Rail transit rear-end collision risk assessment method and system

Abstract

The invention discloses a rail transit rear-end collision risk assessment method and system, and relates to the technical field of data prediction. The method comprises the following steps: acquiring state information of a reference train from a cloud server; when the state information indicates that the reference train is located in the tunnel, first information is obtained from the cloud server; based on the first information, the relative speed and the relative distance of the target train in the first duration are determined, and second information is obtained; and determining a risk coefficient of rear-end collision between the target train and the reference train by using the second information and a prediction model to obtain a risk assessment result. According to the technical scheme, the accuracy and reliability of the positioning result in the train running process can be improved, and therefore the rail transit running safety is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data prediction, and specifically to a rail transit rear-end collision risk assessment method and system. Background Art

[0002] In order to ensure the driving safety of rail transit systems (such as subways, light rails or trains, etc.), it is crucial to conduct real-time risk assessment of rail transit. In existing technologies, rear-end collision risk assessment mostly relies on technologies such as the Global Positioning System (GPS) to locate trains in real time, and uses the train automatic control system (ATC) to achieve speed control and automatic protection of trains, ensuring that the distance between the front and rear vehicles is not less than the minimum safety distance specified in the rail transit implementation standards. However, during the operation of trains, there are often scenes with weak positioning signals such as tunnels. Once the front vehicle cannot be accurately located, the rear vehicle will not be able to know the distance to the front vehicle in time. Especially with the arrival of the full speed increase of trains, it is impossible to make accurate and timely assessments and implement correct braking strategies, which will cause great safety hazards to the driving safety of the rear vehicle and even the entire railway system.

[0003] Therefore, how to overcome the above-mentioned technical problems and defects becomes a key issue that needs to be solved. Summary of the invention

[0004] The purpose of this application is to provide a rail transit rear-end collision risk assessment method and system to solve the problems raised in the above-mentioned background technology, which can improve the accuracy and reliability of the positioning results during train travel, thereby improving the safety of rail transit driving.

[0005] The technical solution of the embodiment of the present application is implemented as follows: The present application provides a rail transit rear-end collision risk assessment method, the method comprising: Acquire status information of a reference train from a cloud server; the status information indicates whether the reference train is inside a tunnel or outside a tunnel; When the state information indicates that the reference train is located in a tunnel, obtaining first information from the cloud server; the first information represents a second operating parameter of the reference train in the tunnel; the first information is determined based on the first operating parameter when the reference train enters the tunnel; Based on the first information, determining the relative speed and relative distance of the target train within a first time period to obtain second information; the first time period is determined based on the time period of the reference train passing through the tunnel; The second information and the prediction model are used to determine the risk factor of rear-end collision between the target train and the reference train, and to obtain a risk assessment result.

[0006] In the above scheme, the method further comprises: When the reference train enters the tunnel, it uploads its first operating parameters to the cloud server.

[0007] In the above scheme, the method further comprises: A Global System for Mobile Communications – Railway (GSM-R) base station determines whether the distance between the reference train and the tunnel entrance is less than a distance threshold, and obtains a second determination result; When the second judgment result indicates that the distance between the reference train and the tunnel entrance is less than the distance threshold, continuously acquiring sensor data of the tunnel entrance according to a preset period; Based on the acquired sensor data, the first operating parameter of the reference train is determined and uploaded to the cloud server.

[0008] In the above scheme, the method further comprises: After receiving the first operating parameter, the cloud server updates the status information of the reference train, and predicts the second operating parameter of the reference train in the tunnel based on the first operating parameter to obtain the first information.

[0009] In the above scheme, the method further comprises: After the reference train enters the tunnel, the GSM-R base station obtains radio frequency identification (RFID) information of the reference train within a first time period; Determine a third operating parameter of the reference train based on the RFID information, and upload the third operating parameter to a cloud server; The cloud server corrects the second operating parameter based on the third operating parameter.

[0010] In the above scheme, the first time period includes a plurality of sampling points; and based on the first information, determining the relative speed and relative distance of the target train in the first time period to obtain the second information includes: Based on the first information, determining the relative speed and relative distance of the target train at each sampling point according to the time axis; Based on the relative speeds and relative distances of all sampling points, second information is generated.

[0011] In the above solution, the use of the second information and the prediction model to determine the risk coefficient of the target train and the reference train rear-ending each other to obtain the risk assessment result includes: Determine the historical relative distance and historical relative speed of the target train in the second time period based on the historical operating parameters of the reference train in the second time period before entering the tunnel; Determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time; Based on the credibility, the second information and the prediction model, a risk coefficient of rear-end collision between the target train and the reference train is determined to obtain a risk assessment result.

[0012] In the above solution, determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time includes: Based on all historical relative speed data within the second time period, determining a change in the historical relative speed, and generating a first change curve; Based on all historical relative distance data within the second time period, determining a change in the historical relative distance, and generating a second change curve; The credibility of the second information is determined based on the fluctuation degrees of the first change curve and the second change curve.

[0013] In the above solution, determining the credibility of the second information based on the fluctuation degree of the first change curve and the second change curve includes: Determining a first credibility of the relative speed in the second information based on the fluctuation frequency and the fluctuation amplitude of the first variation curve; Based on the fluctuation frequency and the fluctuation amplitude of the second change curve, a second credibility of the relative distance in the second information is determined.

[0014] The embodiment of the present application further provides a rail transit rear-end collision risk assessment system, the system comprising a target train and a cloud server; the target train comprises: a communication unit, configured to obtain status information of a reference train from a cloud server; the status information indicating that the reference train is located in a tunnel or outside a tunnel; and, when the status information indicates that the reference train is located in a tunnel, obtain first information from the cloud server; the first information represents a second operating parameter of the reference train in the tunnel; the first information is determined based on the first operating parameter of the reference train when it enters the tunnel; A processing unit, configured to determine, based on the first information, a relative speed and a relative distance of a target train within a first duration, to obtain second information; the first duration being determined based on a duration of the reference train passing through a tunnel; The prediction unit is used to determine the risk coefficient of rear-end collision between the target train and the reference train by using the second information and the prediction model to obtain a risk assessment result.

[0015] The rail transit rear-end collision risk assessment method and system provided in the embodiment of the present application synchronize the status information and operating parameters of the front vehicle when entering the tunnel to the cloud server, so that when the cloud server cannot accurately know the position of the front vehicle through the positioning system, it can estimate the position and speed of the front vehicle based on the received operating parameters, thereby achieving accuracy, comprehensiveness and reliability of positioning during the train travel; further, by obtaining the operating parameters of the front vehicle, the relative distance and relative speed between the front and rear vehicles can be calculated, and the risk of rear-end collision between the rear vehicle and the front vehicle can be predicted based on the relative distance and relative speed, thereby achieving real-time and comprehensive assessment of the risk of rear-end collision during travel, improving the comprehensiveness and reliability of safety monitoring of the train travel process, and thus ensuring the safe and stable operation of the entire rail transit system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a rail transit rear-end collision risk assessment method provided in an embodiment of the present application; Figure 2 This is a schematic diagram of a process for correcting a second operating parameter in a rail transit rear-end collision risk assessment method according to an embodiment of the present application; Figure 3 A schematic diagram of a process for determining second information in a rail transit rear-end collision risk assessment method according to an embodiment of the present application; Figure 4 A schematic diagram of a process for obtaining a risk assessment result in a rail transit rear-end collision risk assessment method according to an embodiment of the present application; Figure 5 A schematic diagram of a process for determining the credibility of second information in a rail transit rear-end collision risk assessment method according to an embodiment of the present application; Figure 6 A schematic diagram of a flow chart of determining the credibility of the second information based on the fluctuation degree of the first change curve and the second change curve in the rail transit rear-end collision risk assessment method according to an embodiment of the present application; Figure 7 A schematic diagram of the architecture of a rail transit rear-end collision risk assessment system provided in an embodiment of the present application; Figure 8 A schematic diagram of the structure of a target train in a rail transit rear-end collision risk assessment system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The present application embodiment provides a rail transit rear-end collision risk assessment method, which is applied to a train control system, such as Figure 1 As shown, the method may include S101 to S104. S101 to S104 are described in detail below.

[0019] S101: Acquire status information of a reference train from a cloud server; the status information indicates whether the reference train is inside a tunnel or outside a tunnel.

[0020] In actual application, obtaining the status information of the reference train from the cloud server can be understood as the target train or the train dispatching control center obtaining the status information of the reference train from the cloud server; the reference train can also be called the front train, that is, the front train of two trains traveling in the same direction, and the target train can also be called the rear train, that is, the rear train of two trains traveling in the same direction.

[0021] In actual application, the cloud server can use GPS, Beidou and other positioning systems to obtain the location information of the reference train in real time, so as to update the status information of the reference train in time when the reference train enters the tunnel.

[0022] It should be noted that the entering of a tunnel described in the embodiments of the present application may be understood as the front end of a train entering a tunnel.

[0023] In actual application, the reference train can also send its own status information to the cloud server when entering the tunnel, that is, before disconnecting from the cloud server, so that the cloud server can know that the driving environment of the reference train has changed from outside the tunnel to inside the tunnel, and thus update the status information of the reference train in time.

[0024] Based on this, in one embodiment, the method may further include: When the reference train enters the tunnel, it uploads its first operating parameters to the cloud server.

[0025] Here, compared with the method of obtaining location information in real time, since there is no need to obtain information continuously, but only needs to update the status information of the corresponding train after receiving the operating parameters when the train enters the tunnel, it can reduce the data transmission volume between the cloud server and other devices, save spectrum resources, and reduce the data processing pressure of the cloud server.

[0026] In actual application, in order to ensure that the cloud server can accurately and timely obtain the information when the reference train enters the tunnel, sensors can also be set at the tunnel entrance. When the train enters the tunnel entrance, the operating parameters of the train are obtained through the detection data of the sensor; for example, visual cameras and ultrasonic sensors can be set at the tunnel entrance to detect the image of the entering train and the distance from the tunnel entrance; when the sensor collects the data of the reference train entering the tunnel, the collected data is sent to the GSM-R base station installed at the tunnel entrance or in the tunnel. The GSM-R base station communicates with the cloud server and sends the operating parameters of the reference train when entering the tunnel to the cloud.

[0027] Based on this, in one embodiment, the method may further include: The GSM-R base station determines whether the distance between the reference train and the tunnel entrance is less than a distance threshold, and obtains a second determination result; When the second judgment result indicates that the distance between the reference train and the tunnel entrance is less than the distance threshold, continuously acquiring sensor data of the tunnel entrance according to a preset period; Based on the acquired sensor data, the first operating parameter of the reference train is determined and uploaded to the cloud server.

[0028] In actual application, the reference train can determine whether the current distance to the tunnel entrance is less than the distance threshold based on the real-time positioning information obtained from the positioning system, and send an alarm signal to the GSM-R base station when the distance is less than the distance threshold. After receiving the alarm signal sent by the reference train, the GSM-R base station can start to continuously obtain the detection data of the sensors installed at the tunnel entrance according to the preset interval time, and judge the position and operating parameters of the reference train based on the detection data. When the reference train enters the tunnel, the operating parameters obtained at the corresponding time are sent to the cloud server.

[0029] In actual application, the first operating parameter may also be referred to as the actual operating parameter, that is, referring to the actual operating parameter before the train enters the tunnel. The embodiment of the present application does not limit this, as long as its function can be achieved.

[0030] In actual application, since both the GSM-R base station and the reference train send the first operating parameters to the cloud server, when the reference train malfunctions and cannot communicate normally with the cloud server, and thus cannot send the first operating parameters normally, the cloud server can also receive the operating parameters of the reference train and update the status information in time, thereby ensuring the accuracy of the status information and the reliability of the entire risk assessment results.

[0031] S102: When the status information indicates that the reference train is located in a tunnel, obtain first information from the cloud server; the first information represents a second operating parameter of the reference train in the tunnel; the first information is determined based on the first operating parameter of the reference train when entering the tunnel.

[0032] In actual application, after receiving the first operating parameter, the cloud server can estimate the running speed and position of the reference train in the tunnel according to the first operating parameter and the design parameters of the tunnel (such as tunnel length, speed limit requirements, etc.).

[0033] Based on this, in one embodiment, the method may further include: After receiving the first operating parameter, the cloud server updates the status information of the reference train, and predicts the second operating parameter of the reference train in the tunnel based on the first operating parameter to obtain the first information.

[0034] In actual application, the second operating parameter can also be called a predicted operating parameter, that is, a predicted operating parameter referring to the train passing through a tunnel. The embodiment of the present application does not limit this, as long as its function can be achieved.

[0035] In actual application, RFID tags can also be deployed in the tunnel, and the position of the train in the tunnel can be calculated based on the position change between the reference train and the RFID tags.

[0036] Based on this, in one embodiment, the method may further include correcting the second operating parameter; Figure 2 As shown, the correction of the second operating parameter may include: S201: After the reference train enters the tunnel, the GSM-R base station obtains the RFID information of the reference train within a first time period; S202: Determine a third operating parameter of the reference train based on the RFID information, and upload the third operating parameter to a cloud server; S203: The cloud server corrects the second operating parameter based on the third operating parameter.

[0037] In actual application, after the reference train enters the tunnel, the RFID reader installed on the reference train identifies the RFID tags at different positions in the tunnel and generates identification information, i.e., RFID information, and sends the obtained identification information to the GSM-R base station. The GSM-R base station determines the position of the corresponding RFID tag according to the number of the RFID tag in the identification information, thereby determining the current position of the reference train, and calculates the travel speed of the reference train according to the time interval between the two RFID tag identification signals and the tag distance interval, thereby obtaining the operating parameters determined according to the RFID tag position, i.e., the third operating parameters; the GSM-R base station uploads the third operating parameters to the cloud server, so that the cloud server can correct the second operating parameters according to the third operating parameters, so as to improve the accuracy and reliability of the predicted second operating parameters, thereby ensuring the accuracy of the risk assessment results.

[0038] Here, based on the RFID information, the third operating parameter of the reference train is determined, and when the third operating parameter is uploaded to the cloud server, the GSM-R base station can upload the third operating parameter once each time an RFID information is obtained. Specifically, after the GSM-R base station obtains the first RFID information (i.e., the identification information collected by the reader when the reference train passes the first RFID tag) and calculates the train position, it uploads the third operating parameter including the train position data at this moment to the cloud server, and starting from the second RFID information obtained, calculates the position and speed of the train and uploads it; the GSM-R base station can also upload the third operating parameter once after obtaining two RFID information; the frequency of the GSM-R base station uploading the third operating parameter can be determined specifically according to the actual application scenario requirements, and the embodiments of the present application are not limited to this.

[0039] S103: Based on the first information, determine the relative speed and relative distance of the target train within a first time period to obtain second information; the first time period is determined based on the time period for the reference train to pass through the tunnel.

[0040] In actual application, after determining the first information, the speed and driving trajectory of the reference train while passing through the tunnel can be known. At this time, the relative speed and relative distance between the two at different time points can be calculated based on the speed and driving trajectory of the target train, thereby realizing the assessment of the collision risk between the two when the position of the reference train cannot be located by the positioning system.

[0041] Based on this, in one embodiment, the first duration may include multiple sampling points; Figure 3 As shown, determining the relative speed and relative distance of the target train within the first time period based on the first information to obtain the second information may include: S301: Based on the first information, determine the relative speed and relative distance of the target train at each sampling point according to the time axis; S302: Generate second information based on the relative speeds and relative distances of all sampling points.

[0042] In actual application, the first duration may be determined based on the estimated time it takes for a reference train to pass through a tunnel; specifically, it may be equal to the time it takes for a reference train to pass through a tunnel, or it may be greater than the time it takes for a train to pass through a tunnel.

[0043] In actual application, the sampling point may also be referred to as a sampling time point, or as a sampling moment. The embodiment of the present application does not limit this, as long as its function can be achieved.

[0044] S104: Determine the risk factor of rear-end collision between the target train and the reference train by using the second information and the prediction model, and obtain a risk assessment result.

[0045] In practical applications, after determining the relative speed and relative distance of the target train with respect to the reference train at each sampling point, the relative speed and relative distance can be used as inputs of the prediction model to output the risk coefficient of the corresponding sampling point.

[0046] It should be noted that the embodiment of the present application introduces the process of determining the relative distance and relative speed when the reference train cannot be positioned in the tunnel. When the reference train is not outside the tunnel, the positioning system can be used to locate the reference train and obtain parameters, and calculate the real-time relative distance and relative speed, so as to evaluate the corresponding rear-end collision risk based on the relative distance and relative speed at each moment.

[0047] In actual application, the reference train may also be referred to as the front train, and the target train may also be referred to as the rear train.

[0048] In actual application, the second operating parameter is determined based on the speed of the reference train when it enters the tunnel. Taking into account the different technical parameters and driving characteristics of different trains, the changing characteristics of the speed and position of the corresponding train during the driving process can be determined according to the historical driving data of each train, and the predicted second operating parameter can be optimized using the determined changing characteristics to improve the accuracy of the estimation results of the train operating parameters in the tunnel.

[0049] Based on this, in one embodiment, if Figure 4 As shown, the use of the second information and the prediction model to determine the risk coefficient of the target train and the reference train rear-end collision to obtain the risk assessment result may include S401 to S403. S401 to S403 are described in detail below in conjunction with specific embodiments.

[0050] S401: Based on the historical operating parameters of the reference train for a second period of time before entering the tunnel, determine the historical relative distance and historical relative speed of the target train for the second period of time.

[0051] S402: Determine the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time.

[0052] In practical applications, a variation curve of relative speed and relative distance can be established to analyze the variation characteristics of the two variables.

[0053] Based on this, in one embodiment, if Figure 5 As shown, determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second time period may include: S501: determining a change in the historical relative speed based on all historical relative speed data within the second time period, and generating a first change curve; S502: Determine a change in the historical relative distance based on all historical relative distance data within the second time period, and generate a second change curve; S503: Determine the credibility of the second information based on the fluctuation degrees of the first change curve and the second change curve.

[0054] In practical applications, the fluctuation degree of historical relative distance and historical relative speed can be evaluated from the two dimensions of change frequency and change amplitude.

[0055] Based on this, in one embodiment, if Figure 6 As shown, determining the credibility of the second information based on the fluctuation degree of the first change curve and the second change curve may include: S601: Determine a first credibility of the relative speed in the second information based on the fluctuation frequency and the fluctuation amplitude of the first change curve; S602: Determine a second credibility of the relative distance in the second information based on the fluctuation frequency and the fluctuation amplitude of the second change curve.

[0056] In actual application, when determining the fluctuation frequency, the position in the first curve where the fluctuation amplitude exceeds the speed fluctuation amplitude threshold can be selected as the speed fluctuation position, and then the fluctuation frequency of the first curve is calculated based on the number of speed fluctuation positions in the second time period; accordingly, the position in the second curve where the fluctuation amplitude exceeds the distance fluctuation amplitude threshold is selected as the distance fluctuation position, and then the fluctuation frequency of the second curve is calculated based on the number of distance fluctuation positions in the second time period.

[0057] In actual application, the slope of the corresponding position can be calculated according to the fluctuation amplitude of the speed fluctuation position in the first change curve, so as to obtain the speed change rate of the position. Specifically, the absolute value of the slope of the position can be used as the corresponding speed change rate, and then the first credibility can be calculated according to the mean of all speed change rates and the corresponding fluctuation frequency. Correspondingly, the slope of the corresponding position can be calculated according to the fluctuation amplitude of the distance fluctuation position in the second change curve, so as to obtain the distance change rate of the position. Specifically, the absolute value of the slope of the position can be used as the corresponding distance change rate, and then the second credibility can be calculated according to the mean of all distance change rates and the corresponding fluctuation frequency.

[0058] In actual application, the first credibility may also be referred to as relative speed credibility, and the second credibility may also be referred to as relative distance credibility. The embodiment of the present application does not limit this, as long as the functions can be achieved.

[0059] Exemplarily, the relative speed credibility can be expressed as: ; in, Represents the relative speed credibility, that is, the first credibility, represents the fluctuation frequency of the first variation curve, represents the mean value of the velocity change rate of the first curve, , represents the weight coefficient; The relative distance credibility can be expressed as: ; in, Represents the relative distance credibility, that is, the second credibility, represents the fluctuation frequency of the second variation curve, represents the mean value of the distance change rate of the second curve, , Represents the weight coefficient.

[0060] S403: Based on the credibility, the second information and the prediction model, determine the risk coefficient of rear-end collision between the target train and the reference train to obtain a risk assessment result.

[0061] In actual application, the second information can be optimized using the credibility first, and then the optimized second information can be used as the input of the prediction model; specifically, the relative speed in the second information can be optimized using the first credibility, and the relative distance in the second information can be optimized using the second credibility.

[0062] For example, the risk factor can be expressed as: ; ; ; in, express The risk factor at the moment, express The relative speed of time, express The relative distance of time, is a constant, , is the coefficient.

[0063] In actual application, after predicting the risk coefficient of each sampling point within the first time period based on the credibility, the second information and the prediction model, the risk assessment result of the target train can be determined according to the value of the risk coefficient and the change pattern of the risk coefficient along the time axis.

[0064] Specifically, it is first determined whether the risk coefficient of each sampling point exceeds the risk threshold. If there are sampling points whose risk coefficients exceed the risk threshold within the first time period, the risk assessment result of the target train is high risk; accordingly, if the risk coefficients of all sampling points within the first time period do not exceed the risk threshold, it is determined whether the risk coefficient increases along the time axis. If so, the risk assessment result of the target train is medium risk. Conversely, if there is no increase, the risk assessment result of the target train is low risk. Here, the risk threshold can be determined based on factors such as safety standards for railway operations, historical accident data, actual application scenarios, etc., and this application does not limit this.

[0065] In summary, a rail transit rear-end collision risk assessment method provided in an embodiment of the present application synchronizes the status information and operating parameters of the front vehicle when entering the tunnel to a cloud server, so that when the cloud server cannot accurately know the position of the front vehicle through the positioning system, it can estimate the position and speed of the front vehicle based on the received operating parameters, thereby improving the accuracy, comprehensiveness and reliability of the train positioning results; further, by obtaining the operating parameters of the front vehicle, the relative distance and relative speed between the front and rear vehicles are calculated, and the rear-end collision risk of the rear vehicle and the front vehicle is predicted based on the relative distance and relative speed, thereby realizing real-time and comprehensive assessment of the rear-end collision risk during driving, improving the comprehensiveness and reliability of safety monitoring of the train driving process, and ensuring the safe and smooth operation of the entire rail transit system.

[0066] In order to implement the rail transit rear-end collision risk assessment method of the present application, the present application embodiment also provides a rail transit rear-end collision risk assessment system, such as Figure 7 As shown, the system includes a cloud server 701, a reference train 702, a target train 703 and a GSM-R base station 704; Figure 8 As shown, the target train 703 may include: The communication unit 801 is used to obtain state information of a reference train from a cloud server; the state information indicates that the reference train is located in a tunnel or outside a tunnel; and, when the state information indicates that the reference train is located in a tunnel, obtain first information from the cloud server; the first information represents a second operating parameter of the reference train in the tunnel; the first information is determined based on the first operating parameter of the reference train when it enters the tunnel; The processing unit 802 is configured to determine the relative speed and relative distance of the target train within a first time period based on the first information to obtain second information; the first time period is determined based on the time period of the reference train passing through the tunnel; The prediction unit 803 is used to determine the risk coefficient of the target train and the reference train rear-ending each other by using the second information and the prediction model, and obtain a risk assessment result.

[0067] In one embodiment, the reference train can be used to: When the reference train enters the tunnel, it uploads its first operating parameters to the cloud server.

[0068] In one embodiment, the GSM-R base station is arranged near a tunnel; the GSM-R base station may be used for: The GSM-R base station determines whether the distance between the reference train and the tunnel entrance is less than a distance threshold, and obtains a second determination result; When the second judgment result indicates that the distance between the reference train and the tunnel entrance is less than the distance threshold, continuously acquiring sensor data of the tunnel entrance according to a preset period; Based on the acquired sensor data, the first operating parameter of the reference train is determined and uploaded to the cloud server.

[0069] In one embodiment, the cloud server may also be used for: After receiving the first operating parameter, the cloud server updates the status information of the reference train, and predicts the second operating parameter of the reference train in the tunnel based on the first operating parameter to obtain the first information.

[0070] In one embodiment, the GSM-R base station may also be used to: after the reference train enters the tunnel, the GSM-R base station obtains the radio frequency identification RFID information of the reference train within a first time period; and, based on the RFID information, determines a third operating parameter of the reference train, and uploads the third operating parameter to a cloud server; The cloud server may also be used to: calibrate the second operating parameter based on the third operating parameter.

[0071] In one embodiment, the first time period includes a plurality of sampling points; determining the relative speed and relative distance of the target train in the first time period based on the first information to obtain the second information may include: Based on the first information, determining the relative speed and relative distance of the target train at each sampling point according to the time axis; Based on the relative speeds and relative distances of all sampling points, second information is generated.

[0072] In one embodiment, the use of the second information and the prediction model to determine the risk coefficient of rear-end collision between the target train and the reference train to obtain a risk assessment result may include: Determine the historical relative distance and historical relative speed of the target train in the second time period based on the historical operating parameters of the reference train in the second time period before entering the tunnel; Determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time; Based on the credibility, the second information and the prediction model, a risk coefficient of rear-end collision between the target train and the reference train is determined to obtain a risk assessment result.

[0073] In one embodiment, determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time includes: Based on all historical relative speed data within the second time period, determining a change in the historical relative speed, and generating a first change curve; Based on all historical relative distance data within the second time period, determining a change in the historical relative distance, and generating a second change curve; The credibility of the second information is determined based on the fluctuation degrees of the first change curve and the second change curve.

[0074] In one embodiment, determining the credibility of the second information based on the fluctuation degree of the first change curve and the second change curve includes: Determining a first credibility of the relative speed in the second information based on the fluctuation frequency and the fluctuation amplitude of the first variation curve; Based on the fluctuation frequency and the fluctuation amplitude of the second change curve, a second credibility of the relative distance in the second information is determined.

[0075] It should be noted that: the rail transit rear-end collision risk assessment system provided in the above embodiment only uses the division of the above program modules as an example when performing rail transit rear-end collision risk assessment. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the rail transit rear-end collision risk assessment system provided in the above embodiment and the rail transit rear-end collision risk assessment method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0076] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0077] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A rail transit rear-end collision risk assessment method, characterized in that: The method comprises: Acquire status information of a reference train from a cloud server; the status information indicates whether the reference train is inside a tunnel or outside a tunnel; When the state information indicates that the reference train is located in a tunnel, obtaining first information from the cloud server; the first information represents a second operating parameter of the reference train in the tunnel; the first information is determined based on the first operating parameter when the reference train enters the tunnel; Based on the first information, determine the relative speed and relative distance of the target train within a first time period to obtain second information; the first time period is determined based on the time period of the reference train passing through the tunnel; The second information and the prediction model are used to determine the risk factor of rear-end collision between the target train and the reference train, and to obtain a risk assessment result.

2. The method according to claim 1, characterized in that The method further comprises: When the reference train enters the tunnel, it uploads its first operating parameters to the cloud server.

3. The method according to claim 2, characterized in that The method further comprises: The railway integrated digital mobile communication system GSM-R base station determines whether the distance between the reference train and the tunnel entrance is less than a distance threshold, and obtains a second determination result; When the second judgment result indicates that the distance between the reference train and the tunnel entrance is less than the distance threshold, continuously acquiring sensor data of the tunnel entrance according to a preset period; Based on the acquired sensor data, the first operating parameter of the reference train is determined and uploaded to the cloud server.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: After receiving the first operating parameter, the cloud server updates the status information of the reference train, and predicts the second operating parameter of the reference train in the tunnel based on the first operating parameter to obtain the first information.

5. The method according to claim 2 or 3, characterized in that: The method further comprises: After the reference train enters the tunnel, the GSM-R base station obtains the radio frequency identification (RFID) information of the reference train within a first time period; Determine a third operating parameter of the reference train based on the RFID information, and upload the third operating parameter to a cloud server; The cloud server corrects the second operating parameter based on the third operating parameter.

6. The method according to claim 1, characterized in that The first time period includes a plurality of sampling points; and based on the first information, determining the relative speed and relative distance of the target train in the first time period to obtain the second information includes: Based on the first information, determining the relative speed and relative distance of the target train at each sampling point according to the time axis; Based on the relative speeds and relative distances of all sampling points, second information is generated.

7. The method according to claim 6, characterized in that The step of using the second information and the prediction model to determine the risk coefficient of rear-end collision between the target train and the reference train to obtain a risk assessment result includes: Determine the historical relative distance and historical relative speed of the target train in the second time period based on the historical operating parameters of the reference train in the second time period before entering the tunnel; Determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second period of time; Based on the credibility, the second information and the prediction model, a risk coefficient of rear-end collision between the target train and the reference train is determined to obtain a risk assessment result.

8. The method according to claim 7, characterized in that The determining the credibility of the second information based on the fluctuation degree of the historical relative distance and the historical relative speed within the second time period includes: Based on all historical relative speed data within the second time period, determining a change in the historical relative speed, and generating a first change curve; Based on all historical relative distance data within the second time period, determining a change in the historical relative distance, and generating a second change curve; The credibility of the second information is determined based on the fluctuation degrees of the first change curve and the second change curve.

9. The method according to claim 8, characterized in that The determining the credibility of the second information based on the fluctuation degree of the first change curve and the second change curve includes: Determining a first credibility of the relative speed in the second information based on the fluctuation frequency and the fluctuation amplitude of the first variation curve; Based on the fluctuation frequency and the fluctuation amplitude of the second change curve, a second credibility of the relative distance in the second information is determined.

10. A rail transit rear-end collision risk assessment system, characterized in that: The system includes a target train and a cloud server; The target train comprises: a communication unit, configured to obtain status information of a reference train from a cloud server, wherein the status information indicates that the reference train is located in a tunnel or outside a tunnel; and, when the status information indicates that the reference train is located in a tunnel, obtain first information from the cloud server, wherein the first information represents a second operating parameter of the reference train in the tunnel, and the first information is determined based on the first operating parameter of the reference train when it enters the tunnel; A processing unit, configured to determine, based on the first information, a relative speed and a relative distance of the target train within a first duration, to obtain second information; the first duration is determined based on a duration of the reference train passing through the tunnel; The prediction unit is used to determine the risk coefficient of rear-end collision between the target train and the reference train by using the second information and the prediction model to obtain a risk assessment result.

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