ΑΤ 0108 receives the departure signal from the driving station 104, and starts the automatic operation operation by the automatic train operation means. After the departure, the load information obtained from the load handler 105, the speed data obtained from the speed detector 106, and the ground detection information obtained from the ground sub-detector 107 are utilized. The load information is used as the weight related information of the train, and the ground sub-test information is used for the compensation of the position information. Using this information, -29 - 1276560 (24) ΑΤΟ 1 0 8 can be used to formulate train control commands (running commands / deceleration commands). When the operation command is prepared as a control command, the operation command is output, and the train is operated by the drive device 109. In addition to the running torque (running traction) command, the running command has a running level command and the like when driving. When the deceleration command is prepared as a control command, the deceleration command is output. The deceleration device 1 10 is used to decelerate the train. The deceleration command is the braking force command, and when the class is driving, it is the braking class command. Next, a detailed description of the action of AT01 08 will be carried out with reference to Fig. 16. When receiving the departure signal from the driver's station 104, first, the pre-business test driving means or the driving after the business is judged by the pre-employment driving determination means 120. The method of judging at this time may be a method using a flexible flag such as "testing when the flag is not set", "having a business trip when the flag is established", and a method of setting the result by using a rigid switch. When the pre-business driving determination means 120 determines that the pre-business test is being carried out, the pre-business characteristic initial setting means 1 2 1 sets the initial characteristic parameters at the time of the pre-business test driving. The method of setting can be considered by using the human-machine interface to manually implement the setting method before the start of driving. In addition, in terms of setting the contents of the 値, it is possible to extract the characteristic parameters from the information that can be obtained in advance, such as the specifications of the train and the route characteristics, and input them. Then, using the characteristic parameters set by the pre-business characteristic initial setting means 1 2 1 , the test driving of the train using the automatic operation is carried out by the pre-service test train automatic operation means 1 22 . In terms of the method of automatic train operation, such as the development of the best driving plan when parking by the station, according to the implementation of the automatic operation, and the large deviation from the best driving plan, re-planning driving -30-1276560 (25) plan Or a method of applying compensation using error feedback to the control command. In addition, in the case of a pre-operating pre-vehicle, for example, a train of a graded train, a test vehicle or the like of a utilization level for the purpose of characteristic estimation is performed, and a vehicle for the purpose of characteristic estimation is executed. Next, the result of the automatic operation performed by the pre-service test train automatic operation means 122 is stored by the driving result storage means 1 23. When storing, the target driving plan, speed information and location data measured at the time of driving are regarded as electronic files stored in a medium such as a hard disk (HD). Next, the calculation of the characteristic parameters is carried out by the pre-service characteristic estimating means 124 using the test driving result stored by the driving result storing means 133. Predicted characteristic parameters such as weight, acceleration characteristics, and deceleration characteristics should be implemented before the business. In terms of the weight of the total number of trains, the number of trains before the operation is “there is no passenger riding”, and the acceleration or deceleration during taxiing and the driving resistance of the train can be used to estimate. Here, a case where the train of the object is expressed by the simple physical form of the equation (7) is considered. In terms of train running resistance, calculations can be performed using the formulas of the route characteristics such as slope and curvature, air resistance, and frictional resistance. In addition, please refer to the document "Operation Theory (DC AC Power Station)" for the calculation of train running resistance. In general, the train running resistance F Γ can be expressed as follows.

Fr= Frg+ Fra+ Frc -31 - (26) 1276560 =s + (A+Bv+Cv2 ) + 800/r But 'Fr is the train resistance [kg weight / ton] ' Frg is the slope resistance [kg weight / ton] ( Uphill is positive and downhill is negative) 'Fra is driving resistance [kg weight/ton], Frc is curve resistance [kg weight/ton], s is slope [%Q], a, B, C are coefficients, v For train speed, r is the radius of curvature. If the above items are considered, the weight can be estimated by the variant of equation (7). M= (F-Fr) la ". (12) In the formula (12), when the vehicle is coasting, the running traction force F may be set to 0 (zero). Further, in terms of acceleration (or deceleration) α, the calculation can be performed using the measurement result (train travel speed) by the minimum level method or the like. In the above process, the weight Μ can be derived. After the calculation of the weight Μ is completed, the weight estimation 値 can be used to estimate the running characteristics and the braking characteristics. First, use the weight estimation VI] VIest, the acceleration aacc at runtime, and the train running resistance Fr to estimate the operating characteristics (the relationship between the operating level and the running traction, etc.). The acceleration a acc and the train resistance Fr in operation can be obtained by the same processing as the aforementioned weight calculation. Using its weight and weight estimation, the running traction can be estimated by the following formula: F° F = Mesta acc + Fr -32- (27) 1276560 When the train is operated with the grade, the running traction of each grade can be calculated by the equation (1 3 ). It can also be used to estimate the relationship between the operating level and the running traction. In addition, the braking force characteristics can be estimated by using the weight estimation 减, the deceleration during deceleration, and the train running resistance. The deceleration at the time of deceleration and the train resistance can be obtained by the same processing as the above weight calculation. Using this and the weight calculation 値, the braking force F can be estimated by the following formula.

14 F = Mesta dec + Fr However, a dec is the deceleration (negative acceleration). When a train that performs a brake operation is used, the brake force of each grade can be estimated by the formula (1 4 ). This result can be used to estimate the relationship between the brake class and the braking force. These calculations are best calculated after the station is driving or when the vehicle is parked. However, it is also possible to perform calculations in the train and confirm the calculation results in the train. By implementing the weight, the running characteristics, and the braking characteristics in this way, the number of trains can be adjusted in the number of trains, and the adjustment can be completed in a shorter period of time before the business trip. Next, the characteristic obtained by the pre-business characteristic estimating means 1 24 is estimated, and the compensation result compensation means 125 is used for compensation. When performing compensation, it should be set within the allowable range of theoretically achievable characteristic parameters and must be corrected to this tolerance. For example, if the characteristic calculation is over-33-(28) 1276560, the setting of the calculation beforehand, or the restriction within the allowable range, etc., can be considered. If the deviation from this allowable range is too large, the operation of the test driving or the like must be re-executed. Next, the characteristic calculation of the compensation is performed by the estimation result compensation means 156, and the characteristic estimation calculation means 126 is stored in the learning characteristic DB 130. The method of storing can be the same as the above-described driving result storage means 1 23 . The learning characteristic DB 130 can store the characteristic learning results obtained after the business trip described later, in addition to the characteristic calculation results obtained by the trial driving before the driving. The case where it is determined by the pre-vehicle driving determination means 120 that it is driving after the business is described below. When driving, the initial parameters of the characteristic parameters are set first by the characteristic initial setting means 1 31. In the first business driving, the characteristic parameters (characteristic estimation results) stored in the storage means 1 26 are estimated using the utilization characteristics obtained from the learning characteristic DB 130. The function parameter (characteristic learning result) obtained from the learning result can be used at the same time as the business travels, and the characteristic parameter set by the characteristic initial setting means 1 31 can be used, and the train automatic operation means 132 Perform automatic running of the train. In terms of the automatic operation of the train, basically, it is the same as the automatic train operation means 122 for the pre-operating test train. When there is an unspecified number of passengers, the weight changes. Therefore, the initial weight of the station must be estimated when running from the station. The weight calculation method can also utilize the load if the load can be obtained. When the load is not available, -34-1276560 (29) can be used to calculate the weight in the same manner as the pre-service characteristic estimation means 124 and the estimation result compensation means 125 at the initial stage of the station departure. If the result of the calculation and the characteristic initial setting means 1 3 1 are different, the processing plan and the like must be re-executed. Figure 17 is a summary of the weight calculations performed during the initial operation from the station. In Fig. 17, the horizontal axis represents the distance from the departure station to the next station, and the vertical axis represents the speed of each position in the speed mode. According to the characteristics of the departure station, the optimal driving mode is calculated according to the characteristics of the departure. 1 3 1 (fine dotted line) After the execution of the driving, the actual driving result according to the initial running interval 130 will be the actual driving mode 1 3 2 (thick solid line) The weight calculation is performed, and based on the weight calculation, the driving mode 13 2 (thick broken line) is calculated by recalculating and implementing the compensation, and the actual running operation is performed accordingly. Next, the result of the automatic operation performed by the automatic train operation means 32 is stored by the post-business driving result storage means 33. The method of storing ' can be the same as the above-described driving result storage means 23. Next, the characteristic learning is performed by the post-business characteristic learning means 134 using the result of the driving stored in the post-business driving result storage means 133. Regular learning aspects of this feature are implemented in the following manner. (1) Learning based on the results of inter-station driving (2) Learning based on the results of the whole route (3) Learning based on the results of the 1 day driving (4) Learning based on the results of several days of driving (5) Driving according to several months The results of the following are explained below for each of (1) to (5). -35- (30) 1276560 (1) Learning based on the results of the inter-station driving According to the inter-station driving results obtained after the inter-station driving, the learning results are reflected in the next station driving. For example, when it starts to rain, learn the correspondence when the braking force is reduced. It is judged that an example of learning must be carried out for the driving result of one station, for example, the corresponding reduction in the vehicle power in the rainy day. In rainy days, if the train uses air brakes, the rain will reduce the friction of the brake blocks and reduce the braking force (deceleration performance). At this point, after the start of rain, you should find that the deceleration performance is reduced. Just learn the characteristics of the braking force based on this result. The learning result at this time is usually temporary, so it can be stored separately and used as a temporary characteristic parameter. (2) Learning based on the results of the full-route driving The learning is performed based on the driving results from the first to the last of the route, and the learning results are reflected on the driving of the next route. For example, when there is almost no shortage of the target stop position (deviation amount) at the end of a route, in order to eliminate the amount of deviation, it is sufficient to perform the learning of the vehicle force characteristic corresponding to the deviation amount. For example, when the target stop position is exceeded, the setting of the braking force characteristic should be slightly larger than the actual 値. 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 carry out the learning in which the setting of the braking force characteristic is slightly smaller. (3) Learning based on the results of the one-day driving The learning is carried out based on the results of the one-day driving, and the results of the learning are reflected in the driving of -36- (31) 1276560 on the next day. For example, when observing the results of the driving on the 1st (for example, the driving result of several trips of the entire route), it is almost certain that the parking between the stations will definitely exceed the same level with respect to the target stop position. It is likely that there is an error in the setting of the route characteristic parameters such as the slope and the curve between the stations. In this case, it is sufficient to perform the learning of the route characteristic parameters such as the slope and the curve with a slight adjustment of the corresponding driving result. (4) Learning based on several-day driving results Store the results of several days of driving and perform learning based on the stored results. For example, 'observing the results of several days of driving, if the deviation of the driving plan will only occur at the same time, it should be affected by certain factors, and only the running traction characteristics or braking force characteristics of the time zone are deviated. The actual situation. If there is no deviation in other time zones, the characteristic parameter itself should not deviate from the actual situation, so only the characteristics of the object time zone are compensated, and then the compensation can be corrected by learning. (5) Learning based on the results of several months of driving When storing the driving results for several months, the learning is performed based on the stored results. For example, the learning is performed based on the stored driving results such as the maintenance check. For example, by observing the results of the three-month driving, it can be seen that the vehicle power of 3 months ago, 2 months ago, and 1 month ago will gradually decrease as time passes. In this situation, it is difficult to judge by studying the results of several days of driving. When using an air brake, it is likely that friction causes the brake block to wear. Therefore, it is necessary to change the (learning) characteristic parameter based on the result, or -37-1276560 (32) is to take measures such as replacing the brake block according to the degree. In addition, measures to change the aging time such as the wheel diameter can be used. In the above study, the examples shown in the flowchart of Figure 18 can be selectively used to implement the learning. In the first drawing, the pre-business driving determination means 1 20 is used to determine the pre-business test driving or the business driving after the business (step 15 1), and the result of the determination is the former (pre-business test driving), and the business is carried out. Before the test is carried out (step 1 52), the initial parameter estimation is performed (step 1 5 3 ) and the processing is terminated. If the result of the determination in step 151 is a business trip, one of the five types of learning corresponding to the driving content is implemented. In other words, it is judged that the vehicle is in the end of the driving mode (step 1 54), and if it is to end the inter-station driving, "(1) learning based on the inter-station driving result" (step 1 5 5) is executed, Then, "(2) Learning based on the results of the entire route is implemented" (step 156). In step 154, if the driving is completed for one day, the data of how many days are stored is further determined (step 157), and based on the result of the judgment, if the data of one day has been stored, "(3) basis 1 is implemented. "Learning of daily driving results" (Step 1 5 8), if it is already stored for several copies of the data, implement "(4) Learning based on several-day driving results" (Step 1 59), if it has been stored for several months The information is implemented as "(5) Learning based on the driving results of several months" (step 160). However, the learning steps 1 5 5, 1 5 6 , 1 5 8 , 1 5 9 , 1 60 0 indicated by thick lines in Fig. 18 are only when the driving result shows the tendency to learn as shown below. Will implement learning. That is, a) when the deviation of the same tendency is continuously exhibited (for example, in the whole route driving result, the same degree of target stop position occurs when all stations exceed -38-1276560 (33), etc.): and b) there is a significant deviation Time. Learning can be considered as a method of increasing or decreasing a certain characteristic parameter by a certain ratio. For example, as mentioned above, in the whole route driving result, when all the stations have the same degree of target stop position exceeding, the setting of the braking force should be slightly larger than the actual braking force, so the braking force characteristic is reduced by a certain ratio. The setting of learning. In particular, depending on the learning outcomes of the inter-station driving results, there are few cases where deviations of the same tendency occur. Therefore, at this time, the following learning should be implemented. That is, • Automatic train operation mode: When there is a considerable deviation in the driving plan and the actual measurement, the automatic train operation mode for the compensation of the control command (running level command, brake level command, etc.) is implemented corresponding to the deviation. • Learning method: When there is a deviation between the driving plan and the actual measurement, the learning is performed in response to the compensation of the control command. Taking the braking force characteristic as an example, for example, when braking, if there is a control command compensation that is greater than the plan, it should be a deceleration that is not assumed. In this case, the setting of the braking force characteristic should be too large, so it is only necessary to implement the learning of setting the braking force characteristic at a certain ratio. If there is a control command that compensates for the brake level less than the plan, the opposite is true, as long as the setting of the brake force characteristic is expanded to a certain extent, -39· (34) 1276560 can be learned. The judgment of the difference between the estimated characteristics and the actual , is based on the acceleration and deceleration obtained in the form of measurement data, using the characteristics of the train, the characteristics of the route shape (slope, curve, etc.), the weight, the running traction or the braking force. To determine whether the formula (7) is satisfied. As described above, the learning result compensation means 135 performs compensation for the result of the learning by the post-business characteristic learning means 134. The method of compensation can be the same as the above-mentioned estimation result compensation means 1 25 . This compensation result is stored as a characteristic learning result and stored in the learning characteristic DB130. As shown above, learning is carried out even during business operations, and business operations are performed while adjusting the characteristic parameters. Most of the above studies are automatically learned on the line during train stop at the time of the station. However, the calculation of the weight of the runtime is automatically calculated on the line in the vehicle. In this way, the automatic operation of the train can be carried out by continuously implementing the learning and calculation, and the automatic operation can be carried out in a situation where the number of trains is different and the timeliness changes are well matched. As described above, with the automatic train running device of the seventh embodiment, the calculation of the weight, the running traction, and the braking force can be performed before the driving. For the number of trains of different trains, it can be adjusted in a shorter period of time than before, and the characteristic parameters can be learned after the operation, so even if the characteristic parameters change, the ride comfort and stopping accuracy can be achieved. Automatic operation. In addition, after the business, the study can be based on the use of the information during the -40- (35) 1276560 period to distinguish between the part-by-station driving section and the route driving section, so that it is possible to learn more realistically. In addition, in the calculation before the business and in the post-business study, compensation for the calculation and learning results will be implemented. In the event of an impossible result, it can be compensated without using the impossible characteristic parameters. Implement calculations and learning. In the above way, as the characteristics learning progresses, an effective driving plan can be developed. In addition, if there is a large learning in the train, the learning will be triggered, and the driving plan will be redesigned to realize the automatic train operation that satisfies the ride comfort, the stopping accuracy of the target stop position, and the driving time. In the seventh embodiment, most of the learning is automatically learned on the line in the train stop when the train arrives at the station, and the weight calculation at the time of the train is automatically calculated on the line in the train. However, if there is a human-machine interface in which the learning progress can be confirmed during train driving, online automatic learning can be performed in the driving, and the system using the learning result can be realized at the judgment of the driver. At this time, it is also possible to use only the learning means as a separate device and use it as a support device for automatic train operation. Fig. 19 is a view showing an essential part of the automatic train running device of the ninth embodiment. In this embodiment, the post-business characteristic learning means includes an automatic characteristic learning means 1341 for each request item, an automatic characteristic learning means 1342, an automatic characteristic learning means 1343, an automatic characteristic learning means 1344, and an automatic characteristic learning means 1 3 45. In addition, the learning result comparison means 1 3 6 having the learning result obtained by inputting the automatic characteristic learning means, and the learning result performing the compensation based on the comparison result of the learning result comparison means 1 36 (36) 1276560 Result compensation means 1 3 7. The automatic characteristic learning means 1341 to 1 3 45 perform the characteristic learning as described in the seventh embodiment. The learning result comparison means 1 3 6 will accept the learning results of the automatic characteristic learning means 1341~1 345, compare the learning results, and check whether there is a large contradiction between them. In the automatic characteristic learning method 1 3 4 1~1 3 4 5, there is a considerable difference in the interval between the learning periods, and basically, one of the longer periods of the learning period is checked according to the result of one of the shorter periods of the learning period. The result is fine. For example, the learning result of the automatic characteristic learning means 1 3 4 5 is obviously the same as the n-time of the learning result of the automatic characteristic learning means 1 3 44 of the same time zone, for example, 10 times, it is judged as a significant abnormality, and will be automatically The learning result of the characteristic learning means 1 345 can be regarded as the result of a major contradiction. Further, the inspection is performed by the complex result in the automatic characteristic learning means 1 3 4 1 to 1345, and the inspection accuracy can be further improved. Secondly, the learning result compensation means 137 will have a major contradiction in the comparison result of the learning result. The comparison result performs compensation. The method of compensation The simplest method is to directly use the learning result of the automatic feature learning method with a short learning period (learning interval). However, the average 値 of these learning results can also be considered when using the complex learning results of the automatic feature learning means 1341~1 345. Moreover, if the learning results of most of the automatic characteristic learning means 1341 to 1345 appear to be contradictory, or when the learning results of the automatic characteristic learning means 1341 to 1345 have large errors, 'the average value may be considered. . Automated feature learning means 1 34 can be performed using an adaptive observer -42 - 1276560 (37) Learning. If the target device has been modeled as in equation (7), the adaptive observer uses the observable (detection) to identify the parameter. The system identification can also be carried out by type, and the train automatic operation means 81 can utilize the identification result of the adaptive observer at any time to form an adaptive control system. In the case of equation (7), the adaptive observer can be used to observe the acceleration and deceleration of the crucible (calculated from the detection speed of the speed detector 106), and the operating traction or braking force of the control command, to identify the weight and the train running resistance at any time. For the calculus of the observer, you can use the method of expanding the least squares, expanding the Kalman observer, and adapting the observer. (For details, please refer to the "Introduction to Strong Adaptive Control" (Taiji Manju, Jinjing Ximeixiong, OHMSHA) Chapter 2 "Inferred and Adapted Observers for Unknown Devices" 47·47~87, or "Optimal Filtering for System Control Series 6" (Xishan Jinglu, Peifeng) Section 3.3 "Adapting to the Viewer" Ρ.50~57 ). As shown above, the comparison of several automatic characteristic learning means with different learning periods (learning intervals) is carried out to eliminate the contradictory learning results, and a more accurate characteristic learning result can be obtained. In the first embodiment, the automatic characteristic learning means 134 can also perform characteristic learning using the interference observer. Interference observers use motion control, etc., to identify interferences (for details, please refer to "Designing with MATLAB Control System" (edited by Noda Kenji, Nishimura Hideo and Hirata Hikaru, Tokyo University of Electrical Engineering Publishing House) Section 4.4" Motion Control Interference Observer "Ρ.99~102). The train running resistance of the formula (1) is regarded as the force disturbance of the motion control, and the interference of the train can be estimated at any time by using the interference observer. Using this calculation result to implement learning, you can perform learning with higher precision. -43- (38) 1276560 A detailed description of the first embodiment of the present invention will be made with reference to the drawings. Fig. 20 is a view showing the configuration of the automatic train running device 1 and the data storage unit 201. The automatic train running device 1 is composed of a train characteristic learning device 207 of a train characteristic learning means and an automatic operation control unit 〇 8 of an automatic train operating means. The train characteristic learning device 207 obtains the characteristic data (train resistance, delay time (described later)) and route information of the train in the train. The data acquired by the train characteristic learning device 207 is stored in the data storage unit 201. The data acquired by the train characteristic learning device 207 and stored in the data storage unit 201 is output to the automatic operation control unit 208. The automatic operation control unit 208 formulates a driving plan based on the data acquired by the train characteristic learning device 20 7 and stored in the data storage unit 20 1 . The train will operate automatically according to this driving plan. The train characteristic learning device 207 is a data storage means data storage unit 20 1 , a train weight calculation means, a train weight calculation unit 209 that operates the traction force deviation detecting means, and a train resistance calculation unit 2 1 0 of the train resistance calculation means, and a braking force calculation means. And the braking force calculation unit 2 1 1 of the braking force deviation detecting means, the delay time calculating unit 2 1 2 of the delay time calculating means, and the riding rate calculating unit 2 1 3 of the riding rate calculating means, and detecting the train speed. The output of the data storage unit 201 is input to the train weight calculating unit 209, the train resistance calculating unit 2 1 0, the braking force calculating unit 2 1 1 , the riding ratio calculating unit 213, and the automatic operation control unit 208. The output of the train weight calculating unit 209 is input to the data storage unit 201. The output of the train resistance calculating unit 2 1 0 is input to the data storage unit 201. The output of the braking force calculation unit 211 inputs -44-(39) 1276560 to the data storage unit 201. The output of the delay time calculation unit 2 1 2 is input to the data storage unit 20 1 . The output of the ride rate calculation unit 213 is input to the data storage unit 201. The output of the operation control unit 8 is input to the train weight calculation unit 209, the braking force calculation unit 2 1 1 , the delay time calculation unit 2 1 2, and the ride rate calculation unit 2 1 3 . When the train acceleration operation is performed, the data storage unit 207 inputs the train resistance 値, and the automatic operation control unit 208 inputs the operation traction force 値F and the train speed V at the current point to the train weight calculation unit 209. The train weight calculation unit 2 〇9 calculates the train weight 以 using the train resistance 値Fr, the running traction force 値F, and the train speed V by Equation 15. The train weight is obtained by the train weight calculation unit 209. The data storage unit is stored. In Equation 15, μ is the train weight, F is the running traction 値, Fr is the train resistance 値, and α is the train acceleration. The train acceleration α can be obtained by using the train speed V. (F — Fr )

(15) The train weight calculating unit 209 can also be used as the running traction force detecting means for the running traction force 値F, and the train weight 计算 calculated by the train weight calculating unit 2 〇 9 can be used, and when the speed V is calculated, the train weight is calculated.値 When the 値V 1 used at the time is different, it can be substituted into the formula 丨5 to obtain the correct running traction 値F. The train weight calculating unit 209 can also detect the deviation between the running traction force 値F and the operating traction force command 値Fk instructed by the automatic operation control unit 208. The deviation of the running traction command 値Fk& running traction 値ρ is output to the data storage unit 20 1 for storage. Since the deviation of the -45- (40) 1276560 traction command 値Fk and the running traction force 値F can be detected, the deviation of the running traction command 値^ and the running traction force 値F can be added to the running traction command 値Fk at the time of detection. It can be calculated as a new running traction command 値Fk, and with this treatment, a more accurate train automatic operation can be realized. When the train is coasting, the data storage unit 20 1 inputs the train weight Μ and the speed V to the train resistance calculating unit 2 1 0 . The train resistance 値Fr can be calculated by Equation 16 using the train weight Μ and the speed V input from the data storage unit 20 1 . When the train is taxiing, the running traction force 値F is 0 because there is no running traction. Since the running traction force 値F is 0, the formula 15 can be deformed to derive the formula 16. The train resistance 値Fr calculated using Equation 16 will be output to the data storage unit and stored. In Equation 16, Μ is the train weight, F is the running traction 値, Fr is the train resistance 値, 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Fr as shown in “Operation Theory (DC AC Power Vehicle), Dating Society, etc.), when the general train (there is a difference in high-speed vehicles), the slope resistance The sum of 値Frg, curve resistance 値Frc, and driving resistance 値Fra can be expressed by Equation 17. Further, it is understood that the slope resistance 値Frg, the running resistance 値Fra, and the curve resistance 値Frc can also be expressed by Equation 18, Formula 19, and Formula 20, respectively. Since the train resistance 値Fr at the time of taxiing can be calculated by using the train weight Μ and the speed V, the train resistance calculating unit 210 can also calculate the slope resistance 値Frg and -46 - (41) 1276560

Driving resistance 値Fra. The driving resistance 値Fra can be calculated using the speed V. Further, the curve resistance 値Frc uses the data stored in advance in the data storage unit 1. Since the train resistance 値Fr, the driving resistance 値Fr, and the curve resistance 値Frc can be used as the data, the train resistance calculating unit 210 can calculate the slope resistance 値Frg using the deformation of the formula 17. The slope resistance 値Frg calculated by the train resistance 2 1 0 is output to the data storage unit 201 and stored. In Equation 18, the s-system slope [%] is positive for uphill and negative for downhill. In Equation 19, the coefficients of the A, B, and C systems and the velocity of the V system are [km/h]. In Equation 20, r is the radius of the curve [m]. The train resistance calculation unit can detect the slope resistance and the train resistance 在 when the train is driving, so the correct data can be detected. Moreover, as long as the data is detected by performing a round trip on the planned route, it has a considerable time reduction effect. In Equation 17, Equation 18, Equation 19, and Equation 20, the train resistance FFr, the slope resistance FFrg, the driving resistance FFra, and the curve resistance FFrc. A, B, C coefficient, r system curve radius.

Fr = Frg + Fra + Frc (17)

Frg = s (18)

Fra= A + B v + Cv2 ( 19 )

Frc = 800 / r (20) For the braking force calculation unit 21 1, the automatic operation control unit 208 inputs the train speed V and the braking command 値Fs, and the data storage unit 201 inputs the train weight Μ and the train resistance 値Fr. The braking force calculation unit 2 1 1 calculates the braking force 値Fb by the formula 2 1 using the train speed V, the train weight Μ, 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 -47-1276560 (42) portion 201 and stored. The description is re-implemented using the aforementioned formula. In Equation 21, the braking force is Fb, the weight is Μ, the acceleration is α, and the train resistance 値 is Fr.

The Fb=Ma + Fr 煞 vehicle force calculating unit 2 1 1 calculates the deviation Fh (refer to Formula 22) of the braking force 値Fb calculated by the braking force calculating unit 2 1 1 and the braking command 値Fs as the braking force deviation detecting means. The deviation Fh between the braking force 値Fb and the braking command 値Fs calculated by the braking calculation unit 21 1 is 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 deviation Fh of the braking force 値Fb calculated by the braking force calculating unit 2 1 1 and the braking command 値Fs to the braking command 値Fs when the deviation Fh is detected. The calculation method can provide a more correct braking force 値Fb for the train. In Formula 22, the vehicle braking system Fb, the braking command system Fs, and the deviation system Fh. (twenty two )

Fh = Fs - Fb When the vehicle is braking, the delay time calculation unit inputs the data of the time T1 at which the automatic operation control unit 208 outputs the braking command 値Fs and the time T2 at which the train speed is decelerated. The delay time calculation unit 2 1 1 calculates the data of the time T1 at which the brake command 値 Fs is output and the deviation Th of the data at the time T2 at which the train speed is decelerated (see Equation 23). The deviation Th calculated by the delay time calculation unit 211 is output to the data storage unit 201 and stored. The delay time Th - 48 - (43) 1276560 is the time until the actual brake command from the automatic operation control unit 208 is received until the brake command reaches the drive unit 205 and the brake unit 206 and the operation is performed. By detecting the delay time Th, a driving plan can be drawn up in consideration of the delay time Th, and a more correct and safer driving plan can be obtained. In the formula 23, the timing at which the automatic operation control unit 208 outputs the braking command 値F is T1, the time at which the train speed is decelerated is T2, and the delay time is Th. (twenty three )

Th = T2 - T1 The data storage unit 201 inputs the train weight Mk at the time of the empty vehicle, the train weight 现时 at the current point, the number N of passengers at the time of full vehicle, and the average weight Me of the human to the boarding rate calculation unit 213. The ride rate calculation unit 213 calculates the ride rate estimation 値 M r a t e using Equation 24 using the train weight Mk at the time of the empty train, the train weight 现时 at the current point, the number of passengers n at the time of full vehicle, and the average weight Me of the human. The ride rate § 10 calculation unit 2 1 3 5 is used to calculate the ride rate 値μ r a t e, which is input to the data storage unit 201 and stored in the data storage unit 201. In Equation 24, the weight of the train at the time of the empty train is Mk, the weight of the train at the current point is μ, the number of passengers at the time of full vehicle is Ν, the average weight of the human being is Mc, and the estimated ride rate is Mr ate. M — Mk

Mrate =————— (24)

N In the train characteristic learning device of this configuration, in the train weight calculation unit 209, the train weight 计算 can be calculated during the train operation, and the traffic rate is calculated via the data storage-49-(44) 1276560 storage unit 20 1 The calculation unit outputs the train weight μ at the current point. Therefore, the ride rate Mrate between stations can be estimated. Since it is possible to estimate the ride rate Mrate between stations, it is possible to analyze the change in the ride rate of each station and the change in the ride rate of time. Further, since the train weight calculating unit 209 can calculate the train weight Μ ' at the current point, the correct data of the train resistance 値Fr and the slope resistance 値Frg are also calculated. In the case of the automatic operation control unit 208, as shown in Japanese Laid-Open Patent Publication No. Hei-5-105052, and Japanese Patent Application Laid-Open No. Hei 6-2845-119, the present position of the train is detected by the ground speed, the train speed, and the elapsed time, and the automatic train is used. Operation mode (Refer to Figure 2 (the vertical axis is the speed and the horizontal axis is the distance (position)). The target speed is determined. The train follows the target speed to implement automatic train operation control. Further, a method of detecting the position by the driving distance and the ground can be used, and the control method of the automatic operation control unit is not particularly limited. The operation control unit 208 of the present embodiment has a delay time compensation means, a running traction force deviation compensation means, and a braking force deviation compensation means which are not provided by the conventional automatic operation control unit. The delay time calculation unit 212 inputs the delay time to the delay time compensation unit (not shown) of the delay time compensation means. The delay time compensation unit (not shown) calculates the starting time of the braking force or the running traction and determines the starting time of the running traction when considering the delay time. The train weight calculation unit 209 that operates the traction deviation detecting means inputs the running traction deviation to the running traction deviation compensation means (not shown). The running traction deviation compensation means (not shown) will calculate the new running traction command 値 and control the running traction when considering the running traction deviation. The braking force calculation unit will input the braking force deviation compensation 値 to the braking force deviation compensation means (not shown in the figure -50- (45) 1276560). The braking force deviation compensation means (not shown) will calculate the new braking force command and control the braking force in consideration of the braking force deviation compensation. In the automatic train operating device according to the first embodiment of the present invention, the train characteristic learning device 207 can collect information such as the ride rate, the train weight, the train resistance, and the braking force during traveling, and collects not only before the automatic operation is performed safely. The information can also be applied to vehicles that use the information collected during driving to further correct the driving plan when the actual passenger is travelling. In the present embodiment, the train characteristic learning device 207 adopts a method of processing data in train driving, and the data processing can be processed after the train is driven. Further, in the present embodiment, although only the braking force is indicated, there is no limitation on the method of braking, including the braking level. Further, the train characteristic learning device of the present embodiment can collect the data of the rainy day, the data of each season, the data of each route, and the data of each station. Therefore, it is not limited to performing data collection once for the route. Fig. 22 is a block diagram showing the train construction of the automatic train running device according to each embodiment of the present invention. The train 0 has a speed detector 322 composed of a pulse generator (PG) or the like mounted on a rotating shaft of the wheel, and an above-ground sub-detector for detecting a ground (inquiry machine) provided on the track. Further, the automatic train running device 1 for inputting the train detecting speed signal and the train detecting position signal, and the driving device 305 and the braking device 306 by which the automatic train running device 1 performs control are further provided. The automatic train control device (ATC), which is omitted from the figure, inputs the relevant ATC signal such as the speed limit and the operating conditions to the automatic train running device 4. (46) 1276560 The automatic train running device 1 has a database 300, an arithmetic circuit 304A when the station is parked, and an arithmetic circuit 3 04B when the inter-station is driven. The train detection speed signal and the train detection position signal are input to the station. The arithmetic circuit 3 04B is implemented while driving. When the station stops, the arithmetic circuit 3 04 A performs a specific calculation to be described later when the train 0 stops at the station, and the arithmetic circuit 3 04B performs the specific calculation or control described later when the train is traveling between the stations of the train 0. . Next, the database 300 stores characteristic data such as route conditions (slope, curvature, etc.), vehicle conditions (restricted speed, vehicle weight, and train characteristics such as acceleration and deceleration performance), and timetables (running tables) ) and other information. This database 3 can be a hard disk that is placed in the automatic train running device 1, or a 1C card that is recently developed and can be carried by the driver. Figure 23 is a block diagram showing the configuration of an automatic train running device 1 according to a thirteenth embodiment of the present invention. The operation circuit 3 04A is provided with the optimal driving plan drafting means 3 07 when the station is parked, and the running calculation circuit 304B is provided with the driving plan recalculation means 3 08, the control command discharging means 3 09, and the control command output. Means 310. Next, the data stored in the database 00 is input to both the arithmetic circuit 307A when the station is parked and the arithmetic circuit 304B when the station is running, and the speed detector 302 and the ground sub-detector. Each of the detection signals of 03 and ATC signals is only input to the inter-station driving circuit 3 04B. The best driving plan drafting method 3 07 will be based on the various data stored in the database 00, and the best driving plan for the train to run from one station to the next, and stop at the target position at the target time. painting. At this time -52- (47) 1276560 "Best" conditions can be various settings. For example, taking the driving time as the first priority, increasing the energy saving efficiency as the highest priority, or avoiding the rapid acceleration and deceleration ride comfort is the highest priority. In the example of the method of holding the best driving rf drawing related information of the best driving plan, the speed target corresponding to the time or distance is regarded as the control command. The best driving rf* drawing method 3 〇7 is to formulate the best driving plan method, for example, using the mechanical train model to predict the train driving behavior (for example, Japanese special Kaiping 5 - 1 9 3 5 0 2 )Wait. As shown in Figure 3, the running curve, the taxiing curve, and the retrograde braking curve are predicted, and the intersection of the sliding curve and the retrograde braking curve is used as the starting point of the braking. The driving plan recalculation means 3 08 will not only input the driving plan proposed by the best driving plan drafting means 307, but also input the train detecting speed and train from the speed detector 3 0 2 and the ground sub-detector 3 0 3 respectively. The detection position and the ATC signal from the ATC will perform a recalculation of the driving plan when the error of the proposed driving plan and the actual driving result reaches a certain level or more. The control command precipitating means 3 09 will calculate the driving plan and the deceleration command for the current point of the driving device 305 and the braking device 306 according to the driving plan input by the driving plan recalculation means 308, and output it to the control command. The output means 3 1 0. The control command output means 3 10 0 outputs the acceleration command and the deceleration command input from the control command means 9 to the drive unit 305 and the brake device 306. Next, the operation of Fig. 22 having the above configuration will be described. Assume that train 0 stops at a certain station, and the best driving plan is to use the information stored in the database 00 to calculate the best driving plan to the next stop. Secondly, when the train starts to run, the driving plan recalculation means 3 08 implements the best driving plan proposed by the best driving plan drafting means 3 07, and based on the speed detector 312 and the ground sub-detector 3 The comparison between the train detection speed of 03 and the actual driving result calculated by the train detection position, when the difference between the two (for example, the speed error of the difference between the speed target and the speed performance of the optimal driving plan) is greater than the preset At the time of a critical threshold, the recalculation of the driving plan will be performed. The difference between the two is greater than the critical state. In addition to the above-mentioned chasing phenomenon, it may happen that the ATC enters the speed limit change command because the other vehicle is parked in the forward direction. Further, the recalculation performed by the driving plan recalculation means 3 08 may be performed by considering the actual performance speed at the time of recalculation, the actual performance distance (train position), or the remaining time allowed for the inter-station driving. Next, the control command precipitating means 9 outputs a control command such as an acceleration command or a deceleration command from the driving plan recalculation means 3 08 to recalculate the driving plan, and the control command output means 3 1 0 outputs the outputted control command to the drive. Device 3 05 or brake device 306. By using such calculation and control of the automatic train running device 3 04, the train can stop at the target position of the next stop at the target time. Thereafter, during the stop of the train stop in the next stop, the best driving plan drafting means 3 07 further develops the best driving plan up to the next stop, and performs the same action as the means 308~3 10 . In addition, when the best driving plan for the best driving plan 3 07 is not better than the specific driving plan, the driving plan is re-counted -54- (49) 1276560 The calculation, and directly outputs the optimal driving plan of the optimal driving plan drawing means 7 to the control command discharging means 3 09. In the thirteenth embodiment of the above-mentioned Fig. 23, after the train 0 starts to drive according to the optimal driving plan proposed by the optimal driving plan drawing means 3 07, if the actual driving result and the driving plan have a certain degree of deviation from the driving plan, The recalculation of the driving plan 3 08 will immediately implement the recalculation of the driving plan, which can greatly suppress the chasing phenomenon that has occurred in the past, so that the energy saving effect can be improved. FIG. 24 is an automatic train operation of the first embodiment of the present invention. A block diagram of the configuration of the device 1. The difference between Fig. 24 and Fig. 23 is that the vehicle plan recalculation means 3 08 of Fig. 23 employs the cumulative error reference type driving plan recalculation means 311. The recalculation method 3 08 of the driving plan in Fig. 23 is because at the time of each recalculation, it is judged whether the error at that time exceeds the critical value, and therefore the recalculation with the chasing feeling is sometimes performed due to the influence of the disturbance. Therefore, in this embodiment, the cumulative error reference type driving plan recalculation means 31 1 performs judgment on the accumulation of an error (for example, an error accumulated in 5 minutes). In this manner, it is possible to prevent the above-described recalculation with the chasing sensation due to the influence of the disturbance. Fig. 25 is a block diagram showing the configuration of the automatic train running device 1 according to the fifteenth embodiment of the present invention. The difference between Fig. 25 and Fig. 24 is that the control command issuance means 3 09 and the control command output means 3 1 0 are provided with a control command compensation means 3 12 . The control command compensation means 312 has a function of determining whether the driving plan output by the driving plan recalculation means 308 and the actual driving result is 1276560 (50) exceeding the critical threshold. When it is determined that the critical value is more than 値, the control command is executed. The control command that is deposited by the deposition means 9 performs compensation. The control command compensation means 3 1 2 is provided to enable the automatic train running device 1 to have a support function. That is, if the train is based on the best driving plan drafting means 3 07 or the driving plan recalculation means 3 08 calculation of the driving plan to perform the actual driving, there is no problem, however, sometimes there is a large deviation from the driving plan. The situation of driving. For example, when one of the plural vehicles has an abnormality. However, in the present embodiment, the control command compensation means 3 1 2 can also perform the support function, and can appropriately compensate the control command to prevent the stop position and the target position of the train from being largely deviated. Further, in the configuration of Fig. 25, an example of the control command compensating means 3 1 2 is provided between the control command precipitating means 3 09 and the control command output means 3 1 0 of Fig. 23. Of course, the control command compensating means 312 It can also be arranged between the control command precipitation means 309 and the control command output means 310 of Fig. 24. Figure 26 is a block diagram showing the configuration of an automatic train running device 1 according to a sixteenth embodiment of the present invention. The difference between Fig. 26 and Fig. 25 is that the control command compensation means 3 1 2 of Fig. 25 employs the cumulative error reference type control command compensation means 313. In the control command compensation means 312 of Fig. 25, even if the occurrence of one driving plan and the judgment of the actual driving result is greater than the critical threshold, the control command compensating means 3 1 2 immediately executes the control command of the control command discharging means 3 09 Compensation, and is susceptible to interference and control with a chasing sensation. Therefore, in this embodiment, the cumulative error reference type control command compensating means 313 performs judgment on the accumulated error (for example, the error accumulated in 5 minutes). In this way, it is possible to prevent the above-mentioned recalculation with a chasing sensation due to the influence of the interference of -56-(51) 1276560. Fig. 2 is a block diagram showing the configuration of an automatic train running device 1 according to a seventeenth embodiment of the present invention. The difference between Fig. 27 and Fig. 6 is the recalculation method of the driving plan recalculation method 3 1 1 . Since the other configurations are the same as those in Fig. 26, detailed descriptions thereof will be omitted. Further, in this embodiment, the cumulative error of the driving plan and the actual driving result is determined by the two means 3 11 and 3 1 3, however, the threshold used in the execution of the cumulative error determination by these means can be set to Corresponding to different conditions. Fig. 2 is a block diagram showing the configuration of an automatic train running device 1 according to a first embodiment of the present invention. The difference between Figure 28 and Figure 27 is that the optimal driving plan for the calculation circuit 3 04A when the station is parked is 3 07, and the optimal driving plan for the delay time is considered 3 1 4 and stored in The train characteristics data of the database 300 contains "delay time" data. In the calculation of the driving plan, the delay time of the train's response to the control command, that is, the time from the output of the control command to the time when the control command affects the actual train, requires a huge computational load to obtain the aforementioned front. There is difficulty in the speed of operation in practical use. Therefore, in the present embodiment, in addition to the delay time pre-determined in the train characteristic data stored in the database 300, the optimal driving plan is also the best driving plan for the "delay time consideration" type. Means 3 1 4, this delay will also be considered when formulating the best driving plan. In this way, the stop accuracy of the target position of the next parking station can be improved. Figure 29 is a block diagram showing the construction of an automatic train running device 1 - 57 - (52) 1276560 in accordance with a nineteenth embodiment of the present invention. The difference between Fig. 29 and Fig. 28 is the cumulative error reference type vehicle recalculation means 3 1 1 of Fig. 28 for the delay time consideration type driving plan recalculation means 3 1 5. The delay time consideration type driving plan recalculation means 3 1 5 is the same as the delay time considering type optimal driving plan drafting means 314, and the vehicle schedule is re-referenced with reference to the delay time data contained in the train characteristic data of the database 300. Calculation. In this way, the stop accuracy of the target position of the next parking station can be further improved. Further, in the configuration of the nineteenth embodiment, the combination of the driving schedule recalculation means 3 1 5 of the "delay time consideration type" and the optimal driving plan drawing means 3 1 4 of the "delay time consideration type" is adopted. However, it is also possible to construct a combination of the means 3 〇 7 of the ordinary best driving plan which is not the "delay time consideration type", that is, the recalculation means 3 of the driving plan of Figs. 23 to 27 〇8, 3 1 1 is replaced by the delay calculation time considering the recalculation means of the driving plan 3 1 5 . Fig. 30 is a block diagram showing the configuration of an automatic train running device 1 according to a twentieth embodiment of the present invention. The difference between Fig. 30 and Fig. 29 is the deferred time considering the best driving plan for drawing in Fig. 29. 3 4 4 is the best predictive driving plan for the forward forecasting type. Previously, the proposed method for predicting the best driving plan 3 16 is also a kind of "delay time consideration type", which is based on the prediction of the direction of travel of train 0 to make the train stop at the target position of the next stop station. For the purpose of the driving plan. That is, as shown in Fig. 38, the train behavior prediction of the train traveling direction is calculated and the convergence operation at the target speed through the target point (or the progressive convergence operation from the deceleration start point) is performed, and the retrograde curve can be used without -58- 1276560 (53) The best driving plan is drawn up. If you do not need to consider the delay time, you only need to refer to the target position braking characteristics and use the reverse thrust position as the starting point of the braking. The calculation will be easier. However, if the delay time must be considered, then the reverse pushing method is used. The operation will be very complicated. Therefore, it takes a lot of calculation time to obtain the start point of the brake, and the point 'may have passed the target position when the start point of the brake is obtained. Further, the method shown in Fig. 3 is to perform the prediction operation of the plurality of traveling directions to obtain the starting point of the braking. Even if the calculation is performed plural times, since it can be implemented in each specific sampling period, it is only necessary to perform the comparison. Short time. Fig. 3 is a block diagram showing the configuration of an automatic train running device 1 according to a second embodiment of the present invention. The difference between Fig. 31 and Fig. 29 is the recalculation means of the delay time consideration type driving plan in Fig. 29, which is the recalculation means 3 1 7 of the forward prediction type driving plan. Previously, the recalculation method for predictive driving plans was the same as the forward-predicted optimal driving plan 3 1 6 , and the recalculation of the driving plan was based on the prediction of the direction of travel of the train. The calculation is performed for the purpose of stopping the train 0 from the target position of the next parking station. Therefore, the recalculation of the vehicle plan considering the delay time can be implemented in a short time. In addition, the previous predictive driving plan recalculation means 317 can replace the delay time considering driving plan recalculation means 315 of FIG. 29, and can also replace the driving meters of Figs. 23 to 27 and Fig. 30. The recalculation means 3 08, 311, 315 are drawn. Figure 32 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-second embodiment of the present invention. The difference between Fig. 32 and Fig. 31 is the recalculation method for the predictive driving plan before the 31st chart. 3 1 7 is the progressive forward predictive type line-59- (54) 1276560 car plan recalculation means 3 1 8. The recalculation means for predicting the type of driving calculation before the third figure is to perform the recalculation of the driving plan by performing the forward prediction operation according to the predetermined specific control periods. However, the progressive forward direction of this embodiment The predictive driving plan recalculation means 3 1 8 does not have to be recalculated in each control cycle. For example, when the sampling control period is 0 · 3 seconds, it can be implemented every 1 second, or even every 1 second. In this way, changing the recalculation cycle can reduce the computational load. Further, the calculation cycle can be appropriately determined in consideration of the place where the line gradient changes rapidly, and the place where the speed change is restricted. Fig. 3 is a block diagram showing the configuration of an automatic train running 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 in Fig. 32. The first method is the speed measurement driving type successive forward prediction type driving plan recalculation means 3 1 9 . That is, if the detection sampling period of the speed detector 302 is l [msec], the implementation of the arithmetic circuit 307B side during the inter-station driving is not directly using the speed detection signal input according to the period, but for the period of 5 to 10 [msec]. The input speed detection signal is subjected to filtering and the like, and then the data update is performed. Secondly, the speed measurement drive type progressive forward predictive driving plan recalculation means 3 1 9 is based on the update cycle of this data to implement the recalculation of the forward predictive driving plan. In this way, the influence of interference and the like can be suppressed, and the arithmetic precision at the time of recalculation can be improved. Fig. 3 is a block diagram showing the configuration of an automatic train running device 10 according to a twenty-fourth embodiment of the present invention. In this embodiment, in addition to the operation of the station between the stations of FIG. 31, the inter-station driving result storage means 32 is implemented on the arithmetic circuit 304B, and (55) 1276560 实施 the delay circuit is added to the arithmetic circuit 304A when the station is parked. Means 21, and the delay time can be estimated based on the latest driving results. Therefore, the database 300 of this embodiment may not store the delay time data. That is, after the departure of the train 0 from a certain station, the inter-station driving result data during the period from the train position, the train speed, the ATC signal, and the like to the next stop station is stored in the inter-station driving result storage means 3 20 . Secondly, after the train arrives at the next stop and stops, during the stop, the delay time estimating means 321 calculates the delay time based on the data stored in the inter-station driving result storage means 3 20, and outputs the estimated result to the delay time. Type of best driving plan development means 3 1 4 and forward prediction type driving plan recalculation means 3 1 7. The delay time consideration type optimal driving plan drafting means 3 1 4 and the forward forecasting driving plan recalculation means 3 1 7 will be further implemented until the next stop station in consideration of the estimated delay time. Formulation and recalculation of the driving plan. If the method of estimating the delay time by the delay time estimating means 32 1 is explained, this method does not use a complicated operation, but is a simple method of estimating the signal level change based on the measurement data. For example, when the vehicle is braked, after the brake control command is output and the level operation is performed, the train speed decreases after a certain period of time elapses. At this time, the time until the threshold 値 which is set in advance is estimated to be a delay time. Moreover, the delay time stored in the database 300 of the above-described 28th to 33rd drawings is particularly complicated because the time is not required to be obtained, so that complicated calculations can be used and the result of the calculation can be stored and implemented. Test driving of train 0, using the delay time estimation means 3 2 1 of this embodiment, data can be more easily obtained -61 - (56) 1276560 This embodiment takes the delay time reflecting the latest train characteristics, and is considered by the delay time. The best driving plan drafting means 3 1 4 and the forward forecasting driving plan recalculation means 3 1 7 The proposed and recalculated driving plan can further improve the reliability. Figure 35 is a block diagram showing the configuration of an automatic train operating device 1 according to a twenty-fifth embodiment of the present invention. The difference between Fig. 5 and Fig. 34 is to implement an additional line delay time derivation means 322 on the arithmetic circuit 307B when driving between stations, and the forward predictive type driving plan recalculation means 3 17 can be considered In the case where the delay time calculation means 22 estimates the delay time, the recalculation is performed. That is, in the composition of the 34th figure, the delay time is calculated based on the inter-station driving result of a certain section, and the calculation result is applied to the recalculation of the driving plan of the next section, and the implementation of the 35th figure is implemented. In the form, even if it is driving in the same section, the delay time can be estimated based on the results of a few stations, so it can also be applied to recalculation. Therefore, the recalculation result of the predictive driving plan recalculation means 3 1 7 before this embodiment is more accurate than the one shown in Fig. 4 to reflect the latest train characteristics. Figure 36 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty sixth embodiment of the present invention. In the embodiment, the arithmetic circuit 3 04B is added to the interim predictive parking temporary parking plan calculation means 3 23 and the driving plan adoption means 3 24 when the vehicle is traveling between the stations in Fig. 5 . Next, in this embodiment, the driving plan is divided into three types of P1, P2, and P3 corresponding to the train driving time, and the train 〇-62-(57) 1276560 is used when the train arrives at a specific place on the front side of the target position. The means 3 2 4 adopts the driving plan P3 calculated by the forward predictive parking temporary driving plan calculation means 3 23 . Hereinafter, the twenty-sixth embodiment will be described in detail. First, the definitions of the driving plans PI, P2, and P3 are as follows. P 1 : When the train 1 stops at the station, the best driving plan proposed by the driving plan 3 1 4 (or 3 07, 3 16 may be) is calculated by the driving plan. P2: In the inter-station driving of train 1, the recalculation method 317 (or 308, 311, 315, 318, 319) may be recalculated to recalculate the driving plan. P3: The time of the N-meter (for example, N = 3 00 [m]) point before the train arrives at the station 0 and the train arrives at the position of the temporary parking plan for the predictive parking. Proposed temporary parking plan for parking. When the train 〇 reaches the target position N meters before, the temporary driving plan calculating means 3 23 formulates the subsequent parking temporary driving plan P3 at a specific cycle (for example, the sampling sampling period of the speed detector 2). This parking use temporary travel plan P3 is designed to use the train detection speed and train detection position at that time to predict the train's parking behavior in consideration of the delay time of the train travel direction. This parking behavior is, for example, pre-planning a basic parking behavior when the train is stopped at a specific braking level position at the current point, and is used to stop. Secondly, in terms of train driving prediction, the physical model of the following formula (25) can also be considered. F— Fr = Μ · α (25 ) -63 - 1276560 (58) F : Running traction or braking force F r : Train Resistance (driving resistance, slope resistance, curve resistance, tunnel resistance, etc.) Μ : Train quality α: Acceleration or deceleration train resistance Fr series of resistance when driving, for the convenience of calculation, as mentioned above, driving resistance is usually considered , slope resistance, curve resistance, and tunnel resistance. Therefore, the train resistance Fr can be obtained by the equation (26).

Fr = Frg + Fra + Frc + Frt (26) The resistance 式 in equation (26) is obtained by using the data stored in the database 00, using the following resistance formulas (2 7 ) to (3 0 ) ( Refer to "Operation Theory (DC AC Power Vehicle)" and the Dating Society). • Slope resistance

Frg = s ( 27)

Frg: slope resistance (kg weight / ton) S: slope () (positive on uphill, negative on downhill) • driving resistance

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

Fra: driving resistance (kg weight / ton) A, B, C: coefficient v: speed (km / h) -64 - (59) 1276560 • Curve resistance

Frc= 800/r ... ( 29 )

Frc: Curvature resistance (kg weight / ton) r : Curve radius [m ] • Tunnel resistance type (Because the tunnel resistance will vary greatly depending on the shape and size of the tunnel section and the train speed, etc., it is sometimes used for convenience. The following 値)

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

Frt: tunnel resistance (kg weight / ton) Temporary driving plan calculation means 3 23 Because the physical model of the above formula (25) is used, the temporary parking for parking will be repeated after reaching the target position N meters away. Plan P3. By repeating the plan, the parking position of the temporary parking plan for parking P3 is gradually approached to the target position. As shown in Figure 39. Further, the distance N from the target position to the start position of the temporary parking plan for parking can be determined by the formula "travel distance" ± "wide distance". Next, the operation of the driving plan employing means 324 of Fig. 3 will be described with reference to the flowchart of Fig. 40. When a driving plan for one of PI, P2, and P3 is formulated or recalculated according to a specific cycle, the flow chart is a processing step of one cycle. First, the driving plan uses the means 324 to determine whether the current train 0 driving state or the driving time is when the station stops or just after the departure from the station, the station line -65-1276560 (60), and whether it is at the target parking position. Nearby (step 1). Secondly, when it is judged as "After the stop at the station or just after the departure from the station", the optimal driving plan P1 (step 2) proposed by the optimal driving plan drafting means 3 1 4 will be adopted. Thereafter, the driving plan employer means 324 outputs the optimum driving plan P 1 to the control command discharging means 3 09. Further, the operation after the control command means 3 09 inputs the driving plan has been described in the above embodiment, and thus the overlapping description will be omitted. When it is judged at the time of "the inter-station driving" in step 1, the driving plan employs means 3 24 to judge whether or not the recalculation of the driving plan of this cycle has been carried out (step 3). Next, if the recalculation has been carried out, the traffic plan P2 is recalculated by the forward prediction type vehicle plan recalculation means 317 (step 4). On the other hand, if it is judged in step 3 that the recalculation of the driving plan of the current cycle is not carried out, it is judged whether or not the first driving cycle P 1 has been adopted in the previous cycle, that is, in the previous cycle (step 5). If the best driving plan P1 has been taken at the previous 1 o'clock, the driving plan employer 324 will use the best driving plan P1 (step 2). However, when the best driving plan P1 is not used in the previous 1 hour, the current point is the time when the best driving plan P 1 has been adopted and the recalculation has been carried out. The former 1 hour adopter is recalculated. plan. Therefore, the driving plan adopts the means 324 to adopt the driving plan adopted at the previous 1 hour (step 6), and the judgment of the step 1 is "near the target parking position", that is, when the target parking position is within N meters, the driving is performed. The plan means 324 will input the parking temporary line -66 - (61) 1276560 car plan P3 which has been prepared by the temporary driving plan calculation means 3 2 3, and determine whether the parking position is located at the "target parking position". Within the range of the error (step 7). Next, if the parking position is within this range, the parking temporary driving plan P3 is adopted (step 8). However, if it is not within this range, return to step 5, and use the driving plan that performs the recalculation at the first 1 o'clock (or before), and after step 1, repeat the judgment of step 7 until it is located. Up to the scope. As described above, in the twenty-sixth embodiment, the parking temporary parking plan in which the train is stopped in the "target parking position" ± "permissible error" near the target parking position is used, and the train can be stopped at the target with good precision. Parking location. Further, since the train is scheduled to move in the traveling direction, the temporary parking plan for parking is proposed, and it is easy to obtain an automatic train running device which is very convenient in considering the delay time and has a very simple calculation. In addition, in this embodiment, an example in which the parking temporary vehicle planning calculation means 323 is "forward prediction type" will be described. However, the parking temporary driving plan calculation means 323 is not limited to " Forward predictive type." The automatic train running device of each of the embodiments described so far is a method of changing the control command in stages by the running level and the braking level of the current general train. However, in the near future, the command signal should be continuously controlled to drive the drive unit and the brake unit. Therefore, as long as the control command during acceleration is made into a continuous traction command or a running torque command, the optimal driving plan or the recalculation of the driving plan can be implemented to achieve better ride comfort and higher energy saving effect. Automatic operation. In addition, the control command during deceleration can be made into a continuous brake 1276560 (62) force command, and the optimal driving plan or recalculation of the driving plan can be implemented, which can also achieve better ride comfort and higher economy. Automatic operation of energy effects. Alternatively, both of the above-described continuous control commands during acceleration and deceleration can further achieve automatic operation with better ride comfort and higher energy saving effects. Next, a twenty-seventh embodiment will be described with reference to the drawings. Figure 41 is a schematic diagram showing the configuration of an embodiment of the present invention. The speed position calculation unit 405 calculates the speed and position of the train 0 in the train based on the information of the speed detecting unit 403 such as the tachometer and the information of the ground detecting unit 404 that detects the signal of the ground. The data acquisition means 4 1 2 now inputs it to the train position stop automatic control device 4 1 0. Further, the map is not indicated, and the information such as the brake level and the stop target position is also input to the train position stop automatic control device 4 1 0 via the train current data acquisition means 412. The train position stop automatic control device 4 1 0 is based on the current speed, the current position, the current brake level, and the like obtained by the train current data acquisition means 4 1 2, and the data stored in the brake characteristic data storage unit 41 1 . The vehicle characteristic data such as the deceleration of the brake class, the delay time of the brake class switching, and the response delay time, using the deceleration control plan drafting means 4 1 3 to formulate the deceleration control in which the combination of the complex levels is used to stop the train at the stop target position plan. For example, when the train is stopped at a specific position by a combination of two levels, the deceleration control plan calculates the time allocation of each brake level. First, the first brake level is maintained after the specific time determined by the time allocation calculation, and then switched. Until the second brake level and until the train stops. Figure 42 is the simplest example of a speed control plan -68-1276560 (63). This example is a deceleration control plan for the location of the remaining distance l〇m, and the level is switched around the remaining distance of 6 m to stop the train at the target stop position. For the time allocation, for example, the fake design drawing uses two levels, and the maintenance time of the first braking level is regarded as a variable for the current speed and the remaining distance, and the driving distance at the first braking level deceleration and the second braking level are decelerated. The total distance of the driving distance is equal to the remaining distance mode, and the maintenance time of the first braking level can be obtained, thereby obtaining the time allocation. If there is no solution that satisfies the condition, the combination of the two levels can be changed and the same calculation can be repeated. In the calculation of the driving distance, it is assumed that the deceleration of the braking level before the switching is performed during the period of the delay switching time after the braking level output command, and the response delay time after the delay time elapses from before the switching. The deceleration of the vehicle level is gradually converted into the deceleration of the braking level after the switching. After the response delay time elapses, the deceleration is performed with the deceleration of the braking level after the switching, and the calculation of the temporary fixed distance is implemented under the above assumption. Consider the plan for the braking response characteristics when the level is switched. The deceleration of each brake class is maintained at a safe time. According to the plan switching level proposed in this way, the train can be stopped at a specific position without frequent switching of the level. In addition, when planning the plan, the first brake level is a level with a large deceleration, and the second brake level is a level with a small deceleration. When the vehicle is parked at a lower level, the ride comfort can be improved. When the deceleration rate of each brake class is changed, for example, when the maintenance time of the first brake % level (the level of the large deceleration) is exceeded (the switching schedule time), the prediction when deceleration is performed by the deceleration used in the plan Speed and actual train speed are compared. If the actual speed is small, that is, deceleration -69- (64) 1276560 is less than the assumed hour, do not immediately switch to the second brake level (lower deceleration level), use Extend the maintenance time of the first brake class to prevent the train from exceeding the target stop position. Fig. 43 is an example of adjusting the stop position by changing the switching schedule time. In this example, the actual deceleration is less than the assumption, and the deceleration is slower. Therefore, the initial plan is scheduled to switch to a lower deceleration level near 5 m and change to a vicinity of 3.2 m to switch and adjust the stop position. Fig. 44 is a flow chart for adjusting the stop position by changing the switching timing. To extend the maintenance time, for example, to calculate the actual deceleration according to the actual train speed at the switching plan time, and to calculate the deceleration control plan at the time when the first brake level command is recalculated by the estimated deceleration, or according to the calculation Deceleration, recalculate the plan to start the switching plan. Also, when formulating the initial deceleration control plan, the maximum preset deceleration is used, and the level switching time can be extended to adjust the stop position regardless of whether the actual deceleration is small or large. Figure 45 is a schematic block diagram of a twenty-eighth embodiment of the present invention. The rest of the configuration is the same as that of the twenty-seventh embodiment except that the deceleration estimating means 41 6 for deducing the deceleration based on the time series data of the train speed during deceleration is the same, and the basic functions are also the same. The deceleration estimation using the deceleration estimating means 4 1 6 can be obtained by, for example, a specific time after a delay of the level switching and a response delay time elapsed at a specific deceleration corresponding to the level. The speed should be slowed down to calculate the 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 estimated based on the data after the interference is removed by appropriate filtering. Deceleration calculation means -70-1276560 (65) 4 1 6 Estimate the deceleration of the time point, and use the deceleration obtained by the calculation to correct the successive deceleration control plan. Thus, the deceleration rate at each brake level is caused by one driving. When the change in time or speed occurs, the correspondence can be obtained to ensure the stop accuracy. Figure 46 is a schematic block diagram of a twenty-ninth embodiment of the present invention. The rest of the configuration is the same as that of the second embodiment, and the basic functions are the same, and the plan deceleration correcting means 417 performs the time points at which the deceleration control plan is decelerated or The comparison between the predicted speed of each position and the actual train speed, and the deceleration used by the difference correction deceleration control plan. According to the calculation of the predicted speed at each time point or each position when the deceleration control plan is decelerating, for example, after calculating the brake level used for the plan and the time allocation, the brake level used according to the current train speed and the plan is used. The deceleration, the level switching delay time, and the response delay time are calculated. The predicted speed can be stored in the array mode from the start to the stop of the project, or can be referred to successively. If the memory capacity of the control computer is limited, the train speed of the previous time can also be used. The deceleration of the brake class is calculated successively. When comparing the predicted speed at this time 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 by the plan. Therefore, 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 deceleration is determined between -71 - (66) 1276560 when the error tolerance is reached. By using the plan deceleration correction means 4 1 7 , the comparison of the predicted speed and the actual train speed is performed successively and the deceleration is corrected, and the deceleration control plan can be appropriately updated corresponding to the time change of the deceleration. Since there is an error in the data of the actual train speed, it is better to use the filtered data or set the upper and lower limits of the deceleration change amount to prevent divergence. [Effect of the Invention] In the present invention, in addition to ensuring that the train is stopped at a specific stop position at a specific time, it is possible to realize an energy-saving operation for reducing energy loss caused by driving. Further, the present invention can implement on-line train characteristics, route characteristics, and automatic learning of control parameters in traveling, and use the learning result to realize efficient train automatic operation. Further, the present invention can provide a device for collecting the necessary information for operating the operation device when the train travels to and from the scheduled route. Further, the present invention achieves an energy saving effect by eliminating the influence of chasing during automatic operation of the train. Further, according to the specific embodiment, it is possible to improve the stop accuracy of stopping the train at the target position by obtaining the delay time, and in other embodiments, it is possible to improve the poor ride feeling due to the change in the stage of the speed control command during the level operation. In addition, the present invention is based on the vehicle characteristic data such as the deceleration of each train level of the train, the delay time of the brake switching, and the response delay time, and -72-1276560 (67) the current speed of the train, the current position, and now the brake For data such as grades, a deceleration control plan is proposed for the purpose of stopping the train at a specific position by using a plurality of brake levels, and even if the deceleration can only be set by discrete turns, it is possible to eliminate the need to frequently switch the ranks. It is also possible to formulate a plan for stopping the train at a specific location, and according to the plan, the ride comfort during the deceleration control is improved and the stop accuracy is ensured. Further, the present invention uses a combination of a plurality of brake classes to calculate a time distribution of each brake class for the purpose of stopping the train at a specific position, and constructs a deceleration control by the switching timing of the used brake level and the brake level. In this way, when the deceleration is changed, the time distribution can be changed, and the stop position can be adjusted without changing the level of the movement, thereby improving the ride comfort and ensuring the stop accuracy. Further, in the deceleration control plan of the present invention, the deceleration is first performed at the braking level with a large deceleration, and then the braking level is switched to a smaller deceleration, and the parking is performed at a reduced deceleration level, thereby improving the riding. In addition, the present invention implements a comparison between the predicted speed at which the switching time is decelerated and the actual train speed at the time of switching according to the deceleration control plan, and the deceleration control plan is changed when the two are different. It is easy to evaluate the actual train deceleration condition, and the deceleration control plan corresponding to the change of the deceleration can be recalculated to improve the stop accuracy. Moreover, the present invention can change the deceleration control plan if the deceleration and the plan used in the proposed plan are different after the deceleration control plan is drafted, and the control for the deceleration fluctuation interference can be improved by using this method. - (68) 1276560 ROUBUST Sex and ensure the accuracy of the stop. Moreover, the present invention calculates the deceleration according to the train speed in the deceleration, and formulates the deceleration according to the deceleration of the estimated deceleration, which can improve the ROUBUST property for the deceleration variation, and ensure that the invention is implemented without complicated adjustment. The deceleration control plan point or the predicted speed of each position and the actual train speed difference correct the deceleration control plan to reduce the deceleration, and change the deceleration control plan. This method can change the ROUBUST of the disturbance control. There is no guarantee to stop the accuracy. Moreover, according to the speed of the previous time step, the deceleration, the level switching delay time, and the response delay according to the deceleration control plan, the present invention performs the speed at each time of deceleration control, and in this way, when the memory of the computer is controlled, It is also possible to improve the accuracy of the disturbance with respect to the deceleration, and to ensure the stop accuracy without complicated adjustment. [Simplified description of the drawings] Fig. 1 is an automatic block diagram of the first embodiment of the present invention. Figure 2 is a machine loss indicator and a general example of the operation. Figure 3 shows the machine loss index when braking

The timing data, push control plan, use the control of interference: stop accuracy. The comparison between the degrees of deceleration and the deceleration according to the correction is made. The delay time is adjusted for the deceleration, and the delay time is used to adjust the plan. The ROUBUST car operation device with the predicted body capacity of each position is controlled. An example of the actual loss and loss index of the loss indicator -74-1276560 (69) and the total loss indicator. Figure 4 is an example diagram of the converter loss indicator and motor loss indicator during operation. Figure 5 is an example diagram of converter losses and motor losses during operation. Fig. 6 is a view showing an example of the driving mode of the first embodiment. Fig. 7 is a block diagram showing an automatic train operating device according to a second embodiment of the present invention. Fig. 8 is a view showing an example of the brake loss when the running load is limited. Fig. 9 is a block diagram showing the automatic train operating device according to the third embodiment of the invention. Fig. 10 is a block diagram showing a train operation support device according to a fourth embodiment of the present invention. Fig. 1 is a block diagram showing a configuration example of a thrust indicating device according to a fourth embodiment. Fig. 12 is a block diagram showing the control system of the thrust indicating device of Fig. 11. Fig. 3 is a block diagram showing a configuration example of a thrust indicating device of the train operation support device according to the fifth embodiment of the present invention. Fig. 14 is a block diagram showing a configuration example of a thrust indicating device of the train operation support device according to the sixth embodiment of the present invention. Fig. 15 is a block diagram showing the entire train having the automatic train running device of the present invention. Figure 16 shows the internal structure of the automatic train running device in Figure 15. Block diagram -75- (70) 1276560 Figure 17 shows the driving mode compensation map based on the weight of the initial operation. Figure 18 is a flow chart showing the steps of learning before and after business. Fig. 19 is a block diagram of a compensation means for the purpose of compensating for automatic characteristic learning results according to an embodiment of the present invention. Figure 20 is a block diagram of an automatic train running device and a data storage unit. Figure 21 is an example of an automatic train operation mode. Fig. 22 is a block diagram showing the construction of a train in which the automatic train operation is mounted in accordance with the embodiments of the present invention. Figure 23 is a block diagram showing the configuration of an automatic train running device 1 according to a thirteenth embodiment of the present invention. Fig. 24 is a block diagram showing the configuration of an automatic train running device 1 according to a fourteenth embodiment of the present invention. Figure 25 is a block diagram showing the configuration of an automatic train running device 1 according to a fifteenth embodiment of the present invention. Figure 26 is a block diagram showing the configuration of an automatic train running device 1 according to a sixteenth embodiment of the present invention. Figure 27 is a block diagram showing the configuration of an automatic train running device 1 according to a seventeenth embodiment of the present invention. Fig. 28 is a block diagram showing the configuration of an automatic train running device 1 according to a first embodiment of the present invention. Figure 29 is a block diagram showing the configuration of an automatic train running device 1 according to a nineteenth embodiment of the present invention. Figure 30 is a block diagram showing the construction of an automatic train running device 1 - 76 - 1276560 (71) according to a twentieth embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of an automatic train running device 1 according to a second embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of the automatic train running device 1 according to the second embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of an automatic train running device i according to a twenty-third embodiment of the present invention. Fig. 4 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-fourth embodiment of the present invention. Figure 35 is a block diagram showing the configuration of an automatic train operating device 1 according to a twenty-fifth embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of an automatic train running device i according to a twenty sixth embodiment of the present invention. Fig. 37 is a diagram showing an example of the characteristics of the optimum driving plan to be proposed in the embodiment of the present invention. Fig. 3 is a diagram showing an example of characteristics of a driving plan to be formulated or recalculated in the embodiment of the present invention. Fig. 39 is a diagram showing an example of the characteristics of the temporary driving plan proposed in the embodiment of the present invention. Fig. 40 is a flow chart showing the operation of the means 24 of the driving plan of Fig. 36. Fig. 4 is a schematic structural view showing a twenty-seventh embodiment of the train position stop automatic control device of the present invention. Figure 42 is a schematic diagram showing an example of a deceleration control scheme of the train position stop automatic control device of the present invention using -77-1276560 (72). Fig. 43 is a schematic diagram showing an example of changing the stop position by changing the switching schedule of the train position stop automatic control device of the present invention. Fig. 44 is a schematic diagram showing an example of the step of adjusting the stop position of the switching schedule adjustment stop position of the train position stop automatic control device of the present invention. Fig. 45 is a schematic block diagram showing a twenty-eighth embodiment of the train position stop automatic control device of the present invention. Fig. 46 is a schematic block diagram showing a twenty-ninth embodiment of the train position stop automatic control device of the present invention. Fig. 47 is a block diagram showing a configuration of a general train system having an automatic train running device. Figure 48 is a block diagram of the automatic train running device of the system of Figure 47. [Description of component symbols]

0 歹ij car 1 automatic train running device (ΑΤΟ) 2 drive brake device 3 data base 4 VVVF variable frequency variable voltage inverter 5 main motor 6 brake control device 7 wheel 8 mechanical brake -78- speed detector above ground detector track Tentative driving plan department best driving plan department thrust command generation part driving mode compensation index calculation unit loss index calculation unit overload index calculation unit addition unit driving mode compensation unit driving distance compensation unit timing determination unit train operation support device main controller Thrust indicator angle command calculation unit Impedance control unit Servo amplifier Servo motor encoder recommendation level indication control unit lamp recommendation level indication control unit • 79- Sound output unit database driving mode separation unit database automatic train control unit (ATC) database (DB) Driver's station load device speed detector above ground detector drive unit deceleration device before driving judgment means pre-operating characteristics initial setting means pre-operation test driving train automatic operation means driving result storage means pre-business characteristic estimation means Estimate Measure of the characteristics of the compensation means 値 Storage means Learning characteristics database (learning characteristics DB) Characteristics Initial setting means Train automatic operation means After driving results Storage means After-service characteristics Learning means -80- (75)1276560 135 Learning result compensation means 136 Learning result comparison means 137 Learning result compensation means 180 Data processing means 18 1 Train white motion running means 1341 to 1345 Automatic characteristic learning means 201 Data storage section 203 Above-ground sub-detector 204 Speed detector 205 Drive means 206 Brake means 207 Train characteristic learning Device 208 White running control unit 209 Train weight calculating unit 210 Train resistance calculating unit 21 1 Brake force calculating unit 212 Delay time calculating unit 213 Ride rate calculating unit 300 Library 302 Speed detector 303 Above ground detector 3 04A Implementing the arithmetic circuit when parking 3 04B Implementing the arithmetic circuit 305 when driving between stations Means

-81 - (76) 1276560 306 Brake device 307 Best driving plan drafting means 308 Driving plan recalculation means 309 Control command precipitating means 3 10 Control command output means 3 11 Cumulative error reference type driving plan weight - frr New calculation Means 3 12 Control command compensation means 3 13 Cumulative error reference type control command compensation means 3 14 Delay time consideration type optimal driving plan drafting means 3 15 Delay time consideration type driving plan recalculation means 3 16 预测 预测 predictive type best Driving Plan Drawing Means 3 17 ····Λ一刖Recalculating means to predictive driving plan 3 1 8 Recurring means for predictive driving plan 3 19 speed: Degree measuring drive type successively 1 To: Predictive driving plan recalculation means 320 Inter-station driving result storage means 321 Delay time estimation means 322 Online delay time Calculation means 3 23 Forward predictive parking temporary travel plan calculation means 324 Driving plan use means 402 Brake device 403 Speed detecting unit 404 Above-ground sub-detection unit 405 Speed position calculation unit (77) (77) 1276560 410 Train position Stop automatic control device 411 Brake characteristic data storage unit 412 Train current data acquisition means 4 13 Deceleration control plan preparation means 414 Deceleration control command precipitation means 415 Deceleration control command output means 416 Deceleration estimation means 4 17 Deceleration correction means

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Claims (1)

1276560 (1) Picking up, applying for patent scope 1. An automatic train running device, which generates a driving mode for stopping the train at a specific time at a specific time, and has an inverter including a variable frequency transformer and a main motor. The driving brake device for the electric machine is provided with a thrust command for realizing the aforementioned driving mode, and is characterized by: a loss index calculating means for calculating a loss index representing an energy loss caused by the driving brake device in the train driving; According to the aforementioned loss index, the first driving mode compensation means for compensating the aforementioned driving mode for the purpose of reducing energy loss. 2. The automatic train running device according to the first aspect of the patent application, wherein the first driving mode compensation means performs the preceding driving mode compensation in such a manner that the time until the train stops at the specific position becomes a specific defect. 3. The automatic train running device of claim 1 or 2, wherein the loss index calculation means includes a brake loss index calculation means, and calculates a brake loss index representing an energy loss caused by mechanical braking during the braking operation. 4. The automatic train running device of claim 1 or 2, wherein the loss index calculation means includes a machine loss index calculation means, and the operation represents the energy of the electric machine including the variable frequency transformer and the main motor. Machine loss indicator for loss. -84 - 1276560 (2) 5. The automatic train running device of claim 1 or 2, wherein: the calculation represents an overload of the overload state of the electric machine including the variable frequency variable voltage inverter and the main motor. The overload indicator calculation means of the index; and the second driving mode compensation means for compensating the driving mode in such a manner that the power device does not form an overload according to the overload factor. 6. If the automatic train running device of claim 1 or 2 is applied, the first driving mode compensation means may also perform the compensation of the preceding driving mode in the train driving. 7. The automatic train running device of claim 3, wherein the foregoing braking loss index calculation means calculates the foregoing braking loss index 〇8 according to the predicted amount of the operating load amount in the same feeding interval or the actual measurement. The automatic train running device of claim 1 or 2, wherein the first driving mode compensation means includes means for calculating the driving mode in advance and storing the same in the storage device, and sequentially reading the corresponding train driving from the storage device. The means of driving mode. 9. A train operation support device that calculates a driving mode for stopping a train at a specific time at a specific time, and provides a driving brake device for an electric machine including a variable frequency variable voltage inverter and a main motor. 85- 1276560 (3) A thrust command for the purpose of the aforementioned driving mode, characterized in that it has a loss index calculation means for calculating a loss index representing the energy loss caused by the train driving; For the purpose of loss, the vehicle mode compensation means for compensating the aforementioned driving mode; and the recommended thrust indicating means for the driver to indicate the thrust recommendation. The train operation support device according to claim 9, wherein the driving mode compensation means performs the preceding mode compensation in such a manner that the time until the train stops at the specific position becomes a specific time. 1 1. The train operation support device according to claim 9 or 10, wherein the main controller has a servo mechanism, and the recommended thrust indicating means controls the position, speed, acceleration, and force of the main controller. 12. The train operation support device according to claim 9 or 10, wherein the recommended thrust indicating means includes means for visually displaying the recommended thrust in the vicinity of the main controller. 13. The train operation support device according to claim 9 or 10, wherein the recommended thrust indicating means includes means for transmitting the suggested thrust in a voice. 1276560 Lu, (1), the designated representative figure of this case is: Figure 1 (2), the representative symbol of the representative figure is a simple description: 3 Database 13 Best Driving Plan Department 15 Driving Mode Compensation Index Calculation Unit 16 Loss Index Calculation unit 17 Overload index calculation unit 18 Addition unit 19 Train mode compensation unit 20 Travel distance compensation unit 2 1 Timing judgment unit 柒 If there is a chemical formula in this case, please disclose the chemical formula that best displays the characteristics of the invention: