Data processing method and device, computer equipment and readable storage medium

Abstract

The invention relates to a data processing method and device, computer equipment and a readable storage medium. The method comprises the steps that a train data set is received, and train data subsets of all trains in the train data set are traversed; the train data subset comprises driving plan information, attribute information and maintenance information of the train; under the condition that the target trains meet preset train constraint conditions, the target trains are combined according to a train scheduling algorithm and the driving plan information, the attribute information and the maintenance information of the target trains, and combination plan information of the target trains is obtained. By adopting the method, the data processing efficiency can be improved.

Description

Technical Field

[0001] This application relates to the technical field of railway transportation, and in particular, to a data processing method, apparatus, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] In order to optimize the channel capacity of heavy-haul railways, trains with a capacity of less than 20,000 tons need to be paired together for combined transportation according to the train pairing plan, and according to the train decomposition plan, after the train arrives at the boundary station, the paired trains are decomposed so that different trains reach different destinations.

[0003] In the traditional technology, the train dispatcher or the station dispatcher constructs the combined plan information of each train according to the train type, cargo flow direction, arrival time, waybill, and transport capacity configuration information of each train. The combined plan information includes pairing plan information and decomposition plan information.

[0004] However, in the traditional technology, constructing the combined plan information of each train manually takes a lot of time. Therefore, the efficiency of the current data processing method is low. Summary of the Invention

[0005] Based on this, it is necessary to provide a data processing method, apparatus, computer device, computer-readable storage medium, and computer program product for the above technical problems.

[0006] In a first aspect, this application provides a data processing method, including:

[0007] Receiving a train data set and traversing the train data subsets of each train in the train data set; the train data subset includes the train operation plan information, attribute information, and maintenance information of the train;

[0008] When the target train meets the preset train constraint conditions, combining the target train according to the train scheduling algorithm, the operation plan information, attribute information, and maintenance information of the target train to obtain the combined plan information of the target train.

[0009] In one embodiment, the train data subset of the target train includes the departure time of the target train. After traversing the train data subsets of each train in the train data set, the method further includes:

[0010] Determining a first time difference between the departure time of the target train and the current time, and determining whether the first time difference is greater than a preset first time period;

[0011] If the first time difference is greater than the first time period, determining that the target train does not meet the preset train constraint conditions;

[0012] If the first time difference is less than or equal to the first time period, it is determined that the target train meets the preset train constraint conditions.

[0013] In one embodiment, the train scheduling algorithm includes a train pairing algorithm and a train decomposition algorithm. According to the train scheduling algorithm, the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train, the target train is combined to obtain the combined plan information of the target train, including:

[0014] If the train data set is a train daily shift plan data set, according to the train pairing algorithm, the train operation plan information, attribute information, and maintenance information, a paired train that pairs with the target train is determined in the train daily shift plan data set, and the target train and the paired train are combined to generate the paired plan information of the target train;

[0015] If the train data set is a train transportation and sales change data set, according to the train decomposition algorithm, the train operation plan information, and attribute information, the target train is decomposed to generate the decomposition plan information of the target train.

[0016] In one embodiment, according to the train pairing algorithm, the train operation plan information, attribute information, and maintenance information, a paired train that pairs with the target train is determined in the train daily shift plan data set, and the target train and the paired train are combined to generate the paired plan information of the target train, including:

[0017] According to the maintenance information and attribute information of the target train, the train type of the target train is determined;

[0018] According to the train type, train operation plan information, and attribute information of the target train, a paired train that matches the target train is determined in the train daily shift plan data set;

[0019] The target train and the paired train are paired together to form the train formation of the target train, and the paired plan information of the train formation is generated.

[0020] In one embodiment, the attribute information includes the locomotive traction mode, and the locomotive traction mode includes a target traction mode and a non-target traction mode; the maintenance information includes requiring technical inspection and not requiring technical inspection. According to the maintenance information and attribute information of the target train, determining the train type of the target train includes:

[0021] If the locomotive traction mode is a non-target traction mode and the maintenance information indicates that technical inspection is not required, determine that the train type of the target train is the second train type;

[0022] If the locomotive traction mode is a target traction mode and / or the maintenance information indicates that technical inspection is required, determine that the train type of the target train is the first train type.

[0023] In one embodiment, the determining of the paired train matching the target train in the train daily plan dataset according to the train type, train operation plan information, and attribute information of the target train includes:

[0024] Determine the next train of the target train in the train daily plan dataset as the initial paired train in the order of arrival time of the departure time;

[0025] According to the train type of the target train, train operation plan information, attribute information of the target train, and the train data subset of the initial paired train, determine whether the initial paired train matches the target train;

[0026] If the initial paired train does not match the target train, determine the next train of the initial paired train in the train daily plan dataset as the initial paired train in the order of arrival time of the departure time;

[0027] Execute the step of determining whether the initial paired train matches the target train according to the train type of the target train, train operation plan information, attribute information of the target train, and the train data subset of the initial paired train or until the traversal of the full train data subset is completed, and determine the initial paired train as the paired train until the initial paired train matches the target train.

[0028] In one embodiment, the decomposing the target train according to the train decomposition algorithm, train operation plan information, and attribute information to generate the decomposition plan information of the target train includes:

[0029] According to the attribute information, determine whether the target train is a decomposable train;

[0030] In the case where the target train is the decomposable train, determine the boundary station information of the target train according to the train operation plan information and the train decomposition algorithm;

[0031] Generate the decomposition plan information of the target train according to the boundary station information, train operation plan information, and attribute information.

[0032] In a second aspect, the present application also provides a data processing device, including:

[0033] A receiving module, configured to receive a train dataset and traverse train data subsets of each train in the train dataset; the train data subsets include train operation plan information, attribute information, and maintenance information of the train;

[0034] A combination module, configured to, when a target train meets preset train constraint conditions, combine the target train according to a train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train, so as to obtain combination plan information of the target train.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Receive a train dataset and traverse train data subsets of each train in the train dataset; the train data subsets include train operation plan information, attribute information, and maintenance information of the train;

[0037] When a target train meets preset train constraint conditions, combine the target train according to a train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train, so as to obtain combination plan information of the target train.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0039] Receive a train dataset and traverse train data subsets of each train in the train dataset; the train data subsets include train operation plan information, attribute information, and maintenance information of the train;

[0040] When a target train meets preset train constraint conditions, combine the target train according to a train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train, so as to obtain combination plan information of the target train.

[0041] In a fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0042] Receive a train dataset and traverse train data subsets of each train in the train dataset; the train data subsets include train operation plan information, attribute information, and maintenance information of the train;

[0043] When the target train meets the preset train constraint conditions, the target train is combined according to the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train to obtain the combined plan information of the target train.

[0044] The above data processing method, device, computer device, computer-readable storage medium, and computer program product receive a train data set and traverse the train data subsets of each train in the train data set; the train data subsets include the train operation plan information, attribute information, and maintenance information of the train; when the target train meets the preset train constraint conditions, the target train is combined according to the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train to obtain the combined plan information of the target train. By using this method, by traversing the train data subsets of each train and combining the target train according to the train scheduling algorithm when the target train data subset meets the train constraint conditions, the automatic combination of trains and the generation of train combination plans are realized, avoiding manual participation, reducing the time for generating train combination plans, and improving the efficiency of the data processing method. Description of the Drawings

[0045] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for describing the embodiments of the present application or related technologies. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is an application environment diagram of the data processing method in an embodiment;

[0047] Figure 2 It is a flowchart of the data processing method in an embodiment;

[0048] Figure 3 It is a flowchart of determining whether the target train meets the train constraint conditions in an embodiment;

[0049] Figure 4 It is a flowchart of determining the combined plan information in an embodiment;

[0050] Figure 5 It is a flowchart of determining the pairing plan information in an embodiment;

[0051] Figure 6 It is a flowchart of determining the train type in an embodiment;

[0052] Figure 7Schematic diagram of the process for determining paired trains in an embodiment;

[0053] Figure 8 Schematic diagram of the process for generating decomposition plan information in an embodiment;

[0054] Figure 9 Flowchart of the process for pairing trains in an exemplary embodiment;

[0055] Figure 10 Flowchart of the process for decomposing trains in an exemplary embodiment;

[0056] Figure 11 Block diagram of the structure of a data processing device in an embodiment;

[0057] Figure 12 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0058] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The data processing method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . Among them, the logical processing server 110 communicates with the terminal 120 of the train dispatcher through a network. The logical processing server 110 is used to execute the data processing method, obtain the combined plan information, and send the combined plan information to the terminal 120 of the train dispatcher, so that the terminal 120 of the train dispatcher verifies the combined plan information, and in the case of passing the verification, sends the verified combined plan information to the logical processing server 110. The logical processing server 110 distributes the running line modification to each dispatching desk affected by the combined plan information, and instructs the CTC each desk operation diagram to complete the automatic laying of the running line. The logical processing server 110 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal 120 can be, but is not limited to, various personal computers, laptop computers, smart phones and tablet computers.

[0060] In an exemplary embodiment, as shown in Figure 2 , a data processing method is provided. Taking the method applied to the logical processing server 110 (hereinafter the label is omitted, simply referred to as the logical processing server) in Figure 1 as an example for description, it includes the following steps 202 to step 204. Among them:

[0061] Step 202: Receive the train dataset and traverse the train data subsets of each train in the train dataset.

[0062] Among them, the train data subset contains the train operation plan information, attribute information, and maintenance information of the train.

[0063] In implementation, the logic processing server receives the train dataset through the CTC system (Centralized traffic control). The train dataset contains at least one train data subset. The train data subset contains the departure time of the train. The logic processing server sorts the train data subsets in the order of the departure time from earliest to latest to obtain a train data list. Then, the logic processing server traverses the train data subsets of each train in the train data list.

[0064] Specifically, there are two different situations for the train dataset. If pairing processing needs to be performed on the train, the train dataset received by the logic processing server is the train daily shift plan dataset. The train daily shift plan dataset contains at least one train plan data subset. The train plan data subset contains the maintenance information, attribute information, and operation plan information of the train. The attribute information includes the formation information, model information, structure information, train number information, etc. of the train. The operation plan information includes the cargo information loaded on the train, the starting point, the destination, the driving route, each stop, the departure time, the end time, and the starting time and arrival time of each stop. The logic processing server sorts the train daily shift plan data subsets in the order of the departure time from earliest to latest to obtain a train daily shift plan data list. Then, the logic processing server traverses each train in the train daily shift plan data list, determines the train as the target train, and determines whether the target train meets the train constraint conditions according to the train daily shift plan data subset of the target train.

[0065] If the train needs to be disassembled, the train dataset received by the logic processing server is the train transportation and sales change dataset. The train transportation and sales change dataset contains each train transportation and sales change data subset. The train transportation and sales change data subset contains the maintenance information, attribute information, and train operation plan information of the train. The train operation plan information contains the cargo information loaded on the train, the starting point, the departure time, the updated end time, the updated destination, the driving route matching the updated destination, each updated stop, and the starting time and arrival time of each updated stop, etc. The logic processing server sorts each train transportation and sales change data subset in ascending order of the departure time to obtain a train transportation and sales change data list. Then, the logic processing server traverses each train in the train transportation and sales change data list, determines the train as the target train, and determines whether the target train meets the train constraint conditions according to the train transportation and sales change data subset of the target train.

[0066] In an exemplary embodiment, in the field of railway transportation, there are currently trains with a load capacity of 5,000 tons, 10,000 tons, and 20,000 tons. To improve the transportation capacity of heavy-haul railways, trains with a load capacity of 5,000 tons and / or 10,000 tons can be paired together so that the two trains run in the same direction in a formation. Therefore, in the above situation, the logic processing server needs to pair the trains. Specifically, the logic processing server receives the train daily plan dataset sent by the CTC3.0 system (Centralized Traffic Control System 3.0, 3.0 is the version number). The train daily plan dataset is the initial operation plan of each train on the same day. The logic processing server sorts each train daily plan data subset in the train daily plan dataset in ascending order of the departure time to obtain a train daily plan data list. Then, the logic processing server traverses each train in the train daily plan data list, determines the train as the target train, and determines whether the target train meets the train constraint conditions according to the train daily plan data subset of the target train.

[0067] Since the two trains paired (formed) together have different destinations. Therefore, it is necessary to disassemble the two trains paired together at a suitable dividing station so that the two trains run towards their respective destinations respectively. Therefore, the logic processing server needs to disassemble the trains. The logic processing server receives the train transportation and sales change dataset sent by the CTC3.0 system. The logic processing server sorts each train transportation and sales change data subset in ascending order of the departure time to obtain a train transportation and sales change data list. Then, the logic processing server traverses each train in the train transportation and sales change data list, determines the train as the target train, and determines whether the target train meets the train constraint conditions according to the train transportation and sales change data subset of the target train.

[0068] Step 204, when the target train meets the preset train constraint conditions, combine the target train according to the train dispatching algorithm, the train operation plan information, the attribute information, and the maintenance information of the target train to obtain the combined plan information of the target train.

[0069] In implementation, when the target train meets the preset train constraint conditions, the logic processing server pairs or decomposes the train according to the train dispatching algorithm, the train operation plan information, the attribute information, and the maintenance information of the target train to obtain the combined plan information of the target train. Among them, the combined plan information of the target train includes the decomposition plan information and the pairing plan information. If the logic processing server performs a decomposition process on the target train, the decomposition plan information is obtained. If the logic processing server performs a pairing process on the target train, the pairing plan information is generated.

[0070] Specifically, if the train data set is the train daily shift plan data set, the logic processing server determines the paired train that pairs with the target train in the train daily shift plan data set according to the train pairing algorithm, the train operation plan information, the attribute information, and the maintenance information, and combines the target train and the paired train to generate the pairing plan information of the target train. If the train data set is the train transportation and sales change data set, the logic processing server decomposes the target train according to the train decomposition algorithm, the train operation plan information, and the attribute information to generate the decomposition plan information of the target train.

[0071] In an optional embodiment, the logic processing server sends the combined plan information of the target train to the terminal of the station train operation staff, instructing the station train operation staff to verify the combined plan information of the target train. During the verification process, the station train operation staff can edit and modify the combined plan information until the combined plan information passes the verification. When the combined plan information passes the verification, the terminal of the station train operation staff sends the verified combined plan information to the logic processing server. The logic processing server distributes the running line modification of each dispatching desk affected by the verified combined plan information to enable the automatic drawing of the running line of the CTC operation diagram for each desk.

[0072] In the above data processing method, by traversing the train data subset of each train and combining the target train according to the train dispatching algorithm when the target train data subset meets the train constraint conditions, the automatic combination of trains and the generation of the train combined plan are realized, avoiding manual participation, reducing the time for generating the train combined plan, and improving the efficiency of the data processing method.

[0073] In an exemplary embodiment, the train data subset of the target train includes the departure time of the target train. After traversing the train data subset, it is also necessary to determine whether the target train meets the preset train constraint conditions, such as Figure 3As shown, after step 102 is executed, the specific processing procedure of the data processing method further includes steps 302 to 306. Among them:

[0074] Step 302, determine a first time difference between the departure time of the target train and the current time, and determine whether the first time difference is greater than a preset first time period.

[0075] In implementation, a first time period is preset in the logic processing server. The first time period is used to restrict whether to decompose or combine the target train. The logic processing server performs a difference process on the departure time of the target train and the current time to obtain a first time difference between the departure time of the target train and the current time. Then, the logic processing server determines whether the first time difference is greater than the preset first time period. If the first time difference is greater than the first time period, the logic processing server executes the following step 304. If the first time difference is less than or equal to the first time period, the logic processing server executes the following step 306.

[0076] In an exemplary embodiment, the first time period is three hours. The logic processing server performs a difference on the departure time of the target train and the current time to obtain a first time difference between the start time of the target train and the current time. The logic processing server determines whether the first time difference is greater than three hours. If the first time difference is less than or equal to three hours, the logic processing server executes the following step 306. If the first time period is greater than three hours, the logic processing server executes the following step 304.

[0077] Optionally, the first time period is generally set to three hours, determined according to data processing requirements, and the embodiments of the present application do not limit the first time period.

[0078] Step 304, if the first time difference is greater than the first time period, determine that the target train does not meet the preset train constraint conditions.

[0079] Among them, the train constraint condition is that the first time difference is less than or equal to the first time period.

[0080] In implementation, a train constraint condition is preset in the logic processing server. If the first time difference is greater than the first time period, the logic processing server determines that the target train does not meet the preset train constraint conditions.

[0081] In an alternative embodiment, when the target train does not meet the preset train constraint conditions, the logic processing server does not process the train data subset of the target train.

[0082] In an optional embodiment, when the target train does not meet the preset train constraint conditions, the logic processing server deletes the train data subset of the target train from the train dataset, thereby preventing the logic processing server from repeatedly determining whether the target train meets the train constraint conditions, reducing the data processing time, and improving the data processing efficiency.

[0083] Step 306: If the first time difference is less than or equal to the first time period, determine that the target train meets the preset train constraint conditions.

[0084] In implementation, if the first time difference is less than or equal to the first time period, the logic processing server determines that the target train meets the preset train constraint conditions. Then, the logic processing server continues to execute step 204 above. The specific processing procedure of step 204 has been described in detail in the above embodiment, and will not be elaborated in this embodiment of the present application.

[0085] In this embodiment, by using the departure time of the target train, the target train that meets the train constraint conditions is determined from the train dataset, and the target train with a relatively close departure time is obtained, which facilitates subsequent combination processing of the target train.

[0086] In an exemplary embodiment, the train scheduling algorithm includes a train pairing algorithm and a train decomposition algorithm, as Figure 4 shown, the specific processing procedure of step 204 includes steps 402 to 404. Among them:

[0087] Step 402: If the train dataset is a train daily shift plan dataset, according to the train pairing algorithm, train operation plan information, attribute information, and maintenance information, determine the pairing train that pairs with the target train in the train daily shift plan dataset, and combine the target train and the pairing train to generate the pairing plan information of the target train.

[0088] In implementation, if the train dataset is a train daily shift plan dataset, it indicates that the logic processing server needs to perform pairing processing on the target train. Specifically, the logic processing server determines the train type of the target train according to the attribute information and maintenance information of the target train. Then, the logic processing server determines the pairing train that pairs with the target train in the train daily shift plan dataset according to the train operation plan information, attribute information, and train type of the target train. Then, the logic processing server combines the target train and the pairing train, and generates the pairing plan information of the target train according to the train daily shift plan data subset of the target train and the train daily shift plan data subset of the pairing train.

[0089] Specifically, if the train dataset is a daily train operation plan dataset, it means that the logic processing server needs to perform pairing processing on the target train, so as to combine the target train and the paired train that matches the target train for combined transportation. The attribute information includes the locomotive traction mode of the target train. The logic processing server determines the train type of the target train according to the locomotive traction mode and maintenance information. The attribute information of the target train includes the model information and formation information of the target train. Then, the logic processing server determines the paired train that matches the target train in the daily train operation plan dataset according to the model information, formation information, train type, and train operation plan information of the target train. Then, the logic processing server combines the target train and the paired train, and generates the paired plan information of the target train according to the daily train operation plan data subset of the target train and the daily train operation plan data subset of the paired train.

[0090] Step 404, if the train dataset is a train transportation and sales change dataset, perform decomposition processing on the target train according to the train decomposition algorithm, train operation plan information, and attribute information to generate the decomposition plan information of the target train.

[0091] In implementation, if the train dataset is a train transportation and sales change dataset, the logic processing server determines whether the target train is a decomposable train. When the target train is a decomposable train, the logic processing server determines the boundary station information of the train according to the train decomposition algorithm and train operation plan information. Then, the logic processing server generates the decomposition plan information of the target train according to the boundary station information, train operation plan information, and attribute information.

[0092] Specifically, the attribute information includes the formation information of the target train. The logic processing server determines whether the target train is a decomposable train according to the formation information of the target train. When the target train is a decomposable train, the logic processing server determines the boundary station information of the target train according to the train operation plan information and train decomposition algorithm of the target train. The decomposition station in the boundary station information is used to decompose the target train into two target sub-trains, and make the two target sub-trains drive to their respective destinations. Then, the logic processing server generates the decomposition plan information of the target train according to the boundary station information, train operation plan information, and attribute information of the target train.

[0093] In this embodiment, the target train is combined through the train combination algorithm and the train decomposition algorithm, realizing the automatic combination of trains and generating the train combination plan, avoiding manual participation, reducing the time for generating the train combination plan, and improving the efficiency of the data processing method. At the same time, it can greatly reduce the labor intensity of train operation command personnel, improve operation efficiency, and ensure the safety of transportation production.

[0094] In an exemplary embodiment, such as Figure 5As shown, the specific processing procedure of step 402 includes steps 502 to 506. Among them:

[0095] Step 502, determine the train type of the target train according to the maintenance information and attribute information of the target train.

[0096] Among them, the maintenance information includes those that require technical inspection and those that do not require technical inspection. The attribute information includes the locomotive traction mode of the target train.

[0097] In implementation, the logic processing server determines whether the maintenance information requires technical inspection to obtain a first judgment result. Then, the logic processing server determines whether the locomotive traction mode of the target train is the target locomotive traction mode to obtain a second judgment result. The logic processing server determines the train type of the target train according to the first judgment result and the second judgment result.

[0098] Specifically, the target locomotive traction mode is the 2+2 locomotive traction mode. The train type includes the first train type and the second train type. The logic processing server determines whether the maintenance information requires technical inspection (technical inspection operation) to obtain a first judgment result. The logic processing server determines whether the locomotive traction mode of the target train is the 2+2 locomotive traction mode to obtain a second judgment result. If the first judgment result is that technical inspection is required and / or the second judgment result is the 2+2 locomotive traction mode, then the logic processing server determines that the train type of the target train is the first train type. If the first judgment result is that technical inspection is not required and the second judgment result is not the 2+2 locomotive traction mode, then the logic processing server determines that the train type of the target train is the second train type.

[0099] Step 504, determine the paired train that matches the target train in the train daily plan dataset according to the train type, train operation plan information and attribute information of the target train.

[0100] In implementation, the logic processing server determines the initial paired train in the train daily plan dataset. Then, the logic processing server determines whether the initial paired train matches the target train according to the train type, train operation plan information, attribute information of the target train and the train daily plan data subset of the initial paired train. If the initial paired train matches the target train, the logic processing server determines the initial paired train as the paired train corresponding to the target train. If the initial paired train does not match the target train, the logic processing server executes the step of determining the initial paired train in the train daily plan dataset until the initial paired train matches the target train, and the logic processing server determines the initial paired train as the paired train corresponding to the target train.

[0101] Specifically, the logic processing server determines the next train of the target train in the train daily plan dataset in the order of the departure time from the earliest to the latest as the initial paired train. The logic processing server determines whether the initial paired train matches the target train according to the train type, train operation plan information, attribute information of the target train, and the subset of the train daily plan data of the initial paired train. If the initial paired train matches the target train, the logic processing server determines the initial paired train as the paired train corresponding to the target train. If the initial paired train does not match the target train, the logic processing server updates the initial paired train to the next train of the initial paired train in the train daily plan dataset in the order of the departure time from the earliest to the latest. Then, the logic processing server executes the step of determining whether the initial paired train matches the target train according to the train type, train operation plan information, attribute information of the target train, and the subset of the train daily plan data of the initial paired train, until the initial paired train matches the target train, and the logic processing server determines the initial paired train as the paired train corresponding to the target train.

[0102] Step 506: Pair the target train and the paired train together to form the train formation of the target train, and generate the pairing plan information of the train formation.

[0103] In implementation, the logic processing server pairs the target train and the paired train together to form the train formation of the target train. Then, the logic processing server generates the pairing plan information according to the subset of the train daily plan data of the target train and the subset of the train daily plan data of the paired train.

[0104] Specifically, the logic processing server pairs the target train and the paired train together to form the train formation of the target train. Then, the logic processing server updates the formation information of the target train according to the subset of the train daily plan data of the paired train, and updates the formation information of the paired train according to the subset of the train daily plan data of the target train. Then, the pairing plan information is generated according to the subset of the train daily plan data of the target train and the subset of the train daily plan data.

[0105] In this embodiment, by determining the train type, train operation plan information, and attribute information, the paired train of the target train is automatically determined, avoiding the manual subjective determination of the paired train, reducing the human factor, and improving the accuracy of the paired train.

[0106] In an exemplary embodiment, the attribute information includes the locomotive traction mode, and the locomotive traction mode includes the target traction mode and the non-target traction mode; the maintenance information includes requiring technical inspection and not requiring technical inspection, as Figure 6 shown, the specific processing process of step 502 includes steps 602 to 604. Among them:

[0107] Step 602: If the locomotive traction mode is a non-target traction mode and the maintenance information indicates that technical inspection is not required, determine that the train type of the target train is the second train type.

[0108] In implementation, if the locomotive traction mode is a non-target traction mode and the maintenance information indicates that technical inspection is not required, the logic processing server determines that the train type of the target train is the second train type. The second train type indicates that the target train can be either an A train or a B train. Since two trains need to be marshaled together, there is a front-back distinction between the trains. The A train indicates that the target train is the first vehicle in the train formation, and the B train indicates that the target train is the second vehicle in the train formation, with the first vehicle in front of the second vehicle.

[0109] Specifically, the target traction mode is the 2+2 locomotive traction mode. If the locomotive traction mode is not the 2+2 locomotive traction mode and the maintenance information indicates that technical inspection is not required (technical inspection operation), it means that the target train can be either the first vehicle or the second vehicle in the train formation. The logic processing server determines that the train type of the target train is the second train type.

[0110] Step 604: If the locomotive traction mode is the target traction mode and / or the maintenance information indicates that technical inspection is required, determine that the train type of the target train is the first train type.

[0111] In implementation, if the locomotive traction mode is the target traction mode and / or the maintenance information indicates that technical inspection is required, the logic processing server determines that the train type of the target train is the first train type. The first train type indicates that the target train is the first vehicle in the train formation, that is, the target train can only be an A train.

[0112] Specifically, the target traction mode is the 2+2 locomotive traction mode. If the locomotive traction mode is the 2+2 locomotive traction mode and / or the maintenance information indicates that technical inspection is required (technical inspection operation), it means that the target train can only be the first vehicle in the train formation. The logic processing server determines that the train type of the target train is the first train type.

[0113] In this embodiment, by determining the train type of the target train based on the maintenance information and locomotive traction mode of the target train, the position of the target train in the train formation can be determined, which is convenient for subsequent determination of the paired train corresponding to the target train.

[0114] In an exemplary embodiment, as Figure 7 shown, the specific processing procedure of step 404 includes steps 702 to 708. Among them:

[0115] Step 702: In the train daily plan dataset, determine the next train of the target train as the initial paired train in the order of departure time from earliest to latest.

[0116] Among them, the train daily plan dataset contains subsets of train daily plan data for each train.

[0117] In implementation, when the train dataset is the train daily plan dataset, the train data subset is the subset of train daily plan data. The logic processing server sorts the subsets of train daily plan data for each train in the train daily plan dataset in ascending order of departure time to obtain a train daily plan data list. Then, the logic processing server determines the next train of the target train in the train daily plan data list as the initial pairing train.

[0118] Step 704: According to the train type, train operation plan information, attribute information of the target train, and the subset of train data of the initial pairing train, determine whether the initial pairing train matches the target train.

[0119] Among them, the attribute information of the target train includes the model information and formation information of the target train.

[0120] In implementation, the logic processing server determines whether the initial pairing train matches the target train according to the train type, train operation plan information, model information, formation information of the target train, and the subset of train daily plan data of the initial pairing train.

[0121] Specifically, the logic processing server determines a second time difference between the departure time of the initial paired train and the departure time of the target train. The logic processing server determines whether the second time difference exceeds a preset interval time threshold. When the second time difference does not exceed the interval time threshold, the logic processing server determines whether the train type of the initial paired train meets the combination conditions of the target train according to the subset of the train daily shift plan data of the initial paired train, the model information of the target train, and the train type. When the train type of the initial paired train meets the combination conditions of the target train, the logic processing server determines whether the destinations of the target train and the initial paired train are the same according to the train operation plan information of the target train and the train operation plan information of the initial paired train. When the destinations of the target train and the initial paired train are the same, the logic processing server determines whether the goods of the target train and the initial paired train are the same according to the goods information of the target train and the goods information of the initial paired train. When the goods of the target train and the initial paired train are the same, the logic processing server determines whether the formation information of the initial paired train and the target train is empty. If the formation information of the initial paired train and the target train is empty, the logic processing server determines whether the total weight of Train A is greater than that of Train B. If the total weight of Train A is greater than that of Train B, the logic processing server determines that the initial paired train matches the target train. If the formation information of the initial paired train is not empty or the formation information of the target train is not empty or the total weight of Train A is less than or equal to the total weight of Train B or the goods of the target train and the initial paired train are not the same or the destinations of the target train and the initial paired train are not the same or the train type of the initial paired train does not meet the combination conditions of the target train or the second time difference exceeds the preset interval time threshold, the logic processing server determines that the initial paired train does not match the target train.

[0122] In an exemplary embodiment, the specific processing procedure for determining whether the train type of the initial paired train meets the combination conditions of the target train includes: The logic processing server determines whether the target train and the initial paired train can be connected according to the model information of the target train and the model information of the initial paired train. When the target train and the initial paired train can be connected, the logic processing server determines the train type of the initial paired train according to the attribute information and maintenance information of the initial paired train. The logic processing server determines whether the train type of the initial paired train matches the train type of the target train. If the train type of the initial paired train matches the train type of the target train, the logic processing server determines that the train type of the initial paired train meets the combination conditions of the target train. If the train type of the initial paired train does not match the train type of the target train or the target train and the initial paired train cannot be connected, the logic processing server determines that the train type of the initial paired train does not meet the combination conditions of the target train. Among them, the situation where the train type of the target train matches the train of the initial paired train is that the train type of one train is the first train type, the train type of the other train is the second train type, or the train types of both trains are the second train type. The situation where the train type of the target train does not match the train type of the initial paired train is that the train types of both trains are the first train type.

[0123] Step 706, if the initial paired train does not match the target train, determine the next train of the initial paired train in the train daily plan dataset as the initial paired train in the order of arrival time from earliest to latest.

[0124] In implementation, if the initial paired train does not match the target train, the logic processing server determines the next train of the initial paired train in the train daily plan dataset as the new initial paired train in the order of arrival time from earliest to latest.

[0125] Step 708, execute the step of determining whether the initial paired train matches the target train according to the train type of the target train, the train operation plan information, the attribute information of the target train, and the train data subset of the initial paired train, until the initial paired train matches the target train or the entire train data subset is traversed, and determine the initial paired train as the paired train.

[0126] In implementation, the logic processing server executes the step of determining whether the initial paired train matches the target train according to the train type of the target train, the train operation plan information, the attribute information of the target train, and the subset of train data of the initial paired train, until the initial paired train matches the target train or the entire subset of train data in the train daily shift plan dataset is traversed. The logic processing server determines the initial paired train as the paired train. Among them, this step is the above step 704, and the specific processing process of step 704 has been elaborated in detail in the above embodiments, and will not be repeated in the embodiments of the present application.

[0127] In an optional embodiment, if the logic processing server traverses the entire subset of train data in the train daily shift plan dataset and still cannot determine a paired train that matches the target train, the logic processing server determines that there is no corresponding matching train for the current target train. The logic processing server updates the target train to the next train of the target train and continues to execute the above step 102.

[0128] In an optional embodiment, after generating the combined plan information, the logic processing server sends the combined plan information to the terminal of the station attendant or the station dispatcher position, instructing the station staff to verify the combined plan information. During the process of the combined plan information, the station staff can modify the combined plan information until the verified combined plan information is obtained. The terminal sends the verified combined plan information to the CTC train operation diagram, which is confirmed by the train dispatcher belonging to the technical station, and at the same time sends it back to the logic processing server. The logic processing server distributes the running line modification of each dispatching desk affected by the verified combined plan information, instructing the CTC operation diagrams of each station to complete the automatic drawing of the running line.

[0129] In this embodiment, by using the train type, train operation plan information, attribute information of the target train, and the subset of train data of the initial paired train, a paired train that matches the target train is determined one by one in the train daily shift plan dataset, realizing the automatic determination of the paired train, avoiding manual participation, reducing subjective factors, and improving the efficiency and accuracy of the data processing method.

[0130] In an exemplary embodiment, as Figure 8 shown, the specific processing process of step 404 includes steps 802 to 806. Among them:

[0131] Step 802, according to the attribute information, determine whether the target train is a decomposable train.

[0132] Among them, the attribute information includes the formation information of the target train.

[0133] In implementation, the logic processing server determines whether the formation information is empty. If the formation information is empty, the logic processing server determines that the target train is not a decomposable train. If the formation information is not empty, the logic processing server determines that the target train is a decomposable train.

[0134] In an alternative embodiment, when the target train is not a decomposable train, the logic processing server does not perform decomposition processing on the target train and updates the target train to the next train of the target train.

[0135] Step 804, when the target train is a decomposable train, determine the boundary station information of the target train according to the train operation plan information and the train decomposition algorithm.

[0136] In implementation, the train decomposition algorithm is pre-set in the logic processing server. The logic processing server determines the boundary station among the stations where the target train stops according to the train decomposition algorithm and the train operation plan information of the target train, and determines the boundary station information.

[0137] Step 806, generate the decomposition plan information of the target train according to the boundary station information, the train operation plan information and the attribute information.

[0138] In implementation, the logic processing server determines the arrival time corresponding to the boundary station in the boundary station information. Then, the logic processing server determines whether the third time difference between the arrival time corresponding to the boundary station and the current time exceeds the second time period. If the third time difference does not exceed the second time period, the logic processing server generates the decomposition plan information of the target train according to the boundary station information, the train operation plan information and the attribute information of the target train.

[0139] In an alternative embodiment, the logic processing server generates the decomposition plan information of the target train according to the boundary station information of the target train and the train data subset.

[0140] In an alternative embodiment, after generating the decomposition plan information, the logic processing server sends the boundary plan information to the staff at the plan adjustment position to instruct the station staff to verify the boundary plan information. During the process of the boundary plan information, the station staff can modify the boundary plan information until the verified boundary plan information is obtained. The terminal sends the verified boundary plan information to the CTC train operation diagram, which is confirmed by the train dispatcher of the technical station and at the same time sent back to the logic processing server. The logic processing server modifies and distributes the operation lines of each dispatching desk affected by the verified boundary plan information, and instructs the CTC operation diagrams of each station to complete the automatic drawing of the operation lines.

[0141] Optionally, the second time period can be but is not limited to being set to three hours, which is determined according to the data processing requirements, and the second time period is not limited in the embodiments of the present application.

[0142] In this embodiment, the dividing station of the target train is determined according to the train decomposition algorithm, and then the decomposition plan information is determined according to the dividing station, realizing the automatic combination of trains and generating a train combination plan, avoiding manual participation, reducing the time for generating the decomposition plan information, and improving the efficiency of the data processing method.

[0143] In an exemplary embodiment, Figure 9 is a flowchart for pairing trains in an exemplary embodiment. As Figure 9 shown, a method for pairing trains is provided, including:

[0144] Step 901, receiving the train daily shift plan data set;

[0145] Step 902, traversing the subset of the train daily shift plan data of the target trains whose departure times are within 3 hours;

[0146] Step 903, judging whether the target train needs inspection operation according to the inspection information of the target train; if the target train needs inspection operation, execute the following step 906, if the target train does not need inspection operation, execute the following step 904;

[0147] Step 904, judging whether the locomotive traction mode of the target train is the 2+2 traction mode according to the locomotive traction information of the target train; if the locomotive traction mode is the target traction mode, execute the following step 905, if the locomotive traction mode is not the 2+2 traction mode, execute the following step 906;

[0148] Step 905, setting the target train as column A or column B, where in a combined train, the first column is column A and the second column is column B;

[0149] Step 906, setting the target train as column A, that is, the first train in the combined train;

[0150] Step 907, determining the next train of the target train in the train daily shift plan data set as the initial paired train in the order of arrival time from earliest to latest;

[0151] Step 908, judging whether the second time difference between the initial paired train and the target train exceeds the preset interval time threshold; if the second time difference does not exceed the interval time threshold, execute step 909, if the second time difference exceeds the interval time threshold, execute step 914;

[0152] Step 909, judging whether the train type of the initial paired train meets the combination conditions of the target train; if the train type of the initial paired train meets the combination conditions, execute step 910, if the train type of the initial paired train does not meet the combination conditions, execute step 914;

[0153] Step 910, determine whether the destinations of the target train and the initial paired train are the same; if the destinations of the target train and the initial paired train are the same, then execute Step 911, if the destinations of the target train and the initial paired train are not the same, then execute Step 914;

[0154] Step 911, determine whether the goods of the target train and the initial paired train are the same; if the goods of the target train and the initial paired train are the same, then execute Step 912, if the goods of the target train and the initial paired train are different, then execute Step 914;

[0155] Step 912, determine whether the formation situations of the target train and the initial paired train meet the combination conditions; if the formation situations of the target train and the initial paired train meet the combination conditions, then execute Step 913, if the formation situations of the target train and the initial paired train do not meet the combination conditions, then execute Step 914,

[0156] Step 913, determine the initial paired train as the paired train and generate paired plan information;

[0157] Step 914, update the initial paired train to the next train of the initial paired train and execute the above Step 908.

[0158] In an exemplary embodiment, Figure 10 is a flowchart for decomposing a train in an exemplary embodiment. As Figure 10 shown, a method for decomposing a train is provided, including:

[0159] Step 1001, receive a train transportation and sales change data set;

[0160] Step 1002, traverse the subset of train transportation and sales change data of the target train whose departure time is within 3 hours;

[0161] Step 1003, determine whether the target train is a decomposable train; if the target train is a decomposable train, then execute Step 1004, if the target train is not a decomposable train, then execute Step 1002;

[0162] Step 1004, calculate the dividing station of the target train according to the train decomposition algorithm and the subset of train transportation and sales change data of the target train; if there is a dividing station, then execute Step 1005, if there is no dividing station, then execute Step 1002;

[0163] Step 1005, determine whether the third time difference between the dividing station time and the current time is less than 3 hours; if the third time difference is less than three hours, then execute Step 1006; if the third time difference is greater than or equal to three hours, then execute Step 1002;

[0164] Step 1006, generate decomposition plan information.

[0165] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0166] Based on the same inventive concept, an embodiment of the present application further provides a data processing device for implementing the data processing method involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the data processing device provided below can refer to the limitations on the data processing method in the above text, and will not be elaborated here.

[0167] In an exemplary embodiment, as Figure 11 shown, a data processing device 1100 is provided, including: a receiving module 1101 and a combining module 1102, where:

[0168] The receiving module 1101 is configured to receive a train data set and traverse the train data subsets of each train in the train data set; the train data subsets include the train operation plan information, attribute information, and maintenance information of the train.

[0169] The combining module 1102 is configured to, when the target train meets the preset train constraint conditions, combine the target train according to the train scheduling algorithm, the operation plan information, attribute information, and maintenance information of the target train, and obtain the combined plan information of the target train.

[0170] In an exemplary embodiment, the train data subset of the target train includes the departure time of the target train, and the data processing device 1100 further includes:

[0171] A judgment module, configured to determine a first time difference between the departure time of the target train and the current time, and judge whether the first time difference is greater than a preset first time period.

[0172] A first determination module, configured to, if the first time difference is greater than the first time period, determine that the target train does not meet the preset train constraint conditions.

[0173] A second determination module, configured to determine that a target train meets a preset train constraint condition if a first time difference is less than or equal to a first time period.

[0174] In an exemplary embodiment, a train scheduling algorithm includes a train pairing algorithm and a train decomposition algorithm. The combination module 1102 includes:

[0175] A pairing sub-module, configured to, if a train data set is a train daily plan data set, determine a paired train that is paired with a target train in the train daily plan data set according to the train pairing algorithm, train operation plan information, attribute information, and maintenance information, and combine the target train and the paired train to generate paired plan information of the target train.

[0176] A decomposition sub-module, configured to, if a train data set is a train transportation and sales change data set, perform decomposition processing on a target train according to the train decomposition algorithm, train operation plan information, and attribute information, and generate decomposition plan information of the target train.

[0177] In an exemplary embodiment, the pairing sub-module includes:

[0178] A first determination sub-module, configured to determine a train type of a target train according to the maintenance information and attribute information of the target train.

[0179] A second determination sub-module, configured to determine a paired train that matches the target train in the train daily plan data set according to the train type, train operation plan information, and attribute information of the target train.

[0180] A first generation sub-module, configured to pair the target train and the paired train together to form a train formation of the target train, and generate paired plan information of the train formation.

[0181] In an exemplary embodiment, the attribute information includes a locomotive traction mode, and the locomotive traction mode includes a target traction mode and a non-target traction mode; the maintenance information includes requiring technical inspection and not requiring technical inspection. The first determination sub-module includes:

[0182] A third determination sub-module, configured to determine that the train type of the target train is a second train type if the locomotive traction mode is a non-target traction mode and the maintenance information is not requiring technical inspection.

[0183] A fourth determination sub-module, configured to determine that the train type of the target train is a first train type if the locomotive traction mode is a target traction mode and / or the maintenance information is requiring technical inspection.

[0184] In an exemplary embodiment, the second determination sub-module includes:

[0185] The fifth determination sub-module is used to determine the next train of the target train in the train daily plan dataset as the initial paired train in the order from the earliest to the latest departure time.

[0186] The first judgment sub-module is used to judge whether the initial paired train matches the target train according to the train type of the target train, the train operation plan information, the attribute information of the target train, and the train data subset of the initial paired train.

[0187] The sixth determination sub-module is used to, if the initial paired train does not match the target train, determine the next train of the initial paired train in the train daily plan dataset as the initial paired train in the order from the earliest to the latest departure time.

[0188] The execution sub-module is used to execute the step of judging whether the initial paired train matches the target train according to the train type of the target train, the train operation plan information, the attribute information of the target train, and the train data subset of the initial paired train or after traversing the full set of train data subsets, until the initial paired train matches the target train, and determine the initial paired train as the paired train.

[0189] In an exemplary embodiment, the decomposition sub-module includes:

[0190] The second judgment sub-module is used to judge whether the target train is a decomposable train according to the attribute information.

[0191] The seventh determination sub-module is used to, when the target train is a decomposable train, determine the dividing station information of the target train according to the train operation plan information and the train decomposition algorithm.

[0192] The second generation sub-module is used to generate the decomposition plan information of the target train according to the dividing station information, the train operation plan information, and the attribute information.

[0193] Each module in the above data processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0194] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 12As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data used by the data processing method. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a data processing method.

[0195] Those skilled in the art can understand that Figure 12 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0196] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0198] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0199] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory can include Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, Artificial Intelligence (AI) processors, etc., without limitation.

[0200] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0201] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Receiving a train dataset and traversing the train data subsets of each train in the train dataset; the train data subsets include the train operation plan information, attribute information, and maintenance information of the train; When the target train meets the preset train constraint conditions, combining the target train according to the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train to obtain the combined plan information of the target train.

2. The method according to claim 1, wherein The train data subset of the target train includes the departure time of the target train. After traversing the train data subsets of each train in the train dataset, the method further includes: Determining a first time difference between the departure time of the target train and the current time, and determining whether the first time difference is greater than a preset first time period; If the first time difference is greater than the first time period, determining that the target train does not meet the preset train constraint conditions; If the first time difference is less than or equal to the first time period, determining that the target train meets the preset train constraint conditions.

3. The method according to claim 1, characterized in that, The train scheduling algorithm includes a train pairing algorithm and a train decomposition algorithm. Combining the target train according to the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train to obtain the combined plan information of the target train includes: If the train dataset is a train daily shift plan dataset, determining a paired train that pairs with the target train in the train daily shift plan dataset according to the train pairing algorithm, the train operation plan information, attribute information, and maintenance information, and combining the target train and the paired train to generate the paired plan information of the target train; If the train dataset is a train transportation and sales change dataset, decomposing the target train according to the train decomposition algorithm, train operation plan information, and attribute information to generate the decomposition plan information of the target train.

4. The method according to claim 3, characterized in that Determining a paired train that pairs with the target train in the train daily shift plan dataset according to the train pairing algorithm, the train operation plan information, attribute information, and maintenance information, and combining the target train and the paired train to generate the paired plan information of the target train includes: Determining the train type of the target train according to the maintenance information and attribute information of the target train; Determining a paired train that matches the target train in the train daily shift plan dataset according to the train type, train operation plan information, and attribute information of the target train; Pairing the target train and the paired train together to form the train formation of the target train, and generating the paired plan information of the train formation.

5. The method according to claim 4, characterized in that, The attribute information includes the locomotive traction mode, and the locomotive traction mode includes a target traction mode and a non-target traction mode; the maintenance information includes requiring technical inspection and not requiring technical inspection. Determining the train type of the target train according to the maintenance information and attribute information of the target train includes: If the locomotive traction mode is a non-target traction mode and the maintenance information indicates that technical inspection is not required, determine that the train type of the target train is the second train type; If the locomotive traction mode is a target traction mode and / or the maintenance information indicates that technical inspection is required, determine that the train type of the target train is the first train type.

6. The method according to claim 4, characterized in that Determining a paired train that matches the target train in the train daily shift plan dataset according to the train type, train operation plan information, and attribute information of the target train includes: Determine the next train of the target train in the train daily shift plan dataset as the initial paired train in the order of arrival time of the departure time; Judge whether the initial paired train matches the target train according to the train type, train operation plan information, attribute information of the target train, and the train data subset of the initial paired train; If the initial paired train does not match the target train, determine the next train of the initial paired train in the train daily shift plan dataset as the initial paired train in the order of arrival time of the departure time; Execute the step of judging whether the initial paired train matches the target train according to the train type, train operation plan information, attribute information of the target train, and the train data subset of the initial paired train or traverse the entire train data subset until the initial paired train matches the target train, and determine the initial paired train as the paired train.

7. The method according to claim 3, characterized in that, Performing decomposition processing on the target train according to the train decomposition algorithm, train operation plan information, and attribute information to generate decomposition plan information of the target train includes: Judge whether the target train is a decomposable train according to the attribute information; In the case that the target train is the decomposable train, determine the boundary station information of the target train according to the train operation plan information and the train decomposition algorithm; Generate the decomposition plan information of the target train according to the boundary station information, train operation plan information, and attribute information.

8. A data processing device, characterized in that, The device includes: A receiving module, configured to receive a train dataset and traverse the train data subsets of each train in the train dataset; the train data subset includes the train operation plan information, attribute information, and maintenance information of the train; A combination module, configured to combine the target train according to the train scheduling algorithm, the train operation plan information, attribute information, and maintenance information of the target train to obtain the combination plan information of the target train when the target train meets the preset train constraint conditions.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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