Group plan and train operation plan matching method and system

Abstract

The invention provides a group plan and train operation plan matching method and system, and relates to the technical field of railway transportation plan compilation, and the method comprises the steps: carrying out the grouping of a plurality of unit trains based on a preset clustering rule, and obtaining a plurality of group train alternative sets; using the maximum on-site saved time as a first objective function to construct a group planning model as an upper layer planning model; constructing a lower-layer planning model by taking the shortest cargo in-transit travel time and the minimum discrete degree of the total arrival amount of the loaded vehicle in unit time as a second objective function; solving the upper-layer planning model to obtain an initial group plan, inputting the initial group plan into a lower-layer planning model, and solving the lower-layer planning model based on a non-dominated sorting genetic algorithm; according to the method, the running form of the heavy haul trains is optimized, the number of the heavy haul trains running on the line at the same time in unit time is increased, and the transportation efficiency and the transportation capacity of the heavy haul railway are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of railway transportation plan compilation, and in particular to a method and system for matching a group plan with a train operation plan. Background Art

[0002] Heavy-haul railways are one of the important modes of freight transportation in my country. They are characterized by heavy axle weight, large traction mass, and large transport volume. Most of them adopt unit and combined train formations. my country's existing heavy-haul trains have high transportation risks when performing operations such as combination, decomposition, giving way, and starting and stopping. These operations not only increase the time cost of transportation, but also increase the complexity and risk of operation. At the same time, with the growth of freight demand, heavy-haul railways have also encountered capacity constraints. Therefore, how to improve the transportation capacity of heavy-haul railways and reduce the time of combination and decomposition operations is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for matching a group plan with a train operation plan 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 matching a group plan with a train operation plan, comprising:

[0005] Acquire data of a number of unit trains, and group the unit trains based on a preset clustering rule to obtain a plurality of group train candidate sets;

[0006] Calculate the time saved by the group trains in the alternative set at the technical operation station, and build a group planning model as the upper-level planning model with the maximum saved time as the first objective function;

[0007] The lower-level planning model is constructed with the shortest travel time of goods and the smallest discrete degree of the total number of heavy vehicles arriving per unit time as the second objective function;

[0008] Solving the upper-level planning model to obtain an initial group plan, inputting the initial group plan into a lower-level planning model, and solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan;

[0009] The adjusted train arrival time is substituted into the upper-level planning model to update the group plan, and the updated group plan is repeatedly input into the lower-level planning model for iteration until a train operation plan that satisfies the second objective function is output.

[0010] In a second aspect, the present application also provides a group plan and train operation plan matching system, including:

[0011] Acquisition module: acquires data of several unit trains, and groups several unit trains based on preset clustering rules to obtain multiple group train candidate sets;

[0012] Calculation module: calculates the time saved by the group trains in the candidate set at the technical operation station, and constructs a group planning model as an upper-level planning model with the maximum saved time as the first objective function;

[0013] Model building module: Taking the shortest travel time of goods and the minimum discreteness of the total number of heavy vehicles arriving per unit time as the second objective function, the lower-level planning model is constructed;

[0014] Adjustment module: solving the upper-level planning model to obtain an initial group plan, inputting the initial group plan into the lower-level planning model, solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan;

[0015] Update module: Substitute the adjusted train arrival time into the upper-level planning model to update the group plan, and repeatedly input the updated group plan into the lower-level planning model for iteration until the output is a train operation plan that meets the second objective function.

[0016] In a third aspect, the present application also provides a group plan and train operation plan matching device, including:

[0017] Memory for storing computer programs;

[0018] A processor is used to implement the steps of the method for matching the group plan with the train operation plan when executing the computer program.

[0019] In a fourth aspect, the present application also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method based on matching group plans with train operation plans are implemented.

[0020] The beneficial effects of the present invention are:

[0021] The train group operation collaborative control technology adopted in the present invention breaks the physical connection mode of the couplers and realizes the virtual connection of heavy-load trains, which can effectively reduce the combination and decomposition operation time, while increasing the number of heavy-load trains running simultaneously on the line per unit time, shortening the tracking distance between trains, and thus improving the heavy-load railway transportation efficiency and transportation capacity, realizing the coordinated compilation of the heavy-load railway group plan and the train operation plan, optimizing the operation form of heavy-load trains, increasing the number of heavy-load trains running simultaneously on the line per unit time, and improving the heavy-load railway transportation efficiency and transportation capacity.

[0022] 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

[0023] 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.

[0024] Figure 1 A schematic diagram of the in-station operation process of a group train disclosed in an embodiment of the present invention;

[0025] Figure 2 The process diagram of the method for matching a group plan with a train operation plan described in an embodiment of the present invention is as follows Figure 1 ;

[0026] Figure 3 The process diagram of the method for matching a group plan with a train operation plan described in an embodiment of the present invention is as follows Figure 2 ;

[0027] Figure 4 is a schematic diagram of chromosome coding described in an embodiment of the present invention;

[0028] Figure 5 It is a schematic diagram of the structure of the group plan and train operation plan matching system described in an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of the group plan and train operation plan matching device described in an embodiment of the present invention.

[0030] Markings in the figure:

[0031] 800, group plan and train operation plan matching device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0032] 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.

[0033] 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.

[0034] Embodiment 1:

[0035] This embodiment provides a method for matching a group plan with a train operation plan.

[0036] Among them, the group plan is a plan proposed for the grouping and degrouping stations of group trains, group size, unit train group type, parking order of unit trains on arrival and departure lines, and group order within group trains through train group operation coordinated control and transport organization technology.

[0037] Train operation plan: a detailed plan for train operation schedule, route planning, stop arrangement and rational allocation of related transportation resources.

[0038] Unit train: A general term for existing heavy-haul train types on heavy-haul railways.

[0039] Group train: A train formed by two or more unit trains virtually connected through train group operation collaborative control technology.

[0040] Grouping: The process in which unit trains are virtually connected and combined into group trains through train group operation cooperative control technology at the station and then dispatched from the station.

[0041] Degrouping: The process of decomposing a group train into unit trains or partial group trains at a station through the train group operation cooperative control technology.

[0042] See also Figure 1The figure shows the process of grouping and degrouping. C1, C2 and C3 are three unit trains from the same departure station. They are grouped into a group train Q1 at the departure station and then run. The final destination of C1 and C2 is the same. When arriving at the final destination of C3, C3 is degrouped and enters the station arrival and departure line. The existing train C4 at the station is regrouped with C1 and C2 into group train Q1 and continues to run.

[0043] See also Figure 2 , Figure 3 , the figure shows that the method includes:

[0044] S1. Acquire data of several unit trains, and group several unit trains based on preset clustering rules to obtain multiple group train candidate sets;

[0045] Specifically, the step S1 includes:

[0046] S11. Obtain the number of unit trains and the operation information of the unit trains, as shown in Table 1 and Table 2, where Table 2 shows the train numbers and OD data tables of some heavy vehicles;

[0047] Table 1

[0048] Train 20002 20004 20006 20008 20010 20012 20014 Departure Station Dongsheng Dongsheng Hanjia Village Dongsheng Hanjia Village Hanjia Village Dongsheng Final destination Wannan South Field Wannan South Field Wannan South Field Wanbei North Field Wanbei North Field Wanbei North Field Wanbei North Field Train 20016 20018 20020 80002 80004 80006 80008 Departure Station Dongsheng Dongsheng Dongsheng Dongsheng Dongsheng Dongsheng Dongsheng Final destination Wanbei North Field Wanbei North Field Wanbei North Field Wannan South Field Wannan South Field Wannan South Field Wannan South Field

[0049] S12. Based on the operation information of the unit trains, the unit trains that meet the OD consistency or the unit trains that arrive at the group station for technical operation time that meets the preset overlap are included in the same set to obtain multiple group train candidate sets;

[0050] Specifically, the OD consistency includes giving priority to grouping unit trains with the same starting and ending stations. For trains with the same starting and ending stations, such trains can go directly to the terminal station after grouping at the starting loading station without any technical operations on the way, and are given priority to be included in the same group set; if the unit trains only have the same starting station or ending station, the latest arrival time of the unit trains needs to be considered.

[0051] S13. Number each group train and generate a group train number.

[0052] Based on the above embodiments, the method further includes:

[0053] S2. Calculate the time saved by the group trains in the candidate set at the technical operation station, and construct a group planning model as an upper-level planning model with the maximum time saved as the first objective function;

[0054] Specifically, step S2 includes:

[0055] S21. Calculate the first time saved when unit trains with the same departure station are grouped as group trains at loading stations Among them, i represents the unit train number, j represents the station number;

[0056] S22. Calculate the second time saved when the unit train passing through the loading station and the originating unit train group are group trains

[0057] S23. Calculate the third time saved by degrouping trains with different terminal stations

[0058] S24. Calculate the fourth time saved by degrouping trains with the same terminal station at the terminal station

[0059] S25. Calculate the sum of the first duration, the second duration, the third duration, and the fourth duration to obtain the time saved by the group train at the technical operation station;

[0060] Specifically, the first objective function is:

[0061]

[0062] In the formula, Z represents the total time saved, S represents the uplink, X represents the downlink, I represents the group train number, j0 represents the departure station, j0′ represents the final destination station, QS I represents the set of uplink group trains with group number I, QX I represents the set of downlink group trains with group number I, Indicates whether unit train i is selected to be included in the uplink group train I or the downlink group train I, is a 0-1 variable, which takes 1 when unit train i is included, otherwise it takes 0. They respectively indicate whether the up train group I or the down train group I is performing group operation at station j. If yes, the value is 1, otherwise it is 0. indicates whether the up train group I or the down train group I is being ungrouped at station j. It indicates whether the up train group I or the down train group I is performing group operation at the departure station j0. Indicates whether the up train group I or the down train group I is being disassembled at the final destination station j0′.

[0063] Based on the above embodiments, the method further includes:

[0064] S3. Taking the shortest travel time of goods in transit and the minimum discreteness of the total number of heavy vehicles arriving per unit time as the second objective function, the lower-level planning model is constructed;

[0065] Among them, the shortest travel time of goods in transit is expressed as:

[0066]

[0067] Where W represents the travel time of the goods in transit, M represents the group size of the group train, m is the number of trains planned to run in the same direction, both up and down, n represents the number of intervals, n+1 represents the number of stations, and m S represents the number of all up trains, where m S =m+1,t jqM / t jqcM They represent the technical operation time of grouping or ungrouping of a group train with a group size of M at station j, It indicates the waiting time of unit train i for the group; represents the time when the down train i arrives at station j; represents the time when down train i departs from station j; represents the actual occupancy corrected time of arrival of down train group I at station j; represents the actual occupancy corrected time of departure of down train group I from station j; It represents the pure running time of the unit train in the section (j, j+1) between stations j and j+1.

[0068] Specifically, the degree of discreteness of the total number of heavy vehicle arrivals per unit time is used to evaluate the degree of balance of the heavy vehicle flow arrival. The discrete rate of heavy vehicle flow arrival refers to dividing the unit time grid T (min), accumulating the number of instant arrival trains within all T in a day and night (group trains are converted into the number of unit trains), and finally performing variance analysis on the obtained number of trains. The smaller the discrete rate value, the more balanced the heavy vehicle arrival, and vice versa.

[0069] Among them, the minimum discrete degree of the total number of heavy vehicles arriving per unit time is expressed as:

[0070]

[0071] In the formula, the end time of the tth cycle in a day and night is expressed as T t ; Indicates that from T t-1 to T t The cumulative number of heavy vehicle trains arriving within the time period; Indicates t The average value of

[0072] in is a unit step function, indicating whether a train arrival time is within the time period (0, T t ), if The step value is incremented by 1, otherwise incremented by 0.

[0073] Based on the above embodiments, the method further includes:

[0074] S4. Solve the upper-level planning model to obtain an initial group plan, input the initial group plan into the lower-level planning model, and solve the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan. In this embodiment, solving the upper-level planning model can obtain several initial group plans, and a grouping scheme of one initial group plan is shown in Table 2;

[0075] Table 2

[0076]

[0077] Specifically, step S4 includes:

[0078] S41. Encoding the initial group plan into chromosomes and inputting them into the lower-level planning model;

[0079] For details, please refer to Figure 4 , the step S41 comprises:

[0080] S411. Get all unit trains in the initial group plan;

[0081] S412. Obtain the departure time, stop plan and arrival time of each unit train;

[0082] S413. Encode the departure time, stop plan and arrival time of all unit trains in the initial group plan into a chromosome:

[0083] R={R l |i,m1,m2,...,m n ,j};

[0084] Among them, R represents the train operation plan set, including the train departure and arrival times and stop conditions, i is the train departure time, m is the train stop condition, m1 represents the first station the train stops at, m n represents the nth station where the train stops, i.e. the second to last station, j is the final arrival time of the train, and l represents the lth train;

[0085] S42. constructing a fitness function according to the second objective function, and defining a dominance relationship of a solution based on the fitness function;

[0086] Specifically, the fitness function is expressed as:

[0087]

[0088] Among them, f1(x) and f2(x) are fitness functions, p1(x), p2(x), p3(x) are the adjustment values ​​of f1(x), p1′(x), p2′(x), p3′(x) are the adjustment values ​​of f2(x);

[0089] Specifically, the dominance relationship between solutions R and R′ is defined as follows: R dominates R′ if and only if any of the following two conditions are met: ①f1(R)>f1(R′)②f1(R)=f1(R′),f2(R)≥f2(R′);

[0090] S43. The sum of the distances between a chromosome individual and its adjacent chromosome individuals is taken as the chromosome individual crowding degree;

[0091] Since the absolute time difference between genes can directly reflect the degree of difference between chromosome individuals under this encoding method, the sum of the distances between each chromosome individual and its adjacent individuals is calculated as its crowding degree. At the same time, the distances between the beginning and end individuals and the adjacent individuals are considered infinite to ensure that they have a good crowding degree on the boundary, ensuring that the adjustment of the train arrival time is always no later than the latest arrival time of the last train. l =i,m1,m2,...,m n ,j, the distance between genes is |m n+1 -m n |, the congestion degree is in, represents the time difference between the middle individual and the starting individual, It represents the time difference between the middle individual and the end individual. A smaller crowding value means that the absolute time difference of the solutions around the individual is smaller, that is, the density of solutions near the individual is higher and has higher diversity.

[0092] S44. Based on the dominance relationship of the solution and the crowdedness of the chromosome, the genetic operator is used to optimize the chromosome;

[0093] Specifically, the step S44 includes:

[0094] S441. Calculate the fitness value of the chromosome individual, and calculate the probability of the second chromosome individual being selected based on the fitness value:

[0095] Specifically, the roulette wheel method is used for selection, and the probability of each individual being selected is

[0096] Among them, popsize represents the population size, f b (x a ) represents individual x a The fitness value of

[0097] S442. Randomly select the crossover point of two chromosome individuals, and exchange the gene segments between the crossover points to generate a new chromosome. Preferably, the fixed crossover probability is Pc;

[0098] In this embodiment, individuals are selected for crossover operation to generate new individuals so that the population can achieve the purpose of evolution. Partial mapping crossover is used to ensure the consistency of the driving plan of each train at different stations. The starting point and the end point of the crossover of two chromosomes are randomly selected, and then the gene segments between the crossover points are exchanged. Finally, the crossover segments are traversed, and the values ​​repeated in the crossover segments in the source chromosome are found in the target chromosome and replaced with the corresponding values.

[0099] S443. traverse the crossover segment of the new chromosome, find the duplicate values ​​in the crossover segment and the original chromosome and replace them;

[0100] S444. Randomly select genes in the new chromosome for mutation to obtain an optimized chromosome. In this embodiment, the fixed mutation probability is Pm.

[0101] S45. When the new population E is less than the preset discreteness E ξ Or when the actual number of iterations reaches the maximum number of iterations maxgen, determine whether the optimized chromosome is the optimal solution of the lower-level planning model:

[0102] If so, the optimal group plan is obtained after decoding the chromosome;

[0103] Otherwise, output the optimized train arrival time.

[0104] In this embodiment, a partial train schedule of the optimal group plan is shown in Table 3:

[0105] Table 3

[0106] Train Types Train Departure Station Departure time Final destination The final moment Travel time Overload 20002 Dongsheng 0:31 Wannan South Field 2:09 1:38 Overload 20004 Dongsheng 0:34 Wannan South Field 2:12 1:38 Overload 20006 Dongsheng 1:12 Wannan South Field 15:05 1:38 Overload 20008 Dongsheng 3:31 Wanbei North Field 23:41 1:44 Overload 20014 Dongsheng 6:06 Wanbei North Field 9:25 1:44 Overload 20016 Dongsheng 7:41 Wanbei North Field 12:40 2:39 Overload 20018 Dongsheng 7:44 Wanbei North Field 19:08 1:58 Overload 20020 Dongsheng 9:13 Wanbei North Field 0:42 1:44 Overload 80010 Dongsheng 13:06 Wannan South Field 6:41 1:38

[0107] Based on the above embodiments, the method further includes:

[0108] S5. Substitute the adjusted train arrival time into the upper-level planning model to update the group plan, and repeatedly input the updated group plan into the lower-level planning model to iterate until the output is a train operation plan that satisfies the second objective function, that is, the new population E is less than the preset discrete degree E ξ .

[0109] In this embodiment, the optimal group plan obtained is compared with the normal operation mode, and the comparison results are shown in Table 4:

[0110] Table 4

[0111]

[0112] According to the comparison results, the optimal group plan in this embodiment reduces the total in-transit time by 15 hours compared with the normal operation mode, and the optimization rate reaches 12%, which can better meet the timeliness requirements of the goods. At the same time, it meets the needs of balanced arrival of heavy vehicles and matching of heavy and empty vehicles, and realizes the orderly transportation of heavy-load trains under the group transportation mode.

[0113] Embodiment 2:

[0114] like Figure 5 As shown, this embodiment provides a group plan and train operation plan matching system, the system comprising:

[0115] Acquisition module: acquires data of several unit trains, and groups several unit trains based on preset clustering rules to obtain multiple group train candidate sets;

[0116] Calculation module: calculates the time saved by the group trains in the candidate set at the technical operation station, and constructs a group planning model as an upper-level planning model with the maximum saved time as the first objective function;

[0117] Model building module: Taking the shortest travel time of goods in transit and the minimum discrete degree of the total number of heavy vehicles arriving per unit time as the second objective function, the lower-level planning model is constructed;

[0118] Adjustment module: solving the upper-level planning model to obtain an initial group plan, inputting the initial group plan into the lower-level planning model, solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan;

[0119] Update module: Substitute the adjusted train arrival time into the upper-level planning model to update the group plan, and repeatedly input the updated group plan into the lower-level planning model for iteration until the output is a train operation plan that meets the second objective function.

[0120] Based on the above embodiment, the acquisition module includes:

[0121] The first acquisition unit: acquires the number of unit trains and the operation information of the unit trains;

[0122] Unit division: Based on the operation information of unit trains, unit trains that meet OD consistency or unit trains whose technical operation time meets the preset overlap are included in the same set to obtain multiple group train candidate sets;

[0123] Numbering unit: Number each group train and generate a group train number.

[0124] Based on the above embodiment, the calculation module includes:

[0125] The first calculation unit: calculates the first time saved when the unit trains with the same departure station are grouped as group trains at the loading station;

[0126] The second calculation unit is used to calculate the second time saved when the unit train passing through the loading station and the originating unit train are grouped into a group train;

[0127] The third calculation unit is used for calculating the third time saved by degrouping trains with different terminal stations;

[0128] The fourth calculation unit is used for calculating the fourth time saved by degrouping trains with the same final destination at the final destination;

[0129] The fifth calculation unit: calculates the sum of the first duration, the second duration, the third duration and the fourth duration to obtain the time saved by the group train at the technical operation station.

[0130] Based on the above embodiment, the adjustment module includes:

[0131] Input unit: encoding the initial group plan into chromosomes and inputting them into the lower-level planning model;

[0132] Function construction unit: constructing a fitness function according to the second objective function, and defining a dominance relationship of a solution based on the fitness function;

[0133] The sixth calculation unit: taking the sum of the distances between the chromosome individual and its adjacent chromosome individuals as the chromosome individual crowding degree;

[0134] Optimization unit: Based on the dominance relationship of the solution and the crowdedness of the chromosome, the genetic operator is used to optimize the chromosome;

[0135] Judgment unit: judge whether the optimized chromosome is the optimal solution of the lower-level planning model:

[0136] If so, the optimal group plan is obtained after decoding the chromosome;

[0137] Otherwise, output the optimized train arrival time.

[0138] Based on the above embodiment, the input unit includes:

[0139] Second, obtain the unit: obtain all unit trains in the initial group plan;

[0140] The third acquisition unit: obtains the departure time, stop plan and final arrival time of each unit train;

[0141] Coding unit: Encode the departure time, stop plan and arrival time of all unit trains in the initial group plan into a chromosome.

[0142] Based on the above embodiment, the optimization unit includes:

[0143] The seventh calculation unit: calculates the fitness value of the chromosome individual, and calculates the probability of the second chromosome individual being selected according to the fitness value;

[0144] Crossover unit: randomly select the crossover point of two chromosome individuals, and exchange the gene segments between the crossover points to generate a new chromosome;

[0145] Traversal unit: traverse the crossover segment of the new chromosome, find the duplicate values ​​in the crossover segment and the original chromosome and replace them;

[0146] Gene mutation unit: randomly select genes in the new chromosome for mutation to obtain the optimized chromosome.

[0147] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0148] Embodiment 3:

[0149] Corresponding to the above method embodiment, this embodiment also provides a group plan and train operation plan matching device. The group plan and train operation plan matching device described below and the group plan and train operation plan matching method described above can be referenced to each other.

[0150] Figure 6 8 is a block diagram of a group plan and train operation plan matching device 800 according to an exemplary embodiment. Figure 6 As shown, the group plan and train operation plan matching device 800 may include: a processor 801 and a memory 802. The group plan and train operation plan matching device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0151] The processor 801 is used to control the overall operation of the group plan and train operation plan matching device 800 to complete all or part of the steps in the above-mentioned group plan and train operation plan matching method. The memory 802 is used to store various types of data to support the operation of the group plan and train operation plan matching device 800, and these data may include, for example, instructions for any application or method operating on the group plan and train operation plan matching 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 group plan and train operation plan matching device 800 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.

[0152] In an exemplary embodiment, the group plan and train operation plan matching device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, 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 to execute the above-mentioned group plan and train operation plan matching method.

[0153] 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 group plan and train operation plan matching method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the group plan and train operation plan matching device 800 to complete the above-mentioned group plan and train operation plan matching method.

[0154] Embodiment 4:

[0155] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the group plan and train operation plan matching method described above can be referred to each other.

[0156] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for matching a group plan with a train operation plan in the above method embodiment.

[0157] 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.

[0158] 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.

[0159] 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 matching a group plan with a train operation plan, characterized in that: include: Acquire data of a number of unit trains, and group the unit trains based on a preset clustering rule to obtain a plurality of group train candidate sets; Calculate the time saved by the group trains in the alternative set at the technical operation station, and build a group planning model as the upper-level planning model with the maximum saved time as the first objective function; The lower-level planning model is constructed with the shortest travel time of goods and the smallest discrete degree of the total number of heavy vehicles arriving per unit time as the second objective function; Solving the upper-level planning model to obtain an initial group plan, inputting the initial group plan into a lower-level planning model, and solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan; The adjusted train arrival time is substituted into the upper-level planning model to update the group plan, and the updated group plan is repeatedly input into the lower-level planning model for iteration until a train operation plan that satisfies the second objective function is output.

2. The method for matching a group plan with a train operation plan according to claim 1, characterized in that , obtain data of several unit trains, group several unit trains based on preset clustering rules to obtain multiple group train candidate sets, including: Obtain the number of unit trains and the operation information of unit trains; Based on the operation information of unit trains, unit trains that meet OD consistency or unit trains whose technical operation time meets the preset overlap are included in the same set to obtain multiple group train candidate sets; Each group train is numbered to generate a group train number.

3. The method for matching a group plan with a train operation plan according to claim 1, characterized in that , calculate the time saved by the group trains in the alternative set at the technical operation station, including: Calculate the first time saved when unit trains with the same departure station are grouped as group trains at the loading station; Calculate the second time saved when the unit train passing through the loading station and the originating unit train are grouped as a group train; Calculate the third time saved by ungrouping trains with different final destinations; Calculate the fourth time saved by degrouping trains with the same final destination at the final destination; The time saved by the group train at the technical operation station is obtained by calculating the sum of the first duration, the second duration, the third duration and the fourth duration.

4. The method for matching a group plan with a train operation plan according to claim 1, characterized in that , inputting the initial group plan into the lower-level planning model, solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan, including: Encoding the initial group plan into chromosomes and inputting them into the lower-level planning model; constructing a fitness function according to the second objective function, and defining a dominance relationship of a solution based on the fitness function; The sum of the distances between a chromosome individual and its adjacent chromosome individuals is taken as the chromosome individual crowding degree; Based on the dominance relationship of the solution and the crowdedness of the chromosome, the genetic operator is used to optimize the chromosome. Determine whether the optimized chromosome is the optimal solution of the lower-level planning model: If so, the optimal group plan is obtained after decoding the chromosome; Otherwise, output the optimized train arrival time.

5. The method for matching group plan and train operation plan according to claim 4, characterized in that , encoding the initial group plan into a chromosome, including: Get all unit trains in the initial group plan; Obtain the departure time, stop plan and arrival time of each unit train; The departure time, stop plan and arrival time of all unit trains in the initial group plan are encoded into a chromosome.

6. A group plan and train operation plan matching system, characterized in that: include: Acquisition module: acquires data of several unit trains, and groups several unit trains based on preset clustering rules to obtain multiple group train candidate sets; Calculation module: calculates the time saved by the group trains in the candidate set at the technical operation station, and constructs a group planning model as an upper-level planning model with the maximum saved time as the first objective function; Model building module: Taking the shortest travel time of goods in transit and the minimum discrete degree of the total number of heavy vehicles arriving per unit time as the second objective function, the lower-level planning model is constructed; Adjustment module: solving the upper-level planning model to obtain an initial group plan, inputting the initial group plan into the lower-level planning model, solving the lower-level planning model based on a non-dominated sorting genetic algorithm with an elite strategy to adjust the train arrival time in the initial group plan; Update module: Substitute the adjusted train arrival time into the upper-level planning model to update the group plan, and repeatedly input the updated group plan into the lower-level planning model for iteration until the output is a train operation plan that meets the second objective function.

7. The group plan and train operation plan matching system according to claim 6, characterized in that: The acquisition module comprises: The first acquisition unit: acquires the number of unit trains and the operation information of the unit trains; Unit division: Based on the operation information of unit trains, unit trains that meet OD consistency or unit trains whose technical operation time meets the preset overlap are included in the same set to obtain multiple group train candidate sets; Numbering unit: Number each group train and generate a group train number.

8. The group plan and train operation plan matching system according to claim 6, characterized in that: The calculation module comprises: The first calculation unit: calculates the first time saved when the unit trains with the same departure station are grouped as group trains at the loading station; The second calculation unit is used to calculate the second time saved when the unit train passing through the loading station and the originating unit train are grouped into a group train; The third calculation unit is used for calculating the third time saved by degrouping trains with different terminal stations; The fourth calculation unit is used for calculating the fourth time saved by degrouping trains with the same final destination at the final destination; The fifth calculation unit: calculates the sum of the first duration, the second duration, the third duration and the fourth duration to obtain the time saved by the group train at the technical operation station.

9. The group plan and train operation plan matching system according to claim 6, characterized in that: The adjustment module comprises: Input unit: encoding the initial group plan into chromosomes and inputting them into the lower-level planning model; Function construction unit: constructing a fitness function according to the second objective function, and defining a dominance relationship of a solution based on the fitness function; The sixth calculation unit: taking the sum of the distances between the chromosome individual and its adjacent chromosome individuals as the chromosome individual crowding degree; Optimization unit: Based on the dominance relationship of the solution and the crowdedness of the chromosome, the genetic operator is used to optimize the chromosome; Judgment unit: judge whether the optimized chromosome is the optimal solution of the lower-level planning model: If so, the optimal group plan is obtained after decoding the chromosome; Otherwise, output the optimized train arrival time.

10. The group plan and train operation plan matching system according to claim 9, characterized in that: The input unit comprises: Second, obtain the unit: obtain all unit trains in the initial group plan; The third acquisition unit: obtains the departure time, stop plan and final arrival time of each unit train; Coding unit: Encode the departure time, stop plan and arrival time of all unit trains in the initial group plan into a chromosome.

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