A freight train operation scheme compiling method, device and equipment and storage medium

Abstract

The application provides a freight train operation scheme compiling method, device and equipment and a storage medium, and relates to the technical field of heavy-load railway freight transportation organization. The application compiles and optimizes the Shenghua railway heavy and empty car operation scheme, obtains the train operation scheme of the heavy and empty car transportation organization operation time, train operation quantity, operation type, operation time period and operation section, has the advantages of unified modeling standard, high calculation efficiency and comprehensive consideration of factors, etc. In the design of the method, the daily dynamic change of the freight transportation demand is combined, the influence of the empty car allocation on the satisfaction of the loading demand is considered, the dynamic coordination is used to compile and optimize the heavy car operation scheme and the empty car allocation scheme, the dynamic freight train operation scheme compiling optimization model is established by taking the minimization of the heavy car transportation operation time and the loading demand error time as the target, and the two-stage genetic-tabu search algorithm is designed, so that the freight train operation scheme can be well solved.

Description

[0001] Technical Field Images

[0002] This invention relates to the field of heavy-haul railway freight transport organization technology, and more specifically, to a method, apparatus, equipment, and storage medium for compiling freight train operation plans. Background Technology

[0003] Shenhua Railway is a crucial bridge for the State Energy Group to achieve integrated production, transportation, and sales. Upstream, there is a large volume of train traffic, and the timing of freight volume and demand is uncertain. When train traffic is not closely coordinated across different sections, it can lead to various problems such as uneven arrival and departure times at stations, difficulties in train connections, underutilization of operating lines in different sections, and train delays. Furthermore, Shenhua Railway's heavy-haul transport capacity is relatively strained, and the existing transport organization plan cannot meet the dynamic changes in market freight transport demand. This prevents trains from operating according to freight train schedules, affecting train efficiency and freight delivery deadlines. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, device, and storage medium for compiling freight train operation plans, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for compiling a freight train operation plan, including:

[0006] The system acquires cargo flow data and vehicle flow data related to cargo transportation demand, calculates them, and constructs a heavy-load train operation plan model. The heavy-load train operation plan model is used to describe the dynamic relationship between the cargo flow data and the vehicle flow data.

[0007] Based on the calculation of the heavy train operation scheme model, the demand for empty cars at each station is determined, and an empty car allocation model is constructed. The empty car allocation model is used to describe the relationship between the demand for empty cars and the actual loading capacity of goods.

[0008] The empty car dispatching model and the loaded car group operation scheme model are solved to obtain the compiled freight train operation scheme.

[0009] Secondly, this application also provides a freight train operation plan compilation device, including a loaded car operation module, an empty car dispatching module, and a solution module, wherein:

[0010] The loaded vehicle operation module is used to acquire freight flow data and vehicle flow data of cargo transportation demand, calculate and construct a loaded vehicle group operation plan model, which is used to describe the dynamic relationship between the freight flow data and the vehicle flow data.

[0011] Empty car dispatching module: It is used to calculate and determine the demand for empty cars at each station based on the loaded train operation scheme model, and to construct an empty car dispatching model. The empty car dispatching model is used to describe the relationship between the demand for empty cars and the actual loading capacity of goods.

[0012] The solution module is used to solve the empty car dispatching model and the loaded car group operation scheme model to obtain the compiled freight train operation scheme.

[0013] Thirdly, this application also provides a freight train operation plan compilation device, including:

[0014] Memory, used to store computer programs;

[0015] A processor is used to implement the steps of the freight train operation plan preparation method when executing the computer program.

[0016] Fourthly, this application also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for compiling freight train operation plans.

[0017] The beneficial effects of this invention are as follows:

[0018] This invention, through the development and optimization of heavy and empty train operation plans for Shenhua Railway, obtains the operational time, number of trains, types of trains, operating times, and operating sections for heavy and empty train transportation. The modeling process is simple, the modeling standards are unified, the method is computationally efficient, reliable, and considers all factors, exhibiting good operability and versatility. In particular, the proposed theory and method for dynamically developing and optimizing freight train operation plans, based on dynamically changing freight transportation demands, is of great significance. By considering dynamic freight transportation demands, the invention rationally organizes the entire heavy-haul train transportation process, determines the heavy-haul train operation mode, meets the transportation needs of freight owners, ensures timely delivery of goods, leverages the role of Shenhua Railway as a link in the integrated production, transportation, and sales model, guarantees the smooth operation of Shenhua Railway's production and sales departments, and maximizes the economic benefits of the enterprise. In terms of methodology, considering the dynamic changes in daily freight transport demand and the impact of empty car dispatching on meeting loading needs, a dynamic freight train operation plan optimization model is established based on the idea of ​​dynamic collaborative compilation of optimized loaded train operation and empty car dispatching plans. With the goal of minimizing the error time between loaded train transport operation and loading demand, a two-stage genetic-tabu search algorithm is designed, which can effectively solve the freight train operation plan. The results obtained have certain reference value for guiding Shenhua Railway to optimize its transport organization model.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the freight train operation plan preparation method described in this embodiment of the invention;

[0022] Figure 2 This is a schematic diagram of the freight train operation plan compilation device described in this embodiment of the invention;

[0023] Figure 3 This is a schematic diagram of the equipment structure for compiling freight train operation plans as described in this embodiment of the invention;

[0024] Figure 4 This is a schematic diagram of the encoding method for the loaded vehicle operation scheme in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the method for generating an initial feasible solution for a loaded vehicle operation scheme in an embodiment of the present invention;

[0026] Figure 6 This is a method for representing the initial solution of the empty vehicle dispatching model in an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the method for generating an initial feasible solution for an empty vehicle dispatching scheme in an embodiment of the present invention.

[0028] In the diagram: 710 - Loaded vehicle operation module; 711 - First acquisition unit; 712 - First construction unit; 713 - Second construction unit; 714 - Third construction unit; 715 - Fourth construction unit; 720 - Empty vehicle dispatching module; 721 - First calculation unit; 722 - Second acquisition unit; 723 - Fifth construction unit; 724 - Sixth construction unit; 725 - Seventh construction unit; 730 - Solving module; 731 - Eighth construction unit; 732 - Second calculation unit; 800 - Freight train operation plan compilation equipment; 801 - Processor; 802 - Memory; 803 - Multimedia component; 804 - I / O interface; 805 - Communication component. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] Example 1:

[0032] This embodiment provides a method for compiling freight train operation plans.

[0033] See Figure 1 The figure shows that the method includes steps S1, S2 and S3.

[0034] Step S1: Obtain cargo flow data and vehicle flow data of cargo transportation demand, calculate and construct a heavy-load train operation plan model, which is used to describe the dynamic relationship between the cargo flow data and the vehicle flow data.

[0035] Understandably, this step involves acquiring daily dynamic freight and vehicle flow data for Shenhua Railway from the detection system. Based on this data, a fitting process is performed to characterize the dynamic changes in freight and vehicle flow under Shenhua Heavy-Haul Railway conditions, and a calculation formula for organizing heavy-haul train operations on Shenhua Railway based on dynamic freight flow is constructed.

[0036] Furthermore, step S1 above includes steps S11, S12, S13, S14 and S15.

[0037] Step S11: Based on the freight flow data and the vehicle flow data, obtain the set of loaded vehicle information and the loading capacity data of each station; the set of loaded vehicle information is a collection of all loaded vehicle information, and each piece of loaded vehicle information includes the transport time information, route station information and load information of the loaded vehicle; each loaded vehicle is a train planned and arranged for transporting goods based on the freight flow data.

[0038] Step S12: Based on the set of loaded vehicle information and the loading capacity data of each station, calculate and construct the train operation mode conditions and the first constraint conditions respectively. The first constraint conditions include the train quantity balance condition, the station combination operation capacity condition, and the loaded vehicle operation direction throughput capacity condition.

[0039] Understandably, in this step, if the total number of stations in the transit station information is less than three, it indicates that the loaded train is going directly and does not need to go to a transfer point for cargo loading, thus establishing the conditions for a direct train operation mode. If the total number of stations in the transit station information is greater than or equal to three, it indicates that the loaded train needs to transfer at other stations, thus establishing the conditions for a combined train operation mode, and constraining the operation plan according to the first constraint condition.

[0040] The method for constructing train operation mode conditions includes steps S12, namely steps S121, S122, and S123.

[0041] Step S121: Obtain a first parameter, a second parameter, and a third parameter based on the loaded vehicle information set. The first parameter includes the departure point and arrival point of the goods. The second parameter is the set of stations along the route of each train corresponding to the first parameter. The set of stations includes at least the departure station and the unloading station. The third parameter is the time period information of the loaded vehicle arriving at each of the stations.

[0042] Step S122: Based on the first parameter, the second parameter, the third parameter, and the loading capacity data of each station, calculate and construct a station loading quantity model.

[0043] It is understandable that, in this step, the loading capacity of different types of trains varies at each station of Shenhua Railway due to the influence of facility and equipment capacity. Stations that meet the facility and equipment capacity requirements and arrival / departure track lengths can directly load 10,000-ton trains; otherwise, they can only load 5,000-ton unit trains. Furthermore, there is an upper limit to the loading capacity of different types of trains at each loading station. Therefore, the total number of various types of trains planned to be operated cannot exceed the corresponding maximum loading capacity and the number of available empty cars at that station. The station loading quantity model describes the relationship between the loading capacity and the number of loaded cars at each station of Shenhua Railway. The station loading quantity model corresponding to the direct train operation mode is shown in formula (1), and the station loading quantity model corresponding to the combined train operation mode is shown in formula (2).

[0044]

[0045]

[0046] In formulas (1) and (2): I represents the set of loading stations, i is the index of the loading station, i∈I; J represents the set of unloading stations, j is the index of the unloading station, j∈J; Z represents the set of combined stations (stations with transfer stations), z is the index of the combined station, z∈Z; T represents the set of decision time periods (time periods when loaded vehicles arrive at the station), t is the index of the decision time period, t∈T; X represents the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit reorders of loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit reorders of loading stations, N2={1,2,...,b,...,B}; This indicates the loading capacity of loading station i for 5000t trains; This indicates the loading capacity of loading station i for 10,000-ton trains; As a 0-1 variable, when train type x, unit 5000t, operates a direct train from station i to j during time period t. Otherwise, take 0; For variables of type 0-1, when unit a of vehicle type x is reassigned from station i to station z for combination during time period t, the result is 5000t. Otherwise, take 0; As a 0-1 variable, when train type x, with 10,000 units per t, operates a direct train from station i to j during time period t. Otherwise, take 0; As a 0-1 variable, when vehicle type x, with 10,000 units per t in time period t, is re-assigned from station i to station z for combination. Otherwise, take 0.

[0047] Step S123: Based on the station loading quantity model and preset departure conditions, construct the train operation mode conditions.

[0048] It is understandable that in this step, the uniqueness constraint of the train operation mode conditions for transporting goods from loading station i to unloading station j after loading includes two types: one is the constraint of choosing between direct and combined operation for 5000t unit trains, as shown in formula (3); the other is the constraint of choosing between direct and combined operation for 10,000t unit trains, as shown in formula (4).

[0049]

[0050]

[0051] In formulas (3) and (4): I represents the set of loading stations, i is the index of the loading station, i∈I; J represents the set of unloading stations, j is the index of the unloading station, j∈J; Z represents the set of combined stations (stations with transfer stations), z is the index of the combined station, z∈Z; T represents the set of decision time periods (time periods when loaded vehicles arrive at the station), t is the index of the decision time period; X represents the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit re-columns of loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit re-columns of loading stations, N2={1,2,...,b,...,B}; As a 0-1 variable, when train type x, unit 5000t, operates a direct train from station i to j during time period t. Otherwise, take 0; For variables of type 0-1, when unit a of vehicle type x is reassigned from station i to station z for combination during time period t, the result is 5000t. Otherwise, take 0; As a 0-1 variable, when train type x, with 10,000 units per t, operates a direct train from station i to j during time period t. Otherwise, take 0; As a 0-1 variable, when vehicle type x, with 10,000 units per t in time period t, is re-assigned from station i to station z for combination. Otherwise, take 0.

[0052] The method for constructing the first constraint mentioned above includes steps S124, S125, and S126.

[0053] Step S124: Construct the constraint formulas for the balance of train combination and decomposition on the Shenhua Railway, i.e., the train quantity balance conditions, as shown in formulas (5) and (6). Currently, the vehicle types on the Shenhua Railway include C80, C70, and C64 open wagons and KM type heavy wagons. For technical considerations, combined trains are usually composed of unit trains of the same type. In order to reduce the time consumed by the decomposition operation of combined trains at the forward technical station, trains going to different destinations cannot be combined. Therefore, for combined station z, the number of 10,000-ton combined trains of type x from this station to the terminal station j is 1 / θ of the number of 5,000-ton unit trains of type x transported from the loading station to this station. k Similarly, the number of 20,000-ton combination trains of type x departing from this station to the terminal station j is 1 / θ of the number of 10,000-ton unit trains of type x transported from the loading station to this station. k The two cases described above are represented by formulas (5) and (6) respectively:

[0054]

[0055]

[0056] In formulas (5) and (6): This represents the number of 10,000-ton combined trains of type x traveling from combined station z to terminal station j; This represents the number of 20,000-ton combined trains of type x traveling from combined station z to terminal station j; θ k Let N represent the combination coefficient of train type k; I represent the set of loading stations, where i is the index of the loading station, i∈I; J represent the set of unloading stations, where j is the index of the unloading station, j∈J; Z represent the set of combined stations (stations with transfer stations), where z is the index of the combined station, z∈Z; T represent the set of decision time periods (time periods when loaded trains arrive at stations), where t is the index of the decision time period, t∈T; X represent the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit reorders at loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit reorders at loading stations, N2={1,2,...,b,...,B}; For variables of type 0-1, when unit a of vehicle type x is reassigned from station i to station z for combination during time period t, the result is 5000t. Otherwise, take 0; As a 0-1 variable, when vehicle type x, with 10,000 units per t in time period t, is re-assigned from station i to station z for combination. Otherwise, take 0.

[0057] Step S125: Construct the constraint formula for the combined operation capacity of stations, i.e., the combined operation capacity condition of stations. Since the types of combined stations on the Shenhua Railway are different, the types and quantities of locomotives that can be used vary, and the combined capacity of different stations is affected by equipment and facility capabilities. Therefore, the number of duplicate trains departing from loading station i to combined station z for combination during time period t should be less than the combined capacity of that station. The combined operation capacity condition of stations is shown in formula (7):

[0058]

[0059] in: Let N1 represent the maximum combination capacity of station z during time period t; I represent the set of loading stations, where i is the index of the loading station, i∈I; J represent the set of unloading stations, where j is the index of the unloading station, j∈J; Z represent the set of combined stations (stations with transfer stations), where z is the index of the combined station, z∈Z; T represent the set of decision time periods (time periods when loaded vehicles arrive at station stations), where t is the index of the decision time period, t∈T; X represent the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit reorders at loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit reorders at loading stations, N2={1,2,...,b,...,B}; For variables of type 0-1, when unit a of vehicle type x is reassigned from station i to station z for combination during time period t, the result is 5000t. Otherwise, take 0; As a 0-1 variable, when vehicle type x, with 10,000 units per t in time period t, is re-assigned from station i to station z for combination. Otherwise, take 0.

[0060] Step S125: Construct the constraint formula for the heavy train direction throughput capacity of Shenhua Railway, that is, the heavy train direction throughput capacity condition, as shown in formula (8). In any time period t, the number of trains passing through a certain section cannot be greater than the throughput capacity of that section.

[0061]

[0062] in: Let t represent the throughput capacity of heavy-load trains between stations i and j during time period t; I represent the set of loading stations, where i is the index of the loading station, i∈I; J represent the set of unloading stations, where j is the index of the unloading station, j∈J; T represent the set of decision time periods (time periods when heavy-load trains arrive at stations), where t is the index of the decision time period, t∈T; X represent the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit reloads at loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit reloads at loading stations, N2={1,2,...,b,...,B}; As a 0-1 variable, when train type x, unit 5000t, operates a direct train from station i to j during time period t. Otherwise, take 0; For variables of type 0-1, when unit a of vehicle type x is reassigned from station i to station z for combination during time period t, the result is 5000t. Otherwise, take 0; As a 0-1 variable, when train type x, with 10,000 units per t, operates a direct train from station i to j during time period t. Otherwise, take 0; As a 0-1 variable, when vehicle type x, with 10,000 units per t in time period t, is re-assigned from station i to station z for combination. Otherwise, take 0.

[0063] Step S13: Based on each piece of loaded train information, determine whether the total number of stations in the transit station information is greater than or equal to three. If the total number of stations in the transit station information is less than three, construct a direct train transportation time model based on the set of loaded train information. If the total number of stations in the transit station information is greater than or equal to three, construct a transfer train transportation time model based on the set of loaded train information.

[0064] Understandably, in this step, a direct train transportation time model and a transfer train transportation time model are constructed according to the operation mode of each train (direct or combined operation) to depict the operation time of each train during transportation.

[0065] The construction methods for the direct train transportation time model and the transfer train transportation time model include steps S131, S132, S133 and S134.

[0066] Step S131: Determine whether the total number of stations in the station set in the second parameter is greater than or equal to three. If the total number of stations in the station set in the second parameter is greater than or equal to three, then obtain the first feature data, the second feature data, and the third feature data according to the third parameter. The first feature data is the time information of the train loading goods at the originating station; the second feature data is the time information of the train running between two adjacent stations; and the third feature data is the time information of the train collecting goods at the transfer stations during transportation.

[0067] Understandably, in this step, if the train needs to load goods together at a transfer station, the transportation organization operation time corresponding to the combined train is divided into loading time, interval running time and combined operation time, and the first feature data, second feature data and third feature data are obtained respectively.

[0068] Step S132: Based on the first feature data, the second feature data and the third feature data, calculate and construct the transit train transportation time model.

[0069] It is understandable that, in this step, the transit train transportation time model is as shown in formula (9):

[0070]

[0071] in, Let N represent the time consumption model for transit train transportation; I represents the set of loading stations, where i is the index of the loading station, i∈I; J represents the set of unloading stations, where j is the index of the unloading station, j∈J; Z represents the set of combined stations (stations with transit stations), where z is the index of the combined station, z∈Z; T represents the set of decision time periods (time periods when loaded trains arrive at the station), where t is the index of the decision time period, t∈T; X represents the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit re-rows at loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit re-rows at loading stations, N2={1,2,...,b,...,B}; This indicates the travel time of a 5000t unit train from the loading station to the assembly station; This indicates the travel time of a 10,000-ton unit train from the loading station to the assembly station; This indicates the combined travel time of a 5000t unit train at station z; This indicates the travel time from the assembly station to the unloading station for a 10,000-ton train formed from 5,000-ton unit trains; This indicates the combined travel time of a 10,000-ton unit train at station z; This indicates the travel time from the assembly station to the unloading station for a 20,000-ton train formed from a 10,000-ton unit train. This represents the number of 10,000-ton combined trains of type x traveling from combined station z to terminal station j; This represents the number of 20,000-ton combined trains of type x traveling from combined station z to terminal station j.

[0072] Step S133: If the total number of stations in the second parameter is less than three, then obtain the first feature data and the second feature data according to the third parameter.

[0073] It is understandable that in this step, if the train is a direct train operation plan, then the first feature data and the second feature data are obtained.

[0074] Step S134: Calculate and construct the direct train transportation time model based on the first feature data and the second feature data.

[0075] Understandably, in this step, the transportation organization time for direct trains is divided into loading time and interval running time, and the transportation time model for direct trains is constructed as shown in formula (10):

[0076]

[0077] Wherein: T ij Let N represent the time consumption model for direct train transportation; I represents the set of loading stations, where i is the index of the loading station, i∈I; J represents the set of unloading stations, where j is the index of the unloading station, j∈J; T represents the set of decision time periods (time periods when loaded trains arrive at the station), where t is the index of the decision time period, t∈T; X represents the set of vehicle types, X={x|x=1,2,3,4}, representing C64, C70, C80, and KM vehicle types respectively; N1 represents the set of 5000t unit reorders at loading stations, N1={1,2,...,a,...,A}; N2 represents the set of 10,000t unit reorders at loading stations, N2={1,2,...,b,...,B}; This indicates the loading time of the 5000t unit train at station i; This indicates the travel time of a 5000t unit train from the loading station to the unloading station; This indicates the loading time of a 10,000-ton unit train at station i; This indicates the travel time of a 10,000-ton unit train from the loading station to the unloading station; As a 0-1 variable, when train type x, unit 5000t, operates a direct train from station i to j during time period t. Otherwise, take 0; As a 0-1 variable, when train type x, with 10,000 units per t, operates a direct train from station i to j during time period t. Otherwise, take 0.

[0078] Step S14: Based on the sum of all the direct train transportation time models and the transfer train transportation time models, obtain the first objective function model.

[0079] Understandably, in this step, based on the total planned daily cargo volume, a corresponding number of trains are arranged for loading and transportation. Each train operates in a different manner, with the goal of minimizing the total transportation time. The total transportation time is described using the first objective function model, as shown in formula (11):

[0080]

[0081] Where: minZ1 is the first objective function model, representing the minimization of heavy-haul train transportation operation time; T ij This represents a model of the transit time for direct trains. This represents a model for the time consumption of transit train transportation.

[0082] Step S15: Based on the train operation mode conditions, the first constraint conditions, and the first objective function model, construct the heavy train set operation scheme model.

[0083] It is understandable that in this step, the train operation mode conditions are used as the solution model, the first constraint condition is used as the constraint condition of the solution model, and the first objective function model is used as the objective function of the solution model to form a heavy train operation scheme model.

[0084] Step S2: Based on the calculation of the loaded train operation scheme model, determine the demand for empty cars at each station and construct an empty car allocation model. The empty car allocation model is used to describe the relationship between the demand for empty cars and the actual loading capacity of goods.

[0085] Understandably, in this step, freight flow data and vehicle flow data are substituted into the calculation based on the heavy train operation plan model to obtain the demand information of each loading station for empty cars, such as the arrival time, number of arrivals, and type of car, and an empty car dispatch model is constructed based on this demand information.

[0086] The method for constructing the empty vehicle dispatch model includes steps S21, S22, S23, S24 and S25.

[0087] Step S21: Calculate and determine the fifth parameter based on the loaded train operation scheme model. The fifth parameter includes the time period information, quantity information and train type information of the first empty train arriving at the station.

[0088] Understandably, in this step, freight flow data and vehicle flow data are substituted into the calculation based on the heavy vehicle group operation plan model to obtain the theoretical empty vehicle demand information, i.e., the fifth parameter.

[0089] Step S22: Obtain the sixth parameter from the traffic flow data in the current time period. The sixth parameter includes the number and type of second empty vehicles in the actual scenario, and the time period information of the second empty vehicles stopping at each of the stations.

[0090] Understandably, in this step, the sixth parameter is obtained based on the train arrival timetable data of each station in the actual scenario.

[0091] Step S23: Based on the fifth and sixth parameters, construct the empty vehicle demand model and the second objective function model respectively.

[0092] It is understandable that in this step, the number of empty columns required by any loading station i is determined to satisfy the loading needs. Part of it comes from empty columns stored on this site. A portion of the empty trains are allocated from other stations. Based on the fifth parameter, an empty train demand model is constructed for each station on the Shenhua Railway, as shown in formula (12):

[0093]

[0094] Based on the calculation of the fifth and sixth parameters, a second objective function model that satisfies the loading requirement error time is constructed, as shown in formula (13):

[0095]

[0096] In formulas (12) and (13): This represents the empty train demand model corresponding to any station i. Let F represent an empty column at any loading station i; F represents the set of decomposition stations, where f is the decomposition station index, f∈F; K represents the set of trains, K={k|k=1,2,3,4}, representing 5000t, 10,000t unit trains, 10,000t, and 20,000t combined trains respectively; I represents the set of loading stations, where i is the loading station index, i∈I; J represents the set of unloading stations, where j is the unloading station index, j∈J; T represents the set of decision time periods (time periods when loaded trains arrive at stations), where t is the decision time period index, t ∈T; N3 represents the set of 5000t empty units from the unloading station to the loading station, N3={1,2,...,n,...,N}; N4 represents the set of 10,000t empty units from the unloading station to the loading station, N4={1,2,...,m,...,M}; N5 represents the set of 10,000t empty units from the unloading station to the sorting station, N5={1,2,...,l,...,L}; N6 represents the set of 10,000t empty units l after being decomposed into empty units, N6={1,2,...,s,...,S}; For 0-1 variables, when the empty column n of vehicle type k class unit x is assigned from station j to station i for loading during time period t. Otherwise, take 0; For 0-1 variables, when the empty column m of vehicle type k is assigned from station j to station i for loading during time period t, the variable is 0-1. Otherwise, take 0; For 0-1 variables, when the empty column l of vehicle type k in time period t is assigned from station j to station f for decomposition... Otherwise, take 0; For 0-1 variables, when the empty column s of vehicle type k of vehicle model x is assigned from station f to station i for loading during time period t. Otherwise, take 0; minZ2 represents the deviation time for the loading station to meet the empty car demand, i.e., the second objective function model; This indicates the time period in which the empty train of type k, numbered w, arrives at the loading station. This represents the time period required to fulfill the w-th loading plan. Specifically, for direct empty trains, the time period for their arrival at the loading station is: For empty trains that have been split into multiple trains, the time period in which they arrive at the loading station is: This indicates the travel time of type k trains from the unloading station to the loading station; This indicates the travel time of type k trains from the unloading station to the sorting station; This indicates the decomposition time of type k trains at the decomposition station; This indicates the travel time of type k trains from the dismantling station to the loading station.

[0097] Step S24: Calculate and construct the second constraint condition based on the fifth parameter. The second constraint condition includes the empty car decomposition balance condition, the station decomposition operation capacity condition, and the empty car direction throughput capacity condition.

[0098] Understandably, in this step, due to limitations in facility and equipment capacity, most loading stations lack the capacity to load 10,000-ton trains, primarily handling 5,000-ton unit trains. To meet loading demands, in addition to operating some direct 10,000-ton empty trains, some 10,000-ton empty trains are sent to the sorting station f for sorting. Therefore, the number of empty x-type, k-type unit trains dispatched from any sorting station... It should be the x model k′ type 10,000 t empty train ρ sent to this station for disassembly. k The constraints for the decomposition and balancing of trains on the Shenhua Railway, namely the empty car decomposition and balancing conditions, are shown in formula (14):

[0099]

[0100] Due to factors such as the type of decomposition station and the number of shunting locomotives, the decomposition capacity of each technical station on the Shenhua Railway is limited. Therefore, the number of empty trains dispatched from unloading station j to decomposition station f for decomposition during time period t should be less than the decomposition capacity of that station. The constraints on the decomposition operation capacity of the technical stations on the Shenhua Railway, namely the station decomposition operation capacity conditions, are shown in formula (15):

[0101]

[0102] Within any time period t, the number of empty trains passing through a certain section cannot exceed the throughput capacity of that section. The constraints on the throughput capacity of Shenhua Railway in the empty direction are constructed as shown in formula (16):

[0103]

[0104] In formulas (14) to (16), ρ k Represents the decomposition coefficients of trains of class k; This represents the maximum decomposition capacity of station z during time period t; Let N1 represent the empty train throughput capacity between stations i and j during time period t; K represents the train set, K = {k | k = 1, 2, 3, 4}, representing 5000t, 10,000t unit trains, 10,000t, and 20,000t combined trains respectively; I represents the loading station set, i is the loading station index, i ∈ I; J represents the unloading station set, j is the unloading station index, j ∈ J; N3 represents the set of 5000t empty unit trains from the unloading station to the loading station, N3 = {1, 2, ..., n, ..., N}; N4 represents the set of 10,000t empty trains from the unloading station to the loading station, N4 = {1, 2, ..., m, ..., M}; N5 represents the set of 10,000t empty trains from the unloading station to the decomposition station, N5 = {1, 2, ..., l, ..., L}; N6 represents the set of 10,000t empty train l after decomposition into unit empty trains, N6 = {1, 2, ..., s, ..., S}; For 0-1 variables, when the empty column n of vehicle type k class unit x is assigned from station j to station i for loading during time period t. Otherwise, take 0; For 0-1 variables, when the empty column m of vehicle type k is assigned from station j to station i for loading during time period t, the variable is 0-1. Otherwise, take 0; For 0-1 variables, when the empty column l of vehicle type k in time period t is assigned from station j to station f for decomposition... Otherwise, take 0; For 0-1 variables, when the empty column s of vehicle type k of vehicle model x is assigned from station f to station i for loading during time period t. Otherwise, take 0.

[0105] Step S25: Based on the empty vehicle demand model, the second objective function model, and the second constraint conditions, construct the empty vehicle dispatch model.

[0106] It is understandable that in this step, the empty vehicle demand model is used as the empty vehicle solution model, the second constraint condition is used as the constraint condition of the empty vehicle solution model, and the second objective function model is used as the objective function of the empty vehicle solution model, thus forming the empty vehicle dispatch model.

[0107] Step S3: Solve the empty car dispatching model and the loaded car group operation scheme model to obtain the compiled freight train operation scheme.

[0108] Understandably, in this step, the empty car dispatching model and the loaded car group operation plan model are solved to obtain the compiled freight train operation plan. The freight train operation plan includes information such as the operation time, number of cars, operation section, and operation type of loaded and empty cars.

[0109] The solution method includes steps S3, including steps S31 and S32.

[0110] Step S31: Construct a solution model with the goal of minimizing the first objective function model and the second objective function model. The solution model is based on the empty car dispatching model and the loaded car group operation scheme model. The first constraint condition and the second constraint condition constitute the relational constraint conditions of the solution model.

[0111] Understandably, in this step, the goal is to minimize the time required for organizing loaded car transport and the delay time required for loading, while satisfying the train combination decomposition balance and capacity constraints. The solution model is shown below:

[0112]

[0113] Furthermore, minimizing minZ1 and minZ2 constitutes the objective function, forming the solution model described above.

[0114] Step S32: Solve the solution model using a two-stage genetic-taboo search algorithm to obtain the compiled freight train operation plan.

[0115] Understandably, in this step, a two-stage genetic-tabu search algorithm is used to solve the model, obtaining information such as the operating times of loaded and empty trains, the number of trains, the operating sections, and the types of trains. The specific solution steps of the two-stage genetic-tabu search algorithm include:

[0116] S321. Set the genetic parameters for the genetic algorithm, such as population size NP, number of generations NG, and crossover probability P. c and the mutation probability P m Etc. NP represents the number of individuals in the set of chromosomes in the genetic algorithm, NG represents the initial maximum iteration count set in the algorithm, and P... c P represents the probability that any two chromosomes will undergo a crossover operation. m This represents the probability of any chromosome undergoing a mutation.

[0117] S322. Generate an initial feasible solution X0 according to the initial solution solution method for loaded vehicles, and set the initial iteration number ng = 0.

[0118] Please see Figure 4 and Figure 5 , Figure 4 A schematic diagram of the coding method for loaded vehicle operation schemes; Figure 5 This diagram illustrates the method for generating an initial feasible solution for a loaded train operation scheme. Each chromosome contains T gene segments, representing the train operation situation at different time periods. Taking the second segment as an example, it contains 6 sub-segments, of which the first sub-segment... This indicates the operation status of 5000t multiple trains departing from the loading station to the unloading station, among which... This indicates the number of 5000t multiple trains operating between the first OD (Original Department) and the second OD (Original Department) set. Similarly, the remaining five sub-segments represent the operation of 10,000t unit trains from the loading station to the unloading station, 5000t unit trains from the loading station to the combined station, 10,000t unit trains from the loading station to the combined station, 10,000t combined trains from the combined station to the unloading station, and 20,000t combined trains from the combined station to the unloading station.

[0119] S323. Since the number of trains to be loaded between each OD is fixed, an initial population is generated by changing the gene position of each gene segment on the chromosome or by altering the position of the gene segment itself, i.e., changing the number of different types of trains, based on the initial feasible solution. An initial population NP is then generated based on the initial feasible solution through gene segment or gene position exchange.

[0120] S324. Perform the following operations on the population to generate a new generation of population.

[0121] S3241, Selection Operation. The fitness function is constructed as shown in formula (17). The fitness function in this paper is F(X). j The value of is the reciprocal of the auxiliary function, indicating that the higher the fitness value of an individual, the smaller its corresponding objective function value, i.e., the shorter the heavy vehicle transportation operation time, and the greater the probability of passing it on to the next generation. Based on the roulette wheel method, NP individuals with high fitness values ​​are selected as the next generation population.

[0122]

[0123] In formula (17), F(X) λ ) represents the fitness function value of the λth chromosome, f(x) represents the value of the first objective function model in formula (11), and g μ (x) represents the μ-th inequality constraint, h τ (x) represents the τth equality constraint, and α and β represent the penalty values ​​for inequality constraints and equality constraints, respectively.

[0124] S3242. Crossover Operation. Since the destinations of trains in each loading plan are fixed, the number of trains operating between each OD is fixed, meaning the total number of real numbers representing the same OD gene location on the chromosome is fixed. To ensure the feasibility of generating new chromosomes after crossover, a crossover operation is performed on the gene segment represented by a certain time period in the chromosome to meet the train operation requirements and to verify its feasibility.

[0125] S3243. Mutation Operations. To ensure chromosome diversity, gene positions in chromosome segments corresponding to any time period can be exchanged, provided that the capability allows, and the feasibility can be verified.

[0126] S325. Judgment: If ng > NG, output the initial heavy vehicle operation plan and go to S326; otherwise, ng = ng + 1 and go to S3241.

[0127] S326. Use the tabu search algorithm to solve the empty car dispatching scheme model.

[0128] S3261. Based on the initial loaded car operation plan, obtain and input the loading requirements of each loading station.

[0129] S3262. Set the taboo length L, the number of iterations NGTS, initialize the taboo table H, and initialize the number of iterations n = 1.

[0130] S3263, The representation method of the initial solution of the empty vehicle dispatching model is as follows: Figure 6 As shown, Figure 6 The diagram illustrates the initial solution representation method for the empty vehicle dispatching model. Taking the second time period as an example, each segment contains 6 sub-segments, where the first sub-segment... This indicates the dispatch status of 5000t empty trains from the supply station to the loading station, where g represents the OD set between the supply station and the loading station. This indicates the number of 5000t empty trains that departed from the g-th OD in the second time period. Similarly, the remaining five sub-segments represent the departure of 10,000t empty trains from the empty station to the loading station, 10,000t empty trains from the empty station to the sorting station, 20,000t empty trains from the empty station to the sorting station, 5000t empty trains from the sorting station to the loading station, and 10,000t empty trains from the sorting station to the loading station. Based on... Figure 7 The method shown for finding the initial solution of the empty car generates an initial solution Y0 as the current optimal solution Y. min And calculate its target value Z2(Y) min ), Figure 7 This is a schematic diagram illustrating the method for generating an initial feasible solution for an empty vehicle dispatching scheme.

[0131] S3264. Generating neighborhood solutions σ(Y0) from the current solution Y0 as candidate solutions: Based on the above-mentioned initial feasible solution for empty trains, it is generated by transforming the position of a single location or time segment in each time segment. To ensure the feasibility of each solution within the neighborhood of the feasible solution, a feasibility check is performed on each generated neighborhood solution during the neighborhood generation process. If a neighborhood solution is generated that does not meet the constraints such as the Shenhua Railway section transport capacity or station operation capacity, this infeasible solution can be removed from its neighborhood. If the requirements are met, the final neighborhood solution set is obtained.

[0132] S3265. Determine if there is a candidate solution Y. i Satisfying Amnesty Rule Z2(Y) i )<Z2(Y min If so, select the candidate solution Y that satisfies the amnesty rule. i The best solution in the neighborhood that is not tabu is taken as the current best solution; otherwise, the best solution in the neighborhood that is not tabu is taken as the current best solution.

[0133] S3266. Replace the feasible solutions in the tabu list with the current optimal solution found in S3265, and update the tabu list.

[0134] S3267. Judgment: If n > NGTS, output the empty car dispatch result and go to S3268; otherwise, n = n + 1, go to S3264.

[0135] S3268. If the empty car allocation meets the loading requirements, the algorithm ends and outputs the initial operation plan as the final freight train operation plan; if the empty car arrival time does not match the loading requirement time, the loading plan loading time is adjusted according to the empty car allocation result, and a new loaded car operation plan is generated; if the number of empty trains is insufficient, the number of daily loading plans is adjusted according to the importance of the goods being loaded, and goods with higher delivery deadlines are prioritized for dispatch. Then, proceed to S322 to generate a new initial solution and continue execution.

[0136] Example 2:

[0137] See Figure 2 , Figure 2 The diagram shown is a structural schematic of the freight train operation plan compilation device of this embodiment, including a loaded car operation module 710, an empty car dispatching module 720, and a solution module 730, wherein:

[0138] The loaded vehicle operation module 710 is used to acquire cargo flow data and vehicle flow data of freight transportation demand, calculate and construct a loaded vehicle group operation plan model, which is used to describe the dynamic relationship between the cargo flow data and the vehicle flow data.

[0139] Preferably, the loaded vehicle operation module 710 includes a first acquisition unit 711, a first construction unit 712, a second construction unit 713, a third construction unit 714, and a fourth construction unit 715, wherein:

[0140] First acquisition unit 711: used to acquire a set of loaded vehicle information and loading capacity data of each station based on the freight flow data and the vehicle flow data; the set of loaded vehicle information is a collection of all loaded vehicle information, and each piece of loaded vehicle information includes the transport time information, route station information and load information of the loaded vehicle; each loaded vehicle is a train scheduled to transport goods based on the freight flow data.

[0141] First construction unit 712: is used to calculate and construct train operation mode conditions and first constraint conditions based on the heavy vehicle information set and the loading capacity data of each station, respectively. The first constraint conditions include train quantity balance conditions, station combination operation capacity conditions and heavy vehicle operation direction throughput capacity conditions.

[0142] The second construction unit 713 is used to determine, based on each piece of loaded train information, whether the total number of stations in the transit station information is greater than or equal to three. If the total number of stations in the transit station information is less than three, a direct train transportation time model is constructed based on the set of loaded train information. If the total number of stations in the transit station information is greater than or equal to three, a transfer train transportation time model is constructed based on the set of loaded train information.

[0143] The third construction unit 714 is used to obtain the first objective function model based on the sum of all the direct train transportation time models and the transfer train transportation time models.

[0144] The fourth construction unit 715 is used to construct the heavy train operation scheme model based on the train operation mode conditions, the first constraint conditions, and the first objective function model.

[0145] Empty car dispatching module 720: It is used to calculate and determine the demand for empty cars at each station based on the loaded train operation scheme model, and to construct an empty car dispatching model. The empty car dispatching model is used to describe the relationship between the demand for empty cars and the actual loading capacity of goods.

[0146] Preferably, the empty vehicle dispatching module 720 includes a first calculation unit 721, a second acquisition unit 722, a fifth construction unit 723, a sixth construction unit 724, and a seventh construction unit 725, wherein:

[0147] First calculation unit 721: used to calculate and determine the fifth parameter based on the heavy train operation scheme model, the fifth parameter including the time period information, quantity information and train type information of the first empty train arriving at the station.

[0148] The second acquisition unit 722 is used to acquire the sixth parameter in the traffic flow data during the current time period. The sixth parameter includes the number and type of the second empty vehicles in the actual scenario, and the time period information of the second empty vehicles stopping at each of the stations.

[0149] Fifth construction unit 723: used to calculate and construct the empty vehicle demand model and the second objective function model based on the fifth and sixth parameters, respectively.

[0150] The sixth construction unit 724 is used to calculate and construct the second constraint condition based on the fifth parameter. The second constraint condition includes the empty car decomposition balance condition, the station decomposition operation capacity condition, and the empty car direction passage capacity condition.

[0151] The seventh construction unit 725 is used to construct the empty vehicle dispatching model based on the empty vehicle demand model, the second objective function model, and the second constraint conditions.

[0152] Solver module 730: used to solve the empty car dispatching model and the loaded car group operation scheme model to obtain the compiled freight train operation scheme.

[0153] Preferably, the solver module 730 includes an eighth construction unit 731 and a second calculation unit 732, wherein:

[0154] The eighth construction unit 731 is used to construct a solution model with the goal of minimizing the first objective function model and the second objective function model. The solution model is based on the empty car dispatching model and the loaded car group operation scheme model. The first constraint condition and the second constraint condition constitute the relational constraint conditions of the solution model.

[0155] The second calculation unit 732 is used to solve the solution model using a two-stage genetic-taboo search algorithm to obtain the compiled freight train operation plan.

[0156] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0157] Example 3:

[0158] Corresponding to the above method embodiments, this embodiment also provides a freight train operation plan preparation device 800. The freight train operation plan preparation device 800 described below and the freight train operation plan preparation method described above can be referred to in correspondence.

[0159] Figure 3 This is a block diagram illustrating a freight train operation scheme compilation device 800 according to an exemplary embodiment. For example... Figure 3 As shown, the freight train operation planning device 800 may include a processor 801 and a memory 802. The freight train operation planning device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0160] The processor 801 controls the overall operation of the freight train operation planning device 800 to complete all or part of the steps in the freight train operation planning method described above. The memory 802 stores various types of data to support the operation of the freight train operation planning device 800. This data may include, for example, instructions for any application or method operating on the freight train operation planning device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented 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 storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the freight train operation planning device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0161] In an exemplary embodiment, the freight train operation plan preparation device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the freight train operation plan preparation method described above.

[0162] In another exemplary embodiment, a computer storage medium including program instructions is also provided, which, when executed by a processor, implements the steps of the freight train operation plan preparation method described above. For example, the computer storage medium may be the memory 802 including program instructions described above, which may be executed by the processor 801 of the freight train operation plan preparation device 800 to complete the freight train operation plan preparation method described above.

[0163] Example 4:

[0164] Corresponding to the above method embodiments, this embodiment also provides a storage medium. The storage medium described below can be referred to in conjunction with the freight train operation plan compilation method described above.

[0165] A storage medium storing a computer program, which, when executed by a processor, implements the steps of the freight train operation plan preparation method described in the above method embodiments.

[0166] The storage medium can be any storage medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0167] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0168] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for making a freight train working diagram, characterized in that, The method comprises the following steps: acquiring freight flow data and vehicle flow data of freight transportation demand, and constructing a heavy vehicle group operation scheme model for describing the dynamic change relationship between the freight flow data and the vehicle flow data; determining the demand for empty vehicles at each station site based on the heavy vehicle group operation scheme model calculation, and constructing an empty vehicle allocation model for describing the relationship between the empty vehicle demand and the actual loading capacity of freight; solving the empty vehicle allocation model and the heavy vehicle group operation scheme model to obtain a compiled freight train operation scheme; the step of acquiring freight flow data and vehicle flow data of freight transportation demand, and constructing a heavy vehicle group operation scheme model comprises the following steps: based on the freight flow data and the vehicle flow data, acquiring a heavy vehicle information set and loading capacity data of each station site; the heavy vehicle information set is a set of all heavy vehicle information, each heavy vehicle information includes transportation time information, passing station information and load information of a heavy vehicle; each heavy vehicle is a train arranged for transporting freight based on the freight flow data; based on the heavy vehicle information set and the loading capacity data of each station site, constructing a train operation mode condition and a first constraint condition respectively, the first constraint condition includes a train number balance condition, a station site group operation capacity condition and a heavy vehicle operation direction passing capacity condition; based on each heavy vehicle information, judging whether the total number of station sites in the passing station information is greater than or equal to three, if the total number of station sites in the passing station information is less than three, constructing a direct train transportation time consumption model according to the heavy vehicle information set; if the total number of station sites in the passing station information is greater than or equal to three, constructing a transit train transportation time consumption model according to the heavy vehicle information set; based on the sum of all direct train transportation time consumption models and transit train transportation time consumption models, obtaining a first objective function model; based on the train operation mode condition, the first constraint condition and the first objective function model, the heavy vehicle group operation scheme model is constructed.

2. The method according to claim 1, wherein, the step of determining the demand for empty vehicles at each station site based on the heavy vehicle group operation scheme model calculation, and constructing an empty vehicle allocation model comprises the following steps: determining a fifth parameter based on the heavy vehicle group operation scheme model calculation, the fifth parameter includes time period information, quantity information and vehicle type category information of the first empty vehicle arriving at the station site; acquiring a sixth parameter in the vehicle flow data in the current time period, the sixth parameter includes the quantity, category, time period information of the second empty vehicle stopping at each station site in the actual scenario; based on the fifth parameter and the sixth parameter calculation, constructing an empty vehicle demand model and a second objective function model respectively; constructing a second constraint condition based on the fifth parameter calculation, the second constraint condition includes an empty vehicle decomposition balance condition, a station decomposition operation capacity condition and an empty vehicle direction passing capacity condition; based on the empty vehicle demand model, the second objective function model and the second constraint condition calculation, the empty vehicle allocation model is constructed.

3. The method according to claim 2, wherein, Solving the empty car allocation model and the heavy car group running scheme model, a compiled freight train running scheme is obtained, including: A solving model is constructed with the first objective function model and the second objective function model minimized as the goal, and the solving model is based on the empty car allocation model and the heavy car group running scheme model; the first constraint condition and the second constraint condition constitute the relationship constraint condition of the solving model; The solving model is solved by using a two-stage genetic-tabu search algorithm, and the compiled freight train running scheme is obtained.

4. A freight train working plan making device characterized by comprising: Comprise: The heavy car running module is used for obtaining freight flow data and car flow data of freight transportation demand, and is used for calculating and constructing a heavy car group running scheme model, which is used to describe the dynamic change relationship between the freight flow data and the car flow data; The empty car allocation module is used for determining the demand for empty cars at each station site based on the heavy car group running scheme model calculation, and an empty car allocation model is constructed, which is used to describe the relationship between the empty car demand and the actual loading capacity of the freight; The solving module is used for solving the empty car allocation model and the heavy car group running scheme model, and obtaining a compiled freight train running scheme; The heavy car running module comprises: The first acquisition unit is used for obtaining a heavy car information set and loading capacity data of each station site based on the freight flow data and the car flow data; the heavy car information set is a set of all heavy car information, each heavy car information includes transportation time information, route station information and load information of the heavy car; each heavy car is a train arranged based on the freight flow data for transporting freight; The first construction unit is used for calculating and constructing train running mode conditions and first constraint conditions based on the heavy car information set and the loading capacity data of each station site, respectively; the first constraint conditions include train quantity balance conditions, station site group operation capacity conditions and heavy car running direction passing capacity conditions; The second construction unit is used for judging whether the total number of the station sites in the route station information is greater than or equal to three based on each heavy car information; if the total number of the station sites in the route station information is less than three, a direct train transportation time consumption model is constructed according to the heavy car information set; if the total number of the station sites in the route station information is greater than or equal to three, a transfer train transportation time consumption model is constructed according to the heavy car information set; The third construction unit is used for obtaining a first objective function model based on the sum of all direct train transportation time consumption models and transfer train transportation time consumption models; The fourth construction unit is used for constructing the heavy car group running scheme model based on the train running mode conditions, the first constraint conditions and the first objective function model.

5. The freight train working diagram preparation apparatus according to claim 4, characterized by, The empty car allocation module comprises: The first calculation unit is used for determining a fifth parameter including time period information, quantity information and car type category information of the first empty car arriving at the station site based on the heavy car group running scheme model calculation; The second obtaining unit is configured to obtain a sixth parameter in the train flow data in the current period, the sixth parameter comprising a number, a type, and period information of the second empty train stopping at each station site in the actual scene; The fifth constructing unit is configured to construct an empty train demand model and a second objective function model based on the fifth parameter and the sixth parameter; The sixth constructing unit is configured to construct a second constraint condition based on the fifth parameter, the second constraint condition comprising an empty train decomposition balance condition, a station decomposition operation capacity condition, and an empty train direction passing capacity condition; The seventh constructing unit is configured to construct the empty train allocation model based on the empty train demand model, the second objective function model, and the second constraint condition.

6. The freight train working diagram preparation apparatus according to claim 5, wherein The solving module comprises: The eighth constructing unit is configured to construct a solving model based on the empty train allocation model and the heavy train group operation scheme model, the solving model being constructed with the first objective function model and the second objective function model being minimized as a target, and the first constraint condition and the second constraint condition being relationship constraint conditions of the solving model; The second calculating unit is configured to solve the solving model by using a two-stage genetic-tabu search algorithm to obtain the compiled heavy train operation scheme.

7. A freight train working diagram preparation device characterized by comprising: The computer program is stored in the readable storage medium and is executed by the processor to implement the steps of the heavy train operation scheme compiling method according to any one of claims 1 to 3. The computer program is stored in the readable storage medium and is executed by the processor to implement the steps of the heavy train operation scheme compiling method according to any one of claims 1 to 3. ​ 8. A readable storage medium characterized by: ​

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