Train control method and device, equipment, storage medium and program product

Abstract

The invention relates to a train control method and device, equipment, a storage medium and a program product. The method comprises the steps that under the condition that a track fault signal is received, driving adjustment information is determined according to train state data and driving environment data of a train, the driving adjustment information comprises a target cruise speed and a train safety distance, and the target cruise speed is adjusted according to the driving adjustment information; determining a train braking curve corresponding to the train and predicted driving information corresponding to the virtual train group according to the position information and the driving state of the train and the position information and the driving state of each train in the virtual train group where the train is located; and according to the predicted driving information and the actual position change condition of the tail train in the virtual train set within the fault passing time, a train braking curve is updated so as to control running of the train, and the fault passing time is the time needed by the virtual train set to drive away from the rail circuit fault area. By adopting the method, the running safety of the train can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of train control, and in particular to a train control method, device, equipment, storage medium and program product. BACKGROUND

[0002] With the continuous development of train operation field, in order to ensure the safety of operation between trains on the same track, virtual coupling train technology appears. That is, multiple trains are physically not actually connected by couplers, and are synchronized to accelerate, cruise and brake like a complete train.

[0003] However, when the above method is used for train control, when the track circuit fails, the trains in the virtual train set cannot obtain the occupation state of the front line, which reduces the safety of train operation. SUMMARY

[0004] Therefore, it is necessary to provide a train control method, device, equipment, storage medium and program product capable of ensuring the safety of train operation in view of the above technical problems.

[0005] In a first aspect, the present application provides a train control method, comprising:

[0006] In the case of receiving a track fault signal, determining travel adjustment information according to train state data and travel environment data of the train; wherein the travel adjustment information includes a target cruise speed and a train safety interval;

[0007] According to the travel adjustment information, and the position information and travel state of each train in the virtual train set in which the train is located, determining a train braking curve corresponding to the train and predicted travel information corresponding to the virtual train set;

[0008] In the process of train running based on the train braking curve, updating the train braking curve according to the predicted travel information and the actual position change of the tail car in the virtual train set within the fault passing time, to control the operation of the train; wherein the fault passing time is the time required for the virtual train set to drive away from the track circuit fault area.

[0009] In one of the embodiments, the train state data includes brake pad wear rate and train load ratio; the travel environment data includes track slope, track curvature and weather information; determining the travel adjustment information according to the train state data and the travel environment data, comprising:

[0010] According to the change of the brake pad wear rate and the train load ratio, processing the basic safety interval of the train to obtain the train safety interval;

[0011] According to the weather information, determining a weather influence factor;

[0012] The track gradient, track curvature and weather influence factor are used to process the basic cruise speed of the train to obtain a target cruise speed.

[0013] In one of the embodiments, the train braking curve is updated according to the predicted travel information and the actual position change of the tail car in the virtual train set within the fault passing time, including:

[0014] The fault passing time is divided into at least two overlapping time windows;

[0015] The train braking curve is updated according to the difference between the actual position change of the tail car in each overlapping time window and the predicted position change of the tail car in the predicted travel information.

[0016] In one of the embodiments, the train braking curve is updated according to the difference between the actual position change of the tail car in each overlapping time window and the predicted position change of the tail car in the predicted travel information, including:

[0017] For each overlapping time window, when the end time of the overlapping time window is reached, the position deviation of the tail car is determined according to the actual position of the tail car and the predicted position of the tail car at the end time in the predicted travel information.

[0018] The train braking curve is updated according to the position deviation of the tail car.

[0019] In one of the embodiments, the method further includes:

[0020] According to the historical state data of the track where the train is located, the track circuit state corresponding to the track is determined;

[0021] In the case of a risk track circuit, the running state of each train in the virtual train set is adjusted according to the indication signal information in each track partition associated with the track circuit corresponding to the track.

[0022] In one of the embodiments, the method further includes:

[0023] When the train departure signal is detected, the fusion position of the tail car is determined according to the fault position contained in the train departure signal and the predicted position of the tail car at the target time in the predicted travel information; wherein the target time is the time when the train departure signal is received;

[0024] According to the position deviation between the fusion position and the fault position, it is determined whether the tail car has left the track circuit fault area.

[0025] In a second aspect, the application also provides a train control device, including:

[0026] The first determining module is configured to determine travel adjustment information according to train state data and travel environment data of the train in the case of receiving the track fault signal; wherein the travel adjustment information comprises a target cruise speed and a train safety interval;

[0027] The second determining module is configured to determine a train braking curve corresponding to the train and predicted travel information corresponding to the virtual train set according to the travel adjustment information and position information and travel state of each train in the virtual train set in which the train is located;

[0028] The train control module is configured to update the train braking curve according to the predicted travel information and actual position change of a tail train in the virtual train set within a fault passing time during running of the train based on the train braking curve, so as to control running of the train; wherein the fault passing time is a time required for the virtual train set to drive away from a track circuit fault area.

[0029] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0030] In the case of receiving the track fault signal, travel adjustment information is determined according to train state data and travel environment data of the train; wherein the travel adjustment information comprises a target cruise speed and a train safety interval;

[0031] The second determining module is configured to determine a train braking curve corresponding to the train and predicted travel information corresponding to the virtual train set according to the travel adjustment information and position information and travel state of each train in the virtual train set in which the train is located;

[0032] The train control module is configured to update the train braking curve according to the predicted travel information and actual position change of a tail train in the virtual train set within a fault passing time during running of the train based on the train braking curve, so as to control running of the train; wherein the fault passing time is a time required for the virtual train set to drive away from a track circuit fault area.

[0033] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the following steps:

[0034] In the case of receiving the track fault signal, travel adjustment information is determined according to train state data and travel environment data of the train; wherein the travel adjustment information comprises a target cruise speed and a train safety interval;

[0035] The second determining module is configured to determine a train braking curve corresponding to the train and predicted travel information corresponding to the virtual train set according to the travel adjustment information and position information and travel state of each train in the virtual train set in which the train is located;

[0036] During the train's operation based on the train braking curve, the train braking curve is updated according to the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, in order to control the train's operation; where fault passage time is the time required for the virtual train group to leave the track circuit fault area.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0038] Upon receiving a track fault signal, the train determines driving adjustment information based on the train status data and driving environment data; the driving adjustment information includes the target cruising speed and the safe distance between trains.

[0039] Based on the driving adjustment information, as well as the position information and driving status of each train in the virtual train group, the train braking curve corresponding to the train and the predicted driving information corresponding to the virtual train group are determined.

[0040] During the train's operation based on the train braking curve, the train braking curve is updated according to the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, in order to control the train's operation; where fault passage time is the time required for the virtual train group to leave the track circuit fault area.

[0041] The aforementioned train control method, device, equipment, storage medium, and program product, upon receiving a track fault signal, determines driving adjustment information based on the train's status data and driving environment data. Based on this driving adjustment information, and the position information and driving status of each train in the virtual train group, it determines the train braking curve and predicted driving information. Then, during the train's operation based on the braking curve, the braking curve is updated based on the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, until the train exits the track circuit fault area. Compared to related technologies where the occupancy status of the track ahead cannot be obtained when a track circuit fault occurs, this method, on the one hand, ensures the accuracy of the braking curve and predicted driving information by determining them based on the driving adjustment information and the position information and driving status of each train in the virtual train group; on the other hand, by continuously updating the braking curve based on the predicted driving information and the actual position change of the last car during the train's operation based on the braking curve, it improves the reliability of train braking. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating a train control method in one embodiment;

[0044] Figure 2 This is a flowchart illustrating the process of determining driving adjustment information in one embodiment;

[0045] Figure 3 This is a schematic diagram of the process for optimizing the train braking curve in one embodiment;

[0046] Figure 4 This is a flowchart illustrating the optimization of driving status in one embodiment;

[0047] Figure 5 This is a flowchart illustrating the process of determining whether a virtual train set has left the fault area in one embodiment.

[0048] Figure 6 This is a flowchart illustrating the train control method in another embodiment;

[0049] Figure 7 This is a structural block diagram of a train control device in one embodiment;

[0050] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] With the continuous development of the train operation field, in order to ensure the safety of trains operating on the same track, virtual coupled train technology has emerged. That is, multiple trains can accelerate, cruise, and brake synchronously as a complete train without being physically coupled together.

[0053] However, when using the above method for train control, if a track circuit malfunctions, the train in the virtual trainset cannot obtain the occupancy status of the track ahead, which reduces the safety of train operation.

[0054] Based on this, in an exemplary embodiment, a train control method is provided, which will be described using an example of the method being applied to an on-board terminal. Figure 1 As shown, the specific steps include:

[0055] S101, upon receiving a track fault signal, determines driving adjustment information based on the train's status data and driving environment data.

[0056] The so-called track fault signal is the signal sent by the track circuit monitoring equipment when it detects an abnormality in the track circuit. The so-called train status data is data related to the train's operating status. The so-called operating environment data is data related to the train's operating environment, which may include, for example, data related to the track on which the train is located.

[0057] The so-called driving adjustment information refers to information related to adjusting the train's driving mode. Furthermore, the driving adjustment information includes the target cruising speed and the safe distance between trains. The target cruising speed is the cruising speed the train is intended to achieve. The safe distance between trains in the virtual trainset is the safe distance between each train.

[0058] Optionally, track circuit monitoring equipment can determine whether there is a fault in the track circuit by analyzing track circuit parameters (such as current, voltage, frequency, etc.) in real time. For example, the collected track circuit parameters in various dimensions can be compared with the corresponding fault thresholds. If any track circuit parameter in any dimension is greater than the corresponding fault threshold, then a fault in the track circuit is determined.

[0059] In the event of a track circuit fault, the track circuit monitoring equipment can send a track fault signal to the trains in the virtual trainset. After receiving the track fault signal, the train's onboard terminal can acquire the train's current status data and driving environment data, and determine driving adjustment information based on the train's status data and driving environment data.

[0060] For example, train status data and driving environment data can be simultaneously input into a trained train adjustment model, which can then output driving adjustment information based on the train status data and driving environment data.

[0061] Understandably, in order to ensure the reliability of coordination between trains in the virtual trainset, the track circuit monitoring equipment can send track fault signals only to the first train in the virtual trainset, and after the first train determines the driving adjustment information, broadcast it to the other trains in the virtual trainset.

[0062] S102, based on the driving adjustment information, as well as the position information and driving status of each train in the virtual train group to which the train belongs, determine the train braking curve corresponding to the train and the predicted driving information corresponding to the virtual train group.

[0063] The term "driving status" refers to the train's operational status information, which may include, but is not limited to, speed information. The "train braking curve" represents the speed curve to be adjusted. The "predicted driving information" refers to the ideal driving information of each train in the virtual trainset after braking.

[0064] In one optional implementation, the driving adjustment information, the position information and driving status of each train in the virtual train group to which the train belongs can be simultaneously input into the cooperative control model. The cooperative control model then outputs the train braking curve and predicted driving information based on the driving adjustment information, the position information and driving status of each train.

[0065] In another alternative implementation, for each train, the driving adjustment information and the status of each train can be processed according to the train's position in the virtual train group to obtain the train braking curve and predicted driving information.

[0066] For example, when the train is the lead car in a virtual trainset, a train braking curve can be directly constructed based on the train's driving state and target cruising speed. Then, combining the current driving state and the train braking curve, predicted driving information for a rational state based on the train's braking curve can be generated. The train braking curve can be a trapezoidal braking curve, including a deceleration phase and a constant speed phase. Further, the deceleration phase reduces the current speed to the target cruising speed, and the constant speed phase involves traveling at the target cruising speed to the target point. The target point is the state point that must satisfy the desired trajectory during the cumulative braking process.

[0067] When the train consists of vehicles other than the lead car in a virtual trainset, the Model Predictive Control (MPC) algorithm can be used to dynamically predict the train's braking curve and driving information based on the driving status of the preceding train and the safe distance between trains. For example, the objective function of the MPC algorithm is to minimize the tracking error and control quantity in the prediction time domain. This can be expressed by the following formula (1).

[0068] (1)

[0069] Where J represents the total control error to be minimized, and the smaller the value, the higher the tracking accuracy of the system on the desired trajectory and the better the control strategy; N is the number of trains in the virtual train group; Let $\frac{k}{k}$ be the position tracking error of the $k$-th vehicle. Let the speed tracking error be that of the k-th vehicle. Let be the acceleration of the k-th vehicle; For position error weights; As the speed error weight; Weights for control quantities (acceleration).

[0070] In addition, the optimal speed curves under typical operating conditions (such as speed, gradient, curvature) can be stored in advance. Then, the cosine similarity algorithm is used to match the train's train status data with each preset typical operating condition, and the optimal speed curve under the typical operating condition with the highest matching degree is used as the train braking curve.

[0071] S103, during the train's operation based on the train braking curve, updates the train braking curve according to the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, in order to control the train's operation.

[0072] The "fault passage time" refers to the time required for the virtual train to leave the track circuit fault area. The "last car" refers to the last car in the virtual train. The track circuit fault area refers to the region where the track circuit is faulty.

[0073] Optionally, during the train's operation based on the train braking curve, the train braking curve can be updated in real time based on the deviation between the predicted train's driving information and the actual position change of the tail car within the fault passage time; then, the train is controlled to operate according to the updated train braking curve.

[0074] For example, for each moment within the fault passage time, the actual position of the tail car at that moment can be compared with the predicted position of the tail car at that moment in the predicted driving information to obtain the position difference; then, the train braking curve is updated according to the position difference, so as to ensure that the train's driving situation is consistent with the predicted driving information.

[0075] It is worth noting that, in order to improve the reliability of train control, emergency measures for various fault types can be preset. These emergency measures can be presented in tabular form. Table 1 shows the train emergency measures table.

[0076] Table 1 Train Emergency Measures Table

[0077]

[0078] In the aforementioned train control method, upon receiving a track fault signal, the system determines driving adjustment information based on the train's status data and driving environment data. Then, based on this driving adjustment information, and the position and driving status of each train in the virtual train group, it determines the train braking curve and predicted driving information. Subsequently, during the train's operation based on the braking curve, the braking curve is updated based on the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, until the train exits the track circuit fault area. Compared to related technologies where the occupancy status of the track ahead cannot be obtained when a track circuit fault occurs, this method, on the one hand, ensures the accuracy of the braking curve and predicted driving information by determining them based on the driving adjustment information and the position and driving status of each train in the virtual train group; on the other hand, by continuously updating the braking curve based on the predicted driving information and the actual position change of the last car during the train's operation based on the braking curve, it improves the reliability of train braking.

[0079] Based on the above embodiments, in this application embodiment, the train status data includes brake pad wear rate and train load ratio; the driving environment data includes track gradient, track curvature, and weather information. Based on this, an optional method for determining driving adjustment information is provided, such as... Figure 2 As shown, the specific steps include:

[0080] S201. Based on the changes in brake pad wear rate and train load ratio, the basic safety distance of the train is processed to obtain the train safety distance.

[0081] Among these, brake pad wear rate refers to the rate at which the brake pad material is lost during braking. Train load ratio is the ratio of the actual load of the train to its designed maximum rated load. Basic safety clearance is the distance between two adjacent trains under normal operating conditions. Train safety clearance is the adjusted train spacing.

[0082] Optionally, the brake attenuation rate can be determined first based on the change in brake pad wear rate; then, referring to the following formula (2), the basic safety distance of the train can be compensated by using the brake attenuation rate and the train load ratio to obtain the train safety distance.

[0083] (2)

[0084] Where, d s For safe train spacing; d s0 The basic safety clearance; L is the braking fade rate; Z is the train load ratio; and These are the weighting coefficients, and and The sum is 1.

[0085] S202, Based on weather information, determine the weather influencing factors.

[0086] The weather impact factor is a parameter that characterizes the degree to which weather affects train operation. The higher the weather impact factor, the greater the impact of weather on train operation.

[0087] Optionally, weather information can be used as an index to query the correspondence between candidate weather conditions and candidate influencing factors to obtain the weather influencing factors. Alternatively, weather information can be input into the influencing factor determination model, which will then output the weather influencing factors based on the weather information.

[0088] S203 uses track gradient, track curvature, and weather influence factors to process the train's basic cruising speed and obtain the target cruising speed.

[0089] The term "track gradient" refers to the gradient of the track on which the train travels. "Track curvature" refers to the degree of curvature of the track. "Basic cruising speed" refers to the cruising speed of the train under normal operating conditions.

[0090] Optionally, the track gradient can be converted into a gradient influence factor, and the track curvature into a curvature influence factor. A larger gradient influence factor indicates a greater gradient; a larger curvature influence factor indicates a greater curvature. Then, referring to the following formula (3), the track gradient, track curvature, and weather influence factor can be used to weight the train's base cruising speed to obtain the target cruising speed. Where v new The target cruising speed; v base Base cruise speed; k slope For track gradient; k curve k is the orbital curvature; weather These are weather-related factors.

[0091] (3)

[0092] In this embodiment, by optimizing the basic safety distance and basic cruising speed based on train status data and driving environment data, the train safety distance and target cruising speed are obtained. This ensures the compatibility between the train safety distance and target cruising speed and the driving environment, thereby guaranteeing the reliability of the train safety distance and target cruising speed.

[0093] Based on the above embodiments, this application provides an optional method for optimizing train braking curves, such as... Figure 3 As shown, the specific steps include:

[0094] S301 splits the fault transit time into at least two overlapping time windows.

[0095] The so-called overlapping time window refers to the overlapping period between two adjacent time windows.

[0096] Optionally, the fault transit time can be divided into at least two overlapping time windows based on a preset time interval. The time interval can be determined based on the urgency of the fault; that is, in cases of more urgent faults, the time interval is shorter and the update frequency is faster.

[0097] S302, based on the difference between the actual position change of the tail car within each overlapping time window and the predicted position change of the tail car in the predicted driving information, the train braking curve is updated.

[0098] Optionally, the last car can use a positioning system to send its actual position information to other cars in the virtual train in real time. Then, for each overlapping time window, the train braking curve can be updated based on the difference between the actual position change of the last car within that overlapping time window and the predicted position change of the last car in the predicted driving information, thereby ensuring that the train can travel based on the predicted driving information.

[0099] For example, the actual position change of the last car within the overlapping time window, the predicted position change of the last car, and the train braking curve can be input into a trained curve update model. The curve update model updates the train braking curve based on the actual position change and the predicted position change, thereby outputting the updated train braking curve.

[0100] Alternatively, for each overlapping time window, upon reaching the end of the overlapping time window, the position deviation of the tail car is determined based on the actual position of the tail car and the predicted position of the tail car at the end of the predicted driving information; the train braking curve is then updated based on the position deviation of the tail car.

[0101] Specifically, for each overlapping time window, when the end time of the overlapping time window is reached, the difference between the actual position of the tail vehicle and the predicted position of the tail vehicle at the end time in the predicted driving information is taken as the position deviation of the tail vehicle.

[0102] Furthermore, the positional deviation of the last car within the overlapping time window can be used to update the train braking curve, and the updated train braking curve can be used as the original train braking curve in the next overlapping time window, thereby realizing real-time updating of the train braking curve and smoothing the braking deviation within the remaining time.

[0103] In this embodiment, by updating the train braking curve based on the difference between the actual position change and the predicted position change of the tail car within each overlapping time window, it can be ensured that the vehicle can travel based on the predicted driving information, thereby improving the reliability of train operation.

[0104] Based on the above embodiments, this application provides an optional method for optimizing driving status, such as... Figure 4 As shown, the specific steps include:

[0105] S401 determines the track circuit status corresponding to the track based on historical status data of the track where the train is located.

[0106] Historical status data refers to relevant data that characterizes the status of the track circuit, such as, but not limited to, track vertical vibration acceleration and track circuit equipment temperature. Track circuit status describes the degree of aging risk associated with the track circuit.

[0107] Optionally, vertical vibration acceleration of the track can be collected using vibration sensors, and the temperature of the track circuit equipment can be monitored using temperature sensors. After cleaning the collected data and removing outliers, target state features reflecting the aging degree of the track circuit are extracted from the remaining data. Then, using the target state features, the track circuit state can be obtained by querying the pre-constructed correspondence between candidate state features and candidate circuit states.

[0108] Alternatively, historical state data of the train's track can be input into a trained state determination model, which can then output the track circuit state based on the historical state data.

[0109] S402, when the track circuit status is a risky track circuit, adjust the running status of each train in the virtual train group according to the indication signal information in each track section associated with the track circuit corresponding to the track.

[0110] The term "risk track circuit" refers to track circuits with a high degree of aging. "Track zone" refers to the pre-defined running areas within the track. "Indication signal information" refers to the indication information provided by the indicator signals within each track zone.

[0111] Optionally, to ensure driving reliability, the train's driving mode can be switched from automatic driving mode to manual driving mode, and the switching event can be recorded in the log (including switching time, location, risk level of track circuit, etc.).

[0112] Furthermore, to ensure the safety of trains operating within the virtual trainset, in cases where the track circuit is classified as a high-risk track circuit, the system complexity can be proactively reduced by switching from the original moving block algorithm to a fixed block algorithm. That is, the virtual trainset operation mode, which involves real-time calculation of safe braking distance, dynamic adjustment based on the precise position of the preceding train, and allowing only minimum distances, is switched to a mode where all types of trains operate in preset track zones, with only one train allowed in each zone, resulting in larger but absolutely safer distances. In this mode, the operating status of each train can be adjusted based on the indicator signal status within its respective track zone.

[0113] For example, when the indicator light is green, you can drive normally; when the indicator light is yellow, you must start to slow down to ensure that you can stop before the next red light; when the indicator light is red, you must stop in front of the signal and the front of the vehicle must not cross the signal.

[0114] In this embodiment of the application, by adjusting the running status of each train in the virtual train group according to the indication signal information in each track section associated with the track circuit when the track circuit is in a risky track circuit state, the safety of train operation can be guaranteed.

[0115] Based on the above embodiments, this application provides an optional method for determining whether a virtual train set has left the fault area, such as... Figure 5 As shown, the specific steps include:

[0116] S501, when a train departure signal is detected, the merged position of the last car is determined based on the fault location contained in the train departure signal and the predicted position of the last car at the target time in the predicted driving information.

[0117] The target time is the time when the train departure signal is received; the train departure signal is used to characterize the signal that the train has left the fault area; the fused position is the position information obtained by fusing the fault position and the predicted position.

[0118] Optionally, a radio frequency identification tag (RFID) can be installed at the exit of the section corresponding to the faulty track circuit. When the last car passes the RFID tag, the faulty area will automatically send a train departure signal.

[0119] To prevent RFID tags from prematurely sending train departure signals due to abnormal detection, when a train departure signal from a faulty area is detected, the predicted position of the last car at that moment can be obtained from the predicted travel information. The predicted position and the fault position contained in the train departure signal are then fused to obtain the fused position of the last car.

[0120] S502 determines whether the tail car has left the track circuit fault area based on the positional deviation between the fusion position and the fault position.

[0121] Optionally, the positional deviation between the merged position and the fault position can be calculated based on the merged position and the fault position. Then, the positional deviation can be compared with a preset deviation threshold. If the positional deviation is less than the deviation threshold, it can be determined that the tail vehicle has left the fault area. If the positional deviation is greater than or equal to the deviation threshold, it can be determined that the tail vehicle has not left the fault area.

[0122] In the example of this application, by determining whether the tail vehicle has left the fault area based on the positional deviation between the fused position determined based on the fault location and the predicted position and the fault location, the reliability of the determination of the tail vehicle leaving the fault area can be guaranteed.

[0123] Figure 6 This is a flowchart illustrating a train control method in another embodiment. Based on the above embodiments, this embodiment provides an optional example of a train control method. (Combined with...) Figure 6 The specific implementation process is as follows:

[0124] S601, upon receiving a track fault signal, processes the basic safety clearance of the train based on the changes in the wear rate of the train's brake pads and the train's load ratio to obtain the train's safe clearance.

[0125] S602, based on weather information, determines the weather influencing factors, and uses the train's track gradient, track curvature, and weather influencing factors to process the train's base cruising speed to obtain the target cruising speed.

[0126] S603 determines the train braking curve and predicts driving information based on the train's safe distance, target cruising speed, and the position and driving status of each train in the virtual train group to which the train belongs.

[0127] S604, during the train's operation based on the train braking curve, divides the fault passage time into at least two overlapping time windows.

[0128] S605 updates the train braking curve based on the difference between the actual position change of the tail car within each overlapping time window and the predicted position change of the tail car in the predicted driving information.

[0129] The fault passage time is the time required for the virtual train to leave the faulty area of ​​the track circuit.

[0130] Optionally, for each overlapping time window, upon reaching the end time of the overlapping time window, the position deviation of the tail car is determined based on the actual position of the tail car and the predicted position of the tail car at the end time in the predicted driving information; the train braking curve is updated based on the position deviation of the tail car in the overlapping time window.

[0131] The specific processes of S601-S605 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar and will not be repeated here.

[0132] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0133] Based on the same inventive concept, this application also provides a train control device for implementing the train control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more train control device embodiments provided below can be found in the limitations of the train control method described above, and will not be repeated here.

[0134] In one exemplary embodiment, such as Figure 7 As shown, a train control device 1 is provided, including: a first determining module 10, a second determining module 20, and a train control module 30, wherein:

[0135] The first determining module 10 is used to determine driving adjustment information based on the train status data and driving environment data when a track fault signal is received; wherein, the driving adjustment information includes the target cruising speed and the train safety distance.

[0136] The second determining module 203 is used to determine the train braking curve corresponding to the train and the predicted driving information corresponding to the virtual train group based on the driving adjustment information, as well as the position information and driving status of each train in the virtual train group to which the train is located.

[0137] The train control module 30 is used to update the train braking curve during the train's operation based on the predicted driving information and the actual position change of the tail car in the virtual train group within the fault passage time, so as to control the train's operation; wherein, the fault passage time is the time required for the virtual train group to leave the track circuit fault area.

[0138] In an exemplary embodiment, the train status data includes brake pad wear rate and train load ratio; the operating environment data includes track gradient, track curvature, and weather information; the first determining module 10 is specifically used for:

[0139] Based on the changes in brake pad wear rate and train load ratio, the basic safety distance of the train is processed to obtain the train safety distance; based on weather information, the weather influence factor is determined; using track gradient, track curvature, and weather influence factor, the basic cruising speed of the train is processed to obtain the target cruising speed.

[0140] In one exemplary embodiment, the train control module 30 includes:

[0141] A splitting unit is used to divide the fault transit time into at least two overlapping time windows;

[0142] The braking adjustment unit is used to update the train braking curve based on the difference between the actual position change of the tail car within each overlapping time window and the predicted position change of the tail car in the predicted driving information.

[0143] In one exemplary embodiment, the brake adjustment unit specifically includes:

[0144] For each overlapping time window, upon reaching the end time of the overlapping time window, the position deviation of the tail car is determined based on the actual position of the tail car and the predicted position of the tail car at the end time in the predicted driving information; the train braking curve is updated based on the position deviation of the tail car.

[0145] In one exemplary embodiment, the train control device 1 includes a state adjustment module, wherein the state adjustment module is specifically used for:

[0146] Based on the historical status data of the track where the train is located, the status of the track circuit corresponding to the track is determined; when the track circuit status is a risky track circuit, the running status of each train in the virtual train group is adjusted according to the indicator signal information in each track section associated with the track circuit corresponding to the track.

[0147] In one exemplary embodiment, the train control device 1 includes a travel determination module, wherein the travel determination module is specifically used for:

[0148] When a train departure signal is detected, the merged position of the tail car is determined based on the fault location contained in the train departure signal and the predicted position of the tail car at the target time in the predicted travel information; where the target time is the time when the train departure signal is received; based on the positional deviation between the merged position and the fault location, it is determined whether the tail car has left the track circuit fault area.

[0149] Each module in the aforementioned train control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0150] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a train control method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0151] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0153] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0154] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0155] It should be noted that the data involved in this application (including but not limited to train operation data) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A train control method, characterized in that, The method includes: Upon receiving a track fault signal, driving adjustment information is determined based on the train's status data and driving environment data; wherein, the driving adjustment information includes the target cruising speed and the train's safe distance. Based on the driving adjustment information, as well as the position information and driving status of each train in the virtual train group to which the train belongs, the train braking curve corresponding to the train and the predicted driving information corresponding to the virtual train group are determined. During the operation of the train based on the train braking curve, the train braking curve is updated according to the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time, so as to control the operation of the train; wherein, the fault passage time is the time required for the virtual train group to leave the track circuit fault area.

2. The method according to claim 1, characterized in that, The train status data includes brake pad wear rate and train load ratio; the operating environment data includes track gradient, track curvature, and weather information; the determination of driving adjustment information based on the train status data and operating environment data includes: Based on the changes in the wear rate of the brake pads and the train load ratio, the basic safety distance of the train is processed to obtain the train safety distance; Based on the weather information, determine the weather influencing factors; The target cruising speed is obtained by processing the basic cruising speed of the train using the track gradient, track curvature, and weather influence factors.

3. The method according to claim 1, characterized in that, The step of updating the train braking curve based on the predicted driving information and the actual position change of the last car in the virtual train group within the fault passage time includes: The fault is split into at least two overlapping time windows; The train braking curve is updated based on the difference between the actual position change of the tail car within each overlapping time window and the predicted position change of the tail car in the predicted driving information.

4. The method according to claim 3, characterized in that, The step of updating the train braking curve based on the difference between the actual position change of the tail car within each overlapping time window and the predicted position change of the tail car in the predicted driving information includes: For each overlapping time window, when the end time of the overlapping time window is reached, the position deviation of the tail vehicle is determined based on the actual position of the tail vehicle and the predicted position of the tail vehicle at the end time in the predicted driving information. The train braking curve is updated based on the positional deviation of the tail car.

5. The method according to claim 1, characterized in that, The method further includes: The track circuit status corresponding to the track is determined based on the historical status data of the track where the train is located; When the track circuit is in a risky track circuit state, the driving status of each train in the virtual train group is adjusted according to the indication signal information in each track section associated with the track circuit corresponding to the track.

6. The method according to claim 1, characterized in that, The method further includes: When a train departure signal is detected, the merged position of the last car is determined based on the fault location contained in the train departure signal and the predicted position of the last car at the target time in the predicted travel information; wherein, the target time is the time when the train departure signal is received. Based on the positional deviation between the fusion position and the fault position, it is determined whether the tail car has left the fault area of ​​the track circuit.

7. A train control device, characterized in that, The device includes: The first determining module is used to determine driving adjustment information based on train status data and driving environment data when a track fault signal is received; wherein, the driving adjustment information includes target cruising speed and train safety distance; The second determining module is used to determine the train braking curve corresponding to the train and the predicted driving information corresponding to the virtual train group based on the driving adjustment information, as well as the position information and driving status of each train in the virtual train group to which the train is located. The train control module is used to update the train braking curve based on the predicted driving information and the actual position change of the last car in the virtual train group during the operation of the train based on the train braking curve, so as to control the operation of the train; wherein, the fault passage time is the time required for the virtual train group to leave the track circuit fault area.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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