Urban rail train delay adjustment method and device for multi-route working diagram

Abstract

The invention discloses an urban rail train delay adjustment method and device oriented to a multi-route working diagram, and relates to the technical field of train operation organization and control, and the method comprises the steps: obtaining basic line operation data, multi-route plan working diagram information and train delay information of an urban rail system; and inputting the basic line operation data, the multi-route plan running diagram and the train delay information into a train delay adjustment model oriented to the multi-route running diagram, and outputting the adjusted urban rail multi-route running diagram, thereby realizing automatic and intelligent adjustment of the multi-route running diagram, reducing the influence of train delay on passenger travel, and improving the passenger travel safety. And the actual running diagram cashing rate is improved to adapt to fine management of an urban rail system.

Description

Technical Field

[0001] The present application relates to the technical field of train operation organization and control, and in particular to a method and device for adjusting urban rail train delays for a multi-circuit operation diagram. Background Art

[0002] In recent years, my country's urban rail transit has developed rapidly, playing an indispensable and important role in meeting residents' travel needs and alleviating urban traffic congestion. By the end of 2023, a total of 59 cities had opened 338 operating lines, with a total length of 11,224.54 kilometers. Due to the diversity of urban regional planning, the temporal and spatial distribution of passenger flow on some urban rail lines connecting urban areas and suburbs is extremely uneven, and the single-route operation mode is difficult to adapt to complex passenger flow needs. Therefore, the rail transit systems in many cities in my country have adopted a multi-route operation organization model, allowing trains to run in different sections and share passenger flow. This model can improve train operation efficiency, shorten passenger waiting time, and reduce operating costs.

[0003] However, the multi-circuit operation organization model poses new challenges to dispatchers, especially when facing train delays caused by faults or emergency conditions. In the traditional single-circuit operation model, the propagation of train delays is mainly limited to a single route, the subsequent operation status of the train is relatively easy to predict, and the conditions and timing of the application of dispatching measures are relatively easy to control. Under the multi-circuit operation organization model, the operation of trains between different routes is mutually restricted, and the propagation of delays has changed significantly. This makes the application conditions and timing of the original dispatching measures more complicated, and introduces new dispatching strategies such as extending the train operation route, which increases the difficulty of adjusting train operations. Therefore, after a train delay occurs on a line using a multi-circuit operation diagram, formulating a reasonable adjustment strategy as soon as possible to reduce the impact of train delays has become one of the key research directions of the current urban rail system.

[0004] At present, train delay adjustments under the multi-route operation organization model still rely mainly on manual processing, and this process has not yet been automated and intelligent. Summary of the invention

[0005] The purpose of this application is to provide a method and device for adjusting urban rail train delays for a multi-route operation diagram, which can realize automatic and intelligent adjustment of the multi-route operation diagram according to the urban rail train delay information, reduce the impact of train delays on passengers' travel, and improve the actual operation diagram fulfillment rate to adapt to the refined management of the urban rail system.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for adjusting urban rail train delays based on a multi-route operation diagram, comprising:

[0008] Obtain basic line operation data, multi-route planned operation diagram information and train delay information of the urban rail system;

[0009] The basic line operation data, the multi-intersection planned operation diagram information and the train delay information are input into a train delay adjustment model for a multi-intersection operation diagram, and an adjusted urban rail multi-intersection operation diagram is output, wherein the train delay adjustment model includes an objective function and a constraint condition set for the multi-intersection operation diagram, the objective function is a function constructed with the goal of minimizing a function value, the function value is a numerical value calculated based on a timetable offset, a number of cancelled services and a change in tracking interval, and the constraint condition set includes a time adjustment constraint set and a train turnover adjustment constraint set.

[0010] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for adjusting urban rail train delays for a multi-crossing operation diagram.

[0011] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for adjusting urban rail train delays for a multi-routes operation diagram.

[0012] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for adjusting urban rail train delays for a multi-routes operation diagram.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0014] The present application provides a method and device for adjusting train delays in a multi-interval operation diagram for a city rail system. By acquiring basic line operation data, multi-interval planned operation diagram information and train delay information of the city rail system, the basic line operation data, the multi-interval planned operation diagram and the train delay information are input into a train delay adjustment model for the multi-interval operation diagram, and an adjusted city rail multi-interval operation diagram is output, thereby realizing automatic and intelligent adjustment of the multi-interval operation diagram, reducing the impact of train delays on passenger travel, and improving the actual operation diagram fulfillment rate to adapt to the refined management of the city rail system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is an application environment diagram of a method for adjusting urban rail train delays based on a multi-routes operation diagram in Example 1 of the present application;

[0017] Figure 2 A schematic flow chart of a method for adjusting urban rail train delays for a multi-routes operation diagram provided in Example 1 of the present application;

[0018] Figure 3 This is a line topology diagram considered in Example 1 of the present application;

[0019] Figure 4 This is a schematic diagram of the structure of the multi-layer perceptron classifier in Example 1 of the present application;

[0020] Figure 5 The adjusted multi-route operation diagram of the urban rail in Example 1 of the present application;

[0021] Figure 6 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] Example 1

[0025] The embodiment of the present application provides a method for adjusting urban rail train delays for a multi-route operation diagram, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The data storage system sends the basic line operation data and multi-crossing planned operation diagram of the urban rail system to the server 104, and the terminal 102 sends the train delay information to the server 104. After receiving the basic line operation data, multi-crossing planned operation diagram and train delay information of the urban rail system, the server 104 inputs them into the train delay adjustment model for the multi-crossing operation diagram, and obtains the adjusted urban rail multi-crossing operation diagram according to the train delay adjustment model for the multi-crossing operation diagram. The server 104 can feedback the obtained adjusted urban rail multi-crossing operation diagram to the terminal 102.

[0026] The terminal 102 may be, but is not limited to, a passenger information system device (such as an LED screen or LCD display in a station or a car), a train operation control system device, a power and environment control device, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0027] According to research, the relevant technology for adjusting train delays under the multi-route operation organization mode still relies mainly on manual processing: after obtaining delay information, the train dispatcher needs to predict the spread of delays and issue dispatching instructions such as detaining trains, changing running routes, and returning to the depot by phone. This process has not yet been automated and intelligentized. The dispatcher needs to take into account both the optimization and issuance of dispatching instructions, and the work intensity and pressure have increased dramatically. In addition, the lines using multi-route operation diagrams have the ability to flexibly change the train running routes, and the demand for trains to turn around at intermediate stations has increased significantly. Due to the relatively limited number of return rails configured at intermediate stations, dispatchers face great difficulties in ensuring the feasibility of the adjusted operation diagram. Finally, dispatching work in complex situations such as multi-route operation diagrams often requires a high degree of professional knowledge and instant decision-making capabilities. It is easy for dispatchers to make mistakes in prediction and decision-making due to their lack of experience and adaptability, and may issue wrong instructions, further expanding the impact of delays; or fail to notify the station in time after changing the train running route, resulting in confusion in passenger organization.

[0028] In summary, the above train delay adjustment method for multi-intersection operation diagram has the following defects:

[0029] 1) Traffic dispatchers need to manually adjust the multi-route operation diagram and issue dispatch instructions by phone, which is very labor-intensive and stressful;

[0030] 2) In the train delay adjustment for multi-route operation diagrams, there is a contradiction between the greatly increased demand for train turnaround at intermediate stations and the relatively limited turnaround track capacity at intermediate stations, and it is difficult to manually ensure the feasibility of the adjusted operation diagram;

[0031] 3) Manual adjustments are prone to decision-making errors and untimely dispatch instructions, which can further expand the impact of delays and cause disorganization for passengers.

[0032] In order to overcome the above technical defects, in an exemplary embodiment, Figure 2 As shown, a method for adjusting urban rail train delays for a multi-route operation diagram is provided. The method is executed by a computer device, and can be executed by a computer device such as a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps A to D. Among them:

[0033] A. Obtain basic line operation data, multi-route planned operation diagrams and train delay information of the urban rail system.

[0034] Step A specifically includes:

[0035] A.1. According to the actual situation of the urban rail system, the basic line operation data required for the adjustment of the operation diagram shall be configured, including the line station set, the shortest stop time of each station in the line station set, the longest stop time of each station in the line station set, the shortest running time between each station in the upward direction, the longest running time between each station in the upward direction, the shortest running time between each station in the downward direction, the longest running time between each station in the downward direction, the set of stations configured with reversing rails, the number of reversing rails configured for each station in the set of stations configured with reversing rails, the set of stations connected to the depot, the number of spare cars in the depot connected to each station in the set of stations connected to the depot, the shortest tracking interval of trains, the shortest reversing time of trains and the shortest time for spare cars to go online.

[0036] like Figure 3 As shown, the basic line operation parameters are expressed as: the set of all stations on the line (i.e. the line station set) The shortest stop time q of station s in the set min and the longest stop time q max , connect station 1 to station S end Defined as the upward direction, station S end To station 1 is defined as the down direction, and the shortest running time from station s to station s+1 in the up direction and maximum running time The shortest running time from station s to station s-1 in the down direction and maximum running time Configure the switchback station collection The number of turnaround rails configured at station s in this set r s , a collection of stations connected to the depot The number of spare cars c in the depot connected to station s in this set s , the shortest train tracking interval h min , the shortest return time of the train u min , the shortest time to prepare a vehicle for online use min .

[0037] A.2. Obtaining multi-route planned operation diagram information, including: a collection of all up-going train trips in the multi-route planned operation diagram Downward train number collection The set of stations where train number n is scheduled to run in these two sets Train At the station Planned arrival time and scheduled departure times

[0038] A.3. Obtain train delay information, including: train delay time t start , delay time t duration , the delayed train number and Station The type of delay δ and the set of stations that all trains have run through when the delay occurs in,

[0039]

[0040] B. With the goal of minimizing the timetable offset, the number of service cancellations and the change in tracking interval, a train delay adjustment model for a multi-intersection timetable is established in combination with actual operational constraints (i.e., a set of constraints for a multi-intersection timetable).

[0041] Step B specifically includes:

[0042] B.1. In order to ensure the feasibility of the arrival and departure times after the train number adjustment, a timetable adjustment constraint set for the multi-route operation diagram is established, including the determined time constraint, stop time constraint, operation time constraint, tracking interval constraint, return time constraint and spare vehicle online time constraint, as follows:

[0043] Before the train delay occurred, each train was running strictly according to the planned operation diagram, so At the station The actual arrival and departure time is equal to the planned time. In addition, for the delayed train number If you leave the station If a delay occurs when the train arrives at the station (i.e., δ = 1), a departure delay is set; if If a delay occurs when (i.e., δ = 0), an additional arrival delay is set. Therefore, the determined time constraint is expressed as:

[0044]

[0045]

[0046] In order to facilitate passengers to board and alight and prevent traffic congestion, it is necessary to set a stop time threshold. Since the stop time constraint is only effective at the station where train number n stops, the stop time constraint is expressed as follows using the big M method:

[0047]

[0048] where a n,s and d n,s is the actual arrival and departure time of train number n at station s, y n,s is a 0-1 variable, y n,s =1 means that train number n stops at station s.

[0049] The running time of trains in the section should also be limited to a certain range. The running time constraint takes effect when train number n stops at stations s and s+1 at the same time; for down trains The running time constraint takes effect when train number n stops at stations s and s-1 at the same time, and is expressed as:

[0050]

[0051] In order to ensure operational safety, a tracking interval constraint is introduced to limit the interval between the departure time of the previous train and the arrival time of the next train. Due to the diversity of running routes, the running order of trains cannot be directly determined by index, so train number n and all train numbers n′<n need to maintain the tracking interval constraint, that is:

[0052]

[0053] After executing a trip, if the train wants to continue running, it needs to turn around and change ends before it can execute the reverse trip. The duration of this process must be greater than the shortest turnaround time. Therefore, the turnaround time constraint is expressed as:

[0054]

[0055] Among them, ξ n′,n,s is a 0-1 variable, ξ n′,n,s =1 means that after completing train number n′, the train enters the return track switching end configured at station s and executes the reverse train number n.

[0056] For trains using spare vehicles, their arrival and departure times should be arranged after the earliest time when the spare vehicle can arrive at the main line. Therefore, the spare vehicle online time constraint is expressed as:

[0057]

[0058] in, is a 0-1 variable, It means that train number n is carried out by the spare car in the depot connected to station s.

[0059] B.2. In order to ensure the feasibility of the adjusted train connection and entry and exit plan, the train turnover adjustment constraints for the multi-circuit operation diagram are established, including binary decision variable constraints, train number and train correspondence constraints, and return track capacity constraints, as follows:

[0060] In order to establish the relationship between the timetable and train turnover, the intermediate variable y n,s The calculation method of (i.e. the expression of binary decision variable constraints) is:

[0061]

[0062] Among them, β n,s is a 0-1 variable, β n,s =1 means that the train returns to the depot connected to station s after completing train number n. Train n stops at station s if it starts at a station with index less than or equal to s and ends at a station with index greater than or equal to s. Train n stops at station s if it starts at a station with index greater than or equal to s and ends at a station with index less than or equal to s.

[0063] Train turnover adjustment must ensure a one-to-one correspondence between trains and train numbers. A train number can be executed in one of the following two ways. The corresponding relationship constraints between train numbers and trains include:

[0064] The train on the line turns back or calls the spare car in the depot, that is:

[0065]

[0066] At the same time, after completing a trip, the train also has two options: turn around and run a trip in the opposite direction or return to the depot, which can be expressed as:

[0067]

[0068] When adjusting the multi-route operation diagram, the number of spare vehicles to be called should not exceed the number of spare vehicles already in the depot, which can be expressed as:

[0069]

[0070] The number of reversing rails in urban rail transit systems is relatively limited. Therefore, when flexibly changing the train route, it is necessary to introduce a reversing rail capacity constraint to ensure the feasibility of the operation diagram. When a train turns back, there must be at least one free reversing rail. The number of free reversing rails is related to the number of times all trains enter and exit the reversing rail. Therefore, before formally establishing the reversing rail capacity constraint, it is necessary to first give a calculation formula for the number of times all trains enter and exit the reversing rail. First, introduce a 0-1 variable γ n′,n,s , expressed as the following IF-THEN rule:

[0071]

[0072] Then, before the train returns after completing trip n, all trains leave station s with return lines for the number of times π n,s It can be calculated as:

[0073]

[0074] At the same time, before the train returns after completing train number n, the number of times all trains enter station s equipped with a return line is σ n,s It can be calculated as:

[0075]

[0076] Based on the above variables, the reentry track capacity constraint can be expressed as:

[0077]

[0078] B.3. In order to evaluate the effect of the multi-crossing train operation diagram adjustment, three operational indicators, namely, timetable offset, number of cancelled services and tracking interval change, are introduced and weighed in a linear weighted manner, as follows:

[0079] The timetable offset is calculated as:

[0080]

[0081] The number of cancelled services is calculated as:

[0082]

[0083] The tracking interval change is calculated as:

[0084]

[0085] Among them, p n′,n,s is a 0-1 variable, p n′,n,s=1 means that train numbers n' and n are continuous at station s. The calculation formula of this variable is:

[0086]

[0087] The objective function can be calculated as:

[0088] minZ=λ1Z deviation +λ2Z cancel +λ3Z headway ;

[0089] Among them, λ1, λ2, and λ3 are the weights corresponding to the three operating indicators, representing the importance of the operating indicators. The traffic dispatcher can input different weight values ​​according to the specific on-site conditions.

[0090] C. The prediction of 0-1 variables (i.e., binary decision variables) in the model is regarded as a binary classification task. A data set containing various train delay adjustment schemes is constructed, and a data-driven variable prediction method is proposed to improve the efficiency of subsequent model solving.

[0091] Step C specifically includes:

[0092] C.1. In the model established in step B, β n,s and n,n′,s plays a key role. Once it is determined β n,s and n,n′,s The value of can determine the train turnover, thereby reducing the solution space of the model. β n,s and n,n′,s There is no need to predict each variable separately because of the mutual constraints. Here we introduce two intermediate variables and Indicates whether train number n starts and ends at station s, expressed as:

[0093]

[0094]

[0095] Will and The prediction is considered as a binary classification task.

[0096] C.2. The binary classification task predicts the start and end stations of the train in the train delay adjustment model for multi-intersection operation diagrams. For binary classification tasks, it is essential to build a reliable dataset to discover the correlation between classification tasks and labels. For the above tasks, the dataset used to train the classifier is a variety of train delay instances and the corresponding optimal adjustment plans, which are characterized by a set of features and their labels. Specifically, the feature representation of the train delay instance is e = (e1, e2, …, e K ), including the time and space distribution (the time and location of the train delay), duration, and spare vehicle distribution in the depot. The label is the corresponding optimal adjustment plan. and The train delay instances are generated by historical data, and the optimal adjustment scheme is obtained by solvers such as CPLEX or GUROBI.

[0097] C.3. Use a multi-layer perceptron as a classifier, such as Figure 4 As shown in Figure 1, the multilayer perceptron includes an input layer, a hidden layer, and an output layer. The feature vector e is used as the input layer, and after a series of linear weighting and nonlinear activation, the output and The predicted value of the output layer can be expressed as: or A threshold of 0.5 is used to determine the classification label. In order to make the predicted value as close to the true value as possible, the cross entropy loss function is used in the training of the multilayer perceptron, namely:

[0098]

[0099] Where J is the number of training instances. During the training process, the weights of the multilayer perceptron are continuously updated to minimize the cross entropy loss.

[0100] D. Use the trained classifier to predict the value of the intermediate variable, and solve the train delay adjustment model after the solution space is reduced to obtain the adjusted urban rail multi-intersection operation diagram.

[0101] Step D specifically includes:

[0102] D.1. Sort the trained classifiers by prediction accuracy and use the classifiers with a higher prediction accuracy threshold than the acceptable one. According to the basic operating parameters and train delay information obtained in step A, determine the feature vector value of the input layer, input them into the multi-layer perceptron in order according to the sorting, and determine the prediction value of the corresponding variable according to the output layer. If there is a constraint conflict between the prediction values ​​of the two classifiers, skip the output of the classifier with lower prediction accuracy. After traversing all available classifiers, obtain the train delay adjustment model with a reduced solution space;

[0103] D.2. Use solvers such as CPLEX or GUROBI to solve the train delay adjustment model after the solution space is reduced, and obtain the adjusted urban rail multi-intersection operation diagram within the specified time. Figure 5 It shows the adjusted operation diagram of Beijing Metro Line 19 Train No. 092037 when it was delayed by 5 minutes at Niujie Station. Gray represents the planned arrival and departure times, red represents the adjusted arrival and departure times, green represents the adjusted train turnover, and yellow represents the cancelled trains.

[0104] This embodiment realizes the adjustment of the multi-intersection operation diagram of the urban rail transit system and has the following advantages:

[0105] 1. The intelligent method replaces the manual adjustment method of dispatchers under the existing multi-route operation diagram under train delay conditions, reducing the workload of dispatchers;

[0106] 2. It can meet the operational constraints such as the capacity of the return line, tracking interval, stop time, return time, etc., and ensure the feasibility of the adjusted operation diagram;

[0107] 3. Comprehensively apply various dispatching measures such as adjusting train arrival and departure times, canceling / adding trains, putting trains online, changing operation routes, etc., and automatically issue dispatching instructions to improve the operation diagram fulfillment rate and avoid decision-making errors or untimely issuance of dispatching instructions due to insufficient experience of dispatching personnel.

[0108] The present application also provides an application scenario, which applies the above-mentioned method for adjusting the delay of an urban rail train for a multi-route operation diagram. Specifically: the method for adjusting the delay of an urban rail train for a multi-route operation diagram provided in this embodiment can be applied in the train dispatching and operation management scenarios of the urban rail transit system. The train dispatching and operation management scenarios of the urban rail transit system include train operation monitoring, delay prediction and adjustment, and passenger information release. The method for adjusting the delay of an urban rail train for a multi-route operation diagram provided in this embodiment belongs to the intelligent optimization link in delay prediction and adjustment. The method for adjusting the delay of an urban rail train for a multi-route operation diagram provided in this embodiment improves the operating efficiency and service quality of the urban rail transit system through intelligent optimization and dynamic adjustment, and reduces the negative impact of delays on passengers.

[0109] Example 2

[0110] This embodiment provides a computer device, which may be a server or a terminal. Its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store any data in the urban rail train delay adjustment method for a multi-crossing operation diagram. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for adjusting the delay of an urban rail train for a multi-crossing operation diagram provided in Example 1.

[0111] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] Example 3

[0113] This embodiment provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a method for adjusting urban rail train delays for a multi-routes operation diagram provided in the above-mentioned embodiment 1 is implemented.

[0114] Example 4

[0115] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for adjusting urban rail train delays for a multi-routes operation diagram provided in the above-mentioned embodiment 1 is implemented.

[0116] Example 5

[0117] This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for adjusting urban rail train delays for a multi-routes operation diagram provided in the above-mentioned embodiment 1 is implemented.

[0118] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0119] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0120] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0122] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for adjusting urban rail train delays based on a multi-route operation diagram, characterized in that: The urban rail train delay adjustment method for a multi-routes operation diagram comprises: Obtain basic line operation data, multi-route planned operation diagram information and train delay information of the urban rail system; The basic line operation data, the multi-intersection planned operation diagram information and the train delay information are input into a train delay adjustment model for a multi-intersection operation diagram, and an adjusted urban rail multi-intersection operation diagram is output, wherein the train delay adjustment model includes an objective function and a constraint condition set for the multi-intersection operation diagram, the objective function is a function constructed with the goal of minimizing a function value, the function value is a numerical value calculated based on a timetable offset, a number of cancelled services and a change in tracking interval, and the constraint condition set includes a time adjustment constraint set and a train turnover adjustment constraint set.

2. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 1, characterized in that: The basic line operation data includes a line station set, the shortest stop time of each station in the line station set, the longest stop time of each station in the line station set, the shortest running time between each station in the upward direction, the longest running time between each station in the upward direction, the shortest running time between each station in the downward direction, the longest running time between each station in the downward direction, a set of stations equipped with reversing rails, the number of reversing rails equipped at each station in the set of stations equipped with reversing rails, a set of stations connected to vehicle depots, the number of spare vehicles in the vehicle depots connected to each station in the set of stations connected to vehicle depots, the shortest tracking interval of trains, the shortest reversing time of trains, and the shortest time for spare vehicles to go online; The multi-route planned operation diagram information includes an up train set, a set of stations where each train in the up train set is planned to run, a down train set, a set of stations where each train in the down train set is planned to run, a planned arrival time at each station, and a planned departure time at each station; The train delay information includes the time when the train delay occurs, the delay duration, the train number of the delayed train, the station where the delay occurs, the delay type, and the set of stations that all train numbers have run through when the delay occurs.

3. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 1, characterized in that: Inputting the basic line operation data, the multi-intersection planned operation diagram information and the train delay information into a train delay adjustment model for a multi-intersection operation diagram, and outputting an adjusted urban rail multi-intersection operation diagram, specifically includes: Determining a feature vector value according to the basic line operation data and the train delay information; The feature vector value is used as input, and the trained classifier is used to output the intermediate variable value in the optimal adjustment scheme, wherein the intermediate variable is a variable determined according to a set of binary decision variables in a train delay adjustment model for a multi-junction operation diagram, and the set of binary decision variables includes a variable used to characterize whether the train returns to a vehicle segment connected to the station after completing the train trip, a variable used to characterize whether the train enters a turnaround track switching end configured at the station and executes a reverse train trip after completing the train trip, and a variable used to characterize whether the train trip is executed by a spare vehicle in a vehicle segment connected to the station; The train delay adjustment model is solved according to the basic line operation data, the multi-intersection planned operation diagram information, the train delay information and the intermediate variable value to obtain an adjusted urban rail multi-intersection operation diagram.

4. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 1, characterized in that: The time adjustment constraint set includes determined time constraints, stop time constraints, running time constraints, tracking interval constraints, turnaround time constraints and standby vehicle online time constraints; the train turnover adjustment constraint set includes binary decision variable constraints, train number and train correspondence constraints and turnaround track capacity constraints.

5. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 1, characterized in that: The expression of the objective function is: minZ=λ1Z deviation +λ2Z cancel +λ3Z headway ; Where Z represents the function value; Z deviation Indicates the timetable offset; Z cancel Indicates the number of services canceled; Z headway Indicates the change in tracking interval; n indicates the train number; Indicates the collection of up train numbers; Indicates the set of downlink train numbers; a n,s represents the actual arrival time of train number n at station s; represents the planned arrival time of train number n at station s; d n,s represents the actual departure time of train number n at station s; represents the scheduled departure time of train number n at station s; S represents the line station set; Represents the set of stations where train number n is scheduled to run; represents the set of stations that train number n has already passed when the delay occurs; y n,s Indicates whether train number n stops at station s; n′ and n″ both represent train numbers; S turn Indicates the configuration of the reentry rail station set; p n′,n,s Indicates whether train number n′ and train number n are continuous at station s; p n,n″,s Indicates whether train number n and train number n″ are continuous at station s; d n′,s represents the actual departure time of train number n′ at station s; y n′,s Indicates whether train number n′ stops at station s; p n″,n,s Indicates whether train number n″ and train number n are continuous at station s; λ1, λ2, λ3 all represent weights.

6. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 3, characterized in that: The training process of the classifier includes: Constructing a sample data set, wherein the sample data set includes a sample feature vector and label data, the sample feature vector is data determined according to historical basic line operation data and historical train delay information in historical train delay instances, and the label data is a real intermediate variable value in an optimal adjustment solution corresponding to the historical train delay instance; Training a classifier according to the sample feature vector and the label data; When the cross entropy loss function value between the predicted variable value output by the classifier and the true intermediate variable value is minimized, the classifier training is completed.

7. The method for adjusting urban rail train delays based on a multi-route operation diagram according to claim 3, characterized in that: The expression of the intermediate variable is: in, represents an intermediate variable used to characterize whether train number n starts at station s; n′ and n both represent train numbers; represents the set of downlink train numbers; n′,n,s Indicates whether the train enters the turnaround terminal configured at station s and executes the reverse train n after completing train number n′; θ n,s Indicates that train number n is carried out by a spare vehicle in the depot connected to station s; Indicates the configuration of the reentry rail station set; Indicates the collection of up train numbers; Represents the set of stations connected to the depot; represents an intermediate variable used to characterize whether train number n ends at station s; β n,s It means that the train returns to the depot connected to station s after completing trip n.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the urban rail train delay adjustment method for a multi-crossing operation diagram according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for adjusting urban rail train delays for a multi-routes operation diagram according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for adjusting urban rail train delays for a multi-routes operation diagram according to any one of claims 1 to 7 is implemented.

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