Method and device for determining heavy-haul train driving curve and computer equipment
By using particle swarm optimization algorithm and index selection, the driving curve of heavy-haul trains is automatically determined, which solves the problems of low accuracy and efficiency caused by relying on driver experience and realizes efficient and accurate driving curve design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHUOHUANG RAILWAY DEV
- Filing Date
- 2026-02-03
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, the design of driving curves for heavy-haul trains relies on driver experience, resulting in poor accuracy and reliability, as well as low efficiency.
By acquiring the target driving information of the current path point of the heavy-haul train, the candidate driving information is updated using the particle swarm optimization algorithm, and the driving information is predicted based on the candidate driving information. The target driving information is selected by combining the stability, punctuality and energy consumption indicators, and the driving curve is automatically determined.
It can improve the accuracy and efficiency of driving curves without human intervention, reduce manpower input, and improve the design quality of driving curves.
Smart Images
Figure CN122143977A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of railway transportation technology, and in particular to a method, apparatus and computer equipment for determining the driving curve of heavy-haul trains. Background Technology
[0002] Heavy-haul rail transport boasts advantages such as large capacity, high efficiency, and low cost, making it a crucial direction for the development of railway freight. Heavy-haul trains typically refer to trains transporting heavy loads or goods, and their operating characteristics differ significantly from ordinary passenger trains, including higher initial traction requirements, longer braking distances, and higher energy consumption. Therefore, the design of driving curves for heavy-haul trains is particularly important.
[0003] Currently, the design of driving curves for heavy-haul trains mainly relies on the experience and skills of the drivers. While this method is simple, it heavily tests the drivers' experience and skills, and the varying levels of experience and ability among different drivers result in poor accuracy and reliability in the designed driving curves, as well as low design efficiency. Summary of the Invention
[0004] Therefore, it is necessary to provide a method, apparatus, and computer equipment for determining the driving curve of heavy-haul trains that can improve the accuracy and reliability of the driving curve of heavy-haul trains, in order to address the above-mentioned technical problems.
[0005] Firstly, this application provides a method for determining the driving curve of a heavy-haul train. The method includes:
[0006] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0007] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0008] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0009] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0010] In one embodiment, the target driving information of the current path point is updated based on the location information of the current path point, the target driving information, and the location information of the next path point to obtain candidate driving information for the heavy-haul train at the next path point, including:
[0011] The particle swarm optimization algorithm is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0012] In one embodiment, the target driving information of the current path point is updated based on the location information of the current path point, the target driving information, and the location information of the next path point to obtain candidate driving information for the heavy-haul train at the next path point, including:
[0013] Based on the current path point location information and target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm; among which, the initial velocity information includes initial acceleration and initial velocity;
[0014] The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point;
[0015] The particle velocity information of each particle at the next path point is used as the candidate driving information for the heavy-load train at the next path point.
[0016] In one embodiment, based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information for the next path point is selected from the candidate driving information, including:
[0017] For each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0018] Based on the train indicator values corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
[0019] In one embodiment, the target driving information for the next path point is selected from the candidate driving information based on the train index value corresponding to each candidate driving information, including:
[0020] For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0021] Select the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0022] In one embodiment, the method further includes:
[0023] If the next path point is not the end point of the driving path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is executed.
[0024] Secondly, this application also provides a device for determining the driving curve of heavy-haul trains. The device includes:
[0025] The acquisition module is used to acquire the target driving information of the current path point of the heavy-haul train on the driving path;
[0026] The first determining module is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point; wherein the number of candidate driving information is at least one.
[0027] The selection module is used to select the target driving information for the next path point from each candidate driving information based on the predicted driving information of the heavy-haul train from the current path point to the next path point.
[0028] The second determining module is used to determine the target driving curve of the heavy-haul train on the driving path based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the path end point, when the next path point is the end point of the driving path.
[0029] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:
[0030] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0031] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0032] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0033] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:
[0035] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0036] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0037] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0038] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0039] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:
[0040] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0041] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0042] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0043] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0044] The aforementioned method, apparatus, and computer equipment for determining the driving curve of heavy-haul trains acquire target driving information of the current path point on the travel path. Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain candidate driving information of the heavy-haul train at the next path point; wherein the number of candidate driving information is at least one. Based on the predicted travel information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from the candidate driving information. When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information of the current path point, the target driving information, the location information and target driving information of the path points preceding the current path point, and the location information and target driving information of the end point. This application can determine the target driving curve of the heavy-haul train on the travel path without relying on human intervention. Compared with the traditional method of determining the driving curve based on human experience, this application not only reduces human input but also effectively improves the efficiency and accuracy of determining the driving curve. Attached Figure Description
[0045] Figure 1 This is an application environment diagram of the heavy-haul train driving curve determination method provided in this embodiment;
[0046] Figure 2 This is a flowchart illustrating the first method for determining the driving curve of a heavy-haul train provided in this embodiment.
[0047] Figure 3 This is a flowchart illustrating the process of determining candidate driving information for a heavy-haul train at the next path point, as provided in this embodiment.
[0048] Figure 4 This is a flowchart illustrating the process of determining target driving information for the next path point, as provided in this embodiment.
[0049] Figure 5 This is a flowchart illustrating the second method for determining the driving curve of a heavy-haul train provided in this embodiment;
[0050] Figure 6 This is a structural block diagram of a heavy-haul train driving curve determination device provided in this embodiment;
[0051] Figure 7 This is an internal structural diagram of the computer device provided in this embodiment. Detailed Implementation
[0052] 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.
[0053] The method for determining the driving curve of heavy-haul trains provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the server obtains the target driving information of the heavy-haul train at the current path point on the travel path. Based on the location information of the current path point, the target driving information, and the location information of the next path point, the server updates the target driving information of the current path point to obtain candidate driving information for the heavy-haul train at the next path point; the number of candidate driving information is at least one. Based on the predicted travel information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the server selects the target driving information for the next path point from the candidate driving information. If the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information of the current path point, the target driving information, the location information and target driving information of the path points preceding the current path point, and the location information and target driving information of the path end point.
[0054] The server can be a standalone server or a server cluster. Heavy-haul trains refer to trains transporting heavy loads or goods, and their operating characteristics differ significantly from ordinary passenger trains, including higher initial traction requirements, longer braking distances, and higher energy consumption. Therefore, the design of driving curves for heavy-haul trains is particularly important.
[0055] In one embodiment, such as Figure 2 As shown, a method for determining the driving curve of a heavy-haul train is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:
[0056] S201, Obtain target driving information of the current path point of the heavy-haul train on the driving path.
[0057] The travel path refers to the route the heavy-haul train will take; the travel path includes several waypoints, including the starting point, ending point, and intermediate waypoints. The current waypoint refers to the current waypoint of the heavy-haul train, or the waypoint predicted by simulation. Target form information refers to the actual or predicted travel information of the heavy-haul train at the current waypoint.
[0058] Optionally, in this embodiment, the target driving information includes train acceleration and train speed.
[0059] As an optional implementation of this application, target driving information of the current path point of the heavy-haul train on the driving path is obtained based on sensors on the heavy-haul train.
[0060] Another optional implementation of this application is to send an information retrieval request to the query device so that the query device can provide the target driving information of the heavy-load train at the current path point on the driving path.
[0061] Another optional implementation of this application involves obtaining the target driving information of the previous path point on the travel path of the heavy-haul train; wherein, the previous path point refers to a path point preceding the current path point on the travel path. Based on the location information of the previous path point, the target driving information, and the location information of the current path point, the target driving information of the previous path point is updated to obtain the first driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one. Based on the predicted travel information of the heavy-haul train from the previous path point to the current path point based on each first driving information, the target driving information of the current path point is selected from the first driving information. For details, please refer to the subsequent method for determining the target driving information of the next path point. It should be noted that if the previous path point is the starting point of the path, then the train acceleration and train speed in the target driving information of the previous path point are both 0.
[0062] Optionally, location information refers to the specific location information of the path points, which can be represented by coordinates in this application.
[0063] S202, based on the location information of the current path point, the target driving information, and the location information of the next path point, update the target driving information of the current path point to obtain the candidate driving information of the heavy-haul train at the next path point.
[0064] The number of candidate driving information is at least one.
[0065] Optionally, in this embodiment, based on the particle swarm optimization algorithm, the target driving information of the current path point is updated according to the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0066] S203, based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, select the target driving information of the next path point from each candidate driving information.
[0067] As an optional implementation of this application, for each candidate driving information, based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, the predicted comfort information of the heavy-haul train traveling from the current path point to the next path point is determined. Based on the predicted comfort information corresponding to each candidate driving information, the candidate driving information pointed to by the optimal predicted comfort information is selected as the target driving information for the next path point.
[0068] As another optional implementation of this application, for each candidate driving information, based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, the predicted energy consumption information of the heavy-haul train traveling from the current path point to the next path point is determined. Based on the predicted energy consumption information corresponding to each candidate driving information, the candidate driving information pointed to by the optimal predicted energy consumption information is selected as the target driving information for the next path point.
[0069] As another optional implementation of this application, for each candidate driving information, based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, the predicted arrival time information of the heavy-haul train traveling from the current path point to the next path point is determined. Based on the predicted arrival time information corresponding to each candidate driving information, the candidate driving information pointed to by the optimal predicted arrival time information (i.e., the predicted arrival time information closest to the specified on-time time) is selected as the target driving information for the next path point.
[0070] S204, when the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0071] Optionally, in this embodiment, when the next path point is the end point of the travel path, a target driving curve for the heavy-haul train on the travel path is drawn based on the location information and target driving information of the current path point, the location information and target driving information of the path points preceding the current path point, and the location information and target driving information of the end point of the path. That is, the target driving curve includes the location information and target driving information of each path point. The target driving information includes the train's acceleration and speed. This embodiment determines the target driving curve by determining the optimal solution for each path point, i.e., the target driving information for each path point.
[0072] Optionally, in this embodiment, if the next path point is not the end point of the travel path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. The process then returns to execute the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain the candidate driving information for the heavy-haul train at the next path point. In other words, if the next path point is not the end point of the path, the target driving information for subsequent path points needs to be obtained sequentially in the manner described above for determining the target driving information of the next path point.
[0073] In this embodiment, target driving information of the heavy-haul train at the current path point on the travel path is obtained. Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain candidate driving information of the heavy-haul train at the next path point; wherein the number of candidate driving information is at least one. Based on the predicted travel information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from the candidate driving information. If the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information of the current path point, the target driving information, the location information and target driving information of the path points preceding the current path point, and the location information and target driving information of the end point of the path. This application can determine the target driving curve of the heavy-haul train on the travel path without relying on manual intervention. Compared with the traditional method of determining the driving curve based on manual experience, this application not only reduces manpower input but also effectively improves the efficiency and accuracy of determining the driving curve.
[0074] In one embodiment, to improve the efficiency of acquiring candidate driving information and to enhance the richness of candidate driving information, such as... Figure 3 As shown, in an optional implementation of S202, it includes:
[0075] S301, based on the current path point's location information and the target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm.
[0076] The initial velocity information includes initial acceleration and initial velocity. In this embodiment, each particle represents a driving curve from the current path point to the next path point. The optimal driving curve from the current path point to the next path point is obtained from the particle swarm, which is to determine the optimal driving information for the next path point.
[0077] Optionally, in this embodiment, the position information of the current path point is used as the initial position information of each particle in the particle swarm. The target driving information of the current path point is used as the initial velocity information of each particle. The target driving information includes train acceleration and train speed.
[0078] S302, update the initial position information and initial velocity information to obtain the particle velocity information of each particle at the next path point.
[0079] Optionally, in this embodiment, the initial position information and initial velocity information are updated based on the velocity update formula, the position update formula, and constraints to obtain the particle velocity information of each particle at the next path point. The constraints include at least one of velocity constraints, traction constraints, and braking constraints. The velocity update formula is a formula that can update the initial velocity information of each particle in the particle swarm. The position update formula is a formula that can update the initial position information of each particle in the particle swarm to the position of the next path point. It should be noted that there can be multiple velocity update formulas and position update formulas to obtain a richer amount of particle velocity information, i.e., candidate driving information for the next path point.
[0080] Optionally, the velocity constraint condition in this embodiment can be expressed by the following formula: Where v represents the train speed; This indicates the maximum speed limit between the current path point and the next path point; This indicates the minimum air braking release speed limit between the current path point and the next path point. Because the coupler force on a heavy-haul train increases sharply when the release speed is too low or the speed is too high, which can cause the coupler to break in severe cases, a speed constraint condition is added to increase the safety of heavy-haul trains during operation.
[0081] Optionally, the traction constraint condition in this embodiment can be expressed by the following formula: F represents the traction force of a heavy-haul train between the current path point and the next path point; This indicates the maximum traction force of a heavy-haul train between the current path point and the next path point.
[0082] Optionally, the braking force constraint condition in this embodiment can be expressed by the following formula: B represents the braking force of a heavy-haul train between the current path point and the next path point; This indicates the maximum braking force of a heavy-haul train between the current path point and the next path point.
[0083] S303 uses the particle velocity information of each particle at the next path point as candidate driving information for the heavy-load train at the next path point.
[0084] In this embodiment, based on the current path point's position information and the target driving information, the initial position and initial velocity information of each particle in the particle swarm are determined; wherein, the initial velocity information includes initial acceleration and initial velocity. The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point. This particle velocity information at the next path point is used as candidate driving information for the heavy-load train at the next path point. This embodiment effectively improves the efficiency of obtaining candidate driving information and enhances the richness of the candidate driving information.
[0085] In one embodiment, to improve the accuracy of the target driving information obtained for determining the next path point, such as Figure 4 As shown, one optional implementation of S203 includes:
[0086] S401, for each candidate driving information, based on the predicted driving information of the heavy-haul train from the current path point to the next path point, the train index value corresponding to the candidate driving information is determined.
[0087] The train performance indicators include at least one of the following: stability performance, punctuality, and energy consumption. Stability performance primarily reflects the train's safety status. Punctuality primarily reflects the train's timely arrival at its designated location. Energy consumption primarily reflects the train's energy consumption. Predicted travel information refers to the predicted travel information of a heavy-haul train from its current path point to the next path point, based on candidate driving information.
[0088] Optionally, in this embodiment, the predicted driving information includes the driving speed of each carriage of the heavy-haul train. Based on this, the stability index value can be determined by the following formula: Where K1 represents the stationarity index value; V i The speed of the i-th carriage is the driving speed, i.e. the operating speed; n represents the number of carriages in the heavy-haul train.
[0089] Optionally, in this embodiment, the predicted driving information includes the travel time information of the heavy-haul train in each location interval. Based on this, the punctuality index value can be determined by the following formula: Where K2 represents the punctuality index value; T i This represents the estimated travel time information of the heavy-haul train in the i-th position interval; T u This indicates the specified travel time information for a heavy-haul train in the i-th position interval.
[0090] Optionally, in this embodiment, the predicted driving information includes the traction force of the heavy-haul train at each time point; based on this, the energy consumption index value can be determined by the following formula: Where E represents the energy consumption index value; express The traction force of a constantly heavily loaded train; express The operating speed of heavily loaded trains at all times; This indicates the travel time of a heavy-haul train between the current path point and the next path point.
[0091] S402: Based on the train index values corresponding to each candidate driving information, select the target driving information for the next path point from each candidate driving information.
[0092] Optionally, for each candidate driving information, the stability index value, punctuality index value, and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information. The candidate driving information with the largest total index value is selected as the target driving information for the next path point.
[0093] In this embodiment, for each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of a stability index value, a punctuality index value, and an energy consumption index value. For each candidate driving information, the stability index value, punctuality index value, and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information. The candidate driving information with the highest total index value is selected as the target driving information for the next path point. This embodiment ensures that the determined target driving information for the next path point satisfies requirements for stability, punctuality, and energy conservation, taking into account more aspects and better meeting user needs.
[0094] In one embodiment, such as Figure 5 As shown, one possible implementation of the method for determining the driving curve of heavy-haul trains is as follows:
[0095] S501, obtain the target driving information of the current path point of the heavy-haul train on the driving path.
[0096] S502, based on the current path point's position information and the target driving information, determines the initial position and initial velocity information of each particle in the particle swarm. The initial velocity information includes initial acceleration and initial velocity.
[0097] S503 updates the initial position and initial velocity information to obtain the particle velocity information of each particle at the next path point.
[0098] S504, the particle velocity information of each particle at the next path point is used as candidate driving information for the heavy-load train at the next path point. The number of candidate driving information is at least one.
[0099] S505, for each candidate driving information, based on the predicted driving information of the heavy-haul train from the current path point to the next path point, determines the corresponding train index value. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0100] S506, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0101] S507 selects the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0102] If the next path point is not the end point of the driving path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is executed.
[0103] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0104] This application obtains the target driving information of a heavy-haul train at the current path point on its travel path. Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain candidate driving information for the heavy-haul train at the next path point; wherein the number of candidate driving information is at least one. Based on the predicted travel information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information for the next path point is selected from the candidate driving information. If the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information of the current path point, the target driving information, the location information and target driving information of the path points preceding the current path point, and the location information and target driving information of the end point. This application can determine the target driving curve of a heavy-haul train on its travel path without relying on manual intervention. Compared with the traditional method of determining the driving curve based on manual experience, this application not only reduces manpower input but also effectively improves the efficiency and accuracy of determining the driving curve.
[0105] 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.
[0106] Based on the same inventive concept, this application also provides a heavy-haul train driving curve determination device for implementing the above-described method for determining heavy-haul train driving curves. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the heavy-haul train driving curve determination device provided below can be found in the limitations of the heavy-haul train driving curve determination method described above, and will not be repeated here.
[0107] In one embodiment, such as Figure 6 As shown, a heavy-haul train driving curve determination device 1 is provided, comprising: an acquisition module 10, a first determination module 20, a selection module 30, and a second determination module 40, wherein:
[0108] The acquisition module 10 is used to acquire the target driving information of the heavy-haul train at the current path point on the driving path;
[0109] The first determining module 20 is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point; wherein the number of candidate driving information is at least one.
[0110] The selection module 30 is used to select the target driving information of the next path point from each candidate driving information based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information.
[0111] The second determining module 40 is used to determine the target driving curve of the heavy-haul train on the driving path based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the path end point when the next path point is the end point of the driving path.
[0112] In one embodiment, Figure 6 The first determining module in the module is also specifically used for:
[0113] The particle swarm optimization algorithm is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0114] In one embodiment, Figure 6 The first determining module in the module is also specifically used for:
[0115] Based on the current path point location information and target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm; among which, the initial velocity information includes initial acceleration and initial velocity;
[0116] The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point;
[0117] The particle velocity information of each particle at the next path point is used as the candidate driving information for the heavy-load train at the next path point.
[0118] In one embodiment, Figure 6 The selection module in the document is also specifically used for:
[0119] For each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0120] Based on the train indicator values corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
[0121] In one embodiment, Figure 6 The selection module in the document is also specifically used for:
[0122] For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0123] Select the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0124] In one embodiment, Figure 6 The device 1 for determining the driving curve of heavy-haul trains also includes:
[0125] The return module is used to, when the next path point is not the end point of the driving path, take the next path point as the new current path point, and take the path point after the next path point as the new next path point, and return to execute the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain the candidate driving information of the heavy-haul train at the next path point.
[0126] Each module in the aforementioned heavy-haul train driving curve determination 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 corresponding operations of each module.
[0127] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to heavy-haul train operation. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the driving curve of a heavy-haul train.
[0128] Those skilled in the art will understand that Figure 7 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.
[0129] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0130] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0131] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0132] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0133] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0134] In one embodiment, when the processor executes the computer program, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0135] The particle swarm optimization algorithm is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0136] In one embodiment, when the processor executes the computer program, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0137] Based on the current path point location information and target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm; among which, the initial velocity information includes initial acceleration and initial velocity;
[0138] The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point;
[0139] The particle velocity information of each particle at the next path point is used as the candidate driving information for the heavy-load train at the next path point.
[0140] In one embodiment, when the processor executes the computer program, it further performs the following steps: selecting target driving information for the next path point from the candidate driving information based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, according to the candidate driving information; including:
[0141] For each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0142] Based on the train indicator values corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
[0143] In one embodiment, when the processor executes the computer program, it further performs the following steps: selecting the target driving information for the next path point from the candidate driving information based on the train index value corresponding to each candidate driving information, including:
[0144] For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0145] Select the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0146] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0147] If the next path point is not the end point of the driving path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is executed.
[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0149] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0150] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0151] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0152] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0153] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0154] The particle swarm optimization algorithm is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0155] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0156] Based on the current path point location information and target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm; among which, the initial velocity information includes initial acceleration and initial velocity;
[0157] The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point;
[0158] The particle velocity information of each particle at the next path point is used as the candidate driving information for the heavy-load train at the next path point.
[0159] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: selecting target driving information for the next path point from the candidate driving information based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, including:
[0160] For each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0161] Based on the train indicator values corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
[0162] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: selecting target driving information for the next path point from the candidate driving information based on the train index values corresponding to each candidate driving information, including:
[0163] For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0164] Select the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0165] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0166] If the next path point is not the end point of the driving path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is executed.
[0167] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:
[0168] Obtain target driving information for the current path point of the heavy-haul train on its travel path;
[0169] Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of candidate driving information is at least one.
[0170] Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information.
[0171] When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
[0172] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0173] The particle swarm optimization algorithm is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
[0174] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain candidate driving information of the heavy-haul train at the next path point, including:
[0175] Based on the current path point location information and target driving information, determine the initial position information and initial velocity information of each particle in the particle swarm; among which, the initial velocity information includes initial acceleration and initial velocity;
[0176] The initial position and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point;
[0177] The particle velocity information of each particle at the next path point is used as the candidate driving information for the heavy-load train at the next path point.
[0178] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: selecting target driving information for the next path point from the candidate driving information based on the predicted driving information of the heavy-haul train traveling from the current path point to the next path point, including:
[0179] For each candidate driving information, the train index value corresponding to the candidate driving information is determined based on the predicted driving information of the heavy-haul train from the current path point to the next path point. The train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value.
[0180] Based on the train indicator values corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
[0181] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: selecting target driving information for the next path point from the candidate driving information based on the train index values corresponding to each candidate driving information, including:
[0182] For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information.
[0183] Select the candidate driving information corresponding to the maximum total index value as the target driving information for the next path point.
[0184] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0185] If the next path point is not the end point of the driving path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is executed.
[0186] Those skilled in the art will understand that all or part of the processes in 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 described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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, etc., and are not limited to these.
[0187] 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 specification.
[0188] 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 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 method of determining a driving curve of a heavy-haul train, characterized in that, The method includes: Obtain target driving information for the current path point of the heavy-haul train on its travel path; Based on the location information of the current path point, the target driving information, and the location information of the next path point, the target driving information of the current path point is updated to obtain the candidate driving information of the heavy-haul train at the next path point; wherein, the number of the candidate driving information is at least one. Based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information, the target driving information of the next path point is selected from each candidate driving information. When the next path point is the end point of the travel path, the target driving curve of the heavy-haul train on the travel path is determined based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the end point of the path.
2. The method of claim 1, wherein, The step of updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain the candidate driving information of the heavy-haul train at the next path point, includes: Using the particle swarm optimization algorithm, the target driving information of the current path point is updated based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point.
3. The method of claim 2, wherein, The step of updating the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, to obtain the candidate driving information of the heavy-haul train at the next path point, includes: Based on the current path point's location information and the target driving information, the initial position information and initial velocity information of each particle in the particle swarm are determined; wherein, the initial velocity information includes initial acceleration and initial velocity; The initial position information and initial velocity information are updated to obtain the particle velocity information of each particle at the next path point; The particle velocity information of each particle at the next path point is used as the candidate driving information of the heavy-load train at the next path point.
4. The method of claim 1, wherein, The step of selecting the target driving information for the next path point from each candidate driving information, based on the predicted driving information of the heavy-haul train from the current path point to the next path point, includes: For each candidate driving information, based on the predicted driving information of the heavy-haul train from the current path point to the next path point, the train index value corresponding to the candidate driving information is determined; wherein, the train index value includes at least one of the following: stability index value, punctuality index value, and energy consumption index value. Based on the train index value corresponding to each candidate driving information, the target driving information for the next path point is selected from each candidate driving information.
5. The method of claim 4, wherein, The step of selecting the target driving information for the next path point from each candidate driving information based on the train indicator value corresponding to each candidate driving information includes: For each candidate driving information, the stability index value, punctuality index value and energy consumption index value corresponding to the candidate driving information are weighted and summed to obtain the total index value corresponding to the candidate driving information. The candidate driving information corresponding to the maximum total index value is selected as the target driving information for the next path point.
6. The method of claim 1, wherein, The method further includes: If the next path point is not the end point of the travel path, the next path point is taken as the new current path point, and the path point after the next path point is taken as the new next path point. Then, the operation of updating the target driving information based on the location information of the current path point, the target driving information, and the location information of the next path point is performed to obtain the candidate driving information of the heavy-haul train at the next path point is returned.
7. A device for determining the driving curve of a heavy-haul train, characterized in that, include: The acquisition module is used to acquire the target driving information of the current path point of the heavy-haul train on the driving path; The first determining module is used to update the target driving information of the current path point based on the location information of the current path point, the target driving information, and the location information of the next path point, so as to obtain the candidate driving information of the heavy-haul train at the next path point; wherein the number of the candidate driving information is at least one. The selection module is used to select the target driving information of the next path point from each candidate driving information based on the predicted driving information of the heavy-haul train from the current path point to the next path point based on each candidate driving information. The second determining module is used to determine the target driving curve of the heavy-haul train on the driving path based on the location information and target driving information of the current path point, the location information and target driving information of the path points before the current path point, and the location information and target driving information of the path end point, when the next path point is the end point of the driving path.
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 for determining the driving curve of a heavy-haul train as described in 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 the processor, it implements the steps of the method for determining the driving curve of a heavy-haul train as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for determining the driving curve of a heavy-haul train as described in any one of claims 1 to 6.