TWI868970B - Rail vehicle monitoring management system and management method thereof - Google Patents

Abstract

A rail vehicle monitoring management system and management method thereof are provided. The rail vehicle monitoring management system includes an unmanned vehicles and a monitoring host. The monitoring host wirelessly receives the driving information output by the unmanned vehicle, and analyzes the driving information to learn the vibration conditions of the unmanned vehicle running on the track. When the vibration amount of the unmanned vehicle in the driving position of the rail does not match a corresponding reference value, the monitoring host further determines whether a vibration assistance condition is established. When the vibration auxiliary condition is judged to be established, the monitoring host determines that the vibration condition is abnormal and outputs an alarm signal. When the auxiliary condition is not established, the monitoring host records the abnormality of the unmanned vehicle's driving position.

Description

Rail vehicle monitoring and management system and rail vehicle monitoring and management method

The present invention relates to a monitoring and management system, and in particular to a rail vehicle monitoring and management system and a rail vehicle monitoring and management method.

The RGV (Rail Guided Vehicle) control system used in existing unmanned vehicles does not have a vibration detection function. Most of them install additional sensors during the test run of the unmanned vehicle to confirm whether there are any abnormalities. However, when the unmanned vehicle actually carries goods, there may not be too much vibration in the initial use, but as the use time increases, the unmanned vehicle or the track may be damaged or deformed, which will lead to increased vibration and ultimately affect the quality of the goods carried.

The technical problem to be solved by the present invention is to provide a rail vehicle monitoring and management system and a rail vehicle monitoring and management method in view of the deficiencies of the existing technology.

The present invention provides a rail vehicle monitoring and management system, including an unmanned vehicle and a monitoring host. When the unmanned vehicle is traveling on a track, it wirelessly outputs a vehicle information, and the vehicle information includes a vehicle position and a vibration momentum of the unmanned vehicle traveling on the track. The monitoring host wirelessly receives the vehicle information and analyzes the vehicle information to obtain a vibration condition of the unmanned vehicle traveling on the track. When the vibration amount of the unmanned vehicle at the driving position does not match a corresponding benchmark value, the monitoring host further determines whether a vibration auxiliary condition is established; when the vibration auxiliary condition is determined to be established, the monitoring host determines that the vibration condition is abnormal and outputs an alarm signal, and when the auxiliary condition is determined not to be established, the monitoring host records that the unmanned vehicle is abnormal at the driving position.

The present invention provides a method for monitoring and managing a rail vehicle, comprising: when an unmanned vehicle is driving on a track, the unmanned vehicle wirelessly outputs a vehicle information to a monitoring host, wherein the vehicle information includes a vehicle position and a vibration momentum of the unmanned vehicle driving on the track; the monitoring host wirelessly receives the vehicle information and analyzes the vehicle information to obtain a vibration condition of the unmanned vehicle driving on the track; wherein the monitoring host wirelessly receives the vehicle information and analyzes the vehicle information to obtain a vibration condition of the unmanned vehicle driving on the track; When the vibration amount of the unmanned vehicle at the driving position does not match a corresponding reference value, the monitoring host further determines whether a vibration auxiliary condition is established; when the vibration auxiliary condition is determined to be established, the monitoring host determines that the vibration condition is abnormal and outputs an alarm signal, and when the auxiliary condition is determined not to be established, the monitoring host records that the unmanned vehicle is abnormal at the driving position.

In summary, the rail vehicle monitoring and management system and rail vehicle monitoring and management method provided by the embodiment of the present invention can accurately detect the vibration condition of the unmanned vehicle, and can further accurately identify the source of the vibration, thereby ensuring that the quality of the transported goods is not affected by the vibration amount.

To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not used to limit the present invention.

1: Autonomous vehicle

10: First controller

12: Driving module

14: Detection device

141: Identify circuits

143: Inertial sensor

16: First wireless circuit

2: Monitor host

20: Second controller

22: Second wireless circuit

24: Database

26: Alarm circuit

GR: Track

GR1: Track 1

GR2: Track 2

OB: Foreign object

IB: Identification card

BS transport station

L1: First route

L2: Second route

S401: The driverless car starts moving

S403: Collect detection data

S405: Send driving information back to the monitoring host

S501: Receive data sent back by each unmanned vehicle

S503: Data is normal

S505: There is a benchmark value

S507: Comparison and match

S509: Record benchmark values in the database

S511: Other unmanned vehicles are abnormal

S513: Issue an alarm for abnormal track in this section

S515: There are also abnormalities in other sections of the track

S517: Issued an abnormality alert for an unmanned vehicle

S519: Abnormal times reached

S521: Issue an alarm about abnormalities in the track and autonomous vehicles in this section

S523: Recording abnormalities

Figure 1 is a circuit block diagram of a rail vehicle monitoring and management system provided in an embodiment of the present invention.

Figure 2 is a schematic diagram of an unmanned vehicle driving on a track according to an embodiment of the present invention.

Figure 3 is a waveform diagram of the vibration quantity obtained when the unmanned vehicle is driving under normal conditions.

Figure 4 is an operational flow chart of the unmanned vehicle driving provided in an embodiment of the present invention.

Figure 5 is a control flow chart of rail vehicle monitoring and management provided in an embodiment of the present invention.

Figure 6 is a schematic diagram of an unmanned vehicle driving on a normal track according to an embodiment of the present invention.

Figure 7 is a schematic diagram of an unmanned vehicle driving on a track with foreign objects provided by an embodiment of the present invention.

Figure 8 is a schematic diagram of an unmanned vehicle driving on a crooked track according to an embodiment of the present invention.

FIG9 is another schematic diagram of an unmanned vehicle driving on a crooked track according to an embodiment of the present invention.

Figure 10 is a schematic diagram of an unmanned vehicle running at abnormal intervals on a track according to an embodiment of the present invention.

The following is a specific embodiment to illustrate the implementation of the present invention. The technical personnel in this field can understand the advantages and effects of the present invention from the content provided in this manual. The present invention can be implemented or applied through other different specific embodiments. The details in this manual can also be modified and changed based on different viewpoints and applications without deviating from the concept of the present invention. In addition, the drawings of the present invention are only for simple schematic illustration and are not depicted according to actual size. Please note in advance. The following implementation will further explain the relevant technical content of the present invention in detail, but the content provided is not intended to limit the scope of protection of the present invention.

It should be understood that although the terms "first", "second", "third" and so on may be used in this article to describe various components or signals, these components or signals should not be limited by these terms. These terms are mainly used to distinguish one component from another component, or one signal from another signal. In addition, the term "or" used in this article may include any one or more combinations of the related listed items depending on the actual situation.

The present invention provides a rail vehicle monitoring and management system and a rail vehicle monitoring and management method. The rail vehicle monitoring and management system described herein is for an unmanned autonomous vehicle (RGV) used for automatically transporting goods. The RGV can transmit relevant data with a remote monitoring host using wireless communication technology. By monitoring the feedback data of the RGV, it can determine whether the vibration of the driving condition is abnormal, and can actively report when an abnormality is identified; thereby ensuring that the goods carried by the RGV are not affected by abnormal vibration.

[Hardware architecture of rail vehicle monitoring and management system]

Please refer to Figure 1 and Figure 2. Figure 1 is a circuit block diagram of a rail vehicle monitoring and management system provided by an embodiment of the present invention, and Figure 2 is a schematic diagram of an unmanned vehicle driving on a track provided by an embodiment of the present invention. The rail vehicle monitoring and management system described in this embodiment includes, for example, but is not limited to, an unmanned vehicle 1 and a monitoring host 2, wherein the unmanned vehicle 1 can actively send back driving information to the monitoring host 2 when driving on the track, so that the monitoring host 2 can analyze various possible vibration conditions of the unmanned vehicle 1 during the driving process on the track after receiving the driving information.

Furthermore, the monitoring host 2 can accurately determine whether there is abnormal vibration and the source of the abnormal vibration based on the analysis results. For example, the monitoring host 2 can determine whether the source of the abnormal vibration comes from at least one of the unmanned vehicle 1 and the track based on the comparison results of multiple judgment conditions. When the monitoring host 2 determines that the vibration is abnormal, it can actively output an alarm to remind relevant personnel to improve the current abnormal situation, thereby effectively ensuring that the unmanned vehicle 1 and the track can still detect abnormal vibration early during long-term use.

Specifically, the unmanned vehicle 1 includes, but is not limited to, a first controller 10, a driving module 12, a detection device 14, and a first wireless circuit 16, wherein the first controller 10 is electrically connected to the driving module 12, the detection device 14, and the first wireless circuit 16. The driving module 12 includes, for example, a motor module and a motor driving circuit that can drive the unmanned vehicle 1. The detection device 14 includes, for example, an identification circuit 141 and an inertia sensor 143. When the unmanned vehicle 1 is driving on the track, the identification circuit 141 can obtain identification information of identification elements on the track, and the identification information at least includes the driving position and driving speed of the unmanned vehicle 1. The inertial sensor 143 is used to obtain the vibration amount of the unmanned vehicle 1 when it is driving. The inertial sensor 143 includes at least one of an accelerometer and a gyroscope. The vibration amount obtained by the inertial sensor 143 includes, but is not limited to, detecting the vibration of the unmanned vehicle during driving (such as Acc X, Acc Y, Acc Z), detecting the stable horizontality of the unmanned vehicle (such as Angle X, Angle Y, Angle X), detecting the transient rotation of the unmanned vehicle during driving (such as Gyro X, Gyro Y, Gyro Z), detecting the stable yaw of the unmanned vehicle (such as Roll (X axis), Pitch (Y axis), Yaw (Z axis)), etc.

In one embodiment, the first controller 10 is configured to process the related control of the driving module 12 and the detection device 14. For example, the first controller 10 can obtain the driving mission of the unmanned vehicle 1 on the track through the first wireless circuit 16, and control the driving module 12 to drive the unmanned vehicle 1 to move according to the driving mission. The first controller 10 obtains the driving information of the unmanned vehicle 1 through the detection result of the detection device 14, and wirelessly outputs the driving information to the monitoring host 2 through the first wireless circuit 16. The driving information includes, but is not limited to, the driving position obtained through the identification circuit 141 and the vibration amount obtained through the inertia sensor 143.

FIG. 2 is an example of an unmanned vehicle driving on a track to obtain driving information, wherein the track GR is provided with a plurality of identification elements IB and a transport station BS. The unmanned vehicle 1 can drive a first route L1 or a second route L2 on the track GR according to different driving tasks, and load or unload goods through the transport station BS. When the unmanned vehicle 1 is driving, it can know which identification element IB it has passed through through the identification circuit 141, so that it can know which section the unmanned vehicle 1 is currently in the track GR, that is, the first controller 10 can obtain the driving position of the unmanned vehicle 1 on the track according to the identification result of the identification circuit 141. At the same time, the first controller 10 can obtain the vibration amount of the unmanned vehicle 1 at the current driving position through the inertial sensor 143. According to this, the unmanned vehicle 1 can obtain driving information of each section in the track GR by operating in this way, and this driving information includes, but is not limited to, the driving position in the track GR, the corresponding vibration amount, and the identity data of the unmanned vehicle 1 itself.

It should be noted that when the unmanned vehicle 1 and the track GR are both in normal condition, the vibration amount obtained by the unmanned vehicle 1 on the track can be considered normal. As shown in Figure 3, it is an example of various normal vibration amounts obtained by the unmanned vehicle 1 on the straight section or right turn section of the track GR. These normal vibration amounts can be used as the benchmark value for the subsequent monitoring host 2 to judge whether the vibration condition is abnormal.

In one embodiment, the monitoring host 2 includes, for example, but not limited to, a second controller 20, a second wireless circuit 22, a database 24, and an alarm circuit 26. The second controller 20 is electrically connected to the second wireless circuit 22, the database 24, and the alarm circuit 26. The second controller 20 is configured to process a vibration abnormality judgment procedure. Here, the vibration abnormality judgment procedure refers to the second controller 20 receiving data returned by each unmanned vehicle 1 through the second wireless circuit 22 (for example, the track GR can be used for one or more unmanned vehicles 1 to drive alone or simultaneously, and the monitoring host 2 can identify different unmanned vehicles 1 according to different identity data obtained). Here, the returned data is, for example, the driving information of the unmanned vehicle 1. Then, the vibration condition of the unmanned vehicle 1 driving on the track is obtained by analyzing the driving information. Database 24 is used to store reference values for judging vibration conditions. For example, this reference value refers to the normal vibration value of the unmanned vehicle 1 when it is driving in various sections of the track under normal conditions (without abnormal vibration).

In one embodiment, the monitoring host 2 compares the vibration amount of the driving position of the unmanned vehicle 1 with a corresponding benchmark value according to the received driving information. When the comparison is consistent, it can be determined that there is no abnormal vibration condition at the current driving position of the unmanned vehicle 1 on the track. On the contrary, when the aforementioned comparison result is inconsistent, it can be preliminarily determined that there may be an abnormal vibration condition, and other vibration auxiliary conditions will be further combined to further confirm the source of the abnormal vibration. If the vibration auxiliary condition is judged to be established, it can be determined that the abnormal vibration condition comes from one or both of the unmanned vehicle 1 itself and the track, and the monitoring host 2 can output the corresponding alarm content through the alarm circuit 26 at the same time. If the vibration-assisted condition is ultimately judged to be unsatisfactory, the monitoring host 2 can record the abnormality of the unmanned vehicle 1 at the current driving position for use as comparison data when the vibration-assisted condition is subsequently executed.

It is worth noting that the vibration auxiliary condition can be a combination of one or more judgment conditions. When the monitoring host 2 performs the judgment of the vibration auxiliary condition, it mainly judges the monitoring data other than the current driving information. The monitoring host 2 can accurately judge the source of the abnormal vibration condition through other monitoring data combined with the current driving information.

For example, the vibration auxiliary condition is to determine whether there are other unmanned vehicles 1 at the same driving position with vibration amounts that do not match the corresponding reference values. If this auxiliary condition is determined to be yes, the monitoring host 2 determines that the track at the current driving position of the unmanned vehicle 1 is abnormal.

Alternatively, the vibration auxiliary condition is, for example, to determine whether the vibration amount of the unmanned vehicle 1 at other different driving positions on the track is inconsistent with the corresponding reference value. If this auxiliary condition is determined to be yes, the monitoring host 2 determines that the unmanned vehicle 1 is abnormal.

Alternatively, the vibration auxiliary condition is, for example, to determine whether the number of times that the vibration amount of the unmanned vehicle 1 at the driving position does not match the corresponding reference value exceeds a preset number. If the auxiliary condition is determined to be yes, the monitoring host 2 determines that the unmanned vehicle 1 and the track of the unmanned vehicle 1 at the current driving position are abnormal.

[Implementation example of driverless car reporting method]

Please refer to Figure 4. Figure 4 is an operational flow chart of the unmanned vehicle driving provided by the embodiment of the present invention. The flow chart shown in Figure 4 is illustrated by taking the structure of Figure 1 as an example, but is not limited thereto. The process shown in Figure 4 includes the following steps, for example.

In step S401, the unmanned vehicle 1 starts to move. When the first controller 10 of the unmanned vehicle 1 obtains the driving task wirelessly output by the monitoring host 2 through the first wireless circuit 16, the first controller 10 can control the driving module 12 to start, and the unmanned vehicle 1 can move on the track.

In step S403, detection data is collected. When the unmanned vehicle 1 is moving, the first controller 10 obtains the driving information of the unmanned vehicle 1 through the detection device 14. For example, the driving position of the unmanned vehicle 1 on the track (such as the section of the track) can be obtained through the identification circuit 141 in the detection device 14, and the vibration amount of the unmanned vehicle 1 at the current driving position can be obtained through the inertia sensor 143.

In step S405, the driving information is returned to the monitoring host 2. After the first controller 10 obtains a driving information including at least the driving position of the unmanned vehicle 1 and the corresponding vibration amount of the driving position according to the detection result of the detection device 14, the first controller 10 wirelessly returns the driving information to the monitoring host 2 through the first wireless circuit 16.

[Implementation example of rail vehicle monitoring and management method]

Please refer to Figure 5. Figure 5 is a control flow chart for rail vehicle monitoring and management provided in an embodiment of the present invention. The flow chart shown in Figure 5 is illustrated by taking the structure of Figure 1 as an example, but is not limited thereto. The process shown in Figure 5 includes the following steps, for example.

In step S501, the data returned by each unmanned vehicle 1 is received. If there are multiple unmanned vehicles 1 running on the track, the second controller 20 in the monitoring host 2 can wirelessly receive the driving information returned by each unmanned vehicle 1 through the second wireless circuit 22.

In step S503, it is determined whether the received data is normal. When the monitoring host 2 receives the driving information sent back by any unmanned vehicle 1, the second controller 20 of the monitoring host 2 determines whether the received driving information contains the data of the driving position and vibration amount. If not, it can be determined that this data is abnormal and the subsequent vibration judgment of the unmanned vehicle 1 cannot be continued. That is, after the judgment of step S503 is negative, it will return to step S501.

In step S505, it is determined whether there is a relative benchmark value. When step S503 is determined to be yes, the monitoring host 2 can then continue to determine whether the vibration amount in the driving information has a corresponding benchmark value in the database 24 for comparison. Here, the second controller 20 determines whether there is a benchmark value corresponding to the driving position in the current driving information in the database 24. When step S505 is determined to be no, it may be the first time that the unmanned vehicle 1 is used under normal circumstances, and there is no relative benchmark value in the database 24 at this time.

In step S507, check whether they match. When step S505 is judged as yes, the monitoring host 2 will compare the vibration of the current unmanned vehicle 1 with the corresponding benchmark value in the database 24. When the comparison result matches, it means that the vibration of the current unmanned vehicle 1 is not abnormal, that is, the second controller 20 determines that the vibration value of the current unmanned vehicle 1 is within the normal range.

In step S509, the reference value is recorded in the database 24. When step S505 is judged as no, the second controller 20 will store the received driving information in the database 24, that is, the corresponding vibration value of the driving position in the current driving information is set in the database 24 as the reference value for subsequent comparison. Each reference value recorded in the database 24 has a corresponding driving position, that is, the database 24 can record the corresponding reference values of different sections in the track, so that the vibration values obtained by the subsequent unmanned vehicle 1 when driving on different sections of the track have corresponding reference values for comparison.

In step S511, it is determined whether other unmanned vehicles 1 are abnormal at the same driving position. When step S507 is determined to be no, it indicates that the vibration condition is abnormal. At this time, the second controller 20 of the monitoring host 2 will confirm whether other unmanned vehicles 1 at the same driving position also have the same mismatch result as in step S507. In this way, it can be further confirmed whether multiple unmanned vehicles 1 have abnormal vibration at the same driving position. In this case, the monitoring host 2 can accurately determine whether the source of the abnormal vibration is the track itself through the judgment of step S511.

In step S513, an alarm is issued for the abnormality of the track in this section. When step S511 is judged as yes, the monitoring host 2 can determine that the track in this section is abnormal based on the fact that multiple unmanned vehicles 1 have experienced abnormal vibrations at the same driving position, so the second controller 20 of the monitoring host 2 can determine that the track in this section is abnormal, and can send an alarm through the alarm circuit 26.

In step S515, it is determined whether the current unmanned vehicle 1 is also abnormal in other sections of the track. When step S511 is determined to be no, the monitoring host 2 can confirm whether the same unmanned vehicle 1 has also experienced abnormal vibration in other sections of the track. Through the determination of step S515, it can be accurately determined whether the source of the abnormal vibration is the current unmanned vehicle 1 itself.

In step S517, an abnormal alarm of the unmanned vehicle 1 is issued. When step S515 is judged as yes, the monitoring host 2 can determine that the unmanned vehicle 1 itself is the source of abnormal vibration based on the abnormal vibration of the driving position of the same unmanned vehicle in different sections of the track, so the second controller 20 of the monitoring host 2 can determine that the unmanned vehicle 1 itself is the source of abnormal vibration at present, and can send an alarm through the alarm circuit 26.

In step S519, it is determined whether the number of abnormalities meets the standard. When step S515 is determined to be negative, the monitoring host 2 can further determine whether the number of abnormal vibrations of the current unmanned vehicle 1 at the same location meets the standard. For example, when the accumulated number of abnormalities reaches the preset value, the monitoring host determines that the standard is met. Since the execution of step S519 means that a single source of abnormal vibration (such as the unmanned vehicle or the track) has been excluded, the execution and judgment of step S519 can confirm whether the source of abnormal vibration comes from the interaction between the unmanned vehicle 1 and the track at the same time.

In step S521, an alarm is issued for abnormality of the track and the unmanned vehicle 1 in this section. When step S519 is judged as yes, the monitoring host 2 can determine that the abnormal vibration source is caused by the interaction between the unmanned vehicle 1 and the track. Therefore, the second controller 20 of the monitoring host 2 can determine that the unmanned vehicle 1 and the track of this section are the sources of abnormal vibration, and can send an alarm through the alarm circuit 26.

In step S523, the abnormality is recorded. When step S519 is judged as no, the monitoring host can accumulate the number of abnormalities that the unmanned vehicle 1 has occurred on the track in this section. For example, the initial value of the number of abnormalities is 0, and each time an abnormality occurs, the current number of abnormalities will be accumulated by 1, and the number of abnormalities will be accumulated until the number of abnormalities reaches the standard. After that, the number of abnormalities can be reset to 0 for the next re-judgment.

According to the above description, when the unmanned vehicle 1 is driving on the track GR, the built-in detection device 14 of the unmanned vehicle 1 can be used to obtain the vibration amount of the unmanned vehicle 1 during driving, and the monitoring host 2 can monitor the vibration amount sent back by each unmanned vehicle 1. The monitoring host 2 can know whether the unmanned vehicle 1 has abnormal vibration during driving based on the monitoring results and can know the source of the abnormal vibration through analysis.

Next, several examples are given to illustrate the conditions that may cause the unmanned vehicle 1 to vibrate abnormally when driving on the track GR. Please refer to FIG6 , which is a schematic diagram of an unmanned vehicle driving on a normal track according to an embodiment of the present invention. The track GR and the unmanned vehicle 1 shown in FIG6 are in a normal state for illustration. At this time, the monitoring host 2 receives the driving information sent back in real time by the unmanned vehicle 1 during driving on the track GR. The monitoring host 2 can then determine the vibration amount in the driving information and immediately know that the unmanned vehicle 1 is not vibrating abnormally when driving. In addition, the vibration amount in the driving information can be obtained through the detection device 14 installed at an appropriate position of the unmanned vehicle 1, and the installation position of the detection device 14 shown in Figure 6 is only an example, and the present invention is not limited to this.

Please refer to FIG. 7, which is a schematic diagram of an unmanned vehicle driving on a track with a foreign object provided by the embodiment of the present invention. Compared with FIG. 6, the track GR shown in FIG. 7 has a foreign object OB, and the location of the foreign object OB on the track GR will cause abnormal vibration to the unmanned vehicle 1 driving at this location. Therefore, after the monitoring host 2 receives the driving information sent back by the unmanned vehicle 1 driving over the foreign object OB, the monitoring host 2 can immediately know that the unmanned vehicle 1 has been determined to have abnormal vibration while driving by judging the vibration amount in the driving information. In addition, the monitoring host 2 can further determine that when different unmanned vehicles 1 are identified to have abnormal vibrations when driving to the position of the track GR where the foreign object OB exists, the monitoring host 2 can clearly confirm that the position of the track GR where the foreign object OB exists has an abnormality, and can further issue an alarm notification of the abnormality of the track GR in this section.

Please refer to FIG8 and FIG9. FIG8 is a schematic diagram of an unmanned vehicle driving crookedly on a track according to an embodiment of the present invention, and FIG9 is another schematic diagram of an unmanned vehicle driving crookedly on a track according to an embodiment of the present invention. The track GR shown in FIG8 and FIG9 is crooked compared to FIG6. The crooked track GR here means that the adjacent first track GR1 and the second track GR2 in the track GR are not aligned with each other. That is, the unmanned vehicle 1 will produce abnormal vibration when driving at the intersection where the first track GR1 and the second track GR2 are not aligned. Therefore, at this time, after the monitoring host 2 receives the driving information sent back by the unmanned vehicle 1 when it passes the intersection of the first track GR1 and the second track GR2, the monitoring host 2 can immediately know that the unmanned vehicle 1 has been identified as having abnormal vibration while driving by judging the vibration amount in the driving information. Similarly, the monitoring host 2 can further judge that different unmanned vehicles 1 are identified as having abnormal vibration when driving to the intersection of the first track GR1 and the second track GR2. At this time, the monitoring host 2 can clearly confirm that there is an abnormality at the intersection of the first track GR1 and the second track GR2, and can further issue an alarm notification of the abnormality of the track GR in this section.

Please refer to FIG. 10 , which is a schematic diagram of an unmanned vehicle driving on a track with abnormal intervals according to an embodiment of the present invention. The track GR shown in FIG. 10 is spaced compared to FIG. 6 , and the track GR interval here means that the adjacent first track GR1 and the second track GR2 in the track are not closely adjacent to each other. That is, the unmanned vehicle 1 will generate abnormal vibrations at the intersection of the first track GR1 and the second track GR2. Therefore, at this time, after the monitoring host 2 receives the driving information sent back by the unmanned vehicle 1 when it passes the intersection of the first track and the second track, the monitoring host 2 can immediately know that the unmanned vehicle 1 has been identified as having abnormal vibration while driving by judging the vibration amount in the driving information. Similarly, the monitoring host 2 can further judge that when different unmanned vehicles 1 are identified as having abnormal vibration when driving at the intersection of the first track and the second track, the monitoring host 2 can clearly confirm that there is an abnormality at the intersection of the first track GR1 and the second track GR2, and can further issue an alarm notification of abnormality of the track GR in this section.

According to the contents disclosed in Figures 8 to 10 above, when the monitoring host 2 learns that the unmanned vehicle 1 has abnormal vibration, it can further analyze whether different unmanned vehicles 1 have abnormal vibration at the same location of the track GR. If it is determined to be so, it can be determined that the track GR in this section has abnormality. The abnormal condition of the track GR is, for example, the abnormal phenomenon of the track GR having foreign objects, the track GR being skewed, or the track GR having gaps, etc., as described in the above examples, but the present invention is not limited to this.

In one embodiment, when the monitoring host 2 learns that the first unmanned vehicle has abnormal vibration, it can further analyze whether the unmanned vehicles 1 other than the first unmanned vehicle have all produced abnormal vibrations at the same location of the track GR. If it is determined to be no and the first unmanned vehicle is determined by the monitoring host 2 to have abnormal vibrations at different sections of the track GR, it can be confirmed that the source of the vibration belongs to the first unmanned vehicle itself rather than the track GR.

In one embodiment, the first wireless circuit 16 and the second wireless circuit 22 are, for example, WI-FI communication circuits, Bluetooth communication circuits, or wireless radio frequency communication circuits. The alarm circuit 26 is, for example, a display circuit, a light indicator circuit, or a speaker circuit. The first controller 10 and the second controller 20 can be, for example, one or any combination of an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a system-on-chip (SOC), and can be used in conjunction with other related circuit components and firmware to implement the above control process, but the present invention is not limited thereto.

In summary, the monitoring host 2 can observe the driving information reported by the unmanned vehicle 1 for a long time. For example, when the unmanned vehicle 1 is used for a long time, it will cause the internal parts to age (such as the elastic fatigue of the belt in the driving module), mechanical wear or long-term load-bearing. These factors will cause the vibration to increase. The present invention can provide early warning before the vibration expands to affect the quality of the goods transported by the unmanned vehicle 1 so that relevant personnel can perform maintenance in advance, avoiding the excessive vibration momentum that generates unexpected dust and affects the production yield of the transported goods.

[Beneficial effects of the embodiment]

The rail vehicle monitoring and management system and rail vehicle monitoring and management method provided by the present invention can accurately detect the vibration of the unmanned vehicle, and can further accurately identify the source of the vibration, thereby ensuring that the quality of the transported goods is not affected by the vibration, and through alarm warning, the unmanned vehicle or track can be handled immediately by personnel in the event of an abnormality.

The above contents are only the preferred feasible embodiments of the present invention, and do not limit the scope of the patent application of the present invention. Therefore, all equivalent technical changes made by using the contents of the description and drawings of the present invention are included in the scope of the patent application of the present invention.

1: Autonomous vehicle

10: First controller

12: Driving module

14: Detection device

141: Identify circuits

143: Inertial sensor

16: First wireless circuit

2: Monitor host

20: Second controller

22: Second wireless circuit

24: Database

26: Alarm circuit

Claims (10)

A rail vehicle monitoring and management system includes: an unmanned vehicle, when driving on a track, wirelessly outputting a vehicle information, the vehicle information including the vehicle position and a vibration momentum of the unmanned vehicle driving on the track; and a monitoring host, wirelessly receiving the vehicle information and analyzing the vehicle information to obtain a vibration condition of the unmanned vehicle driving on the track; wherein the monitoring host is When the vibration amount at the driving position does not match a corresponding reference value, the monitoring host further determines whether a vibration auxiliary condition is established; when the vibration auxiliary condition is determined to be established, the monitoring host determines that the vibration condition is abnormal and outputs an alarm signal, and when the vibration auxiliary condition is determined not to be established, the monitoring host records that the unmanned vehicle is abnormal at the driving position. The rail vehicle monitoring and management system as described in claim 1, wherein the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to judge whether there are other unmanned vehicles at the same driving position where the vibration amount does not match the corresponding reference value. If the judgment is yes, the monitoring host determines that there is an abnormality in the track at the driving position. The rail vehicle monitoring and management system as described in claim 1, wherein the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to judge whether the vibration amount of the unmanned vehicle at other different driving positions on the track is inconsistent with the corresponding benchmark value. If the judgment is yes, the monitoring host determines that the unmanned vehicle is abnormal. A rail vehicle monitoring and management system as described in claim 1, 2 or 3, wherein the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to judge whether the number of times the vibration amount of the unmanned vehicle at the driving position does not match a corresponding benchmark value exceeds a preset number of times, and if the judgment is yes, the monitoring host determines that the unmanned vehicle and the track of the unmanned vehicle at the driving position are abnormal. The rail vehicle monitoring and management system as described in claim 4, wherein the unmanned vehicle has a detection device, the detection device includes an identification circuit for obtaining the vehicle position and an inertia sensor for obtaining the vibration amount, the identification circuit identifies an identification element provided on the track to obtain the vehicle position when the unmanned vehicle is traveling on the track, and the inertia sensor includes at least one of an accelerometer and a gyroscope. A method for monitoring and managing a rail vehicle comprises: when an unmanned vehicle is driving on a track, the unmanned vehicle wirelessly outputs a vehicle information to a monitoring host, wherein the vehicle information includes a vehicle position and a vibration momentum of the unmanned vehicle driving on the track; the monitoring host wirelessly receives the vehicle information and analyzes the vehicle information to obtain a vibration condition of the unmanned vehicle driving on the track; wherein the monitoring host When the vibration amount of the unmanned vehicle at the driving position does not match a corresponding benchmark value, the monitoring host further determines whether a vibration auxiliary condition is established; when the vibration auxiliary condition is determined to be established, the monitoring host determines that the vibration condition is abnormal and outputs an alarm signal, and when the vibration auxiliary condition is determined not to be established, the monitoring host records that the unmanned vehicle is abnormal at the driving position. The rail vehicle monitoring and management method as described in claim 6, wherein the monitoring host determines the vibration auxiliary condition including: the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to determine whether other unmanned vehicles have the vibration amount that does not match the corresponding benchmark value at the same driving position; and if the judgment is yes, the monitoring host determines that the track at the driving position is abnormal. The rail vehicle monitoring and management method as described in claim 6, wherein the monitoring host determines the vibration auxiliary condition including: the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to determine whether the vibration amount of the unmanned vehicle at other different driving positions on the track does not match the corresponding benchmark value; if the judgment is yes, the monitoring host determines that the unmanned vehicle is abnormal. The rail vehicle monitoring and management method as described in claim 6, 7 or 8, wherein the monitoring host determines the vibration auxiliary condition including: the vibration auxiliary condition is a combination of one or more judgment conditions, one of which is to determine whether the number of times the vibration amount of the unmanned vehicle at the driving position does not match a corresponding benchmark value exceeds a preset number; if the judgment is yes, the monitoring host determines that the unmanned vehicle and the track of the unmanned vehicle at the driving position are abnormal; if the judgment is no, the monitoring host records the number of times the unmanned vehicle appears abnormal at the driving position plus 1. The rail vehicle monitoring and management method as described in claim 6 further includes: when the monitoring host wirelessly receives the driving information, it first determines whether the vibration amount of the driving position in the driving information has a corresponding reference value; when the determination is no, the vibration amount is set to the corresponding reference value at the driving position for use.

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