Group train working diagram compilation method, device and equipment and medium

Abstract

The invention provides a group train working diagram compilation method, device and equipment and a medium, and relates to the technical field of computer traffic application. Comprising the steps that group interval time and group turn-back time are constructed based on a preset single-line heavy haul railway train operation constraint model, and a multi-target planning operation diagram compilation model is obtained; processing a particle swarm of the multi-objective planning running diagram compilation model, then performing optimization updating, constructing an adaptive multi-objective particle swarm algorithm, and performing optimization iteration on the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm to obtain an optimal running time table; and outputting the optimal operation timetable according to the special group train operation line and a preset locomotive turnover diagram drawing method to obtain an optimal heavy haul railway group train operation diagram. The problem that in the prior art, due to the fact that coupling and parking of a combined heavy-load train are conducted, the operation interval time is long, and the heavy-load train organized in a station is unfavorable in running is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer traffic application, and in particular to a method, device, equipment and medium for compiling a train group operation diagram. Background Art

[0002] In the existing field of computer traffic application technology, an inter-station block operation mode is generally adopted. In the inter-station block operation mode, the coupling, stopping and letting operation intervals of the combined heavy-load trains are long, resulting in tight single-line capacity and being unfavorable for the station to organize the heavy-load trains. Therefore, a method, device, equipment and medium for compiling a group train operation diagram are urgently needed, which solves the problem in the prior art that the coupling, stopping and letting operation intervals of the combined heavy-load trains are long, resulting in tight single-line capacity and being unfavorable for the station to organize the heavy-load trains, and ensures the safety of the group trains during operation. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and medium for compiling a train group operation diagram to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a method for compiling a train group operation diagram, comprising:

[0005] Acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time;

[0006] The group interval time and the group return time are constructed based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model;

[0007] Performing random initialization processing on the particle swarm of the multi-objective planning operation diagram compilation model to obtain multiple train operation line models;

[0008] Performing Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set;

[0009] Based on the Pareto optimal solution set, determining whether to optimize and update the Pareto optimal solution set, when optimizing and updating the Pareto optimal solution set, constructing an adaptive multi-objective particle swarm algorithm, optimizing and iterating the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm, and obtaining an optimal operation schedule;

[0010] The optimal operation schedule is output according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

[0011] In a second aspect, the present application also provides a device for compiling a train group operation diagram, comprising:

[0012] An acquisition module, used to acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time;

[0013] The first construction module is used to construct the group interval time and the group return time based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model;

[0014] The second construction module is used to randomly initialize the particle swarm of the operation diagram compilation model of the multi-objective planning to obtain multiple train operation line models;

[0015] A third building module is used to perform Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set;

[0016] A fourth construction module is used to determine whether to optimize and update the Pareto optimal solution set based on the Pareto optimal solution set, and when optimizing and updating the Pareto optimal solution set, construct an adaptive multi-objective particle swarm algorithm, and optimize and iterate the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule;

[0017] The fifth construction module is used to output the optimal operation schedule according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

[0018] In a third aspect, the present application also provides a train group operation diagram compilation device, comprising:

[0019] Memory for storing computer programs;

[0020] A processor is used to implement the steps of the method for compiling an operation diagram of a group of trains when executing the computer program.

[0021] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for compiling an operation diagram based on group trains are implemented.

[0022] The beneficial effects of the present invention are:

[0023] The present invention introduces a single-line heavy-load railway train operation constraint model and an adaptive multi-objective particle swarm algorithm, constructs group interval time and group return time through the single-line heavy-load railway train operation constraint model, obtains a multi-objective planning operation diagram compilation model, initializes the particle swarm of the multi-objective planning operation diagram compilation model, and then optimizes it through Pareto optimal solution and the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule, outputs the optimal operation schedule according to a dedicated operation line and a turnover diagram laying method, and obtains an optimal heavy-load railway group train operation diagram, which solves the problem in the prior art that the coupling, parking and stopping operation intervals of combined heavy-load trains are long, resulting in tight single-line line capacity and being unfavorable for the station to organize the operation of heavy-load trains, and ensures the safety of group trains during operation.

[0024] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 It is a schematic flow chart of a method for compiling a train group operation diagram according to an embodiment of the present invention;

[0027] Figure 2 The present invention is a schematic diagram of the structure of a device for compiling a train group operation diagram according to an embodiment of the present invention.

[0028] Markings in the figure: 800, operation diagram compilation equipment for group trains; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] Embodiment 1:

[0032] This embodiment provides a method for compiling an operation diagram of a group of trains.

[0033] See also Figure 1 , the figure shows that the method includes steps S1 to S6, including:

[0034] S1: Acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time;

[0035] In this step, the group interval time is:

[0036]

[0037] In the above formula (1): Denotes the group train set K q The total interval time within a group when the group size is n, Denotes group train k γ Unit train and The departure tracking interval between them; Denotes group train k γ Unit train and The interval tracking interval between; Denotes group train k γ Unit train and The arrival tracking interval between .

[0038] In this step, the group return time includes the time consumed by the continuous operation between group locomotives and the time consumed by the continuous operation between ordinary locomotives;

[0039] The formula for the time spent on the successive operation between the group locomotives is:

[0040]

[0041] In the above formula (2): Indicates that group locomotive u completes group train k γ Unit train With group train k γ+1 Unit train The time spent on connecting operations; Denotes group train k γ+1 Unit train In the train γ+1 The departure time corresponding to the originating station; Denotes group train k γ Unit train In the train γ The arrival time corresponding to the final destination station.

[0042] lmwk indicates the time consumed by the subsequent operation.

[0043] The formula for the time consumed by the continuous operation between ordinary locomotives is:

[0044]

[0045]

[0046] In the above formula (3): Indicates that ordinary rolling stock v completes ordinary train k γ Compared with ordinary trains γ+1 The time spent on connecting operations; represents ordinary train k γ+1 In the train γ+1 The departure time corresponding to the originating station; represents ordinary train k γ In the train γ The arrival time corresponding to the final destination station.

[0047] lmwk indicates the time consumed by the subsequent operation.

[0048] S2: constructing the group interval time and the group turnaround time based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model;

[0049] In order to clarify the specific method of obtaining the operation diagram compilation model of the multi-objective planning, step S2 includes S21 to S25, which are specifically:

[0050] S21: Obtain the departure time of the train at the first station and the arrival time of the train at the first station;

[0051] S22: Calculating based on the arrival time of the train at the first station and the preset arrival time of the train at the second station to obtain the train section running time;

[0052] In this step, the formula for the train section running time is:

[0053]

[0054] In the above formula (4): Indicates that in a group of size n, train k is in the interval (z a ,z b ) interval running time; means train k is at station z b The corresponding arrival time; means train k is at station z a The corresponding departure time; Denotes the group train set K q The total interval time within a group when the group size is n.

[0055] The train k is at station z b The corresponding arrival time represents the arrival time of the train at the first station; the train k arrives at station z a The corresponding departure time indicates the arrival time of the train at the second station.

[0056] S23: Calculating based on the departure time of the train at the first station and the arrival time of the train at the first station to obtain the train stop time;

[0057] In this step, the formula for the train stop time is:

[0058]

[0059] In the above formula (5): represents a 0-1 variable, train k is at station z a The value is 1 when the car is parked, otherwise it is 0; Indicates that group train k of group size n is at station z aStop time; means train k is at station z a The corresponding departure time; means train k is at station z a The corresponding arrival time; Denotes the group train set k q The total interval time within a group when the group size is n.

[0060] The train k is at station z a The corresponding departure time represents the departure time of the train at the first station; the train k at station z a The corresponding arrival time indicates the arrival time of the train at the first station.

[0061] S24: Taking the shortest train stop time and the shortest locomotive turnaround time as the objective function, a single-track heavy-haul railway train operation constraint model is constructed based on the preset group different train crossing constraints and the preset locomotive vehicle connection uniqueness constraints;

[0062] In this step, the formula for the shortest train stop time is:

[0063]

[0064] In the above formula (6): MinZ1 represents the minimum value of the station set Z1; means train k is at station z a The corresponding stop time; represents a 0-1 variable, train j is at station z a The value is 1 when the train stops, otherwise it is 0. J represents the train set; Z represents the station set.

[0065] The formula for the shortest locomotive turnaround operation time is:

[0066]

[0067] In the above formula (7): MinZ2 represents the minimum value of the station set Z2; Indicates that group locomotive u completes group train k γ Unit train With group train k γ+1 Unit train The time spent on connecting operations; Indicates a 0-1 variable, a group train unit car and group train unit cars The value is 1 when the connection is made through the group locomotive and vehicle u, otherwise it is 0; Indicates that group locomotive v completes group train k γ With group train kγ+1 The time spent on connecting operations; represents a 0-1 variable, ordinary train k γ and ordinary trains γ+1 The value is 1 when the connection is made through a common rolling stock v, otherwise it is 0.

[0068] lmwk indicates the time consumed by the subsequent operation.

[0069] The formula for the intersection constraint of different trains in the group is:

[0070] On a single-track railway, when trains in opposite directions are about to meet, if one train passes without stopping, the other train needs to give way in the station. In this model, the up train is assumed to be a loaded train and the down train is an empty train. The constraint is:

[0071] When n≥m(n,m∈N + )hour

[0072]

[0073] In the above formula (8): Indicates an up train At the station a The corresponding arrival time, Indicates down train At the station a The corresponding arrival time; Indicates the time standard for different arrival intervals; Denotes the group train set K q The total interval time within a group when the group size is n; Indicates down train At the station a The corresponding departure time; Indicates an up train At the station a The corresponding departure time; Indicates the time between vehicles. Indicates the up train in the up train set K 上 Indicates the collection of up trains; represents the downlink train in the downlink train set K 下 Indicates the set of down trains.

[0074] The formula for the locomotive vehicle connection uniqueness constraint is:

[0075]

[0076] In the above formula (9): Indicates a 0-1 variable, a group train unit car and group train unit cars The value is 1 when the connection is made through the group locomotive and vehicle u, otherwise it is 0; represents a 0-1 variable, ordinary train k γ and ordinary trains γ+1 The value is 1 when the connection is made through a common rolling stock v, otherwise it is 0; Represents a group train unit car in a group unit train set and group train unit cars represents the group unit train set; u represents the group locomotive vehicle; k γ+1 represents the ordinary train k in the ordinary train set γ and ordinary trains γ+1 ; K p represents a set of ordinary trains; v represents ordinary locomotives and vehicles.

[0077] The single-track heavy-load railway train operation constraint model also includes: train interval operation time constraints, train stop time constraints, station interval time constraints, train pair selection locomotive and vehicle uniqueness constraints, locomotive and vehicle connection time constraints, locomotive and vehicle connection uniqueness constraints, same-direction train prohibition of overtaking constraints, crossing rule constraints, skylight time driving constraints and train prohibition and train must stop constraints.

[0078] The station interval time constraints include the interval time constraints for consecutive train departures in the same direction, the interval time constraints for trains arriving and departing at different times in the same direction, the interval time constraints for trains arriving and departing at different times in the same direction, the meeting interval time constraints and the interval time constraints for trains arriving at different times; the same-direction train prohibition overtaking constraints include section crossing overtaking and station overtaking.

[0079] S25: Based on the single-track heavy-load railway train operation constraint model, the group interval time, the group return time, the train section operation time and the train stop time are constructed to obtain a multi-objective planning operation diagram compilation model.

[0080] S3: performing random initialization processing on the particle swarm of the multi-objective planning operation diagram compilation model to obtain multiple train operation line models;

[0081] The particle group consists of a plurality of particles.

[0082] In order to clarify the specific method of obtaining multiple train line models, step S3 includes S31 to S35, which are:

[0083] S31: Obtain the number of group trains, the number of ordinary trains and the number of stations;

[0084] S32: Calculate the preset number of particles to obtain random train arrival and departure times;

[0085] In this step, Python introduces random numbers and defines the function "Generate Random Timetable Function" to generate random train arrival and departure times.

[0086] S33: Solving the number of group trains, the number of ordinary trains, the number of stations and the random train arrival and departure times to obtain a train timetable;

[0087] In order to clarify the specific method of obtaining the train timetable, step S33 includes S331 to S335, which are:

[0088] S331: Obtaining train time window;

[0089] S332: Generate the number of group trains and the number of ordinary trains to obtain a new group and ordinary train timetable;

[0090] In this step, an outer loop is performed in Python to generate the number of group trains and the number of ordinary trains respectively, and a new group and ordinary train timetable is generated each time.

[0091] S333: Generate the number of sites to obtain a random time for each site;

[0092] In this step, the inner loop in Python generates the number of sites, generates a random time for each site, and obtains the random time for each site.

[0093] S334: Generate the train time window to obtain the time when the train arrives at the station;

[0094] In this step, the inner loop in Python generates random integers within the train time window to obtain the time when the train arrives at the station.

[0095] S335: Construct a train schedule based on the new group and ordinary train schedule, the random time of each station, the time when the train arrives at the station and the random train arrival and departure time to obtain a train schedule.

[0096] S34: judging the train schedule according to the single-track heavy-haul railway train operation constraint model, and obtaining the initial position of the particle when the train schedule satisfies the single-track heavy-haul railway train operation constraint model;

[0097] In this step, all constraints are traversed through Python code to determine whether the train schedule is satisfied. If any constraint is not satisfied, "False" is returned; if all constraints are satisfied, "True" is returned to obtain an initial feasible solution. The initial feasible solution is solved through a loop algorithm to obtain the initial position of the particle.

[0098] S35: randomly generate a plurality of train line models in a preset four-dimensional solution space based on the initial positions of the particles.

[0099] In this step, the four-dimensional solution space is the set space of solutions of four independent variables.

[0100] S4: performing Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set;

[0101] The plurality of train route models include initial velocities of particles.

[0102] In order to clarify the specific method of obtaining the Pareto optimal solution set, step S4 includes S41 to S44, which are:

[0103] S41: judging the initial position and the initial velocity of the particle based on the objective function value of the preset particle to obtain a non-dominated solution;

[0104] A target particle is obtained according to the initial position of the particle and the initial velocity of the particle, an objective function value of the particle is calculated for a plurality of the target particles, and a non-dominated solution is obtained by making a judgment based on the objective function value of the particle.

[0105] S42: inputting the non-dominated solution into a preset Pareto solution set;

[0106] S43: calculating the particles of the Pareto solution set based on the plurality of particles, the objective function values ​​of the preset particles and the maximum value of the objective function of the preset particles to obtain a dynamic dense distance of the particles;

[0107] The objective function value of the particle includes the objective function value of the first particle and the objective function value of the second particle, and the maximum value of the objective function of the particle includes the maximum value of the objective function of the first particle and the maximum value of the objective function of the second particle;

[0108] In this step, the formula for the dynamic dense distance of the particles is:

[0109]

[0110] In the above formula (10): I(x i ) represents particle x iThe dense distance; f1(x j ) represents the objective function value of the first previous particle; f1(x k ) represents the objective function value of the first next particle; f2(x j ) represents the objective function value of the second previous particle; f2(x k ) represents the objective function value of the next particle; represents the maximum value of the objective function of the first particle; The maximum value of the objective function for the second particle.

[0111] x i Represents the current particle; x j Previous particle; x k Next particle.

[0112] S44: judging the dynamic dense distance of the particles according to a preset distance constraint condition to obtain a Pareto optimal solution set.

[0113] In this step, the dynamic dense distance sorting method is used to judge the dynamic dense distance of the particles through the preset distance constraint condition, retain the solutions with a longer distance, eliminate the solutions with a closer distance, and update the Pareto solution set with the retained solutions to obtain the Pareto optimal solution set. It is ensured that after the algorithm is iterated multiple times, the number of Pareto optimal solutions does not exceed the number of particle swarms initially preset.

[0114] S5: Based on the Pareto optimal solution set, determine whether to optimize and update the Pareto optimal solution set. When optimizing and updating the Pareto optimal solution set, construct an adaptive multi-objective particle swarm algorithm, and optimize and iterate the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule.

[0115] In order to clarify the specific method of obtaining the optimal operation schedule, step S5 includes S51 to S54, which are:

[0116] S51: Based on the interval time preset in the Pareto optimal solution set and the maximum interval time preset in the Pareto optimal solution set, a judgment is made, and when the interval time preset in the Pareto optimal solution set is not equal to the maximum interval time preset in the Pareto optimal solution set, the Pareto optimal solution set is optimized and updated;

[0117] S52: Adaptively construct based on a preset multi-objective particle swarm algorithm to obtain an adaptive inertia weight and an adaptive learning factor;

[0118] In this step, the adaptive inertia weight ensures that in the early stage of iteration, particles focus more on exploring the search space, and as the number of iterations increases, particles focus more on using the current search information for mapping. The adaptive learning factor ensures that in the early stage of iteration, particles focus more on the individual optimal solution, and as the number of iterations increases, particles focus more on the global optimal solution.

[0119] S53: constructing based on the adaptive inertia weight, the adaptive learning factor and a preset genetic algorithm to obtain an adaptive multi-objective particle swarm algorithm;

[0120] S54: Optimizing and iterating the Pareto optimal solution set according to the adaptive multi-objective particle swarm algorithm to obtain an optimal operating schedule.

[0121] In order to clarify the specific method of obtaining the optimal operation schedule, step S54 includes S541 to S543, which are:

[0122] S541: updating the multiple train line models based on the adaptive inertia weight and the adaptive learning factor to obtain updated particles;

[0123] The updated particles include updated particle velocities and updated particle positions.

[0124] In order to clarify the specific method of obtaining the updated particle speed and the updated particle position, step S541 includes S5411 to S5413, which are specifically:

[0125] S5411: performing calculation based on the adaptive learning factor, the adaptive inertia weight and a threshold of a preset inertia weight to obtain an update formula for particle velocity;

[0126] In this step, the updating formula of the particle velocity is:

[0127]

[0128] In the above formula (11): V i a+1 Represents the update formula of particle velocity; ω a Indicates the inertia weight value at the current number of iterations; ω max Represents the maximum value of inertia weight; ω min Indicates the minimum value of the inertia weight; A indicates the maximum number of iterations; a indicates the number of current iterations; represents cognitive learning factor; Indicates the maximum value of cognitive learning factor; represents the minimum value of cognitive learning factor; represents the social learning factor; represents the minimum value of the social learning factor; Represents the maximum value of the social learning factor.

[0129] Preferably, the maximum value of the inertia weight is 0.9; the minimum value of the inertia weight is 0.4.

[0130] S5412: updating the initial velocity of the particle according to the particle velocity update formula to obtain an updated velocity of the particle;

[0131] S5413: Calculate the moving distance of the initial position of the particle based on the speed of the updated particle to obtain the position of the updated particle.

[0132] In this step, the formula for the updated particle position is:

[0133]

[0134] In the above formula (12): Indicates the updated position of the particle; represents the initial position of the particle; V i a+1 The update formula representing the particle velocity.

[0135] S542: performing crossover and mutation operations on the updated particles based on a preset genetic algorithm to obtain replaced particles;

[0136] In order to clarify the specific method of obtaining the replaced particles, step S542 includes S5421 to S5427, which are:

[0137] S5421: performing crossover and mutation operations on the updated particles based on a preset genetic algorithm to obtain a crossover rate of the updated particles and a mutation rate of the updated particles;

[0138] In this step, p c represents the crossover rate of particles after update; p m Indicates the mutation rate of particles after update.

[0139] S5422: In each iteration process, randomly select two particles from the updated particles for iteration;

[0140] S5423: constructing the two particles according to the preset crossover weight to obtain a crossover operation formula;

[0141] The two particles include a randomly selected first particle and a randomly selected second particle;

[0142] In this step, the crossover operation formula is:

[0143]

[0144] In the above formula (13), x′ i represents the new particle generated by the first crossover operation; x′ j represents the new particle generated by the second crossover operation; α represents a randomly selected number in the interval [0,1] as the crossover weight; x i represents the first particle randomly selected; x j represents a randomly selected second particle;

[0145] The randomly selected number in the interval [0,1] as the cross weight represents the preset cross weight.

[0146] S5424: When the crossover rate of the updated particles meets the preset target crossover rate, a crossover operation is performed, and the two particles are calculated based on the crossover operation formula to obtain particles after the crossover operation;

[0147] In this step, when the crossover rate of the updated particles is the preset target crossover rate, the crossover operation is performed. When the crossover rate of the updated particles is 1-p c Keep the two particles unchanged.

[0148] S5425: performing calculation based on the particles after the crossover operation and the particles after the preset mutation operation to obtain a mutation operation formula;

[0149] In this step, the mutation operation formula is:

[0150] x" i,mn = x′ i,mn +β·24·(2r-1) (14)

[0151] In the above formula (14), x" i,mn Represents the mutated particle x′ i,mn The value of the element in the mth row and nth column; x′ i,mn Represents the new particle x′ generated by the crossover operation i The value of the element in the mth row and nth column; β represents the coefficient of variation; 24 represents the difference between the maximum and minimum values ​​of the element in the mth row and nth column; r represents a random number in the interval [0,1].

[0152] S5426: When the mutation rate of the updated particles meets the preset target mutation rate, a mutation operation is performed, and the particles after the crossover operation are calculated based on the mutation operation formula to obtain particles after the crossover and mutation;

[0153] In this step, when the mutation rate of the updated particle is the preset target mutation rate, the mutation operation is performed. m No mutation operation is performed.

[0154] S5427: Replace the two randomly selected particles with the crossover and mutated particles to obtain replaced particles.

[0155] S543: Input the replaced particles into the Pareto optimal solution set for iteration, and determine whether the number of iterations meets the preset number of iterations. If not, continue to iterate. If it meets the preset number of iterations, stop iterating, and output the optimal operation schedule.

[0156] S6: Outputting the optimal operation schedule according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

[0157] In order to clarify the specific method of obtaining the optimal heavy-haul railway group train operation diagram, step S6 includes S61 to S65, which are specifically:

[0158] S61: Obtaining the departure time of a group train, where the departure time of the group train includes the departure time of the first train of the group and the departure time of the last train of the group;

[0159] S62: Calculate according to the group interval time, the group turnaround time and the preset train group station to obtain the group train that needs to be operated;

[0160] S63: scheduling the group trains to be operated based on a preset scheduling model to obtain updated group train numbers;

[0161] In this step, the group car numbers are Q1 to QN; the group unit car numbers are respectively specified as Q1-1 to Q1-N, Q2-1 to Q2-N, ..., QN-1 to QN-N according to the group car numbers.

[0162] S64: Based on the departure time of the first train of the group, the departure time of the last train of the group and the group interval time, a dedicated running line for group trains is obtained;

[0163] In this step, no matter how many unit trains there are in the group, only a black single line is used to indicate the departure time of the first train in the group and another black single line is used to indicate the departure time of the last train in the group. A gray shadow is used between the two black single lines to indicate the total interval time between the trains in the group.

[0164] S65: Solving the optimal operation schedule and the updated group train number according to the preset locomotive turnover diagram drawing method and the group train dedicated operation line to obtain the optimal heavy-load railway group train operation diagram.

[0165] In this step, since group trains are basically operated in pairs, the group train locomotive route drawing is completed first according to the principle of continuing to operate the group train in the group train locomotive turnaround section to facilitate the operation of the group train locomotive, and after the group train locomotive operation diagram is completed, the ordinary train operation diagram is drawn. When the group locomotive turnover is drawn, on the basis of the group train dedicated operation line, the different unit train locomotives of the group train of a larger group scale can be divided into different unit train locomotives of the group train of a smaller group scale (the reverse direction is also possible).

[0166] Embodiment 2:

[0167] This embodiment provides a device for compiling a train group operation diagram, the device comprising:

[0168] An acquisition module, used to acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time;

[0169] The first construction module is used to construct the group interval time and the group return time based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model;

[0170] In order to clarify the specific acquisition methods of the operation diagram compilation model for multi-objective planning, the specific methods are as follows:

[0171] A first acquisition unit, used to acquire the departure time of the train at the first station and the arrival time of the train at the first station;

[0172] A first calculation unit, configured to calculate based on the arrival time of the train at the first station and the preset arrival time of the train at the second station to obtain the train section running time;

[0173] A second calculation unit is used to calculate based on the departure time of the train at the first station and the arrival time of the train at the first station to obtain the train stop time;

[0174] The first construction unit is used to take the shortest train stop time and the shortest locomotive turnaround operation time as the objective function, and to construct based on the preset group different train crossing constraints and the preset locomotive vehicle connection uniqueness constraints to obtain a single-line heavy-duty railway train operation constraint model;

[0175] The second construction unit is used to construct the group interval time, the group return time, the train section running time and the train stop time based on the single-line heavy-load railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model.

[0176] The second construction module is used to randomly initialize the particle swarm of the operation diagram compilation model of the multi-objective planning to obtain multiple train operation line models;

[0177] A third building module is used to perform Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set;

[0178] A fourth construction module is used to determine whether to optimize and update the Pareto optimal solution set based on the Pareto optimal solution set, and when optimizing and updating the Pareto optimal solution set, construct an adaptive multi-objective particle swarm algorithm, and optimize and iterate the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule;

[0179] The fifth construction module is used to output the optimal operation schedule according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

[0180] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0181] Embodiment 3:

[0182] Corresponding to the above method embodiment, this embodiment also provides a group train operation diagram compilation device, and the group train operation diagram compilation device described below and the group train operation diagram compilation method described above can correspond to each other.

[0183] Figure 2 FIG. 8 is a block diagram of a train group operation diagram preparation device 800 according to an exemplary embodiment. Figure 2 As shown, the train group operation diagram preparation device 800 may include: a processor 801 and a memory 802. The train group operation diagram preparation device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0184] The processor 801 is used to control the overall operation of the group train operation diagram preparation device 800 to complete all or part of the steps in the above-mentioned group train operation diagram preparation method. The memory 802 is used to store various types of data to support the operation of the group train operation diagram preparation device 800, and these data may include, for example, instructions for any application or method operated on the group train operation diagram preparation device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by 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 memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, 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 signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the operation diagram compilation device 800 of the group train and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0185] In an exemplary embodiment, the operation diagram compilation device 800 for a group train can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned method for compiling the operation diagram of the group train.

[0186] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for compiling a train group operation diagram are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the train group operation diagram compilation device 800 to complete the above-mentioned method for compiling a train group operation diagram.

[0187] Embodiment 4:

[0188] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the method for compiling a train group operation diagram described above can refer to each other.

[0189] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for compiling a train group operation diagram of the above method embodiment.

[0190] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0192] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for compiling a train group operation diagram, characterized in that: include: Acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time; The group interval time and the group return time are constructed based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model; Performing random initialization processing on the particle swarm of the multi-objective planning operation diagram compilation model to obtain multiple train operation line models; Performing Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set; Based on the Pareto optimal solution set, determining whether to optimize and update the Pareto optimal solution set, when optimizing and updating the Pareto optimal solution set, constructing an adaptive multi-objective particle swarm algorithm, optimizing and iterating the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm, and obtaining an optimal operation schedule; The optimal operation schedule is output according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

2. The method for compiling a train group operation diagram according to claim 1 is characterized in that the group interval time and the group return time are constructed based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective programming operation diagram compilation model, including: Get the departure time of the train at the first station and the arrival time of the train at the first station; Calculating based on the arrival time of the train at the first station and the preset arrival time of the train at the second station to obtain the train section running time; Calculating based on the departure time of the train at the first station and the arrival time of the train at the first station to obtain the train stop time; Taking the shortest train stop time and the shortest locomotive turnaround time as the objective function, a single-track heavy-haul railway train operation constraint model is constructed based on the preset group different train crossing constraints and the preset locomotive vehicle connection uniqueness constraints; Based on the single-line heavy-haul railway train operation constraint model, the group interval time, the group return time, the train section operation time and the train stop time are constructed to obtain a multi-objective planning operation diagram compilation model.

3. The method for compiling a train group diagram according to claim 2, characterized in that a particle swarm of the multi-objective programming diagram compilation model is randomly initialized to obtain a plurality of train line models, wherein the particle swarm is composed of a plurality of particles, including: Get the number of group trains, ordinary trains and stations; Calculate the preset number of particles to obtain random train arrival and departure times; Solving the number of group trains, the number of ordinary trains, the number of stations and the random train arrival and departure times to obtain a train timetable; The train timetable is judged according to the single-track heavy-haul railway train operation constraint model, and when the train timetable satisfies the single-track heavy-haul railway train operation constraint model, an initial position of the particle is obtained; Based on the initial position of the particle, a preset four-dimensional solution space is randomly generated to obtain multiple train line models, and the four-dimensional solution space is a collection space of four independent variable solutions.

4. The method for compiling a train operation diagram of a group of trains according to claim 3 is characterized in that, based on the Pareto optimal solution set, it is determined whether to optimize and update the Pareto optimal solution set, and when optimizing and updating the Pareto optimal solution set, an adaptive multi-objective particle swarm algorithm is constructed, and the Pareto optimal solution set is optimized and iterated based on the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule, including: Based on the interval time preset in the Pareto optimal solution set and the maximum interval time preset in the Pareto optimal solution set, a judgment is made, and when the interval time preset in the Pareto optimal solution set is not equal to the maximum interval time preset in the Pareto optimal solution set, the Pareto optimal solution set is optimized and updated; Adaptive construction is performed based on the preset multi-objective particle swarm algorithm to obtain adaptive inertia weight and adaptive learning factor; Based on the adaptive inertia weight, the adaptive learning factor and a preset genetic algorithm, an adaptive multi-objective particle swarm algorithm is constructed to obtain; The Pareto optimal solution set is optimized and iterated according to the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule.

5. The method for compiling a train group operation diagram according to claim 4, characterized in that the Pareto optimal solution set is optimized and iterated according to the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule, comprising: updating the multiple train route models based on the adaptive inertia weight and the adaptive learning factor to obtain updated particles; Performing crossover and mutation operations on the updated particles based on a preset genetic algorithm to obtain replaced particles; The replaced particles are input into the Pareto optimal solution set for iteration, and it is determined whether the number of iterations meets the preset number of iterations. If it does not meet the preset number of iterations, the iteration is continued. If it meets the preset number of iterations, the iteration is stopped, and the optimal operation schedule is output.

6. The method for compiling a group train operation diagram according to claim 1, characterized in that the optimal operation schedule is output according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-haul railway group train operation diagram, comprising: Obtaining the departure time of a group train, wherein the departure time of the group train includes the departure time of the first train of the group and the departure time of the last train of the group; Calculate according to the group interval time, the group turnaround time and the preset train group station to obtain the group train that needs to be operated; The group trains to be operated are scheduled based on a preset scheduling model to obtain updated group train numbers; Based on the departure time of the first train of the group, the departure time of the last train of the group and the group interval time, a dedicated running line for group trains is obtained; The optimal operation timetable and the updated group train number are solved according to the preset locomotive turnover diagram laying method and the group train dedicated operation line to obtain the optimal heavy-load railway group train operation diagram.

7. A device for compiling a train group operation diagram, characterized in that: include: An acquisition module, used to acquire train operation information and group train dedicated operation lines, wherein the train operation information includes group interval time and group return time; The first construction module is used to construct the group interval time and the group return time based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model; The second construction module is used to randomly initialize the particle swarm of the operation diagram compilation model of the multi-objective planning to obtain multiple train operation line models; A third building module is used to perform Pareto optimal solutions on the multiple train operation line models to obtain a Pareto optimal solution set; A fourth construction module is used to determine whether to optimize and update the Pareto optimal solution set based on the Pareto optimal solution set, and when optimizing and updating the Pareto optimal solution set, construct an adaptive multi-objective particle swarm algorithm, and optimize and iterate the Pareto optimal solution set based on the adaptive multi-objective particle swarm algorithm to obtain an optimal operation schedule; The fifth construction module is used to output the optimal operation schedule according to the group train dedicated operation line and the preset locomotive turnover diagram laying method to obtain the optimal heavy-load railway group train operation diagram.

8. The operation diagram compilation device for group trains according to claim 7 is characterized in that the first construction module is used to construct the group interval time and the group return time based on a preset single-line heavy-haul railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model, including: A first acquisition unit, used to acquire the departure time of the train at the first station and the arrival time of the train at the first station; A first calculation unit, configured to calculate based on the arrival time of the train at the first station and the preset arrival time of the train at the second station to obtain the train section running time; A second calculation unit is used to calculate based on the departure time of the train at the first station and the arrival time of the train at the first station to obtain the train stop time; The first construction unit is used to take the shortest train stop time and the shortest locomotive turnaround operation time as the objective function, and to construct based on the preset group different train crossing constraints and the preset locomotive vehicle connection uniqueness constraints to obtain a single-line heavy-duty railway train operation constraint model; The second construction unit is used to construct the group interval time, the group return time, the train section running time and the train stop time based on the single-line heavy-load railway train operation constraint model to obtain a multi-objective planning operation diagram compilation model.

9. A train group operation diagram preparation device, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of the method for compiling a train group operation diagram as claimed in any one of claims 1 to 6 when executing the computer program.

10. A readable storage medium, characterized in that The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for compiling a train group operation diagram as described in any one of claims 1 to 6 are implemented.

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