High-speed rail express train operation scheme compiling method and device, equipment and medium

Abstract

The application provides a high-speed rail express train operation scheme compiling method, device and equipment and a medium, relates to the technical field of train route planning, and comprises the following steps: acquiring first information, wherein the first information comprises freight station data, section data between two freight stations, train data and freight flow data; constructing a train allocation constraint model according to the first information; randomly generating a plurality of initial schemes under the condition of the train allocation constraint model; constructing a target function with the maximum income and the shortest train running time as targets, calculating the target value of the initial scheme through the target function; and cyclically optimizing the plurality of initial schemes by using a genetic algorithm to obtain a high-speed rail express train operation scheme with the minimum target value. The application is used for solving the technical problem that the traditional freight train formation plan cannot be implemented according to the plan due to the large fluctuation of high-speed rail express market freight transportation demand and the strict characteristics of the time limit for freight transportation.

Description

Technical Field

[0001] This invention relates to the field of train route planning technology, and more specifically, to a method, apparatus, equipment, and medium for compiling high-speed rail express train operation plans. Background Technology

[0002] The high-speed rail express train operation plan is a transportation organization scheme to realize the flow of goods to trains. Traditional railway freight transportation relies on statistical and forecasted freight demand to formulate freight train formation plans, and arranges train operations according to freight train timetables and daily actual needs. Without considering the freight EMU dedicated train transportation mode under network conditions or under large-scale network conditions, fluctuations in freight flow can lead to situations where there is a line but no flow, or a flow but no line, resulting in an inability to reflect the dynamic changes in market freight demand. At the same time, due to the large fluctuations in freight transportation demand and the strict requirements for freight delivery time in the high-speed rail express market, traditional freight train formation plans are prone to being unable to be implemented as planned. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and medium for compiling high-speed rail express train operation plans, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for compiling a high-speed rail express train operation plan, including:

[0005] Obtain first information, which includes freight station data, interval data between two freight stations, train data, and freight flow data;

[0006] Construct a train allocation constraint model based on the first piece of information;

[0007] Several initial schemes are randomly generated under the constraints of the train allocation model. Each initial scheme includes the train's operating route, operating time, train formation type, and cargo loading flow.

[0008] An objective function is constructed with the goals of maximizing revenue and minimizing train travel time, and the target value of the initial scheme is calculated using the objective function.

[0009] By using a genetic algorithm to iteratively optimize several initial schemes, a high-speed rail express train operation scheme with the minimum objective value is obtained.

[0010] Secondly, this application also provides a high-speed rail express train operation plan compilation device, including:

[0011] Acquisition module: Acquires first information, which includes freight station data, interval data between two freight stations, train data, and freight flow data;

[0012] Constraint Model Construction Module: Constructs a train allocation constraint model based on the initial information;

[0013] Generation module: Randomly generates several initial schemes under the conditions of the train allocation constraint model. An initial scheme includes the train's operating route, operating time period, train formation type, and loading cargo flow.

[0014] Objective function construction module: Constructs an objective function with the goals of maximizing revenue and minimizing train travel time, and calculates the target value of the initial plan using the objective function;

[0015] Optimization module: The genetic algorithm is used to iteratively optimize several initial schemes to obtain the high-speed rail express train operation scheme with the minimum objective value.

[0016] Thirdly, this application also provides a high-speed rail express train operation plan compilation device, including:

[0017] Memory, used to store computer programs;

[0018] A processor is used to implement the steps of the high-speed rail express train operation plan compilation method when executing the computer program.

[0019] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for compiling high-speed rail express train operation plans.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention, based on the market demand characteristics of high-speed rail freight and combined with the principles and processes of developing dynamic train operation plans for high-speed rail freight, aims to maximize the profits of the high-speed rail freight sector and minimize freight transport time. It establishes a high-speed rail express train operation plan development model based on a dynamic spatiotemporal network. Using a genetic algorithm as a foundation, it designs a chromosome encoding method, an initial population generation method, and a selection and crossover method to solve the model, ultimately obtaining a high-speed rail express train operation plan for the specified period. This plan can effectively assist the development of high-speed rail freight transport in my country, and its calculation process is convenient and simple, with unified modeling standards, high algorithm efficiency, and good operability and versatility.

[0022] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the high-speed rail express train operation plan preparation method described in this embodiment of the invention;

[0025] Figure 2 This is a schematic diagram of freight station data as described in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of chromosome encoding as described in an embodiment of the present invention;

[0027] Figure 4 This is a diagram illustrating the algorithm iteration process described in the embodiments of the present invention;

[0028] Figure 5 This is a schematic diagram of the high-speed rail express train operation plan compilation device described in this embodiment of the invention;

[0029] Figure 6 This is a schematic diagram of the high-speed rail express train operation plan compilation device described in this embodiment of the invention;

[0030] Figure 7 This is a schematic diagram of the equipment structure for compiling high-speed rail express train operation plans as described in this embodiment of the invention.

[0031] Marked in the image:

[0032] 01. Acquisition Module; 02. Constraint Model Construction Module; 021. First Construction Unit; 022. Second Construction Unit; 023. Third Construction Unit; 024. Fourth Construction Unit; 025. Fifth Construction Unit; 03. Generation Module; 031. First Acquisition Unit; 032. Second Acquisition Unit; 033. Generation Unit; 04. Objective Function Construction Module; 041. Sixth Construction Unit; 042. Merging Unit; 043. Calculation Unit; 05. Optimization Module; 051. Initial Scheme Set Construction Unit; 052. Selection and Crossover Unit; 0521. Determination Unit; 0522. First Operation Unit; 0523. Second Operation Unit; 0524. Second Judgment Unit; 053. Mutation Unit; 0531. Third Operation Unit; 0532. Third Judgment Unit; 054. First Judgment Unit;

[0033] 800. High-speed rail express train operation plan compilation equipment; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0035] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0036] Example 1:

[0037] This embodiment provides a method for compiling high-speed rail express train operation plans.

[0038] See Figure 1 The figure shows that this method includes:

[0039] S1. Obtain first information, the first information including freight station data, interval data between two freight stations, train data, and freight flow data;

[0040] In this embodiment, the freight station data is shown in Figure 2, which includes 9 freight stations, with adjacent freight stations connected by a straight line;

[0041] Specifically, the interval data includes interval mileage and interval throughput capacity. The interval data in this embodiment is shown in Table 1:

[0042] Table 1

[0043] 1 2 1000 6 1 3 1000 3 1 5 1500 3 2 4 500 10 2 9 2000 5 3 6 1500 4 4 5 500 6 5 6 1000 5 4 7 500 5 6 8 500 5 5 7 1000 3 5 8 1000 2 7 8 1000 5

[0044] Specifically, the train data includes train operating speed, fixed costs of train operation, variable costs of train operation, costs of train station operations, and train carrying capacity;

[0045] In this embodiment, the train operating speed is 250 km / h, the fixed cost of train operation is 8,000 yuan / train, the variable cost of train operation is 34.5 yuan / car, the cost of train station operation is 500 yuan / train, and the carrying capacity of each 8-car train is 120 tons / train.

[0046] Specifically, the freight flow data includes a freight flow number, the corresponding OD pair, and the delivery time limit of the freight flow. The OD represents two freight stations, one of which is the origin station and the other is the destination station. In this embodiment, the freight flow data is shown in Table 2.

[0047] Table 2

[0048]

[0049] Based on the above embodiments, this method further includes:

[0050] S2. Construct a train allocation constraint model based on the first information;

[0051] Specifically, step S2 includes:

[0052] S21. Construct time constraints based on interval data, train data, and freight flow data. The time constraints are used to constrain the earliest and latest arrival times of the trains.

[0053]

[0054] In the formula, G represents the set of cargo flows, E represents the set of running arcs, and g od Let T represent a flow of goods originating at points o and d, where g represents any single flow of goods. o Indicates the flow of goods g od The earliest time period during which transportation is possible; T′ d Indicates the flow of goods g od The earliest delivery time, T d Indicates the flow of goods g od The latest delivery time; t e This indicates the transport time of arc e. This indicates whether cargo flow g should select arc e for transportation. If selected, otherwise, This indicates whether the freight flow g selects the running arc e′ as the stopping arc. If selected, otherwise, t g t represents the transport time of arc e. e' This indicates the dwell time of the running arc e′.

[0055] S22. Construct interval throughput constraints based on interval data, wherein the interval throughput constraints are used to constrain the maximum number of trains passing through the interval in a preset time period;

[0056]

[0057] In the formula, i and j correspond to the freight station information of the running arc e, and i and j constitute a segment. This indicates whether the arc e provides transportation service in segment ij during time period t. If so, then... otherwise, This indicates the maximum throughput capacity of segment ij during time period t;

[0058] S23. Construct the constraint capability of the running arc based on the interval data, train data and freight flow data, wherein the running arc is a transportation path, and the constraint capability of the running arc is used to constrain the freight flow of the running arc to not exceed the maximum carrying capacity of the train;

[0059]

[0060] In the formula, This indicates whether cargo flow g selects arc e for transportation in segment ij, m ξ This represents the maximum carrying capacity when the number of train formations is ξ. This indicates the quantity of cargo flow selected for running arc e.

[0061] S24. Construct a train capacity constraint model based on train data. The train capacity constraint model is used to constrain the train to operate only when the freight capacity of the train is greater than the minimum carrying capacity.

[0062]

[0063] In the formula, This represents the minimum carrying capacity when the number of train formations is ξ. Indicates whether arc e provides transportation services during time period t;

[0064] S25. Construct a train stop count constraint model based on freight station data, section data, train data, and freight flow data. The train stop count constraint model is used to constrain the number of stops during train operation to not exceed the maximum number of stops.

[0065]

[0066] This indicates a cargo flow g with o and d as the origin and destination points. od Does it stop at station i? If so, then... otherwise P represents the maximum number of stops the train can make during its journey.

[0067] Furthermore, this embodiment also includes train stopping constraints:

[0068]

[0069] In this embodiment, each train stops at an intermediate station at most once. When a train stops at an intermediate station, the running arc becomes two arcs: running arc e and stopping arc e'. The departure time of e' differs from the departure time of e by a time interval tt. e Meanwhile, the terminal station of e is the starting station of e', where o e, Indicates the starting point of e', d e Indicates the endpoint of arc e. This indicates whether the arc e′ provides transportation services during time period t. Indicates the running arc e in tt e Whether transportation services are available during the specified time period.

[0070] Furthermore, this embodiment also includes:

[0071] Freight flow balance constraints at freight stations:

[0072]

[0073]

[0074]

[0075] In the formula, o e O represents the starting point of arc e. g d represents the starting point of the flow of goods g. e d represents the endpoint of arc e. g Let G represent the destination of the freight flow g, and V represent the set of freight station nodes.

[0076] Furthermore, this embodiment also includes logical relationship constraints:

[0077]

[0078]

[0079]

[0080] In the formula, M represents a preset positive number. Only when the running arc e is determined to provide transportation services can the cargo flow g select the running arc e for transportation activities.

[0081] Based on the above embodiments, this method further includes:

[0082] S3. Under the conditions of the train allocation constraint model, several initial schemes are randomly generated. Each initial scheme includes the train's operating route, operating time period, train formation type, and cargo loading flow.

[0083] Further, step S3 includes:

[0084] S31. Obtain alternative routes between any two freight stations based on freight station data and interval data. The alternative routes between the two freight stations are one or more, and each alternative route corresponds to a preset loading cargo flow.

[0085] In this embodiment, taking freight station A and freight station B as examples, the k-shortest path algorithm based on Dijkstra's algorithm is used to generate alternative paths between A and B, specifically including the following steps:

[0086] S311. Starting from A and ending at B, set up sets P and T. Let set A be used to store the freight stations and corresponding mileages of the alternative routes that have been found, and set T be used to store the freight stations for which the shortest path has not yet been found and the distance from the freight station to the starting point A. If the freight station is not adjacent to A, let the distance be ∞.

[0087] S312. Calculate the freight stations along the shortest path from A to B, and store the freight stations along the shortest path in set P as alternative paths from A to B.

[0088] S313. Remove B from set T;

[0089] S314. Starting from A and ending at C, repeat step S12 until all alternative routes between any two freight stations have been searched. In this embodiment, a total of 23 alternative routes are obtained, as shown in Table 3:

[0090] Table 3

[0091]

[0092] S32. Obtain the operating time slots included in the preset cycle. Specifically, let one day be a cycle, and the length of one operating time slot is 2 hours. After deducting the 4-hour high-speed railway comprehensive maintenance window, there are 10 operating time slots in one cycle.

[0093] S33. Under the conditions of the train allocation constraint model, one or more operating routes are randomly assigned for each operating period according to the alternative routes, and one operating route corresponds to one train.

[0094] S34. Obtain the loading cargo flow corresponding to the operating route, and randomly generate a train formation type for each train to obtain the operating plan for each train. The operating plan for a train includes the train formation type, the operating route corresponding to the train, the operating time period corresponding to the operating route, and the loading cargo flow.

[0095] S35. An initial plan is formed by the train operation plans for all operating periods within a preset cycle.

[0096] In this embodiment, an initial scheme is considered as a chromosome, as shown in Figure 3. The vertical axis of the chromosome represents the Nth opening period. In each opening period, the odd-numbered positions of the chromosome segments represent the opening path, and the even-numbered positions represent the grouping type of the opening path. If the value of the opening path is 1, it indicates that the opening path has been assigned, and the grouping type of the opening path is randomly generated. The grouping type is either 8 groups or 16 groups. If the value of the opening path is 0, it indicates that it has not been assigned, and the grouping type is also 0.

[0097] Based on the above embodiments, this method further includes:

[0098] S4. Construct an objective function with the goals of maximizing revenue and minimizing train travel time, and calculate the target value of the initial scheme using the objective function;

[0099] Specifically, step S4 includes:

[0100] S41. Construct the first model with the highest revenue and the second model with the shortest train running time, respectively;

[0101]

[0102] In the formula, c represents the revenue of the high-speed rail freight department, and p e f represents the unit transportation price for arc e; e Indicates the flow rate of the running arc e; dis e c represents the transport distance of the arc e; 固 ξ represents the fixed cost of operating a train; ξ represents the number of train formations: ξ = 8 indicates a single-formation train, ξ = 16 indicates a double-unit train; c e This represents the unit mileage cost of running arc e.

[0103]

[0104] In the formula, t represents the train travel time.

[0105] S42. The first and second models are normalized using the maximum-minimum method and then merged into the objective function;

[0106]

[0107] In the formula, c min Let c represent the minimum value. max t represents the maximum value of c; min Let t represent the minimum value of t. max c represents the maximum value of t. 0-1 t represents the value of c after normalization, which takes values ​​in the range (0,1). 0-1 This represents the value of t after normalization, which takes values ​​in the range (0,1).

[0108] minT=λ1c 0-1 +λ2t 0-1 (16)

[0109] In the formula, T represents the target value, λ1 represents the first weighting coefficient, and λ2 represents the second weighting coefficient;

[0110] In this embodiment, for the overall objective T, since the train allocation constraint model has guaranteed that the goods are delivered according to the delivery time limit, the primary objective is to minimize the total operating cost, and the secondary objective is to minimize the goods delivery time. Therefore, let λ1 = 0.8 and λ2 = 0.2.

[0111] S43. Calculate the target value for each initial scheme using the objective function.

[0112] Based on the above embodiments, this method further includes:

[0113] S5. Use a genetic algorithm to iteratively optimize several initial schemes to obtain the high-speed rail express train operation scheme with the minimum objective value.

[0114] Specifically, step S5 includes:

[0115] S51. An initial scheme set is formed by several of the aforementioned initial schemes;

[0116] In this embodiment, the initial scheme set includes 100 initial schemes. Let the initial scheme set be the initial population. Thus, the parent population size popsize = 100 can be obtained, and one initial scheme represents one individual.

[0117] S52. Perform a selection crossover operation on the initial schemes in the initial scheme set to generate a sub-scheme set with the same number of initial schemes as the initial scheme set;

[0118] Specifically, step S52 includes:

[0119] S521. Determine the probability of the initial solution being selected based on the objective value of the initial solution. Specifically, the objective function is transformed into an unconstrained optimization model using the external penalty function method. The fitness of the initial solution is calculated based on the objective value of the initial solution, and then the probability of the initial solution being selected is calculated based on the fitness. The smaller the objective value, the greater the fitness, that is, the better the initial solution, and the greater the probability of being selected. The initial solution will be selected as the parent generation to form new offspring more often. It should be noted that the calculation of the probability of selection based on fitness is existing technology and will not be elaborated here.

[0120] S522. Based on the probability of being selected, use the roulette wheel method to select two initial schemes from the initial scheme set and perform a crossover operation to generate a sub-scheme;

[0121] Specifically, the roulette wheel sampling method with replacement is used to select two individuals from the parent population as parents for crossover.

[0122] Set the crossover probability P crass =0.75;

[0123] Generate a uniformly distributed random number between 0 and 1. If the random number is less than the set P... crass Then, the two parent chromosomes are crossed over. For example, the entire T1 segment of chromosome 1 is crossed over with the entire T1 segment of chromosome 2 to obtain offspring of chromosome 1 and offspring of chromosome 2. Then, chromosome 1 and chromosome 2 are put back into the parent population.

[0124] S523. Store the sub-schemes in a sub-scheme set and repeat the crossover operation to generate sub-schemes until the number of sub-schemes in the sub-scheme set is the same as the number of initial schemes in the initial scheme set. The sub-scheme set represents the offspring population. Stop the selection crossover operation when the size of the offspring population is 100.

[0125] S524. Determine whether the sub-scheme satisfies the train allocation constraint model:

[0126] If not, discard the sub-solution and randomly generate an initial solution, which is then stored as a sub-solution in the sub-solution set.

[0127] If so, then the sub-scheme is retained.

[0128] S53. Perform mutation operations on the sub-solutions in the sub-solution set to obtain the mutated sub-solution set;

[0129] Specifically, step S53 includes:

[0130] S531. Randomly change the train operation path and / or formation type in the sub-scheme to obtain the modified sub-scheme;

[0131] One sub-scheme corresponds to one chromosome;

[0132] Obtain the probability P of chromosome mutation mutation =0.05;

[0133] Generate a uniformly distributed random number between 0 and 1. If the random number is less than P... mutation If so, then a mutation operation will be performed on the current chromosome;

[0134] The value of the open path in the chromosome is randomly changed. If the value is changed from 1 to 0, it means that the open path is not selected and the grouping type of the open path is modified to 0. If the value is changed from 0 to 1, it means that the open path is selected and the grouping type of the open path is modified to 8 or 16.

[0135] S532. Determine whether the optimized sub-scheme conforms to the train allocation constraint model:

[0136] If not, discard the optimized sub-solution and randomly generate an initial solution, which is then stored as a sub-solution in the optimized sub-solution set.

[0137] If so, then retain the optimized sub-solution.

[0138] S54. Determine whether the number of selection crossover operations has reached the set iteration number. In this embodiment, let the set iteration number Generation = 500:

[0139] If so, calculate the target value of each sub-scheme, obtain the sub-scheme with the smallest target value, and determine the train's route in the sub-scheme with the smallest target value as the train's route. The sub-scheme with the smallest target value is then used as the high-speed rail express train operation plan.

[0140] If not, calculate the target value for each sub-solution, record the minimum target value in the current sub-solution set, and repeat the selection and crossover operation on the sub-solutions in the sub-solution set.

[0141] like Figure 4 As shown, this embodiment obtains the target minimum value after 75 iterations. The sub-schemes corresponding to the target minimum value are decoded to obtain the high-speed rail express train operation and scheduling scheme, as shown in Tables 4 and 5.

[0142] Table 4

[0143] DH1 1 4 1 16 groups 1-2-4 H1, H6, H36 DH2 1 9 3 16 groups 1-2 H2,H13 DH3 1 2 4 8 groups 1-2 H3 DH4 1 3 7 8 groups 1-3 H5 DH5 1 4 4 8 groups 1-2-4 H7 DH6 1 5 2 8 groups 1-5 H8 DH7 1 5 6 8 groups 1-2-4-5 H9 DH8 1 5 4 8 groups 1-5 H10 DH9 1 8 2 16 groups 1-5-8 H10 DH10 1 8 4 16 groups 1-3-6-8 H4,H12 DH11 2 4 5 8 groups 2-4 H15 DH12 2 4 6 8 groups 2-4 H16 DH13 2 6 3 16 groups 2-4-5-6 H14, H17 DH14 2 6 5 16 groups 2-4-5-6 H18 DH15 2 8 2 16 groups 2-4-5-6-8 H19 DH16 2 8 5 8 groups 2-4-7-8 H20 DH17 2 9 5 8 groups 2-9 H21 DH18 3 6 6 8 groups 3-6 H23

[0144] Table 5

[0145] DH20 3 8 5 8 groups 3-6-8 H25 DH21 4 7 2 8 groups 4-7 H26 DH22 4 7 4 8 groups 4-7 H27 DH23 4 8 5 8 groups 4-7-8 H28 DH24 4 8 6 8 groups 4-5-8 H29 DH25 4 9 3 8 groups 4-2-9 H30 DH26 5 8 6 16 groups 5-8 H31 DH27 5 8 6 8 groups 5-6-8 H32 DH28 7 8 2 16 groups 7-8 H33 DH29 7 8 4 8 groups 7-8 H34 DH30 7 8 7 8 groups 7-8 H35

[0146] Example 2:

[0147] like Figure 5 , Figure 6 As shown, this embodiment provides a high-speed rail express train operation plan compilation device, the device comprising:

[0148] Acquisition Module 01: Acquires first information, which includes freight station data, interval data between two freight stations, train data, and freight flow data;

[0149] Constraint Model Construction Module 02: Constructs a train allocation constraint model based on the first information;

[0150] Generation Module 03: Randomly generates several initial schemes under the conditions of the train allocation constraint model. Each initial scheme includes the train's operating route, operating time period, train formation type, and loading cargo flow.

[0151] Objective function construction module 04: Constructs an objective function with the goals of maximizing revenue and minimizing train travel time, and calculates the target value of the initial scheme through the objective function;

[0152] Optimization Module 05: Uses a genetic algorithm to iteratively optimize several initial schemes to obtain the high-speed rail express train operation scheme with the minimum objective value.

[0153] Based on the above embodiments, the constraint model construction module 02 includes:

[0154] First construction unit 021: Construct time constraints based on interval data, train data and freight flow data. The time constraints are used to constrain the earliest and latest arrival times of the trains.

[0155] Second construction unit 022: Constructs interval throughput constraints based on interval data, wherein the interval throughput constraints are used to constrain the maximum number of trains passing through the interval in a preset time period;

[0156] The third construction unit 023: constructs the constraint capability of the running arc based on the interval data, train data and freight flow data. The running arc is a transportation path, and the constraint capability of the running arc is used to constrain the freight flow of the running arc to not exceed the maximum carrying capacity of the train.

[0157] Fourth construction unit 024: Construct a train capacity constraint model based on train data. The train capacity constraint model is used to constrain the train to operate when the freight capacity of the train is greater than the minimum carrying capacity.

[0158] Fifth Construction Unit 025: Construct a train stop count constraint model based on freight station data, section data, train data, and freight flow data. The train stop count constraint model is used to constrain the number of stops during train operation to not exceed the maximum number of stops.

[0159] Based on the above embodiments, the generation module 03 includes:

[0160] First acquisition unit 031: Based on freight station data and interval data, acquire alternative routes between any two freight stations. The alternative routes between the two freight stations are one or more, and each alternative route corresponds to a preset loading cargo flow.

[0161] Second acquisition unit 032: Acquires the operating time period included within a preset period;

[0162] Allocation Unit: Under the conditions of the train allocation constraint model, one or more operating routes are randomly allocated for each operating period according to the alternative routes, and one operating route corresponds to one train.

[0163] Generation Unit 033: Obtain the loading cargo flow corresponding to the operating route, and randomly generate a train formation type for each train to obtain the operating plan for each train. The operating plan for a train includes the train formation type, the operating route corresponding to the train, the operating time period corresponding to the operating route, and the loading cargo flow.

[0164] Initial scheme component unit 034: An initial scheme is formed by the train operation schemes for all operating periods within a preset cycle.

[0165] Based on the above embodiments, the objective function construction module 04 includes:

[0166] Sixth building unit 041: Construct the first model with the highest revenue and the second model with the shortest train running time, respectively;

[0167] Merging Unit 042: The first and second models are normalized using the maximum-minimum method and merged into the objective function;

[0168] Calculation unit 043: Calculates the target value for each initial scheme using the objective function.

[0169] Based on the above embodiments, the optimization module 05 includes:

[0170] Initial scheme set composition unit 051: The initial scheme set is composed of several of the initial schemes;

[0171] Select cross unit 052: Perform a cross operation on the initial schemes in the initial scheme set to generate a sub-scheme set with the same number of initial schemes as the initial scheme set;

[0172] Mutation Unit 053: Performs mutation operations on the sub-solutions in the sub-solution set to obtain the mutated sub-solution set;

[0173] First judgment unit 054: Determine whether the number of selection crossover operations has reached the preset number of iterations.

[0174] If so, calculate the target value of each sub-scheme, obtain the sub-scheme with the smallest target value, and determine the train's route in the sub-scheme with the smallest target value as the train's route. The sub-scheme with the smallest target value is then used as the high-speed rail express train operation plan.

[0175] If not, calculate the target value for each sub-solution and repeat the selection crossover operation on the sub-solutions in the sub-solution set.

[0176] Based on the above embodiments, the selection cross unit 052 includes:

[0177] Unit 0521: Determine the probability of the initial scheme being selected based on the target value of the initial scheme;

[0178] First operation unit 0522: Based on the probability of being selected, use the roulette wheel method to select two initial schemes in the initial scheme set and perform cross operations to generate sub-schemes;

[0179] Second operation unit 0523: Store the sub-scheme into the sub-scheme set, and repeatedly perform crossover operations to generate sub-schemes until the number of sub-schemes in the sub-scheme set is the same as the number of initial schemes in the initial scheme set.

[0180] Second judgment unit 0524: Determines whether the sub-scheme satisfies the train allocation constraint model.

[0181] If not, discard the sub-solution and randomly generate an initial solution, which is then stored as a sub-solution in the sub-solution set.

[0182] If so, then the sub-scheme is retained.

[0183] Based on the above embodiments, the mutation unit 053 includes:

[0184] Third operation unit 0531: Randomly change the train's operating path and / or formation type in the sub-scheme to obtain the modified sub-scheme;

[0185] Third Judgment Unit 0532: Determines whether the optimized sub-scheme conforms to the train allocation constraint model.

[0186] If not, discard the optimized sub-solution and randomly generate an initial solution, which is then stored as a sub-solution in the optimized sub-solution set.

[0187] If so, then retain the optimized sub-solution.

[0188] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0189] Example 3:

[0190] Corresponding to the above method embodiments, this embodiment also provides a high-speed rail express train operation plan compilation device. The high-speed rail express train operation plan compilation device described below and the high-speed rail express train operation plan compilation method described above can be referred to in correspondence.

[0191] Figure 7 This is a block diagram illustrating a high-speed rail express train operation plan compilation device 800 according to an exemplary embodiment. For example... Figure 7 As shown, the high-speed rail express train operation plan compilation device 800 may include: a processor 801 and a memory 802. The high-speed rail express train operation plan compilation device 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0192] The processor 801 controls the overall operation of the high-speed rail express train operation plan compilation device 800 to complete all or part of the steps in the high-speed rail express train operation plan compilation method described above. The memory 802 stores various types of data to support the operation of the high-speed rail express train operation plan compilation device 800. This data may include, for example, instructions for any application or method operating on the high-speed rail express train operation plan compilation device 800, as well as application-related data, such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the high-speed rail express train operation planning device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0193] In an exemplary embodiment, the high-speed rail express train operation plan preparation device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the high-speed rail express train operation plan preparation method described above.

[0194] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the high-speed rail express train operation plan preparation method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the high-speed rail express train operation plan preparation device 800 to complete the high-speed rail express train operation plan preparation method described above.

[0195] Example 4:

[0196] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the high-speed rail express train operation plan compilation method described above.

[0197] A readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the high-speed rail express train operation plan preparation method described in the above method embodiments.

[0198] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0199] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0200] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for compiling a high-speed rail express train operation plan, characterized in that, include: Obtain first information, which includes freight station data, interval data between two freight stations, train data, and freight flow data; Construct a train allocation constraint model based on the first piece of information; Several initial schemes are randomly generated under the constraints of the train allocation model. Each initial scheme includes the train's operating route, operating time, train formation type, and cargo loading flow. An objective function is constructed with the goals of maximizing revenue and minimizing train travel time as the objectives, and the target value of the initial scheme is calculated using the objective function. A genetic algorithm is used to iteratively optimize several initial schemes to obtain a high-speed rail express train operation scheme with the minimum objective value, including: The initial scheme set consists of several of the aforementioned initial schemes; Perform a selection crossover operation on the initial schemes in the initial scheme set to generate a sub-scheme set with the same number of initial schemes as the initial scheme set; Perform mutation operations on the sub-solutions in the sub-solution set to obtain the mutated sub-solution set; Determine if the number of crossover operations has reached the preset number of iterations: If so, calculate the target value of each sub-scheme, obtain the sub-scheme with the smallest target value, and determine the train's route in the sub-scheme with the smallest target value as the train's route. The sub-scheme with the smallest target value is then used as the high-speed rail express train operation plan. If not, calculate the target value for each sub-solution and repeat the selection crossover operation on the sub-solutions in the sub-solution set.

2. The method for compiling a high-speed rail express train operation plan according to claim 1, characterized in that... The process involves randomly generating several initial plans under the constraints of the train allocation model. Each initial plan includes the train's operating route, operating time, train formation type, and freight loading flow, including: Based on freight station data and interval data, alternative routes between any two freight stations are obtained. The alternative routes between the two freight stations can be one or more, and each alternative route corresponds to a preset loading cargo flow. Retrieve the operating time periods included within the preset period; Under the constraints of the train allocation model, one or more operating routes are randomly assigned for each operating period based on the alternative routes, and one operating route corresponds to one train. Obtain the loading cargo flow corresponding to the operating route, and randomly generate a train formation type for each train to obtain the operating plan for each train. The operating plan for a train includes the train formation type, the operating route corresponding to the train, the operating time period corresponding to the operating route, and the loading cargo flow. An initial plan is formed by the train operation plans for all operating periods within a preset cycle.

3. The method for compiling a high-speed rail express train operation plan according to claim 1, characterized in that... The objective function is constructed with the goals of maximizing railway sector revenue and minimizing train travel time. The objective function is then used to calculate the target value of the initial plan, including: Construct a first model that maximizes revenue and a second model that minimizes train travel time; The first and second models are normalized using the maximum-minimum method and then merged into the objective function. The objective value for each initial scheme is calculated using the objective function.

4. A high-speed rail express train operation plan compilation device, characterized in that, include: Acquisition module: Acquires first information, which includes freight station data, interval data between two freight stations, train data, and freight flow data; Constraint Model Construction Module: Constructs a train allocation constraint model based on the initial information; Generation module: Randomly generates several initial schemes under the conditions of the train allocation constraint model. An initial scheme includes the train's operating route, operating time period, train formation type, and loading cargo flow. Objective function construction module: Constructs an objective function with the goals of maximizing revenue and minimizing train travel time, and calculates the target value of the initial plan using the objective function; Optimization module: The genetic algorithm is used to iteratively optimize several initial schemes to obtain the high-speed rail express train operation scheme with the minimum objective value; The optimization module includes: Initial scheme set constituent unit: The initial scheme set is composed of several of the aforementioned initial schemes; Select crossover unit: Perform a crossover operation on the initial schemes in the initial scheme set to generate a sub-scheme set with the same number of initial schemes as the initial scheme set; Mutation unit: Performs mutation operations on the sub-solutions in the sub-solution set to obtain the mutated sub-solution set; First judgment unit: Determines whether the number of selection crossover operations has reached the preset number of iterations. If so, calculate the target value of each sub-scheme, obtain the sub-scheme with the smallest target value, and determine the train's route in the sub-scheme with the smallest target value as the train's route. The sub-scheme with the smallest target value is then used as the high-speed rail express train operation plan. If not, calculate the target value for each sub-solution and repeat the selection crossover operation on the sub-solutions in the sub-solution set.

5. The high-speed rail express train operation plan compilation device according to claim 4, characterized in that, The generation module includes: First acquisition unit: Based on freight station data and interval data, acquire alternative routes between any two freight stations. The alternative routes between the two freight stations can be one or more, and each alternative route corresponds to a preset loading cargo flow. Second acquisition unit: Acquires the operating time periods included within a preset period; Allocation Unit: Under the conditions of the train allocation constraint model, one or more operating routes are randomly allocated for each operating period according to the alternative routes, and one operating route corresponds to one train. Generation unit: Obtains the loading cargo flow corresponding to the operating route, and randomly generates a train formation type for each train to obtain the operating plan for each train. The operating plan for a train includes the train formation type, the operating route corresponding to the train, the operating time period corresponding to the operating route, and the loading cargo flow. Initial scheme component unit: An initial scheme is composed of the train operation schemes for all operating periods within a preset cycle.

6. The high-speed rail express train operation plan compilation device according to claim 4, characterized in that, The objective function construction module includes: The sixth building unit: Construct the first model with the highest revenue and the second model with the shortest train running time, respectively; Merging Units: The first and second models are normalized using the maximum-minimum method and merged into the objective function; Calculation unit: Calculates the target value for each initial scheme using the objective function.

7. A high-speed rail express train operation plan compilation device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the high-speed rail express train operation plan preparation method as described in any one of claims 1 to 3 when executing the computer program.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the high-speed rail express train operation plan preparation method as described in any one of claims 1 to 3.

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