Train operation adjustment method, device, system and storage medium
By configuring a train operation adjustment model and optimizing train operation strategies by combining multiple parameters, the problem that static distribution fitting models cannot adapt to actual train operation conditions has been solved, thereby improving the disturbance recovery capability and passenger transport efficiency of high-speed railways.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- KUNMING UNIV OF SCI & TECH
- Filing Date
- 2024-01-16
- Publication Date
- 2026-05-15
AI Technical Summary
Existing static distribution fitting models cannot adapt to actual train operation conditions in high-speed railway train operation adjustments, resulting in a single method for adjusting train delays and an inability to effectively cope with the disturbances caused by train malfunctions.
By acquiring the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters, and the alternative train parameters, a train operation adjustment model is configured, including an objective function and multiple constraint expressions, to optimize the train operation strategy to maximize the volume of passengers affected by the disruption and minimize the weighted total delay of trains.
It improves the disturbance recovery capability of high-speed railways under temporary speed restrictions, reduces losses caused by train failures, and improves passenger transport efficiency and train operation flexibility.
Smart Images
Figure CN117944743B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of train operation adjustment and control technology, and in particular to a train operation adjustment method, device, system, and storage medium. Background Technology
[0002] With the rapid development of high-speed rail technology in China in recent years, high-speed rail stations are covering more and more towns and cities across the country. Due to its advantages such as speed, comfort, affordable fares, and safety, more and more people are choosing high-speed rail as their preferred mode of long-distance travel. As the number of passengers on high-speed trains increases, the demand for their disturbance recovery capabilities also rises. When a high-speed train malfunctions during operation, not only are the passengers on board disrupted, but subsequent trains on the same railway line are also affected, experiencing delays or even cancellations. Therefore, timely adjustments and optimizations to train operations are particularly important.
[0003] In the relevant technical solutions for train operation, static distribution fitting models are usually used to assess train delays. These models are mainly based on historical train punctuality and delay records to predict transportation bottlenecks in the operating network and to estimate the associated delays when an initial delay occurs.
[0004] However, the static distribution fitting model uses relatively simple evaluation parameters for high-speed railway operation scenarios. It mainly evaluates and controls based on the data recorded on train punctuality and delays, and the corresponding control strategies are also relatively simple. As a result, the train operation adjustment methods cannot adapt to the actual train operation situation.
[0005] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0006] The main objective of this invention is to provide a train operation adjustment method, which aims to solve the problem of how to adjust the train operation mode to adapt to the actual train operation conditions.
[0007] To achieve the above objectives, the present invention provides a train operation adjustment method, the method comprising:
[0008] Obtain basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters;
[0009] Based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters, and / or the alternative train parameters, a train operation adjustment model is configured. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum time of the weighted total delay of the train.
[0010] Based on the configured train operation adjustment model, output the train operation adjustment strategy.
[0011] Optionally, the basic parameters of the high-speed railway network model include: high-speed railway information, station information, and train operation sequence.
[0012] Optionally, the target train operating parameters include the number of passengers in the target train, the first arrival time, the first departure time, the first stop plan, the maximum first stop duration, and the minimum first stop duration for each station, the first speed curve, the first running time, the first operating energy consumption, and the passenger destination for the train under different first stop plans.
[0013] Optionally, the train malfunction operation scenario parameters include the station where the malfunctioning train stops, the total number of affected passengers, the destination station of the affected passengers, the passenger groups, and the number of affected passengers in each passenger group.
[0014] Optionally, the alternative train parameters include the alternative train's second arrival and departure times, second stopping plan, second stopping duration, and number of available seats at each station.
[0015] Optionally, the step of configuring the train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters, and / or the alternative train parameters includes at least one of the following:
[0016] The first constraint expression is determined based on the passenger groups, the number of disturbed passengers in each passenger group, the number of available seats, and the first constraint parameter.
[0017] The second constraint expression is determined based on the faulty train's stopping station, the maximum first stop duration, the first arrival time, the first departure time, the passenger's destination, and the second constraint parameter.
[0018] Based on the first velocity curve and the third constraint parameters, determine the expression for the third constraint;
[0019] Based on the first operating energy consumption and the fourth constraint parameter, determine the expression for the fourth constraint;
[0020] The fifth constraint expression is determined based on the first arrival time, the first departure time, the maximum first stop duration, the first running time, the first speed curve, and the fifth constraint parameter;
[0021] The sixth constraint expression is determined based on the maximum first stop duration, the minimum first stop duration, and the sixth constraint parameter;
[0022] Based on the first velocity curve and the seventh constraint parameter, determine the expression for the seventh constraint;
[0023] Based on the train operation sequence and the eighth constraint parameter, determine the expression for the eighth constraint;
[0024] The ninth constraint expression is determined based on the first arrival and departure times, the first running time, the station information, and the ninth constraint parameter.
[0025] Optionally, the step of outputting a train operation adjustment strategy based on the configured train operation adjustment model includes:
[0026] Obtain the multi-objective optimization strategy output by the configured train operation adjustment model;
[0027] Based on the multi-objective optimization strategy, a train fault adjustment operation plan and alternative train matching scheme are generated or determined.
[0028] Furthermore, to achieve the above objectives, the present invention also provides a train operation adjustment device, the train operation adjustment device comprising:
[0029] The parameter acquisition module is used to acquire basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters.
[0030] The model configuration module is used to configure a train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters and / or the alternative train parameters. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum weighted total delay of the train.
[0031] The operation adjustment module is used to output train operation adjustment strategies based on the configured train operation adjustment model.
[0032] In addition, to achieve the above objectives, the present invention also provides a train operation adjustment system, the train operation adjustment system comprising: a memory, a processor, and a train operation adjustment program stored in the memory and executable on the processor, wherein when the train operation adjustment program is executed by the processor, it implements the steps of the train operation adjustment method as described above.
[0033] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a train operation adjustment program, which, when executed by a processor, implements the steps of the train operation adjustment method as described above.
[0034] This invention provides a train operation adjustment method, apparatus, system, and storage medium, which comprehensively considers the basic parameters of the high-speed railway network model, the operating parameters of the target train, the parameters under train failure operation scenarios, and the parameters of alternative trains. The train operation adjustment model is configured based on one or more of the above parameters, aiming to obtain the maximum transport capacity of the affected passengers and the minimum weighted total delay time of the train from multiple dimensions, thereby improving the disturbance recovery capability of high-speed railways under temporary speed restrictions and minimizing losses caused by train failures or other special circumstances. Attached Figure Description
[0035] Figure 1 This is a schematic diagram of the hardware operating environment of the train operation adjustment system according to an embodiment of the present invention;
[0036] Figure 2 This is a flowchart illustrating the first embodiment of the train operation adjustment method of the present invention;
[0037] Figure 3 This is a flowchart illustrating the second embodiment of the train operation adjustment method of the present invention;
[0038] Figure 4 This is a schematic diagram of the structure of the train operation adjustment device of the present invention.
[0039] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0040] This application proposes a train operation adjustment method that comprehensively considers the basic parameters of the high-speed railway network model, the operating parameters of the target train, the parameters under train failure operation scenarios, and the parameters of alternative trains. The train operation adjustment model is configured based on one or more of these parameters, aiming to obtain the maximum transport capacity of affected passengers and the minimum weighted total delay time of trains from multiple dimensions. This improves the disturbance recovery capability of high-speed railways under temporary speed restrictions, thereby minimizing losses caused by train failures or other special circumstances.
[0041] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.
[0042] As one implementation scheme, Figure 1 This is a schematic diagram of the hardware operating environment of the train operation adjustment system involved in the embodiments of the present invention.
[0043] like Figure 1 As shown, the train operation adjustment system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0044] Those skilled in the art will understand that Figure 1 The architecture of the train operation adjustment system shown in the figure does not constitute a limitation on the train operation adjustment system. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0045] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a train operation adjustment program. The operating system is a program that manages and controls the hardware and software resources of the train operation adjustment system, as well as the operation of the train operation adjustment program and other software or programs.
[0046] exist Figure 1 In the train operation adjustment system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the back-end server; and the processor 1001 can be used to call the train operation adjustment program stored in the memory 1005.
[0047] In this embodiment, the train operation adjustment system includes: a memory 1005, a processor 1001, and a train operation adjustment program stored in the memory and executable on the processor, wherein:
[0048] When processor 1001 calls the train operation adjustment program stored in memory 1005, it performs the following operations:
[0049] Obtain basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters;
[0050] Based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters, and / or the alternative train parameters, a train operation adjustment model is configured. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum time of the weighted total delay of the train.
[0051] Based on the configured train operation adjustment model, output the train operation adjustment strategy.
[0052] When processor 1001 calls the train operation adjustment program stored in memory 1005, it performs the following operations:
[0053] The first constraint expression is determined based on the passenger groups, the number of disturbed passengers in each passenger group, the number of available seats, and the first constraint parameter.
[0054] The second constraint expression is determined based on the faulty train's stopping station, the maximum first stop duration, the first arrival time, the first departure time, the passenger's destination, and the second constraint parameter.
[0055] Based on the first velocity curve and the third constraint parameters, determine the expression for the third constraint;
[0056] Based on the first operating energy consumption and the fourth constraint parameter, determine the expression for the fourth constraint;
[0057] The fifth constraint expression is determined based on the first arrival time, the first departure time, the maximum first stop duration, the first running time, the first speed curve, and the fifth constraint parameter;
[0058] The sixth constraint expression is determined based on the maximum first stop duration, the minimum first stop duration, and the sixth constraint parameter;
[0059] Based on the first velocity curve and the seventh constraint parameter, determine the expression for the seventh constraint;
[0060] Based on the train operation sequence and the eighth constraint parameter, determine the expression for the eighth constraint;
[0061] The ninth constraint expression is determined based on the first arrival and departure times, the first running time, the station information, and the ninth constraint parameter.
[0062] When processor 1001 calls the train operation adjustment program stored in memory 1005, it performs the following operations:
[0063] Obtain the multi-objective optimization strategy output by the configured train operation adjustment model;
[0064] Based on the multi-objective optimization strategy, a train fault adjustment operation plan and alternative train matching scheme are generated or determined.
[0065] Based on the hardware architecture of the train operation adjustment system based on the above-mentioned train operation adjustment and control technology, an embodiment of the train operation adjustment method of the present invention is proposed.
[0066] First Embodiment
[0067] Reference Figure 2 The train operation adjustment method includes the following steps:
[0068] Step S10: Obtain the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters, and the alternative train parameters;
[0069] In this embodiment, the data acquisition module in the train operation adjustment system (hereinafter referred to as the system) acquires pre-configured parameters in multiple dimensions.
[0070] The basic parameters of the high-speed railway network model are characterized as the basic information of the high-speed railway network, including but not limited to the detailed topology of the railway network (including but not limited to information such as stations, intersections, and track sections, passenger flow distribution at each station, i.e., passenger arrival distribution at different times, and the specific models and configurations of each train in the railway network, including the number of seats).
[0071] The target train's operating parameters are characterized as the detailed information of the corresponding train timetable of a selected target train among all trains in the railway network (including but not limited to all stations it passes through, stop times, and departure intervals), as well as the passengers' travel needs (including but not limited to departure point, destination, and travel time).
[0072] Train malfunction operation scenario parameters are characterized by train condition information (including but not limited to train performance loss, malfunction time and location distribution) under different malfunction conditions, as well as passenger information on the malfunctioning train (including but not limited to the number of passengers and their respective destinations).
[0073] The alternative train parameters refer to the information of trains located behind the target train that can be used for passengers to transfer when the target train fails. As an alternative train, it must meet at least the following conditions: 1. Its arrival time is later than the arrival time of the failed train plus the passenger transfer time; 2. Its direction of travel is the same as or close to the direction of the passenger's destination.
[0074] Step S20: Configure a train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters and / or the alternative train parameters. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum weighted total delay of the train.
[0075] Step S30: Based on the configured train operation adjustment model, output the train operation adjustment strategy.
[0076] In this embodiment, the system's model configuration module configures the train operation adjustment model based on one or more of the following: basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters.
[0077] The train operation adjustment model refers to the data processing model proposed in this application, which is trained based on the aforementioned parameters and is used to match alternative trains for passengers to transfer to when a train malfunctions.
[0078] In this embodiment, the train operation adjustment model mainly includes an objective function and several constraint expressions. The objective function aims to minimize the delay time of affected passengers on the faulty train and to maximize the transportation of affected passengers to their destinations. The constraint expressions are used to constrain the model to search for alternative trains that meet the transfer conditions for passengers on the faulty train. The determination of the constraint expressions will be explained in detail in subsequent embodiments and will not be repeated here.
[0079] In the technical solution provided in this embodiment, the basic parameters of the high-speed railway network model, the operating parameters of the target train, the parameters under the train failure operation scenario, and the parameters of the alternative train are comprehensively considered. The train operation adjustment model is configured according to one or more of the above parameters. The maximum transport capacity of the number of passengers affected by the disturbance and the minimum weighted total delay of the train are obtained from multiple dimensions. This improves the disturbance recovery capability of the high-speed railway under temporary speed limits, thereby minimizing the losses caused by train failures or other special circumstances.
[0080] Further, in this embodiment, step S30 includes:
[0081] Step S31: Obtain the multi-objective optimization strategy output by the configured train operation adjustment model;
[0082] Step S32: Based on the multi-objective optimization strategy, generate or determine the train fault adjustment operation diagram and alternative train matching scheme.
[0083] Optionally, in this embodiment, after the model is configured, the model will output a multi-objective optimization strategy generated or determined based on the above parameters, and use multiple dimensions to filter the operation diagrams of candidate trains and faulty trains.
[0084] Optionally, the multi-objective optimization strategy may include, but is not limited to, the following strategies:
[0085] 1. Minimize total passenger delay time to reduce passenger delays.
[0086] Second, maximize train utilization to ensure that train passenger load is as high as possible.
[0087] Third, minimize additional delays caused by train malfunctions.
[0088] IV. Minimize train operating costs.
[0089] V. Balance the operational stability of different road sections.
[0090] 6. Minimize conflicts between trains.
[0091] In this embodiment, each target optimization strategy can be assigned a corresponding weight. When selecting candidate trains, based on the above strategies and their corresponding weights, the optimal candidate train that meets the conditions is selected and pushed to the terminal of the disturbed passenger, thereby guiding the passenger to transfer.
[0092] Second Embodiment
[0093] Based on the first embodiment, referring to Figure 3 Step S20 includes at least one of the following steps:
[0094] Step S21: Determine the first constraint expression based on the divided passenger groups, the number of disturbed passengers in each passenger group, the number of available seats, and the first constraint parameter;
[0095] Step S22: Determine the second constraint expression based on the faulty train's stopping station, the maximum first stopping time, the first constraint expression, and the second constraint parameter;
[0096] Step S23: Determine the third constraint expression based on the first velocity curve and the third constraint parameter;
[0097] Step S24: Determine the fourth constraint expression based on the first operating energy consumption and the fourth constraint parameter;
[0098] Step S25: Determine the fifth constraint expression based on the first arrival / departure time, the first running time, the first speed curve, and the fifth constraint parameter;
[0099] Step S26: Determine the sixth constraint expression based on the maximum first stop duration, the minimum first stop duration, and the sixth constraint parameter;
[0100] Step S27: Determine the expression for the seventh constraint based on the first velocity curve and the seventh constraint parameter;
[0101] Step S28: Determine the eighth constraint expression based on the train running sequence and the eighth constraint parameter;
[0102] Step S29: Determine the ninth constraint expression based on the first arrival / departure time, the first running time, the station information, and the ninth constraint parameter.
[0103] As an optional implementation, in this embodiment, the basic parameters of the high-speed railway network model include: high-speed railway information, station information, and train operation sequence;
[0104] The target train's operating parameters include the target train's passenger capacity, the target train's first arrival and departure times, first stop plan, maximum first stop duration, and minimum first stop duration at each station, as well as the train's first speed curve, first running time, and first operating energy consumption under different first stop plans.
[0105] The parameters for train malfunction operation scenarios include the stops of the malfunctioning train, the total number of affected passengers, the destination stations of the affected passengers, the passenger groups, and the number of affected passengers in each passenger group.
[0106] The parameters for alternative trains include the second arrival and departure times, second stop plans, second stop duration, and number of available seats for each station.
[0107] In this embodiment, the first constraint expression is characterized as a passenger flow balance constraint expression.
[0108] Optionally, the first constraint expression is as follows:
[0109]
[0110] In the formula, ρ i,a This represents the number of passengers in passenger group a who are assigned to subsequent train i; u a This represents the number of passengers affected by the disturbance in passenger group a; A and I* are the first constraint parameters.
[0111] Furthermore, the first constraint expression can also be as follows:
[0112]
[0113] In the formula, τ i I represents the number of available seats for candidate train i, where I is a constraint parameter.
[0114] In this embodiment, the second constraint expression is characterized as a constraint expression for passenger reallocation and train stopping scheme.
[0115] Optionally, the second constraint expression is as follows:
[0116]
[0117]
[0118] In the formula, Indicates the station where the malfunctioning train stopped;
[0119] In the formula, M, S i A, ρ i,a All are second constraint parameters, where M represents a maximum value; A binary 0-1 variable representing a train stopping at a station. If train i is at a station in the adjusted train timetable... A stop is represented by 1; otherwise, it is represented by 0.
[0120] Furthermore, the second constraint expression can also be as follows:
[0121]
[0122]
[0123]
[0124] In the formula, Indicates the destination of passengers in passenger group a. This indicates the first departure time of train i from station φ; This indicates the first arrival time of train i at station φ; This represents the maximum first stop duration of train i at station s;
[0125] In the formula, α i The second constraint parameter represents the travel time for a passenger to transfer from the platform where the faulty train is located to the platform where the subsequent train i is located.
[0126] In this embodiment, the third constraint expression is characterized as a mapping constraint expression between the adjusted train stopping plan and the train speed curve selection.
[0127] Optionally, the third constraint expression is as follows:
[0128]
[0129]
[0130]
[0131] In the formula, Let represent the initial velocity of train i on the first velocity curve p in the interval (s, s+1). This represents the final velocity of train i on the first velocity curve p in the interval (s, s+1);
[0132] In the formula, φ i,s,p , φ i,s,p M, I, and S are the third constraint parameters. The binary 0-1 variable represents the selection of the train speed curve. If train i selects the first speed curve p in the interval (s, s+1), it is 1; otherwise, it is 0. This represents a binary 0-1 variable indicating when the train stops at station s+1. If train i stops at station s+1 in the adjusted train timetable, then this variable is 1.
[0133] In this embodiment, the fourth constraint expression is characterized as an energy consumption constraint expression.
[0134] Optionally, the fourth constraint expression is as follows:
[0135]
[0136]
[0137] In the formula, δ i This represents the total energy consumption of train i. Let ω represent the traction energy consumption of train i on the first speed curve p in the interval (s, s+1), and let ω represent the first operating energy consumption of all trains.
[0138] In the formula, I is the fourth constraint parameter.
[0139] In this embodiment, the fifth constraint expression is characterized as the train arrival and departure time constraint expression.
[0140] Optionally, the fifth constraint expression is as follows:
[0141]
[0142]
[0143]
[0144]
[0145] In the formula, This indicates the planned stop time of train i at station s. This indicates the first departure time that train i is scheduled to depart from station s; This indicates the duration of train i's first stop at station s; This represents the first running time of train i in the interval (s, s+1); This represents the time that train i travels in the interval (s, s+1) at the first speed curve p;
[0146] In the formula, P, S, I, d i φ i,s,p , This is the fifth constraint parameter.
[0147] In this embodiment, the sixth constraint expression is represented as a stop time constraint expression.
[0148] Optionally, the sixth constraint expression is as follows:
[0149]
[0150] In the formula, Let represent the minimum and maximum first stop durations of train i at station s, respectively; The input parameter is 0-1, representing the planned stop of train i. In the initial train timetable, it is 1 if train i stops at station s, and 0 otherwise.
[0151] In the formula, S, I, d i π π is the sixth constraint parameter; where π represents the additional stop time required to satisfy the reassigned passengers disembarking at their destination.
[0152] In addition, train departure times should also follow the following constraint expression:
[0153]
[0154] In this embodiment, the seventh constraint expression is represented as a train speed selection constraint expression.
[0155] Optionally, the seventh constraint expression is as follows:
[0156]
[0157] In the formula, φ i,s,pThis is the first velocity curve; P, S, and I are the seventh constraint parameters.
[0158] In this embodiment, the eighth constraint expression is represented as a train running sequence constraint expression.
[0159] Optionally, the eighth constraint expression is as follows:
[0160]
[0161] In the formula, σ i,i's and σ i',i,s This indicates the train running sequence. If train i' is scheduled after train i in the interval (s, s+1), then σ is the train running sequence. i,i's +σ i',i,s =1; otherwise, it is 0;
[0162] In the formula, S i S i' I is the eighth constraint parameter.
[0163] In this embodiment, the ninth constraint expression is characterized as a safety interval time constraint expression.
[0164]
[0165]
[0166] In the formula, This represents the safe interval between train i and train i' leaving the same arrival / departure track at station s; This represents the safe interval between trains i and i' arriving at the same arrival / departure track at station s.
[0167] In the formula, S i S i' I, M, σ i,i's This is the ninth constraint parameter.
[0168] In the technical solution provided in this embodiment, different constraint expressions are constructed according to different parameters. The constraint expressions are interconnected and together constitute a train operation adjustment model as a whole. This model can obtain the maximum transport capacity of the number of passengers affected by the disturbance and the minimum weighted total delay of the train from multiple dimensions, thereby improving the disturbance recovery capability of high-speed railways under temporary speed limits and minimizing the losses caused by train failures or other special circumstances.
[0169] In addition, refer to Figure 4 This embodiment also proposes a train operation adjustment device, which includes:
[0170] The parameter acquisition module 100 is used to acquire basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters.
[0171] The model configuration module 200 is used to configure a train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters and / or the alternative train parameters. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum time of the weighted total delay of the train.
[0172] The operation adjustment module 300 is used to output train operation adjustment strategies based on the configured train operation adjustment model.
[0173] Furthermore, 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 includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the train operation adjustment system to implement the process steps of the embodiments of the above methods.
[0174] Therefore, the present invention also provides a computer-readable storage medium storing a train operation adjustment program, which, when executed by a processor, implements the various steps of the train operation adjustment method described in the above embodiments.
[0175] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.
[0176] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.
[0177] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0178] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0179] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0180] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0181] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.
[0182] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.
[0183] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A method for adjusting train operation, characterized in that, The train operation adjustment method includes the following steps: The system acquires basic parameters of a high-speed railway network model, target train operation parameters, train malfunction scenario parameters, and alternative train parameters. The basic parameters of the high-speed railway network model include high-speed railway information, station information, and train operation sequence. The target train operation parameters include the target train's passenger capacity, its first arrival time, first departure time, first stop plan, maximum first stop duration, and minimum first stop duration at each station, as well as the train's first speed curve, first running time, first operating energy consumption, and passenger destination under different first stop plans. The train malfunction scenario parameters include the malfunctioning train's stopping stations, the total number of affected passengers, the destination stations of the affected passengers, the passenger groups, and the number of affected passengers in each passenger group. The alternative train parameters include the alternative train's second arrival and departure times, second stop plan, second stop duration, and number of available seats at each station. Based on the basic parameters of the high-speed railway network model, the target train operating parameters, the train fault operation scenario parameters, and / or the alternative train parameters, a train operation adjustment model is configured. The step of configuring the train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operating parameters, the train fault operation scenario parameters, and / or the alternative train parameters includes at least one of the following: Based on the passenger groups, the number of disturbed passengers in each passenger group, the number of available seats, and the first constraint parameter, the first constraint expression is determined as follows: In the formula, Indicates the passenger group division They were assigned to subsequent trains. The number of passengers; Indicates passenger group The number of passengers affected; A, I This is the first constraint parameter; Based on the faulty train's stopping station, the maximum first stop duration, the first arrival time, the first departure time, the passenger's destination, and the second constraint parameter, determine the second constraint expression: In the formula, Indicates the station where the malfunctioning train stopped; All are second constraint parameters, where, Represents a maximum value; A binary 0-1 variable representing a train stopping at a station. If train i is at a station in the adjusted train timetable... A stop at a station is represented by 1; otherwise, it is represented by 0. Based on the first velocity curve and the third constraint parameters, determine the expression for the third constraint: In the formula, Indicates train In the interval First velocity curve The corresponding initial velocity, Indicates train In the interval First velocity curve The corresponding final velocity; , , M , This is the third constraint parameter. The binary 0-1 variable represents the selection of the train speed curve. In the interval Select the first velocity curve The result is 1 if the condition is true and 0 otherwise. This represents a binary 0-1 variable indicating when the train stops at station s+1. If train i stops at station s+1 in the adjusted train timetable, then the value is 1. Based on the first operating energy consumption and the fourth constraint parameter, the expression for the fourth constraint is determined as follows: In the formula, Indicates train Total energy consumption Indicates train In the interval First velocity curve Traction energy consumption, This represents the initial operating energy consumption of all trains; This is the fourth constraint parameter; Based on the first arrival time, the first departure time, the maximum first stop duration, the first running time, the first speed curve, and the fifth constraint parameter, determine the fifth constraint expression: In the formula, Indicates train Plans at the station The duration of the stop, Indicates train Plans from the station The first departure time of the train; Indicates train At the station The first stop duration; Indicates train In the interval The first runtime; Indicates train In the interval With the first velocity curve Travel time; P, S, I, , , This is the fifth constraint parameter; Based on the maximum first stop duration, the minimum first stop duration, and the sixth constraint parameter, determine the expression for the sixth constraint: In the formula, , They represent trains At the station The minimum and maximum first stop durations; Indicates train The 0-1 input parameters for the planned stop are in the initial train timetable, if the train At the station A stop is 1; otherwise, it's 0; S, I, , , , This is the sixth constraint parameter; where, This indicates the additional stop time to accommodate reassigned passengers disembarking at their destination; In addition, train departure times should also follow the following constraint expression: Based on the first velocity curve and the seventh constraint parameter, determine the expression for the seventh constraint: In the formula, This is the first velocity curve; P, S, and I are the seventh constraint parameters. Based on the train operation sequence and the eighth constraint parameter, the expression for the eighth constraint is determined as follows: In the formula, and Indicates the train running sequence, if the train Arranged in the interval train Then it is Conversely, it is 0. This is the eighth constraint parameter; Based on the first arrival and departure times, the first running time, the station information, and the ninth constraint parameter, the ninth constraint expression is determined as follows: In the formula, Indicates train and train Leaving the station The safe interval between the same arrival and departure lines; Indicates train and train Arrival at the station The safe interval between the same arrival and departure lines; This is the ninth constraint parameter; Based on the configured train operation adjustment model, output the train operation adjustment strategy.
2. The method as described in claim 1, characterized in that, The step of outputting the train operation adjustment strategy based on the configured train operation adjustment model includes: Obtain the multi-objective optimization strategy output by the configured train operation adjustment model; Based on the multi-objective optimization strategy, a train fault adjustment operation plan and alternative train matching scheme are generated or determined.
3. A train operation adjustment device, used to execute the train operation adjustment method according to any one of claims 1 to 2, characterized in that, The train operation adjustment device includes: The parameter acquisition module is used to acquire basic parameters of the high-speed railway network model, target train operation parameters, train fault operation scenario parameters, and alternative train parameters. The model configuration module is used to configure a train operation adjustment model based on the basic parameters of the high-speed railway network model, the target train operation parameters, the train fault operation scenario parameters and / or the alternative train parameters. The train operation adjustment model includes an objective function and multiple constraint expressions. The objective function consists of the maximum transport capacity of the number of passengers affected by the interference and the minimum weighted total delay of the train. The operation adjustment module is used to output train operation adjustment strategies based on the configured train operation adjustment model.
4. A train operation adjustment system, characterized in that, The train operation adjustment system includes: a memory, a processor, and a train operation adjustment program stored in the memory and executable on the processor. When the train operation adjustment program is executed by the processor, it implements the steps of the train operation adjustment method as described in any one of claims 1 to 2.
5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a train operation adjustment program, which, when executed by a processor, implements the steps of the train operation adjustment method as described in any one of claims 1 to 2.