Motor train unit dispatching production organization information management system and method

Abstract

The invention discloses a motor train unit dispatching production organization information management system and method, and relates to the technical field of rail transit dispatching. Multi-source information such as motor train unit assignment, quarterly train working diagrams and advanced repair plans is obtained through a data input module; the rule configuration module is used for setting a mapping relation between vehicle types and route attributes and a transportation peak time identifier; and the application scheme generation module generates a quarterly application scheme based on the data, and determines the vehicle type arrangement of each intersection every day. And the train arrangement algorithm execution module adopts a greedy algorithm to distribute the motor train units to the intersections. And the checking module checks the road vehicle type compliance, the uniqueness of the motor train unit and the reasonability of the repair time. And the visual output module displays the vehicle arrangement result in the form of a table and a Gantt chart. The intelligent level of dispatching of the motor train unit is improved, the maintenance cost is effectively reduced, the vehicle utilization rate and the peak transport capacity guarantee capability are improved, and technical support is provided for high-quality operation of a high-speed railway.

Description

Technical Field

[0001] This invention relates to the field of rail transit dispatching technology, and in particular to an information management system and method for EMU (Electric Multiple Unit) dispatching and production organization. Background Technology

[0002] With the continuous expansion of my country's high-speed railway network and the rapid growth of its EMU (Electric Multiple Unit) fleet, the organization of EMU operations and maintenance scheduling are becoming increasingly complex. As a high-value, high-tech core transportation equipment, the efficiency of EMU operations directly affects the quality of railway transportation services, operating cost control, and traffic safety.

[0003] In current railway operation practices, "timetable adjustment" is an important task involving the systematic adjustment of train operation schedules every quarter. It involves the reconfiguration of information across multiple dimensions, including train numbers, routes, train types, times, and sections. After the timetable adjustment, a quarterly operation plan (also known as an AABB table) for high-speed trains must be prepared simultaneously, clearly defining the daily route arrangements for each high-speed train within the quarter. At the same time, maintenance plans must be developed based on the high-speed trains' mileage and operating days, according to maintenance procedures of levels one to five (levels one and two being routine maintenance, and levels three to five being advanced maintenance), especially the accurate prediction and arrangement of advanced maintenance delivery times.

[0004] Currently, the above-mentioned scheduling and maintenance plans mainly rely on manual experience, supplemented by Excel spreadsheets and simple macro programs. Dispatchers need to manually refer to the operation diagram and fill in the vehicle allocation in Excel every day; maintenance personnel then formulate maintenance plans based on this spreadsheet and use macros to calculate downtime and travel statistics. This model has the following significant drawbacks:

[0005] High cost: Data in each stage is scattered in different unstructured files. Once the operation diagram or the associated information changes, a lot of manpower is needed to synchronize and verify across tables, resulting in high communication and coordination costs.

[0006] Low efficiency: Text-based planning information lacks intuitiveness, the review and analysis process is time-consuming, and it is difficult to quickly respond to temporary adjustment needs;

[0007] Low data utilization: Valuable information such as historical running data, maintenance records, and routine running plans have not been effectively integrated and mined, failing to support intelligent decision-making;

[0008] Risks are easily overlooked: There are multiple restrictions on the matching of routes and vehicle types (such as vehicle type restrictions in restricted areas, maintenance cycle constraints, and maintenance restrictions during peak hours). Manual processing can easily overlook key risk points, resulting in insufficient compliance of the solution.

[0009] Despite some attempts at informatization, the industry still lacks an integrated system that uses advanced maintenance data as its core, integrates multi-source dynamic information, and automatically generates and verifies train scheduling plans through intelligent algorithms. Therefore, there is an urgent need for a new type of EMU (Electric Multiple Unit) scheduling production organization information management system and method to achieve a transformation and upgrade from "manual + spreadsheet" to "data-driven + intelligent algorithm". Summary of the Invention

[0010] To overcome the shortcomings in the prior art, the present invention discloses a high-speed train scheduling and production organization information management system and method.

[0011] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0012] A high-speed train scheduling and production organization information management system includes:

[0013] The data input module is used to acquire EMU allocation information, quarterly train operation schedule data, advanced maintenance plan data, current EMU running mileage data, maintenance cycle information, and peak transportation period information.

[0014] The rule configuration module is used to set vehicle scheduling rules, which include the correspondence between vehicle type and route attributes, the route types that each vehicle type is allowed or prohibited from undertaking, and peak transportation period identifiers.

[0015] The application scheme generation module is used to generate a quarterly application scheme based on the quarterly train timetable data and the train scheduling rules. The quarterly application scheme includes the daily train type arrangement for each route.

[0016] The train scheduling algorithm execution module is used to read the quarterly operation plan, advanced maintenance plan data, current running mileage data of EMUs, maintenance cycle information and peak transportation period information, and use a greedy algorithm to allocate EMUs in the available state to each route, so that the average daily running mileage of EMUs during the advanced maintenance cycle is as close as possible to the average daily running mileage of the assigned route, and to avoid scheduling maintenance during peak transportation periods.

[0017] The scheme verification module is used to verify the correctness of the generated train scheduling scheme. The verification content includes: whether the train type of the route conforms to the timetable regulations, whether the same EMU is repeatedly assigned to multiple routes, and whether EMUs are arranged for maintenance during peak transportation periods.

[0018] The visualization output module is used to display the verified train scheduling plan in tabular and Gantt chart formats. The table displays the daily route information and expected downtime of each EMU, while the Gantt chart displays the advanced maintenance and repair schedule of each EMU.

[0019] Preferably, when the train scheduling algorithm execution module executes the greedy algorithm, it prioritizes selecting the train set whose current remaining running kilometers best match the average daily running kilometers required for the target route for allocation.

[0020] Preferably, the system further includes a route management module for storing and managing route combinations formed by multiple routes. Each route in each route combination has the same vehicle type and includes the mileage, start and end times, and average daily mileage information for each route.

[0021] Preferably, the advanced repair plan data includes the planned repair dates for level three to five repairs, the repair cycle threshold, and the repair requirements for the corresponding vehicle model.

[0022] A method for scheduling and organizing high-speed train production includes the following steps:

[0023] S1: Configure vehicle scheduling rules, establish a mapping relationship between vehicle type and route attributes, and set peak transportation periods;

[0024] S2: Generate a quarterly operation plan based on the quarterly train operation schedule data and the aforementioned train scheduling rules;

[0025] S3: Read the quarterly operation plan, advanced maintenance plan data, current running distance data of the EMU, maintenance cycle information and peak transportation period information;

[0026] S4: Using a greedy algorithm, EMUs that are not under maintenance are assigned to various routes to minimize the deviation between the average daily mileage of the EMUs during the advanced maintenance cycle and the average daily mileage of the assigned routes, and to avoid scheduling maintenance during peak transport periods.

[0027] S5: Verify the generated train scheduling plan, including the compliance of the train model, the uniqueness of the EMU, and the reasonableness of the repair time.

[0028] S6: Visualize the validated vehicle scheduling plan in the form of tables and Gantt charts.

[0029] Preferably, the specific strategy of the greedy algorithm is as follows: iterate through the route demand on a daily basis, and for each route, select the EMU with the current remaining mileage and the daily average mileage of the corresponding route from the set of available EMUs for allocation.

[0030] Preferably, it also includes: dynamically updating the remaining mileage of the EMU based on the actual running data, and re-executing steps S3 to S6 in the next scheduling cycle to achieve rolling optimization of train scheduling.

[0031] Preferably, the advanced maintenance plan data is used to predict the expected downtime of the EMU, and the expected downtime is used as a scheduling constraint in the decision-making process of the greedy algorithm.

[0032] Preferably, the quarterly operation plan is in the form of an AABB table, which records the routes and train types assigned to each EMU each day within the quarter.

[0033] By employing the technical solution described above, the present invention has the following beneficial effects:

[0034] (1) This invention significantly improves the intelligence, automation, and refinement of EMU operation and maintenance planning by constructing a complete EMU scheduling and production organization information management system and method. The system uses advanced maintenance data as its core constraint, integrating multi-dimensional dynamic data such as EMU allocation information, quarterly train timetables, current mileage, maintenance cycles, and peak transport periods. This fundamentally solves the problems of data fragmentation, low efficiency, and risk omissions caused by traditional reliance on manual labor and Excel spreadsheets. It not only achieves the digitalization, automation, and intelligence of the entire EMU scheduling and production organization process, but also achieves significant technical effects in reducing maintenance costs, improving vehicle utilization, ensuring peak-period transport capacity supply, and preventing risks of exceeding operating time limits, providing solid technical support for high-quality operation of high-speed railways.

[0035] (2) The system setting rule configuration module of this invention supports the flexible definition of the mapping relationship between vehicle type and route attributes, clarifies the route types that each vehicle type is allowed or prohibited from undertaking, and can mark transportation peak periods such as Spring Festival travel rush, summer travel rush, and statutory holidays. This mechanism ensures that the subsequent vehicle scheduling process always operates within the compliance boundary, effectively avoids the operational risks caused by vehicle type mismatch or peak period repairs, and provides a prior guarantee for the legality and safety of the solution.

[0036] (3) Further, in the scheme generation stage, the present invention uses a scheme generation module to automatically generate a quarterly operation scheme (AABB table) based on the operation diagram and train scheduling rules, and accurately arranges the allocation of train types for each route every day; the train scheduling algorithm execution module then introduces a greedy algorithm constrained by the advanced maintenance cycle, prioritizing the allocation of available EMUs that best match the remaining running kilometers with the average daily running kilometers of the target route to the corresponding route. This strategy not only makes the running distribution of EMUs more balanced throughout the entire advanced maintenance cycle, avoiding "rush running" or "idle waste", but also accurately predicts the shutdown date and actively avoids peak transportation periods when arranging maintenance, thereby maximizing the available transport capacity of EMUs while ensuring the rigid requirements of maintenance.

[0037] (4) The system of this invention is also equipped with a scheme verification module, which performs multi-dimensional automatic verification of the generated train scheduling results, including whether the train types of the routes comply with the timetable regulations, whether the same EMU is repeatedly assigned, and whether the maintenance time is during off-peak periods, etc. This closed-loop verification mechanism greatly reduces the possibility of human error, ensures that the output scheme can be directly used for production scheduling, and improves the reliability of decision-making. The visualization output module presents the final scheme intuitively in two forms: tables and Gantt charts. The tables list in detail the routes, times and expected downtime of each EMU each day, which is convenient for daily verification. The Gantt chart clearly shows the operation and advanced maintenance time periods of each EMU in a timeline manner, providing a global view for dispatching and command. This dual-mode display method greatly improves the efficiency of scheme review and communication.

[0038] (5) The system of this invention has a built-in trip plan management module, which uniformly stores and manages legal trip combinations consisting of multiple routes of the same train type, ensuring the consistency and integrity of trip logic during the train scheduling process. Combined with the rolling optimization mechanism—that is, dynamically updating the remaining mileage based on the actual travel data of the EMU and automatically rearranging subsequent routes in the next scheduling cycle—the system can flexibly respond to emergencies such as the addition of temporary trains and temporary maintenance, realize the continuous dynamic optimization of the train scheduling plan, and truly realize the transformation from "static planning" to "dynamic control". Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0040] 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 embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0041] In the description of this invention, it should be noted that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0042] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0043] Example 1:

[0044] Combined with appendix Figure 1 A high-speed train scheduling and production organization information management system and method, including

[0045] The data input module is used to acquire EMU allocation information, quarterly train operation schedule data, advanced maintenance plan data, current EMU running mileage data, maintenance cycle information, and peak transportation period information.

[0046] The rule configuration module is used to set vehicle scheduling rules, which include the correspondence between vehicle type and route attributes, the route types that each vehicle type is allowed or prohibited from undertaking, and peak transportation period identifiers.

[0047] The application scheme generation module is used to generate a quarterly application scheme based on the quarterly train timetable data and the train scheduling rules. The quarterly application scheme includes the daily train type allocation for each route.

[0048] The train scheduling algorithm execution module is used to read the quarterly operation plan, advanced maintenance plan data, current train mileage data, maintenance cycle information, and peak transportation period information. It employs a greedy algorithm to allocate available trains to various routes, ensuring that the average daily mileage of each train during its advanced maintenance cycle is as close as possible to the average daily mileage of its assigned route, while avoiding scheduling maintenance during peak transportation periods. Furthermore, when executing the greedy algorithm, the module prioritizes allocating trains whose remaining mileage best matches the average daily mileage required for the target route. The advanced maintenance plan data includes planned maintenance dates for levels three to five maintenance, maintenance cycle thresholds, and maintenance requirements for the corresponding train models. The peak transportation period information includes the time ranges for Spring Festival travel, summer travel, and national statutory public holidays.

[0049] The scheme verification module is used to verify the correctness of the generated train scheduling scheme. The verification content includes: whether the train types on the route comply with the timetable regulations, whether the same EMU is repeatedly assigned to multiple routes, and whether EMUs are arranged for maintenance during peak transportation periods.

[0050] The visualization output module is used to display the verified train scheduling plan in tabular and Gantt chart formats. The table displays the daily route information and expected downtime of each EMU, while the Gantt chart displays the advanced maintenance and repair schedule of each EMU.

[0051] In addition, it includes a route management module, which is used to store and manage route combinations formed by multiple routes. Each route in each route combination has the same vehicle type and includes the mileage, start and end time and average daily mileage information of each route.

[0052] The method for scheduling and organizing high-speed train production includes the following steps:

[0053] S1: Configure vehicle scheduling rules, establish a mapping relationship between vehicle type and route attributes, and set peak transportation periods.

[0054] S2: Based on the quarterly train operation schedule data and the train scheduling rules, a quarterly operation plan is generated; the quarterly operation plan is in the form of an AABB table, which records the routes and train types assigned to each EMU each day within the quarter.

[0055] S3: Read the quarterly operation plan, advanced maintenance plan data, current running distance data of the EMU, maintenance cycle information and peak transportation period information; the advanced maintenance plan data is used to predict the expected downtime of the EMU, and the expected downtime is used as a scheduling constraint in the decision-making process of the greedy algorithm.

[0056] S4: A greedy algorithm is used to allocate EMUs not under maintenance to various routes, minimizing the deviation between the average daily mileage of each EMU during its advanced maintenance cycle and the average daily mileage of its assigned route, while avoiding scheduling maintenance during peak transport periods. Further, the specific strategy of the greedy algorithm is as follows: iterate through the route demand daily, and for each route, select the EMU with the current remaining mileage closest to the average daily mileage of the route's assigned route from the set of available EMUs for allocation.

[0057] S5: Verify the generated train scheduling plan, including the compliance of the train model, the uniqueness of the EMU, and the reasonableness of the repair time.

[0058] S6: Visualize the validated vehicle scheduling plan in the form of tables and Gantt charts.

[0059] Furthermore, the remaining mileage of the EMU is dynamically updated based on the actual running data, and steps S3 to S6 are re-executed in the next scheduling cycle to achieve rolling optimization of train scheduling.

[0060] To more clearly illustrate the technical solution of this invention, the following describes the intelligent trainset scheduling scheme in the context of an actual scheduling scenario following a quarterly timetable adjustment. Assume that after a timetable adjustment in the third quarter of a certain year, a railway bureau needs to develop operational and advanced maintenance scheduling schemes for 30 CR400AF EMUs at its subordinate EMU depot A. The system first obtains necessary information through the data input module, including EMU allocation information, quarterly train timetable data, advanced maintenance plan data, current mileage data, peak transport period information, and a database of trainset operation plans. Simultaneously, the user sets scheduling rules through the rule configuration module, such as prohibiting CR400AF EMUs from operating on plateau lines, requiring all routes to be continuously operated by the same trainset type, and prohibiting any EMUs from being stopped for maintenance during peak transport periods.

[0061] Next, the scheme generation module reads the operation diagram and rules, automatically generating the AABB table for the third quarter (92 days) of a given year, i.e., the quarterly operation scheme. This scheme specifies that each route on each day will be handled by CR400AF trains, complying with train type regulations and avoiding restricted areas. Moving to the core step, the train scheduling algorithm execution module is activated. It groups route demands by day, filters out available EMUs that haven't undergone maintenance that day, and calculates the remaining mileage for each EMU within the current Level 3 maintenance cycle. For G10XS EMUs approaching their maintenance threshold, the system prioritizes assigning them to short-distance routes, ensuring they are ready for maintenance immediately after completing their runs. Furthermore, a greedy algorithm selects the EMU with the "remaining mileage / remaining days" ratio closest to the target daily average mileage for each route, ensuring efficient utilization and safe operation of the EMUs.

[0062] The scheme verification module automatically performs verification, checking whether the routes assigned to the EMUs are compliant, whether they have been repeatedly assigned, and whether the maintenance dates fall within off-peak periods. After successful verification, the visualization output module displays the daily route information and expected downtime dates for each EMU in tabular form, and visually shows the expected advanced maintenance dates for each EMU using a Gantt chart. If, during scheduling, it is found that an EMU has traveled extra mileage due to temporary additional trains, the system can dynamically update its travel data and readjust subsequent route assignments to avoid exceeding the scheduled operating time.

[0063] Example 2:

[0064] Combined with appendix Figure 1This document describes a high-speed train scheduling and production organization information management system and method. The difference from Embodiment 1 lies in that, based on Embodiment 1, in the third quarter timetable adjustment of a certain railway bureau in a certain year, the timetable includes 150 routes for the two main train models, CR400AF and CRH380B. Of these, 80 routes are designated for CR400AF and 70 for CRH380B. The system is equipped with 20 CR400AF trainsets (numbered G1001–G1020) and 18 CRH380B trainsets (numbered G2001–G2018). After the system obtains all basic data through the data input module, the user explicitly sets the following rules in the rule configuration module: CR400AF is prohibited from entering mountainous lines in Southwest China (such as the Chengdu-Guiyang line), and CRH380B is prohibited from operating nighttime routes on the Beijing-Shanghai High-Speed ​​Railway.

[0065] The application scheme generation module generates a quarterly application scheme (AABB table) based on this, ensuring that the vehicle types allocated to each route are consistent with the timetable regulations and rules. Subsequently, the vehicle allocation algorithm execution module processes the route groups on a daily basis and executes a greedy matching strategy for the two types of vehicle types respectively. For example, on day 30, there are 5 CR400AF routes (average daily mileage 2,600 km) and 4 CRH380B routes (average daily mileage 2,400 km). The system selects unmaintained vehicles from the pool of available EMUs for each group and calculates their remaining mileage within the advanced maintenance cycle. For the CR400AF group, G1012 has 28,000 km of remaining mileage, 11 days before the level 3 maintenance threshold, and an average daily mileage of approximately 2,545 km, which is closest to the target, so it is prioritized for allocation; similarly, G20XX in the CRH380B group is selected. The entire process strictly follows the aforementioned greedy strategy of "closest average daily mileage" and ensures that cross-type vehicles are not mixed.

[0066] The scheme verification module further confirmed: no vehicle model mismatch, no duplicate dispatches, and no peak-hour repair requests (the day was not a holiday). Finally, the visualization output module generated tables and Gantt charts, clearly distinguishing the operation and maintenance plans for the two vehicle types. This fully demonstrates the aforementioned system management capabilities—the system has two built-in independent system databases (CR400AF system set and CRH380B system set), ensuring vehicle consistency within the same system and effectively supporting multi-vehicle collaborative scheduling.

[0067] Example 3:

[0068] Combined with appendix Figure 1A high-speed train scheduling and production organization information management system and method, based on Embodiment 1 or 2, describes an scenario where, on the 50th day of operation in the third quarter of a certain year, high-speed train G10XX requires premature Level 3 maintenance due to wheelset abnormalities, having actually traveled only 1.12 million kilometers, far below the original threshold of 1.2 million kilometers. The maintenance department urgently enters this temporary advanced maintenance plan, and the system receives updated maintenance status and shutdown requests in real time through the data input module.

[0069] At this point, the system activates the aforementioned "rolling optimization" mechanism: First, G10XX (CR400AF type) is marked as unavailable, and the remaining running capacity of the remaining 19 CR400AF EMUs is recalculated; then, the train scheduling algorithm execution module re-executes a greedy allocation for subsequent routes starting from day 51. For example, the "Beijing-Shanghai morning train + Shanghai-Kunming evening train" route (average 2,800 km per day) originally handled by G10XX needs to be transferred to other train sets. The system assessment found that G10XA currently has a cumulative 1.08 million km, with 120,000 km remaining, and 5 days until the original scheduled maintenance, with an average daily reserve of 2,400 km. Although this is slightly lower than the target, considering its good condition and the absence of dense subsequent routes, it is adjusted to be the new train set to be used, and its maintenance date is simultaneously extended to day 55.

[0070] The scheme verification module immediately verifies the new scheme: confirming that the G10XA model is compliant, not repeatedly assigned, and that the repair date (day 55) avoids the National Day peak period (October 1-7, i.e., days 92-98). The visualization output module automatically refreshes the Gantt chart, with G10XX turning into a red maintenance block from day 50 onwards, and the G10XA's operating block extending backward, allowing dispatchers to intuitively grasp the impact of the change. This entire process requires no manual intervention in the Excel spreadsheet, avoiding the large-scale manual adjustment errors caused by temporary maintenance in the traditional model, fully verifying the robustness and automation advantages of this invention in dealing with sudden operating conditions.

[0071] The parts of this invention not described in detail are prior art. It will be apparent to those skilled in the art that this invention is not limited to the details of the above exemplary embodiments, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and are intended to encompass all changes falling within the meaning and scope of equivalents within this invention.

Claims

1. A high-speed train scheduling and production organization information management system, characterized in that, include: The data input module is used to acquire EMU allocation information, quarterly train operation schedule data, advanced maintenance plan data, current EMU running mileage data, maintenance cycle information, and peak transportation period information. The rule configuration module is used to set vehicle scheduling rules, which include the correspondence between vehicle type and route attributes, the route types that each vehicle type is allowed or prohibited from undertaking, and peak transportation period identifiers. The application scheme generation module is used to generate a quarterly application scheme based on the quarterly train timetable data and the train scheduling rules. The quarterly application scheme includes the daily train type arrangement for each route. The train scheduling algorithm execution module is used to read the quarterly operation plan, advanced maintenance plan data, current running mileage data of EMUs, maintenance cycle information and peak transportation period information, and use a greedy algorithm to allocate EMUs in the available state to each route, so that the average daily running mileage of EMUs during the advanced maintenance cycle is as close as possible to the average daily running mileage of the assigned route, and to avoid scheduling maintenance during peak transportation periods. The scheme verification module is used to verify the correctness of the generated train scheduling scheme. The verification content includes: whether the train type of the route conforms to the timetable regulations, whether the same EMU is repeatedly assigned to multiple routes, and whether EMUs are arranged for maintenance during peak transportation periods. The visualization output module is used to display the verified train scheduling plan in tabular and Gantt chart formats. The table displays the daily route information and expected downtime of each EMU, while the Gantt chart displays the advanced maintenance and repair schedule of each EMU.

2. The EMU scheduling and production organization information management system as described in claim 1, characterized in that: When executing the greedy algorithm, the train allocation algorithm execution module prioritizes the train sets whose current remaining running kilometers match the average daily running kilometers required for the target route.

3. The EMU scheduling and production organization information management system as described in claim 1, characterized in that: The system also includes a route management module for storing and managing route combinations formed by multiple routes. Each route in each route combination has the same vehicle type and includes information on the mileage, start and end times, and average daily mileage of each route.

4. The EMU scheduling and production organization information management system as described in claim 1, characterized in that: The advanced repair plan data includes the planned repair dates for level three to five repairs, the repair cycle threshold, and the repair requirements for the corresponding vehicle models.

5. A method for scheduling and organizing high-speed train production, characterized in that, Includes the following steps: S1: Configure vehicle scheduling rules, establish a mapping relationship between vehicle type and route attributes, and set peak transportation periods; S2: Generate a quarterly operation plan based on the quarterly train operation schedule data and the aforementioned train scheduling rules; S3: Read the quarterly operation plan, advanced maintenance plan data, current running distance data of the EMU, maintenance cycle information and peak transportation period information; S4: Using a greedy algorithm, EMUs that are not under maintenance are assigned to various routes to minimize the deviation between the average daily mileage of the EMUs during the advanced maintenance cycle and the average daily mileage of the assigned routes, and to avoid scheduling maintenance during peak transport periods. S5: Verify the generated train scheduling plan, including the compliance of the train model, the uniqueness of the EMU, and the reasonableness of the repair time. S6: Visualize the validated vehicle scheduling plan in the form of tables and Gantt charts.

6. The method for scheduling and organizing high-speed train production as described in claim 5, characterized in that: The specific strategy of the greedy algorithm is as follows: iterate through the route demand on a daily basis, and for each route, select the EMU with the current remaining distance of running kilometers that is closest to the average daily kilometers of the route's running plan from the set of available EMUs for allocation.

7. The method for scheduling and organizing high-speed train production as described in claim 5, characterized in that: It also includes: dynamically updating the remaining mileage of the EMU based on the actual running data, and re-executing steps S3 to S6 in the next scheduling cycle to achieve rolling optimization of train scheduling.

8. The method for scheduling and organizing high-speed train production as described in claim 5, characterized in that: The advanced maintenance plan data is used to predict the expected downtime of the EMU (Electric Multiple Unit) trains, and the expected downtime is used as a train scheduling constraint in the decision-making process of the greedy algorithm.

9. The method for scheduling and organizing high-speed train production as described in claim 5, characterized in that: The quarterly operation plan is in the form of an AABB table, which records the routes and train types assigned to each EMU each day during the quarter.

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