Operating scheme compiling method and device, equipment and storage medium

Abstract

The invention provides a driving scheme compilation method and device, equipment and a storage medium. The method comprises the steps that a passenger train driving scheme complete set of a designated route or region of a next graph period is determined; based on the basic data of the high-speed railway road network, basic constraints of an HHTPV framework are determined; the HHTPV framework is initialized based on the constraint condition; wherein the constraint condition is determined according to the basic constraint and the dynamic threshold constraint set; generating a driving scheme in the current state according to the evaluation model, the optimization strategy, the driving scheme optimization adjustment direction and the passenger train driving scheme complete set of the designated route or area of the next graph period; in the HHTPV framework, the operation scheme set in the current state is verified, and the compiled operation scheme is determined according to the verification result, so that automatic compilation and verification of the operation scheme are realized, and the compilation efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of rail transit, and particularly to a method, device, equipment, and storage medium for compiling operation plans. Background Art

[0002] With the continuous expansion of the scale of high-speed rail operations, the design of high-speed rail train operation plans has become increasingly complex. In the design process, various factors such as train operation parameters, line operation conditions, passenger demands, and station capabilities need to be comprehensively considered. These factors restrict and influence each other, making it very difficult to design operation plans. How to balance the needs of all parties and formulate a scientific and reasonable operation plan has become an urgent problem in high-speed rail operations.

[0003] Traditional operation plans are compiled by professionals based on their experience. This method not only has low efficiency but also cannot guarantee the quality of the compiled operation plans (such as unreasonable operation plans). If the operation plan is unreasonable, it is easy to cause the conflict of train operation lines to be unsolved, which will seriously affect the efficiency of compiling operation plans. Summary of the Invention

[0004] To solve one of the above technical defects, this application provides a method, device, equipment, and storage medium for compiling operation plans.

[0005] In the first aspect of this application, a method for compiling an operation plan is provided. The method includes: Determine the complete set of passenger train operation plans for a specified line or area in the next planning period; Based on the basic data of the high-speed rail network, determine the basic constraints of the High-Feasibility High-Speed Rail Operation Plan Verification (HHTPV) framework; Initialize the HHTPV framework based on the constraint conditions; where the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set; Generate the operation plan in the current state according to the evaluation model, optimization strategy, the optimization adjustment direction of the operation plan, and the complete set of passenger train operation plans for a specified line or area in the next planning period; Verify the set of operation plans in the current state within the HHTPV framework, and determine the compiled operation plan according to the verification results.

[0006] In the second aspect of this application, an apparatus for compiling an operation plan is provided. The apparatus includes: The first determination module is used to determine the complete set of passenger train operation plans for a specified line or area in the next planning period; The second determination module is used to determine the basic constraints of the High-Feasibility High-Speed Rail Operation Plan Verification (HHTPV) framework based on the basic data of the high-speed rail network; A processing module for initializing the HHTPV framework based on constraint conditions, where the constraint conditions are determined according to basic constraints and a set of dynamic threshold constraints; A generation module for generating a train operation plan in the current state according to an evaluation model, an optimization strategy, an adjustment direction for optimizing the train operation plan, and the entire set of train operation plans for a specified line or area in the next diagram period; A compilation module for verifying the set of train operation plans in the current state within the HHTPV framework and determining the compiled train operation plan according to the verification results.

[0007] In a third aspect of the present application, an electronic device is provided, including: A memory; A processor; and A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method described in the first aspect above.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the method described in the first aspect above.

[0009] The present application provides a method, device, equipment, and storage medium for compiling a train operation plan. The method includes: determining the entire set of train operation plans for a specified line or area in the next diagram period; determining the basic constraints of the HHTPV framework based on the basic data of the high-speed railway network; initializing the HHTPV framework based on the constraint conditions, where the constraint conditions are determined according to the basic constraints and a set of dynamic threshold constraints; generating a train operation plan in the current state according to an evaluation model, an optimization strategy, an adjustment direction for optimizing the train operation plan, and the entire set of train operation plans for a specified line or area in the next diagram period; verifying the set of train operation plans in the current state within the HHTPV framework and determining the compiled train operation plan according to the verification results. The method provided by the present application determines the basic constraints of the HHTPV framework based on the basic data of the high-speed railway network; initializes the HHTPV framework based on the basic constraints, verifies the set of train operation plans in the current state within the HHTPV framework, and determines the compiled train operation plan according to the verification results, realizing the automatic compilation and verification of the train operation plan and improving the compilation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1A flowchart of a train operation plan compilation method provided by an embodiment of the present application; Figure 2 A schematic diagram of the principle of an evaluation model provided by an embodiment of the present application; Figure 3 A schematic diagram of the division of a high - speed railway network provided by an embodiment of the present application; Figure 4 A schematic diagram of the structure of a train operation plan compilation device provided by an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0011] In order to make the technical solutions and advantages in the embodiments of the present application clearer and more understandable, the following further describes the exemplary embodiments of the present application in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0012] In the process of implementing the present application, the inventors found that the traditional train operation plan is compiled by professionals based on their experience. This method not only has low efficiency but also cannot guarantee the quality of the compiled train operation plan (such as an unreasonable compiled train operation plan). If the train operation plan is unreasonable, it is easy to cause the conflict of train operation lines to be unsolved, which will seriously affect the efficiency of compiling the train operation plan.

[0013] To address the above problems, an embodiment of the present application provides a train operation plan compilation method, device, equipment, and storage medium. The method includes: determining the complete set of passenger train operation plans for a specified line or area in the next planning period; determining the basic constraints of the HHTPV framework based on the basic data of the high - speed railway network; initializing the HHTPV framework based on the constraint conditions, where the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set; generating the train operation plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the train operation plan, and the complete set of passenger train operation plans for a specified line or area in the next planning period; verifying the set of train operation plans in the current state within the HHTPV framework, and determining the compiled train operation plan according to the verification result. The method provided by the present application determines the basic constraints of the HHTPV framework based on the basic data of the high - speed railway network; initializes the HHTPV framework based on the basic constraints, verifies the set of train operation plans in the current state within the HHTPV framework, and determines the compiled train operation plan according to the verification result, realizing the automatic compilation and verification of the train operation plan and improving the compilation efficiency.

[0014] See Figure 1, this embodiment provides a method for formulating an operation plan, and the implementation process of this method is as follows: 101. Determine the complete set of operation plans for passenger trains on the specified line or area in the next diagram period.

[0015] When performing step 101, based on the pre-input estimated data of passenger travel demand, combined with the operation plan of passenger trains in the current diagram period, comprehensively considering new plans, cancellation plans, and adjustment plans, determine the complete set of operation plans for passenger trains on the specified line or area in the next diagram period.

[0016] In addition, the core of the operation plan formulation method provided in this embodiment lies in how to determine whether the set of operation plans in the current state meets the constraint conditions of the HHTPV (High-feasibility High-speed Train Plan Validation) framework. Among them, the passenger trains corresponding to the set of operation plans in the current state can be divided into two categories. One category is the passenger trains that are clearly required to operate and have been laid out in the operation diagram. The set of operation plans for this type of passenger trains is called the set of operation plans for fixed-operating passenger trains. For newly built high-speed railway lines, such operation plans mainly refer to the operation plans that need to be implemented on the high-speed railway line, such as the benchmark trains operating on this line, or the important trains starting or ending at key hub stations and passing through this line. Although such trains are not laid out in the operation diagram at the initial stage of the implementation of the operation plan formulation method provided in this embodiment, due to the importance of relevant plans, they need to be determined to be implemented at the initial stage of the operation plan formulation method provided in this embodiment and completed for layout. For non-newly built high-speed railway lines, in addition to the above situations, such operation plans mainly include the operation plans of passenger trains that have been laid out in the operation diagram and have not been cancelled in the current diagram period.

[0017] The other category is the set of alternative operation plans for passenger trains. At the initial stage of the operation plan formulation method provided in this embodiment, such operation plans are not fully laid out in the operation diagram, and the number of operation plans can be greater than the number of trains actually required to operate and the carrying capacity of the high-speed railway network. By setting a redundant set of alternative operation plans, the results of the iterative compilation process can be made more flexible. At the same time, during the compilation process, the alternative train operation plans can be adjusted according to the actual situation to obtain a better operation plan.

[0018] It should be noted that in order to more accurately implement the optimization process of the train operation plan, a certain degree of redundancy is set for the train operation plans when initializing the set of alternative train operation plans. The purpose of the redundancy setting is to finely adjust the alternative train operation plans according to the actual situation during the compilation process to obtain a better comprehensive train operation plan. In practical applications, the number of train operation plans with redundancy settings should be reasonably set according to the actual situation to ensure the flexibility and efficiency of the compilation process.

[0019] In summary, the complete set of passenger train operation plans for the specified line or area in the next diagram period determined in step 101 .

[0020] Among them, is the set of passenger train operation plans that are fixedly operated on the specified line or area.

[0021] , is the set of trains that meet the preset conditions among the passenger train operation plans newly planned for the specified line or area, is the set of passenger train operation plans that have been laid out in the train operation diagram for the specified line or area and have not been cancelled in the current diagram period.

[0022] , is the set of passenger train operation plans for the specified line or area in the current diagram period, is the set of passenger train operation plans for the trains planned to be taken out of service on the specified line or area.

[0023] is the set of alternative passenger train operation plans for the specified line or area in the next diagram period, , is the set of passenger train operation plans for the newly planned trains.

[0024] It should be noted that in the set of train operation plans in this embodiment and subsequent embodiments, it is allowed that there are cases where the basic train operation information of the train plans is the same but the serial numbers are different.

[0025] 102. Based on the basic data of the high-speed railway network, determine the basic constraints of the HHTPV framework.

[0026] When executing step 102, based on the specified line or area targeted for the compilation of the train operation plan, according to the pre-input basic data of the high-speed railway network, comprehensively considering the topological connectivity of the high-speed railway network, the passing capacity of key intervals and stations (yards) can be deduced, the basic parameters of the HHTPV framework can be calculated, and the basic constraints of the HHTPV framework can be determined. This basic constraint is the static constraint of the HHTPV framework.

[0027] Therefore, in the specific implementation, the implementation process of step 102 is as follows: 102-1. Based on the basic data of the high-speed railway network and the topological connectivity of the high-speed railway network, calculate the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the stop capacity limit value of the station, and the limit of each train type in the section.

[0028] 102-2. According to the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the stop capacity limit value of the station, and the limit of each train type in the section, determine the passing limit constraint and train type limit constraint of each section, and the passing limit constraint and stop limit constraint of each station.

[0029] 1. Passing limit constraint of the section Among them, the passing limit constraint of any section is , is the section identifier, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next planning period, is the train in the section the marked parameter of passing, is the train enabled parameter, is the section upper limit of passing capacity.

[0030] is 0 or 1, indicating that the train passes through the section , indicating that the train does not pass through the section .

[0031] is 0 or 1, indicating that the train is not enabled, indicating that the train is enabled.

[0032] 2. Train type limit constraint of the section The train type limit constraint of any section is , is the train type identifier, is the train belonging to the train type marked parameter, section inside the train type limit.

[0033] For the train The type of train to which it belongs.

[0034] Subject to the combined influence of the operation plan and the passing capacity limit.

[0035] 3. Restriction constraints for passing through stations The restriction constraint for passing through any station is , is the station identifier, for the train at the station The marked parameter for passing through, for the station The upper limit of the passing capacity.

[0036] is 0 or 1, indicating that the train at the station passes through, indicating that the train does not pass through the station .

[0037] 4. Stopping time limit constraints for stations The stopping time limit constraint for any station is , for the train at the station The marked parameter for stopping, for the station The stopping capacity limit value.

[0038] is 0 or 1, indicating that the train at the station stops, indicating that the train does not stop at the station .

[0039] Steps 101 and 102 are the preprocessing stage of the train operation plan compilation method provided in this embodiment. Through this stage, the determination of the basic constraints of the HHTPV framework and the generation of the complete set of passenger train operation plans for the specified line or area in the next graph period are carried out.

[0040] 103. Initialize the HHTPV framework based on the constraint conditions.

[0041] Among them, the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set.

[0042] Step 103 is the initialization process of the HHTPV framework. In step 103, the HHTPV framework is initialized according to the constraint conditions of the HHTPV framework in the current state, and the preparation work of the HHTPV framework is completed.

[0043] Among them, the constraint conditions include the basic constraints obtained in step 102 and the dynamic threshold constraint set.

[0044] The dynamic threshold constraint set is obtained through the iterative process in the operation diagram coordination stage. This iterative process will obtain the latest train operation constraints, and the latest constraints constitute the continuously updated dynamic threshold constraint set.

[0045] 104. According to the evaluation model, the optimization strategy, the preferred adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period, generate the train operation plan in the current state.

[0046] Step 104 is the process of optimizing the train operation plan. In this process, within the HHTPV framework, according to the optimization strategy and combined with the preferred adjustment direction of the train operation plan, select the alternative passenger train operation plans; determine the train operation plans for newly added, suspended, and adjusted trains, and generate the set of passenger train operation plans in the current state.

[0047] First of all, this embodiment does not limit the optimization strategy, which can be determined according to actual needs. For example, if the actual need is passenger demand (such as the least number of train stops), then under the constraint of meeting passenger demand, with the goal of the least number of train stops, the optimization strategy is determined as .

[0048] Among them, is the train identification, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the station identification, is the set of stations on the high-speed railway network, is the train at the station stop mark parameter.

[0049] In specific implementation, according to the optimization goals of multiple types of train operation plans, a weighted multi-objective optimization strategy can be proposed. For example, simultaneously setting goals such as the least number of train stops, uniform stop distribution, and reasonable train type setting as multiple objectives for weighting to form a multi-objective optimization strategy. In reality, the goals of the optimization strategy have a certain degree of complexity, and some goals cannot be numerically quantified. At this time, according to the specific situation, an evaluation model based on artificial intelligence algorithms can be established, using the subjective opinions of professionals as input to ensure the rationality of the actual operation of the train operation plan.

[0050] Secondly, steps 103 and 104 are the plan formulation stage of the train operation plan formulation method provided in this embodiment. Through this stage, the HHTPV framework is initialized, and the iteration of the train operation plan optimization process is executed to gradually optimize the train operation plan, and finally the train operation plan in the current HHTPV framework state is obtained.

[0051] In addition, after selecting the alternative train operation plan according to the optimization strategy within the HHTPV framework and combining the adjustment direction of the train operation plan optimization, the formulated train operation plan will also be optimized. This optimization process can accurately evaluate the transportation efficiency of the train operation plan, improve the supply-demand matching degree and the utilization rate of transport capacity resources.

[0052] This optimization process designs a comprehensive multi-dimensional transportation efficiency evaluation index system for the railway supply-demand efficiency and benefit, and based on the historical operation big data, mines the mapping relationship between the characteristics of the trains in the train operation plan and the transportation efficiency, and accurately estimates the formulated train operation plan.

[0053] 1. Transportation Efficiency Evaluation Index System Since the optimization design of the train operation plan needs to consider the benefits of both the railway supply and demand sides, and there is a mutual coordination and restriction relationship between them, therefore, to evaluate the transportation efficiency of the train operation plan, it is necessary to construct the user transportation efficiency evaluation index system shown in Table 1 from both the supply and demand aspects.

[0054] Table 1

[0055] (1) The service level evaluation is to evaluate the quality of the train service provided for passengers. The evaluation indicators are mainly designed from aspects such as accessibility and convenience, and mainly include the number of passenger arrivals and departures, service frequency, O-D coverage, direct transfer ratio, etc., which are used to measure the quality of passengers' travel.

[0056] (2) The operation efficiency evaluation is to evaluate the operation efficiency and transport capacity utilization of the train operation from the perspective of railway operation enterprises, and mainly includes transport capacity configuration, running distance, number of car bodies, seat occupancy rate, speed coefficient, operation benefit, etc., which are used to measure the transport organization efficiency of the train operation plan.

[0057] (3) The capacity utilization is to evaluate the utilization rate of the resources of the train operation plan from the perspective of the transport capacity resources of the high-speed railway network, and mainly includes the number of trains passing through the section, the number of trains originating and terminating at the station, the section capacity utilization rate, etc.

[0058] To balance the efficiency and benefit of both the railway supply and demand sides, the seat occupancy rate of the train is selected as the main index for the transportation efficiency evaluation. The higher the seat occupancy rate of the train, the higher the supply-demand matching degree and the better the effect of transport capacity configuration.

[0059] 2. Characteristics of Trains in the Train Operation Plan If the high-speed railway network , then the operation plan .

[0060] Among them, is the set of stations of the high-speed railway network, is the set of sections of the high-speed railway network.

[0061] is the train identification, .

[0062] is the total number of stations passed by train , is the set of stations passed by train , , is the first station along the running route of train in the operation plan, is the second station along the running route of train in the operation plan, The th station along the running route of train in the operation plan.

[0063] is the set of stop signs of the th stations passed by train . If the train stops at the first station, then ; otherwise, . If the train stops at the second station, then ; otherwise, . …. If the train stops at the th station, then ; otherwise, . .

[0064] is the seating capacity of train , is the departure time of train .

[0065] The characteristics of the trains in the operation plan include: (1) Running route

[0066] Since the running routes of each train are different, to ensure the consistency of dimensions, the stations of the high-speed railway network are sorted. If the train passes through a station, it is recorded as 1; otherwise, it is recorded as 0.

[0067] (2)Stop Station Identification

[0068] Similarly, for the sorted high - speed railway network stations, if a train stops at a station, it is recorded as 1; otherwise, it is recorded as 0.

[0069] (3)Train Capacity

[0070] Since the train capacities of different vehicle types vary greatly, for standardization, let the maximum capacity among all vehicle types be , then the train capacity is converted to , which is the standardized value of .

[0071] (4)Departure Time

[0072] For the processing of the departure time, it is converted into the proportion in the whole - day period, that is , which is the standardized value of .

[0073] Then, the feature vector of train can be expressed as , and the feature vectors of all trains in the train operation plan can be expressed as .

[0074] Among them, is the total number of trains in the train operation plan .

[0075] Based on this, the process of optimizing the compiled train operation plan is as follows: 201. Obtain the occupancy rate of each train.

[0076] 202. According to the compiled train operation plan and the occupancy rate of each train, determine the optimized train operation plan and its transport efficiency evaluation value through the evaluation model.

[0077] Among them, the evaluation model includes a two - dimensional wavelet packet decomposition module and a convolutional neural network.

[0078] 1. Two - Dimensional Wavelet Packet Decomposition Module The decomposition function of the two - dimensional wavelet packet decomposition module is Daubechies4, and the two - dimensional wavelet packet decomposition module is used to implement the wavelet packet decomposition process, that is, the two - dimensional wavelet packet decomposition module is used to convert the feature vectors of all trains in the train operation plan into multi - channel data.

[0079] For example, when the two-dimensional wavelet packet decomposition module implements the wavelet packet decomposition process, it reduces the instability of the original signal by decomposing the original signal into several sub-signals. The principle of wavelet packet decomposition is the spatial decomposition theory of multi-resolution analysis, that is, according to the spatial scale factor, the space is decomposed into several wavelet packet sub-spaces. At the same time, the frequency of the low-frequency sub-space (scale space) and the high-frequency sub-space (detail space) is subdivided, so that the time-frequency resolution of both the low and high frequency bands is improved, and better frequency-domain localization information than wavelet decomposition is obtained.

[0080] The two-dimensional wavelet packet decomposition module has two decomposition layers, and can convert the eigenvectors of all trains in the input train operation plan into multi-channel data . The two-dimensional wavelet packet decomposition module selects the function Daubechies4 as the decomposition process function of wavelet packet decomposition, and the generated two-dimensional multi-channel data can be used as the input of the convolutional neural network.

[0081] 2. Convolutional Neural Network The convolutional neural network is used to evaluate the transportation efficiency based on multi-channel data and the occupancy rate of each train.

[0082] The convolutional neural network is a type of feedforward neural network that contains convolutional calculations and has a deep structure, and can perform learning on multi-dimensional data. The convolutional neural network has an input layer, a hidden layer, and an output layer. The hidden layer mainly includes two types of feature extraction layers, namely the convolutional layer and the sampling layer (or pooling layer), and the fully connected layer.

[0083] Based on the two-dimensional multi-channel data generated by the two-dimensional wavelet packet decomposition module in the evaluation model, the convolutional neural network in the evaluation model has 3 convolutional layers, and each convolutional layer has convolution kernels, and the number of channels is respectively , the activation function is the rectified linear unit; the pooling function uses average pooling, and the last layer of the network is the fully connected layer; the loss function and the optimization function of this convolutional neural network are the mean square error and the Adam algorithm respectively.

[0084] That is, the convolutional neural network includes: an input layer, a hidden layer, and an output layer.

[0085] The hidden layer includes: a convolutional layer, a pooling layer, and a fully connected layer.

[0086] There are 3 convolutional layers, and each convolutional layer has convolution kernels, and the activation function is the rectified linear unit.

[0087] The pooling function of the pooling layer is the average pooling function.

[0088] The loss function of the convolutional neural network is the mean square error function, and the optimization function is the Adam algorithm.

[0089] In addition, to maximize the transport efficiency of the train operation plan, the convolutional neural network uses the overall passenger occupancy rate level of the train operation plan as the optimization objective during the transport efficiency evaluation, and based on the evaluated passenger occupancy rate of the operating trains , the overall passenger occupancy rate level of the operation plan can be calculated as . Since the transport efficiency is oriented towards maximization, in order to adapt to the minimization-oriented objective of the operation plan optimization model, the adjustment and optimization objective of the train operation plan is adjusted. Therefore, the objective function during the transport efficiency evaluation is: .

[0090] Among them, is the train identification,[[]] is the operation plan,[[]] is the target value of,[[]] is the train total number of passing stations,[[]] is the passing station identification of the train ,[[]] , is the train capacity,[[]] is the mileage of the section ,[[]] is the section identification,[[]] is the train For the section of the th station,[[]] , is the station identification,[[]] is the th station along the passing route of the train in the operation plan,[[]] ,[[]] is the th station along the passing route of the train in the operation plan,[[]] ,[[]] The passenger occupancy rate of the train .

[0091] The constraints during the pre-evaluation of transport efficiency are: transport capacity allocation constraint, section passing capacity constraint, number of trains originating and terminating at stations constraint, basic diagram constraint, originating and terminating time constraint, train capacity constraint, service frequency constraint.[[]]

[0092] 1) Transport capacity allocation constraint The upper limit of the transport capacity allocation of the high-speed railway network can be expressed by the train kilometers. According to the formation capacity of each train , the accounting coefficient of the train kilometers is determined: If the train is a single-group train, then ; otherwise, . Then the transport capacity allocation constraint is: .

[0093] Among them, is the accounting coefficient of the train kilometer number, is the upper limit of the high-speed railway network transport capacity allocation (i.e., the upper limit of the available EMU kilometer number of the high-speed railway network).

[0094] 2) Section passing capacity constraint Affected by railway operation safety factors, the number of trains passing through the section within a unit time (per day or per hour) must meet the corresponding passing capacity limit. Denote the number of trains entering the section throughout the day as , then the section passing capacity constraint is: . .

[0095] Among them, , is the section set of the high-speed railway network, is the number of trains entering the section throughout the day, is the upper limit of the passing capacity of the section .

[0096] 3) Constraint on the number of origin and destination trains at stations The capacity of origin and destination trains at stations is affected by various factors such as station level, technical operation nature, the number of arrival and departure lines at the station, and the distribution of EMU depots (yards). When designing the train operation plan, the number of origin and destination trains at each station cannot exceed its upper limit of origin and destination train capacity. The number of origin trains at station is equal to , and the number of destination trains is equal to , and they must respectively meet their origin and destination capacity constraints, that is, the constraint on the number of origin and destination trains at stations is: , .

[0097] Among them, , is the station set of the high-speed railway network, is the first station along the route of train in the train operation plan, is the th station along the route of train in the train operation plan, is the upper limit of the origin and destination capacity of station .

[0098] If station cannot originate or terminate trains, then .

[0099] 4) Basic diagram constraints The trains in the train operation plan must come from the alternative trains in the basic operation diagram during the diagram period. Therefore, the basic diagram constraints are as follows: .

[0100] Among them, is the set of trains in the basic operation diagram, .

[0101] 5) Origin and destination time constraints The origin and destination times of the trains should be within the operation period. Assuming the whole-day operation period is , then the origin and destination time constraints are: .

[0102] Among them, is the start time of the whole-day operation period, is the end time of the whole-day operation period, , is the origin time of train , is the arrival time of train at the th station.

[0103] If the running time of the given train in the section, as well as the sum of the start-up and stop additional time and the stop time at the station are known, the arrival times and departure times of the train at the stations along the line can be determined .

[0104] Among them, is the station identifier, .

[0105] 6) Train capacity constraints The train types selected for the trains in the train operation plan must meet the actually equipped EMU types. Therefore, the train formation must meet the feasible capacity set of the equipped train types, that is, the train capacity constraints are: .

[0106] Among them, is the capacity set of the equipped train types, .

[0107] 7) Service frequency constraints To meet the travel demands of the arriving and departing passengers at the stations, the service at each station must meet a certain service level, that is, there is a certain lower limit of service frequency. Therefore, the stop frequency at each station must meet this lower limit constraint, that is, the service frequency constraint is: 。

[0108] Among them, is the lower limit of the service frequency of station , is the associated identifier of train and station . If train stops at station , then , otherwise .

[0109] When performing step 202, the eigenvectors of all trains in the train operation plan are a two-dimensional matrix, that is , and the occupancy rate of each train is represented as a one-dimensional label vector, that is . In step 202, refer to Figure 2 . Based on the eigenvectors of all trains in the train operation plan, use the two-dimensional wavelet packet decomposition process of the two-dimensional wavelet packet decomposition module in the evaluation model to convert them into multi-channel data; for the decomposed data, use the convolutional neural network in the evaluation model to perform mapping training between the operation elements of the train operation plan and the occupancy rate of the train, and then evaluate the transport efficiency of the train operation plan to obtain the efficiency evaluation value of the train operation plan. Finally, determine the optimized train operation plan and its transport efficiency evaluation value based on the efficiency evaluation value.

[0110] It should be noted that the above is an explanation of the implementation process of step 202 in terms of principle. The train operation plan therein is a superordinate concept. According to the specific situation during execution, the train operation plan can be the compiled train operation plan, or the train operation plan of adjacent trains. That is to say, the two-dimensional wavelet packet decomposition module in the evaluation model will process the train operation plan, and does not limit the specific train operation plan to be processed. According to the specific requirements during implementation, input the accurate train operation plan into the two-dimensional wavelet packet decomposition module in the evaluation model.

[0111] In specific implementation, the implementation details of step 202 are as follows: 202-1. Convert the eigenvectors of all trains in the compiled train operation plan into multi-channel data through the two-dimensional wavelet packet decomposition module .

[0112] Among them, is the compiled train operation plan.

[0113] That is to say, the compiled train operation plan The two-dimensional wavelet packet decomposition module in the input evaluation model converts the feature vectors of all trains in into multi-channel data . .

[0114] 202-2. Through the convolutional neural network, based on and the occupancy rate of each train, pre-evaluate the transportation efficiency to obtain the efficiency evaluation value . . Determine the current solution , determine the objective value of the current solution , determine the optimal solution , determine the objective value of the optimal solution , determine the number of iterations , determine the current temperature .

[0115] Among them, is the preset initial temperature.

[0116] For example, through the convolutional neural network, using the objective function to obtain the efficiency evaluation value . .

[0117] 202-3. According to , construct the operation plan of the neighboring trains.

[0118] In step 202-3, the following processing will be performed on the target trains in to obtain the operation plan of the neighboring trains: Stop the trains Stop the first target train in.

[0119] Among them, the first target train is a single-group train, and its occupancy rate is lower than , is the preset first ratio value, such as .

[0120] That is to say, for the single-group trains in the current solution , if the occupancy rate is lower than (such as 25%), then stop the train.

[0121] Change the train capacity 1. Adjust the second target train in to a long formation train with a probability of .

[0122] Among them, is the preset first probability value, such as . The second target train is a long-formation train or a coupled train, and its passenger occupancy rate is within and is a preset second ratio value, is a preset third ratio value, , such as . .

[0123] 2. Adjust the third target train in to a single-group train with a probability of .

[0124] Among them, is a preset second probability value, , such as . The third target train is a long-formation train or a coupled train, and its passenger occupancy rate is lower than .

[0125] That is to say, for the long-formation train or the coupled train in the current solution , if the passenger occupancy rate is within a certain lower range (such as ), then it is adjusted to a long-formation train with a smaller seating capacity with a certain probability (such as 0.7); if the passenger occupancy rate is lower than a certain threshold (such as 40%), then it is adjusted to a single-group train with a certain probability (such as 0.9).

[0126] Adding trains For long-formation / coupled trains with a passenger occupancy rate higher than a certain threshold or short-formation trains that cannot perform formation increase, check whether there is a peak-line train pair in the adjacent time points of the basic diagram that is similar to the high-utilization section of this train (the entire route needs to be added for an independent peak line). If there is, add the corresponding train.

[0127] Therefore, add the train corresponding to the fourth target train in.

[0128] Among them, the fourth target train is a long-formation train or a coupled train or a short-formation train that cannot perform formation increase, and its passenger occupancy rate is higher than , is a preset fourth ratio value, and at the same time, there is a peak-line train pair corresponding to the high-utilization section of the fourth target train in the adjacent time points of the basic diagram.

[0129] The train corresponding to the added fourth target train is the train corresponding to the peak-line train pair corresponding to the high-utilization section of the fourth target train in the adjacent time points of the basic diagram.

[0130] Adjusting stop stations For a station If the lower limit constraint of the service frequency is to be met, then, on the principle that the stop distribution of trains at the station satisfies spatio-temporal uniformity, trains with relatively low stop frequencies are selected to add stop stations without causing operation conflicts, and the arrival and departure times of trains along the line are updated.

[0131] Therefore, adjust the arrival and departure times of the fifth target train along the line in

[0132] Among them, the stations along the line of the fifth target train include the target station, and the target station meets the service frequency constraint, and the service frequency constraint is: .

[0133] Adjusting the operating section For trains with relatively low passenger occupancy rates, determine the start and end points of the extended section according to the capacity constraints of the origin and destination adjacent to the originating and terminating stations and the section, and adjust the operating section of the train.

[0134] Therefore, adjust according to the capacity constraint of the section the operating section of the sixth target train in

[0135] Among them, the passenger occupancy rates of the sixth target trains are all lower than , which is the preset fifth proportional value.

[0136] The capacity constraint of the section is: .

[0137] It should be noted that this embodiment does not limit the relationship among the above , , , . Their values can be exactly the same, partially the same, or completely different. Similarly, this embodiment does not limit the relationship among the above , , , . Their values can be exactly the same, partially the same, or completely different. However, it needs to satisfy .

[0138] 202-4, convert the eigenvectors of all trains in the operating plan of neighboring trains into multi-channel data through the two-dimensional wavelet packet decomposition module .

[0139] Among them, is the operating plan of neighboring trains constructed in the th iteration.

[0140] That is to say, the operation plan of neighborhood trains will be input into the two-dimensional wavelet packet decomposition module in the evaluation model, and through the two-dimensional wavelet packet decomposition module, the feature vectors of all trains will be converted into multi-channel data .

[0141] 202-5. Through the convolutional neural network, based on and the occupancy rate of each train, a pre-evaluation of the transportation efficiency is carried out to obtain the efficiency evaluation value .

[0142] For example, through the convolutional neural network, using the objective function the efficiency evaluation value is obtained.

[0143] 202-6. Determine the evaluation difference .

[0144] 202-7. If , then update , update , update , update .

[0145] If but , then update , update .

[0146] Among them, is the natural exponential function, is the random function.

[0147] 202-8. .

[0148] 202-9. If it is determined that the termination condition is not satisfied according to and , then re-execute the steps of constructing the operation plan of neighborhood trains according to (i.e., step 202-3) and subsequent steps for looping. If it is determined that the termination condition is satisfied according to and , then determine that the optimized operation plan is , and the transportation efficiency evaluation value of the optimized operation plan is .

[0149] Specifically, if , then it is determined that the termination condition is not satisfied.

[0150] Among them, is the preset total number of iterations.

[0151] If , then update , update . If , then it is determined that the termination condition is not satisfied. If , then it is determined that the termination condition is satisfied.

[0152] Among them, is the temperature drop ratio, is the preset termination temperature.

[0153] In specific implementation, step 202 is based on the high-speed railway network , the full-day operation period , the compiled train operation plan , the train set of the basic operation diagram , the upper limit of the transport capacity configuration of the high-speed railway network , the station 's upper limit of the originating and terminating capacity , the sum of the start-up and stop additional time and the stop time of the train at the station , the lower limit of the service frequency , the upper limit of the passing capacity of the section , the train 's kilometer accounting coefficient , the initial temperature , the termination temperature , the total number of iterations , the temperature drop ratio , the occupancy rate of each train, and an optimized train operation plan is obtained through the evaluation model and its transport efficiency evaluation value . .

[0154] The train formation accounting coefficient , the initial temperature and termination temperature of the algorithm , the number of iterations of the inner loop , the temperature drop ratio .

[0155] The above process of optimizing the compiled train operation plan starts from the efficiency and benefits of both the railway supply and demand sides, selects the overall occupancy rate level of the train operation plan to construct a transport efficiency evaluation index system, and can evaluate the transport efficiency of the compiled train operation plan from multiple dimensions such as service level, operation efficiency, and capacity utilization.

[0156] The evaluation model designs a two-dimensional feature matrix based on the train operation elements of the train operation plan, performs two-dimensional wavelet packet decomposition using the two-dimensional wavelet packet decomposition module, converts it into multi-channel data, and uses a convolutional neural network to evaluate the transport efficiency.

[0157] ​When evaluating the transportation efficiency, the overall occupancy rate level is used as the optimization goal for the formulated train operation plan. Considering the constraints such as transport capacity allocation, transport organization, and service level, the train operation plan for neighboring trains is constructed based on the transportation efficiency evaluation results and solved.

[0158] The above optimization process for the formulated train operation plan can improve the accuracy of the transportation efficiency evaluation of the formulated train operation plan. The above optimization process for the formulated train operation plan comprehensively extracts the characteristic elements of the train operation plan based on historical operation big data, deeply explores the mapping and correlation relationships between the operation elements of the train operation plan and between the operation elements and the transportation efficiency, realizes the prediction of the occupancy rate distribution of the operating trains, and improves the accuracy of the transportation efficiency evaluation of the formulated train operation plan.

[0159] The above optimization process for the formulated train operation plan can improve the supply-demand matching degree of the formulated train operation plan. The above optimization process for the formulated train operation plan uses the pre-evaluation technology of the transportation efficiency of the train operation plan to obtain the distribution of the supply-demand matching degree of the formulated train operation plan, designs the corresponding adjustment strategy for the operation elements according to the distribution of the supply-demand matching degree, and continuously optimizes the train operation plan using the iterative algorithm to improve the supply-demand matching degree of the formulated train operation plan.

[0160] The above optimization process for the formulated train operation plan can optimize the transport organization efficiency. The above optimization process for the formulated train operation plan constructs a pre-evaluation model of the transportation efficiency of the train operation plan, accurately and quantitatively evaluates the transportation efficiency of the formulated train operation plan, provides a reference basis for the optimal design of the train operation plan, and optimizes the implementation effect of the formulated train operation plan.

[0161] 105. Within the HHTPV framework, verify the set of train operation plans in the current state, and determine the formulated train operation plan according to the verification results.

[0162] Among them, the verification includes constraint verification and feasibility verification. Therefore, the implementation process of step 105 is: within the HHTPV framework, conduct constraint verification on the set of train operation plans in the current state.

[0163] If the constraint verification fails, then: 1) Update the preferred adjustment direction of the train operation plan.

[0164] 2) Re-execute the steps of generating the train operation plan in the current state according to the evaluation model, preferred strategy, preferred adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period (i.e., step 104) and subsequent steps.

[0165] If the constraint verification passes, then: 1) Conduct pre-drawing of the train operation diagram for the set of train operation plans in the current state to generate the pre-drawing result of the train operation diagram.

[0166] During the preliminary drawing of the train operation diagram, the preliminary drawing of the train operation diagram can be carried out according to the set of train operation plans in the current state generated in the plan compilation stage, and the result of the preliminary drawing of the train operation diagram can be generated. In order to balance the accuracy and efficiency of the preliminary drawing of the train operation diagram, a passenger train operation constraint model can be established, and the preliminary drawing of the train operation diagram can be automatically completed through a computer program to improve the efficiency of the entire process. In the specific implementation, since the preliminary drawing process is to verify the high feasibility of the train operation plan, some of the constraints for the actual drawing of the train operation diagram can be appropriately relaxed, the difficulty of drawing the train operation diagram can be reduced, the resource consumption in the verification process can be reduced, and the verification efficiency can be improved.

[0167] 2) Conduct feasibility verification based on the result of the preliminary drawing of the train operation diagram.

[0168] The feasibility verification process can be realized based on the discrimination of train operation diagram conflicts. For example, based on the result of the preliminary drawing of the train operation diagram, it is judged whether the set of train operation plans in the current state is feasible.

[0169] 3) If the feasibility verification fails, update the set of dynamic threshold constraints, and re-execute the steps of initializing the HHTPV framework based on the constraint conditions (i.e., step 103) and subsequent steps.

[0170] If the feasibility verification passes, determine the set of train operation plans in the current state as the compiled train operation plan.

[0171] For example, if the feasibility verification fails, through the analysis of train operation diagram conflicts, new dynamic threshold constraints are generated to form a new set of dynamic threshold constraints, which are introduced into the HHTPV framework, and the process of the plan compilation stage is re-executed until the final high-feasibility passenger train operation plan is generated. This final high-feasibility passenger train operation plan is the compiled train operation plan.

[0172] If the feasibility verification passes, there are no train operation conflicts. At this time, the set of train operation plans in the current state is the final high-feasibility passenger train operation plan. This train operation plan meets the constraint conditions of the HHTPV framework and has high feasibility, and can enter the subsequent railway passenger transport operation organization process to provide efficient and safe travel services for passengers. Therefore, this final high-feasibility passenger train operation plan is the compiled train operation plan.

[0173] Among them, the process of updating the set of dynamic threshold constraints is: conduct train operation diagram conflict analysis based on the result of the preliminary drawing of the train operation diagram, and through Update the set of dynamic threshold constraints.

[0174] Among them, is the train identification, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train type identification, is the set of train types, is an interval identifier, is the set of intervals of the high - speed railway network, is the dynamic threshold constraint identifier in the set of dynamic threshold constraints, is the dynamic threshold constraint belongs to the interval 's marking parameter, is for the train in the interval the marking parameter passed through, is for the train belongs to the train type 's marking parameter, is the dynamic threshold constraint for the train type constraint weighting, is for the train 's enabling parameter, is the dynamic threshold constraint 's limit value.

[0175] is 0 or 1, indicating that the dynamic threshold constraint belongs to the interval , indicating that the dynamic threshold constraint does not belong to the interval. Allowing the constraint to manage multiple related intervals can thus achieve the resolution of train conflicts in multi - interval management and expand the scope of action of the constraint.

[0176] , that is, the interval is composed of partially ordered station pairs.

[0177] is the set of train types, that is, the refined set of train categories, where subdivision is allowed according to train grade, train speed, and whether it belongs to a benchmark train.

[0178] Cooperating with the limit value of the dynamic threshold constraint can achieve the control ability for grouping multiple types of trains.

[0179] The iterative update process of the dynamic threshold constraint set in this step is an important part of the HHTPV framework, and its core function is to verify the high feasibility of the train operation plan. Through the multi-group dynamic control of the carrying capacity of the train operation plan in the line section, if combined with the operation plan optimization process in step 104, a mathematical programming problem for optimizing the passenger train operation plan can be constructed within the closed-loop iterative process in step 105 and calculated and solved. At the same time, verification is carried out through dynamic threshold constraints to ensure that the operation plan meets the limitations of the current constraint combination.

[0180] The dynamic threshold constraint generation process adopts a closed-loop iterative mechanism. First, based on the current operation plan, the train operation diagram is preliminarily laid out. The section with irreconcilable conflicts is identified through the conflict detection algorithm. Subsequently, threshold dynamic adjustment is performed for the problem section: the existing threshold constraint limit value parameters are corrected, targeted grouped threshold constraints are added to exclude conflicting operation plan combinations, and redundant threshold constraints generated in historical iterations are cleared. After multiple rounds of cyclic iteration of "plan optimization - conflict detection - threshold constraint adjustment", a high-feasibility operation plan that meets both the transportation capacity limit and the optimization mechanism is finally generated.

[0181] This constraint condition has different application characteristics in different scenarios. For non-core lines or regions, the feasibility judgment can be quickly completed by simplifying the threshold parameter settings of relevant sections, improving the efficiency of plan compilation; while in core lines or regions, grouped constraints can be refined based on dimensions such as train speed and car body formation, and the combined characteristics of the train operation plan can be accurately controlled through multi-level threshold constraints to ensure the high feasibility of the train operation plan.

[0182] After the dynamic threshold constraint set initializes the HHTPV framework in step 103, the dynamic threshold constraint set remains unchanged in step 104 (that is, the dynamic threshold constraint set is fixed during the operation plan optimization process in step 104), but in the iterative process of step 105, it will be dynamically adjusted according to the result of the preliminary layout of the train operation diagram.

[0183] In addition, by adjusting to make equal to 1, the parameters can be reduced, that is, corresponding to the new constraint . However, retaining can make this constraint more in line with the physical meaning of train operation. In practical applications, the values of and should be reasonably set according to the characteristics of the train type to ensure the effectiveness of the constraint.

[0184] Step 105 is the operation diagram coordination stage of the train operation plan compilation method provided in this embodiment. Through this stage, the train operation plan set in the current state can be verified according to the constraint conditions of the HHTPV framework in the current state to determine whether it meets the constraint conditions of the HHTPV framework. If it meets, it enters the operation diagram preliminary layout stage; if it does not meet, a new preferred adjustment direction for the train operation plan is generated according to the adjustment feedback information of the HHTPV framework. Steps 104 and subsequent steps are re-executed until the constraint conditions of the HHTPV framework are met.

[0185] In the operation diagram preliminary layout stage, the operation diagram of the train operation plan set in the current state can be preliminarily laid out to determine its feasibility. For infeasible train operation plans, a new dynamic threshold constraint set is generated through operation diagram conflict discrimination, and the HHTPV framework is introduced, and steps 103 and subsequent steps are re-executed until a final highly feasible passenger train operation plan is generated. This final highly feasible passenger train operation plan is the compiled train operation plan.

[0186] The train operation plan compilation method provided in this embodiment can be applied to any high-speed railway network structure. However, for the execution efficiency of the train operation plan compilation method provided in this embodiment and the accuracy of the results, the high-speed railway network can be reasonably divided and transformed to verify the train operation plan involving specified lines or regions in a targeted manner. Although the division of the high-speed railway network reduces the verification scope of the HHTPV framework, through the combination of the static constraints and dynamic threshold constraints of the HHTPV framework, the feasibility of the train operation plan outside the verification scope can still be evaluated.

[0187] The division of the high-speed railway network should be based on the actual situation to ensure that the divided high-speed railway network structure has a certain degree of independence and can effectively interact with other high-speed railway network structures. When dividing the high-speed railway network, the following factors can be considered. First, the core lines or regions for train operation plan compilation need to be completely retained to ensure the complete and accurate feasibility verification of the train operation plan in the core lines or regions. Second, for the lines connected to the core lines or regions but without cross-line operation of the train operation plan, since they are not involved in the verification of the high feasibility of the train operation plan, they can be directly deleted from the high-speed railway network to reduce the calculation amount. Finally, for the lines connected to the core lines or regions and with cross-line operation of the train operation plan, a simplified high feasibility verification of the involved train operation plan needs to be carried out according to the carrying capacity of the relevant lines and the actual train operation situation. Therefore, the basic connection relationship with the core lines or regions needs to be retained, that is, the intermediate stations on other lines in the route are deleted, and the connection relationship with the core lines or regions is established by means of virtual sections to ensure the high feasibility of the relevant train operation plan.

[0188] Figure 3Shows a schematic diagram of the division of a high - speed railway network, Figure 3 where the "railway network" in it refers to the high - speed railway network. Figure 3 Displays the line A composed of stations A1 to A7 as the core section for the compilation of the train operation plan. Simplify the complete high - speed railway network (i.e., Figure 3 the complete railway network in it) to generate the result of the converted high - speed railway network (i.e., Figure 3 the converted railway network in it). Lines B, C, D, and E respectively represent 4 lines connected to line A. Through the connection between line A and other lines, the interaction between line A and other lines can be realized. For line B, assuming that there is no existing train operation plan in the current or following diagrams, and a train crosses from station A4 to station B1, then line B in the converted railway network can be deleted. For line C, assuming there is a train operation plan for crossing lines, a train crosses from station A5 to stations C1 and C3, and the final relevant train operation plan has C5 as the origin - destination station, then line C in the converted railway network is retained, where stations C1 and C5 are retained to completely model the relevant train operation plan. Since line C is not the core line for the compilation of this train operation plan, in the converted railway network, simplify the structure of line C and only retain the connection relationship with line A to reduce the calculation amount. At the same time, in the operation process of the HHTPV framework, through the dynamic threshold constraints related to the section from station C1 to station A7 and the virtual section from station A7 to station C5, ensure the high feasibility of the train operation plan for crossing to line C. For lines D and E, assuming there are relevant train operation plans passing through station D3 and having station E1 or station E5 as the origin - destination station, then lines D and E in the converted railway network are retained, where stations D3, E1, and E5 are retained to completely model the relevant train operation plan. In the operation process of the HHTPV framework, ensure the high feasibility of the relevant train operation plans by establishing dynamic threshold constraints for the corresponding virtual sections.

[0189] The train operation plan compilation method provided in this embodiment divides the compilation of the train operation plan into three stages. After the pre - processing stage, perform cyclic iteration on the latter two stages. That is, in the pre - processing stage, input the estimated passenger demand data to determine the complete set of passenger train operation plans, input the basic data of the high - speed railway network to determine the basic constraints of the HHTPV framework; in the plan compilation stage, enter the iteration, initialize the HHTPV framework, repeatedly optimize the passenger train operation plan, and obtain the train operation plan in the current state, that is, the passenger train operation plan with high feasibility; in the train operation diagram coordination stage, execute the pre - drawing process of the train operation diagram, judge whether the train operation plan generated in the plan compilation stage can be drawn in the train operation diagram. If it is not feasible, through the train operation diagram conflict discrimination, calculate the stations or sections where conflicts may occur, generate a new combination of dynamic threshold constraints for high - speed trains according to the corresponding topological structure, and introduce it into the HHTPV framework. Otherwise, complete the compilation of the final train operation plan.

[0190] This embodiment provides a method for formulating an operation plan, which determines the complete set of operation plans for passenger trains on a specified line or area in the next diagram period; determines the basic constraints of the HHTPV framework based on the basic data of the high-speed railway network; initializes the HHTPV framework based on the constraint conditions, where the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set; generates the operation plan in the current state according to the evaluation model, the optimization strategy, the preferred adjustment direction of the operation plan, and the complete set of operation plans for passenger trains on a specified line or area in the next diagram period; and verifies the set of operation plans in the current state within the HHTPV framework, and determines the formulated operation plan according to the verification result. The method provided by this embodiment determines the basic constraints of the HHTPV framework based on the basic data of the high-speed railway network; initializes the HHTPV framework based on the basic constraints, verifies the set of operation plans in the current state within the HHTPV framework, and determines the formulated operation plan according to the verification result, realizing the automatic formulation and verification of the operation plan and improving the formulation efficiency.

[0191] On the basis of reasonable verification of the operation plan formulation, railway passenger train timetables can be generated, which is equivalent to full-chain optimization. The main considerations for generating railway passenger train timetables are: The passenger flow information of the current station. Through the passenger flow information of the current station, passenger flow heat learning is carried out to obtain the passenger flow heat of the current station, determine the local area where the current station is located, and construct a local heat matrix according to the passenger flow heat of other stations corresponding to the local area where the current station is located.

[0192] Perform eigenvalue decomposition on the local heat matrix to obtain the heat learning features corresponding to the local area where the current station is located, and generate the initial passenger train timetable of the current station through the heat learning features. And determine the in-station dispatching coupling degree of the current station according to the passenger flow heat and the passenger transport adjustment ratio of the current station.

[0193] Determining the in-station dispatching coupling degree of the current station specifically includes: Based on a preset coupling degree extraction period, trend value collection is respectively performed on the passenger flow heat change trend and the passenger transport adjustment trend to obtain a passenger flow heat trend value sequence and a passenger transport adjustment trend value sequence; After normalizing the passenger flow heat trend value sequence and the passenger transport adjustment trend value sequence, perform coupling degree analysis according to the passenger flow heat trend value sequence and the passenger transport adjustment trend value sequence to obtain the in-station dispatching coupling degree of the current station.

[0194] In specific implementation, the coupling degree extraction period can be obtained by mapping based on the current passenger flow density. In some embodiments, the coupling degree extraction period can be fixed as a constant value of 6h. Furthermore, the Pearson correlation coefficient between the passenger flow heat trend value sequence and the passenger transport adjustment trend value sequence can be used as the in-station scheduling coupling degree.

[0195] Dynamically feedback and update the initial passenger transport timetable according to the in-station scheduling coupling degree and the inter-station scheduling coupling degree to generate an optimized passenger transport timetable.

[0196] Aiming at the problems of insufficient dynamic response, difficult multi-professional collaboration, and low calculation efficiency caused by the independent operation of passenger flow prediction, train adjustment, seat allocation, and timetable compilation in traditional methods, a modular collaborative system for multiple specialties of passenger transport, transportation, scheduling, and vehicles is constructed, a closed-loop process of "adjustment plan generation → timetable dynamic generation → operation evaluation → feedback optimization" is established, and lightweight data interaction is realized by using a standardized interface.

[0197] Based on the same inventive concept of the train operation plan compilation method, this embodiment provides a train operation plan compilation device. See Figure 4 and the device includes: The first determination module 401 is used to determine the complete set of passenger train operation plans for the specified line or area in the next graph period.

[0198] The second determination module 402 is used to determine the basic constraints of the high-feasibility high-speed rail operation plan verification HHTPV framework based on the basic data of the high-speed rail network.

[0199] The processing module 403 is used to initialize the HHTPV framework based on the constraint conditions. Among them, the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set.

[0200] The generation module 404 is used to generate the operation plan in the current state according to the evaluation model, the optimization strategy, the operation plan optimization adjustment direction, and the complete set of passenger train operation plans for the specified line or area in the next graph period.

[0201] The compilation module 405 is used to verify the set of operation plans in the current state within the HHTPV framework and determine the compiled operation plan according to the verification result.

[0202] Among them, the second determination module 402 is configured to calculate the upper limit of passing capacity of sections, the upper limit of passing capacity of stations, the stop capacity limit value of stations, and the limit of each train type in the section based on the basic data of the high-speed railway network and the topological connectivity of the high-speed railway network. Determine the passing limit constraints and train type limit constraints of each section, and the passing limit constraints and stop limit constraints of each station according to the upper limit of passing capacity of the section, the upper limit of passing capacity of the station, the stop capacity limit value of the station, and the limit of each train type in the section.

[0203] Among them, the passing limit constraint of any section is , is the section identifier, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train in the section passing mark parameter, is the train enabling parameter, is the section upper limit of passing capacity.

[0204] The train type limit constraint of any section is , is the train type identifier, is the train belonging to the train type mark parameter, section the train type limit in.

[0205] The passing limit constraint of any station is , is the station identifier, is the train in the station passing mark parameter, is the station upper limit of passing capacity.

[0206] The stop limit constraint of any station is , is the train stopping in the station stop mark parameter, is the station stop capacity limit value.

[0207] Among them, according to the evaluation model, the optimization strategy, the optimization adjustment direction of the operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period, generate the operation plan in the current state, including: Within the HHTPV framework, according to the optimal strategy, combine the train operation plan to optimize the adjustment direction, and select the alternative passenger train operation plans. Determine the planned newly added, suspended, and adjusted passenger train operation plans, and generate the set of passenger train operation plans in the current state.

[0208] Among them, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the station identifier, is the set of stations in the high-speed railway network, is the train at the station stop mark parameter.

[0209] Among them, the verification includes constraint verification and feasibility verification.

[0210] The compilation module 405 is used to perform constraint verification on the set of operation plans in the current state within the HHTPV framework. If the constraint verification fails, update the optimal adjustment direction of the operation plan and re-execute module 404. If the constraint verification passes, perform pre-drawing of the train operation diagram on the set of operation plans in the current state to generate the pre-drawing result of the train operation diagram. Perform feasibility verification based on the pre-drawing result of the train operation diagram.

[0211] If the feasibility verification fails, update the set of dynamic threshold constraints and re-execute module 403. If the feasibility verification passes, determine the set of operation plans in the current state as the compiled operation plan.

[0212] Among them, updating the set of dynamic threshold constraints includes: Perform train operation diagram conflict analysis based on the pre-drawing result of the train operation diagram, and update the set of dynamic threshold constraints.

[0213] Among them, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train type identifier, is the set of train types, is the section identifier, is the set of sections in the high-speed railway network, is the dynamic threshold constraint identifier in the set of dynamic threshold constraints, is the dynamic threshold constraint belongs to the section mark parameter, is the train in the section The passed marking parameter, for the train belonging to the train type of the marking parameter, is the dynamic threshold constraint for the train type constraint weighting, for the train enable parameter, is the dynamic threshold constraint limit value.

[0214] Among them, the first determination module 401 is used to determine the complete set of passenger train operation plans for the specified line or area in the next graph period .

[0215] Among them, is the set of passenger train operation plans fixedly operated on the specified line or area, , is the set of trains that meet the preset conditions in the passenger train operation plans newly planned for the specified line or area, is the set of passenger train operation plans that have been laid out in the operation graph for the specified line or area and have not been cancelled in the current graph period. , is the set of passenger train operation plans for the specified line or area in the current graph period, is the set of passenger train operation plans for the trains planned to be out of service on the specified line or area.

[0216] is the set of alternative passenger train operation plans for the specified line or area in the next graph period, , is the set of passenger train operation plans newly planned.

[0217] Among them, the operation plan compilation device further includes: an optimization module.

[0218] This optimization module is used to obtain the occupancy rate of each train. According to the compiled operation plan and the occupancy rate of each train, the optimized operation plan and its transport efficiency evaluation value are determined through the evaluation model.

[0219] Among them, the evaluation model includes a two-dimensional wavelet packet decomposition module and a convolutional neural network.

[0220] The decomposition function of the two-dimensional wavelet packet decomposition module is Daubechies4, and the two-dimensional wavelet packet decomposition module is used to convert the feature vectors of all trains in the operation plan into multi-channel data.

[0221] The convolutional neural network is used to evaluate the transport efficiency based on the multi-channel data and the occupancy rate of each train.

[0222] The device provided in this embodiment verifies the set of train operation plans in the current state within the HHTPV framework, and determines the compiled train operation plan according to the verification result, realizing the automatic compilation and verification of the train operation plan and improving the compilation efficiency.

[0223] Based on the same inventive concept of the train operation plan compilation method, this embodiment provides an electronic device, which is as Figure 5 shown and includes: a memory 501, a processor 502, and a computer program.

[0224] Among them, the computer program is stored in the memory 501 and is configured to be executed by the processor 502 to implement the above-mentioned train operation plan compilation method.

[0225] Specifically, Determine the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0226] Based on the basic data of the high-speed railway network, determine the basic constraints of the high-feasibility high-speed train operation plan verification HHTPV framework.

[0227] Initialize the HHTPV framework based on the constraint conditions. Among them, the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set.

[0228] Generate the train operation plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0229] Within the HHTPV framework, verify the set of train operation plans in the current state, and determine the compiled train operation plan according to the verification result.

[0230] Among them, based on the basic data of the high-speed railway network, determine the basic constraints of the high-feasibility high-speed train operation plan verification HHTPV framework, including: Based on the basic data of the high-speed railway network and the topological connectivity of the high-speed railway network, calculate the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the limit value of the stopping capacity of the station, and the limit of each train type within the section.

[0231] According to the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the limit value of the stopping capacity of the station, and the limit of each train type within the section, determine the passing limit constraint and train type limit constraint of each section, and the passing limit constraint and stopping limit constraint of each station.

[0232] Among them, the passing limit constraint of any section is , is the section identifier, is the train identifier, The complete set of passenger train operation plans that specify lines or areas for the next diagram period For the train In the section Marking parameters passed by For the train Enabling parameters of For the section Upper limit of passing capacity

[0233] The train type limit constraint for any section is , Train type identifier For the train Belongs to the train type Marking parameters Section Train type within the section Limit

[0234] The passing limit constraint for any station is , Station identifier For the train At the station Marking parameters passed by For the station Upper limit of passing capacity

[0235] The stop limit constraint for any station is , For the train At the station Marking parameters for stopping For the station Stop capacity limit value

[0236] Among them, according to the evaluation model, optimization strategy, optimization adjustment direction of the operation plan, and the complete set of passenger train operation plans that specify lines or areas for the next diagram period, the operation plan in the current state is generated, including: Select alternative passenger train operation plans within the HHTPV framework according to the optimization strategy, combined with the optimization adjustment direction of the operation plan; Determine the operation plans of newly planned, discontinued, and adjusted passenger trains, and generate a set of passenger train operation plans in the current state.

[0237] Among them, Train identifier The complete set of passenger train operation plans that specify lines or areas for the next diagram period Station identifier Set of stations on the high-speed railway network For the train At the station Marking parameters for station stops.

[0238] Among them, the verification includes constraint verification and feasibility verification.

[0239] Within the HHTPV framework, perform constraint verification and feasibility verification on the set of train operation plans in the current state, and determine the compiled train operation plan according to the results of the constraint verification and feasibility verification, including: Within the HHTPV framework, perform constraint verification on the set of train operation plans in the current state.

[0240] If the constraint verification fails, update the preferred adjustment direction of the train operation plan, and re-execute the steps of generating the train operation plan in the current state and subsequent steps according to the evaluation model, preferred strategy, preferred adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0241] If the constraint verification passes, perform pre-drawing of the train operation diagram on the set of train operation plans in the current state to generate the pre-drawing result of the train operation diagram. Perform feasibility verification based on the pre-drawing result of the train operation diagram.

[0242] If the feasibility verification fails, update the set of dynamic threshold constraints, and re-execute the steps of initializing the HHTPV framework based on the constraint conditions and subsequent steps.

[0243] If the feasibility verification passes, determine the set of train operation plans in the current state as the compiled train operation plan.

[0244] Among them, updating the set of dynamic threshold constraints includes: Perform train operation diagram conflict analysis according to the pre-drawing result of the train operation diagram, and Update the set of dynamic threshold constraints.

[0245] Among them, is the train identification, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train type identification, is the set of train types, is the section identification, is the set of sections of the high-speed railway network, is the dynamic threshold constraint identification in the set of dynamic threshold constraints, is the dynamic threshold constraint belongs to the section marking parameter, is the train in the section passing through marking parameter, is the train belongs to the train type The marking parameter is the dynamic threshold constraint for the train type constraint weighting is the enabling parameter for the train and is the limiting value of the dynamic threshold constraint is the limiting value of the dynamic threshold constraint Among them, determining the complete set of passenger train operation plans for a specified line or area in the next diagram period includes:

[0246] Determining the complete set of passenger train operation plans for a specified line or area in the next diagram period .

[0247] Among them, is the set of passenger train operation plans that are fixedly operated on the specified line or area , is the set of trains that meet the preset conditions in the passenger train operation plans newly planned for the specified line or area is the set of passenger train operation plans that have been drawn in the operation diagram for the specified line or area and have not been cancelled in the current diagram period , is the set of passenger train operation plans for the specified line or area in the current diagram period is the set of passenger train operation plans for the trains that are planned to be taken out of service on the specified line or area

[0248] is the set of alternative passenger train operation plans for the specified line or area in the next diagram period , is the set of passenger train operation plans for the newly planned trains

[0249] Among them, determining the passenger train operation plans for the newly planned, out-of-service, and adjusted trains, and generating the set of passenger train operation plans in the current state, includes: Obtaining the occupancy rate of each train

[0250] According to the prepared operation plan and the occupancy rate of each train, the optimized operation plan and its transportation efficiency evaluation value are determined through the evaluation model

[0251] Among them, the evaluation model includes a two-dimensional wavelet packet decomposition module and a convolutional neural network

[0252] The decomposition function of the two-dimensional wavelet packet decomposition module is Daubechies4, and the two-dimensional wavelet packet decomposition module is used to convert the feature vectors of all trains in the operation plan into multi-channel data

[0253] The convolutional neural network is used to evaluate the transportation efficiency based on the multi-channel data and the occupancy rate of each train​

[0254] In the electronic device provided in this embodiment, the computer program thereon is executed by a processor to verify the set of train operation plans in the current state within the HHTPV framework, and determine the compiled train operation plan according to the verification result, realizing the automatic compilation and verification of the train operation plan and improving the compilation efficiency.

[0255] Based on the same inventive concept of the train operation plan compilation method, this embodiment provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the above-mentioned train operation plan compilation method.

[0256] Specifically, Determine the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0257] Based on the basic data of the high-speed railway network, determine the basic constraints for verifying the HHTPV framework of high-feasibility high-speed train operation plans.

[0258] Initialize the HHTPV framework based on the constraint conditions. Among them, the constraint conditions are determined according to the basic constraints and the dynamic threshold constraint set.

[0259] Generate the train operation plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0260] Within the HHTPV framework, verify the set of train operation plans in the current state, and determine the compiled train operation plan according to the verification result.

[0261] Among them, based on the basic data of the high-speed railway network, determine the basic constraints for verifying the HHTPV framework of high-feasibility high-speed train operation plans, including: Based on the basic data of the high-speed railway network and the topological connectivity of the high-speed railway network, calculate the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the limit value of the stopping capacity of the station, and the limit of each train type within the section.

[0262] According to the upper limit of the passing capacity of the section, the upper limit of the passing capacity of the station, the limit value of the stopping capacity of the station, and the limit of each train type within the section, determine the passing limit constraint and train type limit constraint of each section, and the passing limit constraint and stopping limit constraint of each station.

[0263] Among them, the passing limit constraint of any section is , is the section identifier, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train Within the interval The marked parameter passed through For the train Enabled parameter For the interval Upper limit of passing capacity

[0264] The train type limit constraint for any interval is , Is the train type identifier For the train Belongs to train type Marked parameter Interval Train type within the interval Limit quantity

[0265] The passing limit constraint for any station is , Is the station identifier For the train At the station Marked parameter passed through For the station Upper limit of passing capacity

[0266] The stopping limit constraint for any station is , For the train At the station Marked parameter for stopping For the station Stopping capacity limit value

[0267] Among them, according to the evaluation model, optimization strategy, preferred adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period, the current operation plan is generated, including: Select alternative passenger train operation plans within the HHTPV framework according to the optimization strategy and in combination with the preferred adjustment direction of the train operation plan; Determine the passenger train operation plans for newly planned, suspended, and adjusted trains, and generate the set of passenger train operation plans in the current state.

[0268] Among them, Is the train identifier Is the complete set of passenger train operation plans for the specified line or area in the next diagram period Is the station identifier Is the set of stations on the high-speed railway network For the train At the station Marked parameter for stopping

[0269] Among them, the verification includes constraint verification and feasibility verification.

[0270] Within the HHTPV framework, perform constraint verification and feasibility verification on the set of train operation plans in the current state, and determine the compiled train operation plan according to the results of the constraint verification and feasibility verification, including: Within the HHTPV framework, perform constraint verification on the set of train operation plans in the current state.

[0271] If the constraint verification fails, update the preferred adjustment direction of the train operation plan, and re-execute the steps of generating the train operation plan in the current state and subsequent steps according to the evaluation model, preferred strategy, preferred adjustment direction of the train operation plan, and the complete set of passenger train operation plans for the specified line or area in the next diagram period.

[0272] If the constraint verification passes, perform pre-drawing of the train operation diagram on the set of train operation plans in the current state to generate the pre-drawing result of the train operation diagram. Perform feasibility verification based on the pre-drawing result of the train operation diagram.

[0273] If the feasibility verification fails, update the set of dynamic threshold constraints, and re-execute the steps of initializing the HHTPV framework based on the constraint conditions and subsequent steps.

[0274] If the feasibility verification passes, determine the set of train operation plans in the current state as the compiled train operation plan.

[0275] Among them, updating the set of dynamic threshold constraints includes: Perform train operation diagram conflict analysis according to the pre-drawing result of the train operation diagram, and Update the set of dynamic threshold constraints.

[0276] Among them, is the train identifier, is the complete set of passenger train operation plans for the specified line or area in the next diagram period, is the train type identifier, is the set of train types, is the section identifier, is the set of sections of the high-speed railway network, is the dynamic threshold constraint identifier in the set of dynamic threshold constraints, is the dynamic threshold constraint belongs to the section marking parameter, is the train in the section passing through marking parameter, is the train belongs to the train type marking parameter, is the dynamic threshold constraint Constraint weighting for train types is the enabling parameter for trains and is the limit value for dynamic threshold constraints

[0277] Among them, determining the complete set of passenger train operation plans for a specified line or area in the next diagram period includes: Determining the complete set of passenger train operation plans for a specified line or area in the next diagram period

[0278] Among them, is the set of passenger train operation plans fixedly operated on the specified line or area, is the set of trains that meet the preset conditions in the passenger train operation plans newly planned for the specified line or area, is the set of passenger train operation plans that have been drawn in the operation diagram for the specified line or area and have not been cancelled in the current diagram period.

[0279] is the set of passenger train operation plans for the specified line or area in the current diagram period, is the set of passenger train operation plans planned to be suspended for the specified line or area.

[0280] is the set of alternative passenger train operation plans for the specified line or area in the next diagram period, is the set of passenger train operation plans newly planned.

[0281] Among them, determining the passenger train operation plans newly planned, suspended, and adjusted, and generating the set of passenger train operation plans in the current state includes: Obtaining the occupancy rate of each train.

[0281] Based on the prepared operation plan and the occupancy rate of each train, determine the optimized operation plan and its transport efficiency evaluation value through the evaluation model.

[0282] Among them, the evaluation model includes a two-dimensional wavelet packet decomposition module and a convolutional neural network.

[0283] The decomposition function of the two-dimensional wavelet packet decomposition module is Daubechies4, and the two-dimensional wavelet packet decomposition module is used to convert the feature vectors of all trains in the operation plan into multi-channel data.

[0284] The convolutional neural network is used to evaluate the transport efficiency based on the multi-channel data and the occupancy rate of each train.

[0285] The computer-readable storage medium provided by this embodiment, on which the computer program is executed by a processor to verify the set of train operation plans in the current state within the HHTPV framework, and determine the compiled train operation plans according to the verification results, realizes the automatic compilation and verification of train operation plans and improves the compilation efficiency.

[0286] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented 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. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0287] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 process or multiple processes and / or blocks Figure 1 block or multiple blocks.

[0288] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the specified functions in one Figure 1 process or multiple processes and / or blocks Figure 1 block or multiple blocks.

[0289] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one Figure 1 process or multiple processes and / or blocks Figure 1 block or multiple blocks.

[0290] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0291] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for compiling a train operation plan, characterized in that: The method comprises: Determine the complete set of passenger train operation plans for the designated lines or areas in the next chart period; Based on the basic data of the high-speed railway network, a highly feasible high-speed railway operation plan is determined to verify the basic constraints of the HHTPV framework; Initializing the HHTPV framework based on constraint conditions; wherein the constraint conditions are determined according to the basic constraint and the dynamic threshold constraint set; Generate a running plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the running plan and the complete set of passenger train running plans for the designated line or area in the next diagram period; Within the HHTPV framework, the set of operation plans in the current state is verified, and the compiled operation plan is determined based on the verification results.

2. The method according to claim 1, characterized in that The basic constraints of the HHTPV framework are verified by determining a highly feasible high-speed railway operation plan based on the basic data of the high-speed railway network, including: Based on the basic data of the high-speed railway network and the topological connectivity of the high-speed railway network, the upper limit of the section's capacity, the upper limit of the station's capacity, the station's stop capacity limit, and the limit of each type of train in the section are calculated; According to the upper limit of the section's throughput capacity, the upper limit of the station's throughput capacity, the station's stop capacity limit value, and the limit of each type of train in the section, determine the passing restriction constraints and train type restriction constraints of each section, the passing restriction constraints and stop restriction constraints of each station; Among them, the restriction constraint of any interval is , is the interval identifier, For train identification, A complete set of passenger train operation plans for a specified line or area in the next chart period. For trains In the interval Passed tag parameters, For trains The enabling parameters of For interval The upper limit of the passing capacity; The train type restriction constraint for any section is , is the train type identifier, For trains Train type The tag parameter, Interval Train Type The limit of The passing restriction for any station is , For station identification, For trains At the station Passed tag parameters, For the station The upper limit of the passing capacity; The stop restriction constraint at any station is , For trains At the station The marking parameters of the stop, For the station The stopping capacity limit value.

3. The method according to claim 1, characterized in that The generating of the running plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the running plan and the complete set of passenger train running plans for the designated lines or areas in the next diagram period includes: According to the optimization strategy within the HHTPV framework, the alternative passenger train operation plan is selected in combination with the optimization adjustment direction of the operation plan; Determine the passenger train operation plans for planned additions, suspensions, and adjustments, and generate a set of passenger train operation plans under the current status.

4. The method according to claim 1, characterized in that: The verification includes constraint verification and feasibility verification; In the HHTPV framework, the constraint verification and feasibility verification are performed on the set of operation plans in the current state, and the operation plan to be compiled is determined according to the results of the constraint verification and feasibility verification, including: In the HHTPV framework, constraints are verified on the set of operation plans under the current state; If the constraint verification fails, the preferred adjustment direction of the operation plan is updated, and the steps and subsequent steps of generating the operation plan in the current state according to the evaluation model, the preferred strategy, the preferred adjustment direction of the operation plan and the complete set of passenger train operation plans for the specified line or area in the next diagram period are re-executed; If the constraint verification is passed, a running diagram is pre-drawn for the running scheme set in the current state to generate a running diagram pre-drawing result; and feasibility verification is performed based on the running diagram pre-drawing result; If the feasibility verification fails, the dynamic threshold constraint set is updated, and the step of initializing the HHTPV framework based on the constraint conditions and subsequent steps are re-executed; If the feasibility verification is passed, the operation plan set in the current state will be determined as the compiled operation plan.

5. The method according to claim 4, characterized in that The updating of the dynamic threshold constraint set includes: According to the operation diagram pre-layout results, the operation diagram conflict analysis is performed, and the Update the dynamic threshold constraint set; in, For train identification, A complete set of passenger train operation plans for a specified line or area in the next chart period. is the train type identifier, is a set of train types, is the interval identifier, is the interval set of the high-speed railway network, is the dynamic threshold constraint identifier in the dynamic threshold constraint set, Dynamic threshold constraint Belong to the interval The tag parameter, For trains In the interval Passed tag parameters, For trains Train type The tag parameter, Dynamic threshold constraint For train types The constraint weighting, For trains The enabling parameters of Dynamic threshold constraint limit value.

6. The method according to claim 1, characterized in that The complete set of passenger train operation plans for the designated lines or areas for the next chart period includes: Determine the passenger train operation plan for the specified line or area in the next chart period ; in, A collection of passenger train operation plans for a specified line or region. , A set of trains that meet preset conditions in the passenger train operation plan for a specified line or area. It is a set of passenger train operation plans that have been completed in the operation diagram for a specified line or area and have not been cancelled in the current diagram period; , It is a set of passenger train operation plans for the specified line or area in the current time period. A collection of passenger train operation plans for designated lines or areas that are scheduled to be suspended; A set of alternative passenger train operation plans for designated routes or regions in the next chart period. , A collection of new passenger train operation plans.

7. The method according to claim 3, characterized in that The step of determining the passenger train operation plans for planned addition, suspension, and adjustment, and generating a set of passenger train operation plans in the current state includes: Get the passenger load factor of each train; According to the prepared operation plan and the passenger load factor of each train, the evaluation model is used to determine the optimized operation plan and its transport efficiency evaluation value; Wherein, the evaluation model includes a two-dimensional wavelet packet decomposition module and a convolutional neural network; The decomposition function of the two-dimensional wavelet packet decomposition module is Daubechies4, and the two-dimensional wavelet packet decomposition module is used to convert the characteristic vectors of all trains in the operation plan into multi-channel data; The convolutional neural network is used to evaluate transportation efficiency based on multi-channel data and the passenger load factor of each train.

8. A device for compiling a train operation plan, characterized in that: The device comprises: The first determination module is used to determine the complete set of passenger train operation plans for the specified line or area in the next chart period; The second determination module is used to determine the basic constraints of the HHTPV framework based on the basic data of the high-speed railway network and verify the high-feasibility high-speed railway operation plan; A processing module, configured to initialize the HHTPV framework based on constraint conditions, wherein the constraint conditions are determined according to the basic constraint and the dynamic threshold constraint set; A generation module, for generating a running plan in the current state according to the evaluation model, the optimization strategy, the optimization adjustment direction of the running plan and the complete set of passenger train running plans for the specified line or area in the next diagram period; The compilation module is used to verify the set of operation plans in the current state within the HHTPV framework, and determine the compiled operation plan based on the verification results.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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