Cross-professional multi-mode production and maintenance planning and scheduling method for urban rail

Abstract

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit technology, and in particular to a multi-modal production and maintenance planning and scheduling method, electronic equipment, and readable medium for urban rail transit. Background Technology

[0002] The purpose of urban rail transit (urban rail) production and maintenance operations is to control operational equipment risks and improve equipment reliability through effective equipment maintenance methods. Currently, due to complex factors such as operation time, location, human resources, maintenance resources, and multi-disciplinary collaboration, traditional production and maintenance planning and scheduling mainly rely on manual experience, making it difficult to achieve the optimal balance between scientific rigor and economic efficiency.

[0003] Currently, although production and maintenance planning for single specialties has improved production efficiency and management to some extent with the help of information technology, the basic data systems of production and maintenance for various specialties (such as signaling, communication, rolling stock, mechanical automation, power supply, and track maintenance) are not interconnected, making data sharing difficult. This hinders cross-specialty collaborative data applications and the lack of unified data management, preventing more scientific and economical production and maintenance decision-making and resource optimization from a multi-specialty collaborative perspective. This restricts the lean transformation and development of production and maintenance systems and models. Therefore, it is necessary to further utilize cross-specialty intelligent production planning and scheduling technology to meet the needs of lean transformation in production and maintenance.

[0004] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art. Summary of the Invention

[0005] The purpose of this invention is to provide a method, electronic equipment, and readable medium for cross-professional multimodal production and maintenance planning and scheduling in urban rail transit, which solves the problems of inconsistent data standards and difficulty in sharing among various professional systems, thus failing to support cross-professional joint analysis and collaborative planning.

[0006] To achieve the above objectives, this invention provides a multi-modal production and maintenance planning and scheduling method for urban rail transit, comprising the following steps: S1, Dynamic generation steps of cross-professional production plan: By establishing a unified data interface and data standards, data from different professional systems are aggregated and integrated, the production and maintenance needs of each professional are analyzed, and a preliminary cross-professional production and maintenance plan is generated by combining existing plans with real-time construction information. S2, Cross-disciplinary production scheduling steps: Based on the preliminary production and maintenance plan, perform preliminary scheduling of three resources: personnel, time, and tools, and then generate a globally optimized production and maintenance plan and resource scheduling scheme based on the improved genetic algorithm; S3, Multimodal Production Planning Execution and Tracking Steps: Based on the work order type, execution management is carried out using a precise mode with a fixed cycle and a coarse mode with a non-fixed cycle, with real-time monitoring and dynamic adjustment.

[0007] Optionally, in step S1, the step of establishing a unified data interface and data standards to aggregate and integrate data from different professional systems includes: Build a unified data interface based on RESTful APIs; Construct a unified data model covering the core business entities of various specialties; A unified coding rule is adopted to assign a unique identifier to equipment in each specialty; It aggregates equipment status data, maintenance record data, and testing and monitoring data from different professional systems into a central data pool.

[0008] Optionally, in step S1, the analysis of the production and maintenance needs of each specialty includes: Based on the equipment status data, maintenance record data, and testing and monitoring data of each specialty in the central data pool, data analysis algorithms are used to analyze the production and maintenance needs of each specialty. The production and maintenance needs include the target equipment, specialty, demand type, priority, prediction window, and estimated resources.

[0009] Optionally, in step S1, a preliminary cross-disciplinary production and maintenance plan is generated by combining existing plans with real-time construction information, including: Automatically extract existing annual and monthly production and maintenance plans from various professional systems, and combine them with the production and maintenance needs of each profession to form a cross-professional integrated plan library; By combining the cross-disciplinary integrated planning library with real-time construction information obtained from the construction system, a preliminary cross-disciplinary production and maintenance plan is generated.

[0010] Optionally, in step S2, a preliminary scheduling of personnel is performed, including: Access the personnel skills database, which records the employees' skill levels and professional qualifications; Based on the employees' skill levels and professional qualifications, and considering the complementary skills of the employees in multi-professional collaborative operations in the preliminary production and maintenance plan, a preliminary personnel scheduling plan is formulated.

[0011] Optionally, in step S2, preliminary scheduling of the time is performed, including: Prioritize the production and maintenance tasks in the preliminary production and maintenance plan, and formulate a preliminary time schedule based on the work characteristics and resource needs of each specialty.

[0012] Optionally, in step S2, preliminary scheduling of tools and equipment is performed, including: The tool and equipment resource library is invoked, and a preliminary scheduling plan for tool and equipment resources is formulated based on the needs of production and maintenance tasks in the preliminary production and maintenance plan, the frequency of tool and equipment use, the possibility of sharing tools and equipment across disciplines, and the tool and equipment upgrade and replacement period.

[0013] Optionally, in step S2, the step of generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm includes: The preliminary production and maintenance plan, as well as the preliminary scheduling scheme for personnel, time, and tools, are encoded using a four-dimensional chromosome coding structure that includes a spatiotemporal layer, a personnel layer, a priority layer, and a resource layer. Construct a multi-objective fitness function that takes into account scheduling efficiency, personnel optimization, priority satisfaction, and resource utilization; During iterative optimization, cross operators including spatiotemporal-resource block cross, priority-guided cross, and team collaboration cross are used, as well as mutation operators including scheduling mutation, personnel mutation, and resource mutation. A Pareto optimal solution set search is performed using a multi-objective optimization mechanism based on the improved NSGA-II algorithm to obtain a globally optimized production plan and resource scheduling scheme.

[0014] Optionally, in step S3, the precise mode of the solidification cycle includes: refining the time interval of work orders to the day and accurately assigning personnel to individuals; establishing a strict quality control system and a multi-node inspection mechanism; and establishing a detailed database for tracking legacy work orders for refined tracking management.

[0015] Optionally, in step S3, the rough mode of the non-fixed cycle includes: dividing the time interval into weekly or monthly units, adopting a phased planning and flexible adjustment strategy; evaluating and adjusting progress through regular meetings and reports; and classifying and differentiating legacy work orders.

[0016] Optionally, the real-time monitoring and dynamic adjustment includes: During the execution of a work order, the execution status is dynamically tracked. When the actual situation deviates from the plan, the production plan and resource scheduling scheme are dynamically adjusted based on the execution status of the work order.

[0017] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the urban rail transit cross-professional multimodal production and maintenance planning and scheduling method as described above.

[0018] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the urban rail transit cross-professional multimodal production and maintenance planning and scheduling method as described above.

[0019] This invention has at least the following technical effects: 1. Dynamic generation of cross-disciplinary production plans: Breaking down professional barriers, combining existing monthly and annual plans and construction systems of various disciplines, the production plan can be formulated intelligently, collaboratively, and dynamically to improve the efficiency and quality of urban rail transit operations, reduce maintenance costs, and ensure the reliability and stability of the system. 2. Cross-disciplinary production scheduling: By scientifically and rationally arranging the production tasks of various urban rail transit specialties, the system optimizes the allocation of resources such as personnel, time, and tools to improve production efficiency, reduce costs, and ensure the safe and stable operation of the urban rail system. This breaks through the limitations of traditional single-discipline scheduling, fully considering the interrelationships and resource sharing needs among multiple specialties in urban rail operation, and providing strong support for achieving efficient cross-disciplinary collaborative work. 3. Multimodal Production Planning Execution and Tracking: Covering both fixed-cycle (precise mode) and non-fixed-cycle (coarse mode) to adapt to different types of production plans and work order requirements. Through meticulous management and differentiated handling of time intervals, legacy work orders, and work order completion rates, coupled with real-time tracking of production and maintenance plan execution and dynamic adjustments, the efficient and smooth operation of urban rail production and maintenance work is ensured. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the drawings described below are one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort: Figure 1 This is a flowchart illustrating a multi-modal production and maintenance planning and scheduling method for urban rail transit, provided as an embodiment of the present invention. Figure 2 This is a schematic diagram of a cross-disciplinary integrated planning library provided in an embodiment of the present invention. Detailed Implementation

[0021] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the solution proposed by the present invention. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the embodiments of the present invention. Please refer to the drawings to make the objectives, features, and advantages of the present invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by the present invention, should still fall within the scope of the technical content disclosed in the present invention.

[0022] This invention provides a multi-modal production and maintenance planning and scheduling method for urban rail transit, aiming to solve problems such as the lack of interoperability of basic production and maintenance data systems among various urban rail transit specialties (such as signaling, communication, rolling stock, automation, power supply, and track maintenance), reliance on manual experience in planning, and difficulty in globally optimizing resource scheduling. The following section combines... Figure 1 The flowchart shown illustrates specific embodiments of the present invention in detail.

[0023] Figure 1 This is a flowchart illustrating a multi-modal production and maintenance planning and scheduling method for urban rail transit, provided in an embodiment of the present invention. This method can be applied to a central data platform, which refers to a core data processing and service center for centrally aggregating, storing, managing, processing, and analyzing heterogeneous data from various rail transit specialties. Its specific technical implementation may encompass various technical forms such as big data platforms, data middleware, and data warehouses, and typically features modularity, configurability, support for integrated batch and stream processing, and provision of standard data service interfaces.

[0024] like Figure 1 As shown, the method mainly includes the following steps: S1, Dynamic Generation Steps for Cross-Disciplinary Production Plans: By establishing a unified data interface and data standards, data from different professional systems are aggregated and integrated, the production and maintenance needs of each profession are analyzed, and a preliminary cross-disciplinary production and maintenance plan is generated by combining existing plans with real-time construction information.

[0025] The process of establishing a unified data interface and data standards to aggregate and integrate data from different professional systems includes: building a unified data interface based on RESTful APIs; building a unified data model covering the core business entities of each professional system; assigning unique identification to equipment in each professional system using unified coding rules; and aggregating equipment status data, maintenance record data, and testing and monitoring data from different professional systems into a central data pool.

[0026] First, a unified data interface based on RESTful APIs is constructed as a standardized channel for data interaction between various professional systems (such as vehicle maintenance systems, engineering management systems, and power supply dispatching systems) and the central data platform. Second, a unified data model covering all core business entities (such as "equipment," "maintenance work orders," "personnel," "maintenance windows," and "materials") is built. This model defines the basic attributes, data types, constraints, and interrelationships of these entities. For example, a basic model is defined for "equipment" in all professional fields, including core fields such as "equipment ID," "professional affiliation," "geographical location," "model," and "current status," upon which each professional system extends. Simultaneously, a unified coding rule is implemented, such as using a rule of "professional code (2 digits) + line code (2 digits) + serial number (6 digits)" to assign a unique identifier to all equipment across the network. Through the aforementioned interfaces, models, and rules, equipment status data (such as real-time / historical sensor data), maintenance record data (historical work orders, fault records, maintenance reports, replacement component information), and detection and monitoring data (track geometry inspection vehicle data, catenary inspection vehicle data, vehicle online monitoring data, rail flaw detection data, etc.) from various professional systems are aggregated into the central data pool.

[0027] The analysis of production and maintenance needs for each specialty includes: using data analysis algorithms to analyze the production and maintenance needs of each specialty based on equipment status data, maintenance record data, and testing and monitoring data from the central data pool. The production and maintenance needs include: target equipment for maintenance, specialty, need type (such as predictive maintenance, corrective maintenance, periodic maintenance), priority, prediction window, and estimated resources.

[0028] The process of generating a preliminary cross-disciplinary production and maintenance plan by combining existing plans with real-time construction information includes: automatically extracting existing annual and monthly production and maintenance plan tables from various professional systems, combining them with the production and maintenance needs of each professional system to form a cross-disciplinary integrated plan library; and combining the cross-disciplinary integrated plan library with real-time construction information obtained from the construction system to generate a preliminary cross-disciplinary production and maintenance plan.

[0029] The existing annual and monthly production and maintenance plans in various professional systems (such as vehicle maintenance systems, engineering management systems, and power dispatching systems) are usually pre-defined and include routine maintenance, periodic overhauls, and known modification projects. The production and maintenance needs obtained from the aforementioned analysis are integrated with the extracted existing plans to form a... Figure 2 The diagram shows a structured, visual, and computable library of cross-disciplinary integrated plans.

[0030] The central data platform also establishes a data connection with the construction system to obtain real-time information such as construction windows, construction progress, project quality, and on-site safety. Combined with the cross-professional integrated planning library, it generates a preliminary cross-professional production and maintenance plan.

[0031] S2, Cross-disciplinary production scheduling steps: Based on the preliminary production and maintenance plan, perform preliminary scheduling of three resources: personnel, time, and tools, and then generate a globally optimized production plan and resource scheduling scheme based on the improved genetic algorithm.

[0032] The cross-disciplinary production scheduling mainly includes human resource scheduling, time resource scheduling, and tool and equipment resource scheduling.

[0033] The initial personnel scheduling includes: accessing a personnel skills database that records employees' skill levels and professional qualifications, enabling accurate allocation of suitable personnel to appropriate tasks during scheduling; and developing an initial personnel scheduling plan based on employees' skill levels, professional qualifications, and the complementary skills of employees in multi-disciplinary collaborative work within the initial production and maintenance plan. In other words, based on the task requirements in the initial production and maintenance plan, an attempt is made to match personnel with the corresponding skills and qualifications to tasks, prioritizing personnel with multi-disciplinary skills for cross-disciplinary collaborative tasks to improve the efficiency of human resource utilization and collaborative effectiveness.

[0034] The preliminary scheduling process includes: prioritizing production and maintenance tasks in the preliminary production and maintenance plan; and developing a preliminary schedule based on the work characteristics and resource needs of each specialty. For example, the priority order of production and maintenance tasks is determined based on factors such as their urgency, importance, and impact on operational safety. Based on the determined task priorities, and considering the work characteristics and resource needs of each specialty, the execution time sequence of tasks is rationally arranged. Advanced scheduling algorithms, such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT), are used to analyze the logical relationships and sequence constraints between tasks, and to develop a preliminary schedule.

[0035] The preliminary scheduling of tools and equipment includes: accessing the tool and equipment resource library; based on the needs of production and maintenance tasks in the preliminary production and maintenance plan and the frequency of tool and equipment usage, combined with the possibility of sharing tools and equipment across disciplines and the tool and equipment replacement cycle, formulating a preliminary scheduling plan for tool and equipment resources. Specifically, when arranging production and maintenance tasks, the types and quantities of tools and equipment required are predicted in advance, and allocated from inventory to the corresponding work locations. At the same time, the possibility of tool and equipment sharing is fully considered, and the time and sequence of different professional tasks are rationally arranged so that some shareable tools and equipment can be efficiently circulated and used between different disciplines. When formulating the production scheduling plan, the time nodes for tool and equipment replacement and their impact on production tasks are considered, and the replacement work of tools and equipment is rationally arranged to ensure that production and maintenance work can always use appropriate and efficient tool and equipment resources.

[0036] Then, the preliminary production and maintenance plan, preliminary personnel scheduling plan, preliminary time arrangement plan, and preliminary tooling resource scheduling plan are used as the initial population and input into a unified improved genetic algorithm framework for global collaborative optimization. Specifically, the generation of globally optimized production plans and resource scheduling plans based on the improved genetic algorithm includes: S21 uses a four-dimensional chromosome coding structure, which includes a spatiotemporal layer, a personnel layer, a priority layer, and a resource layer, to encode the preliminary production and maintenance plan and the preliminary scheduling scheme for the three resources of personnel, time, and tools. S22, construct a multi-objective fitness function that takes into account scheduling efficiency, personnel optimization, priority satisfaction and resource utilization; S23, during iterative optimization, uses cross operators including spatiotemporal-resource block cross, priority-guided cross, and team collaboration cross, as well as mutation operators including scheduling mutation, personnel mutation, and resource mutation; S24. A Pareto optimal solution set search is performed using a multi-objective optimization mechanism based on the improved NSGA-II algorithm to obtain a globally optimized production plan and resource scheduling scheme.

[0037] In step S21, during encoding, a chromosome (individual) represents a complete scheduling scheme, whose genes encode the arrangement of all tasks in the "spatiotemporal layer" (execution time, window allocation, spatial location), "personnel layer" (team configuration, skill matching, workload), "priority layer" (task urgency, risk assessment, synergy benefits), and "resource layer" (material allocation, tool scheduling, distribution network).

[0038] In step S22, the multi-objective fitness function is expressed as: F = α·scheduling efficiency + β·personnel optimization + γ·priority satisfaction + δ·resource utilization - ε·constraint violation. Scheduling efficiency includes: window utilization rate and collaborative work efficiency; personnel optimization includes: skill matching degree, teamwork, and load balancing; priority satisfaction includes: emergency task response and risk assessment accuracy; and resource utilization includes: resource utilization rate, cost control, and delivery efficiency. This multi-objective fitness function simultaneously optimizes window utilization rate, personnel skill matching degree and load balancing, high-priority task completion rate, resource utilization rate, and cost, and penalizes schemes that violate hard constraints (such as safety rules and resource conflicts).

[0039] In step S23, the spatiotemporal-resource block cross operator is used for the overall exchange of high-quality time periods and resource configurations; the priority-guided cross operator is used for the optimized reorganization of high-priority task resources; the team collaboration cross operator is used for the reorganization of teams based on complementary skills; the scheduling mutation operator is used for the optimized adjustment of time windows and spatial distribution; the personnel mutation operator is used for the intelligent improvement of skill configuration and team structure; and the resource mutation operator is used for the optimized selection of alternative resources and delivery routes.

[0040] In step S24, an improved NSGA-II algorithm is used to handle the multi-objective optimization problem. Through fast non-dominated sorting (based on Pareto ranking of four-dimensional objective values), crowding calculation (maintaining the distribution of the solution set in the objective space), and elite preservation (preserving high-quality individuals at each Pareto level), a Pareto-optimal solution set that performs well across multiple optimization objectives is maintained during the evolutionary process. Ultimately, one or more globally optimized production plans and resource scheduling schemes that achieve the best balance between scheduling efficiency, personnel load, priority response, and resource cost are provided from the Pareto front.

[0041] Furthermore, this invention employs a hierarchical constraint processing mechanism, balancing the rigor and flexibility of the optimization process. The hard constraint processing strategy includes: pre-limiting the solution space based on preset encoding rules to ensure the initial population satisfies core constraints; configuring dedicated repair operators to correct individuals violating hard constraints in real time during evolution, ensuring the effectiveness of algorithm iteration. The soft constraint processing strategy includes: constructing a penalty function to transform soft constraint indicators into penalty terms of the objective function, adjusting constraint weights through penalty coefficients; and using the penalty mechanism to guide the population to evolve towards satisfying soft constraints, balancing constraint satisfaction and objective optimization effects. The dynamic constraint strategy includes: real-time monitoring of dynamic changes in the external environment and task requirements during runtime, automatically adjusting the optimization strategy for sudden constraint conflicts, achieving adaptive satisfaction of dynamic constraints.

[0042] This embodiment employs an improved genetic algorithm to generate globally optimized production plans and resource scheduling schemes, offering the following advantages: Global optimization effect: By adopting an integrated optimization framework, it avoids the difficulty of goal coordination inherent in step-by-step optimization, effectively preventing getting trapped in local suboptimal solutions; Multi-objective balancing effect: Through hierarchical constraints and penalty mechanisms, it achieves collaborative optimization of conflicting goals across four dimensions, improving the overall performance of the solution; Dynamic adaptation effect: Relying on dynamic constraint checking and resolution strategies, it can quickly respond to real-time changes in operating conditions and sudden anomalies, enhancing the algorithm's robustness; Flexible decision-making effect: It provides multiple sets of optimization schemes with different tendencies, offering a flexible decision-making space for practical application scenarios.

[0043] The improved genetic algorithm described above ensures iterative efficiency and optimization effect through the following parameter configurations: Population size: set to 150-400 individuals, thus balancing computational efficiency and population diversity; Number of generations: set to 2000-8000 generations, ensuring that the algorithm fully converges to the optimal solution region; Crossover probability: set to 0.75-0.95, promoting the recombination and transmission of superior genes within the population; Mutation probability: set to 0.08-0.20, maintaining population diversity and avoiding premature convergence.

[0044] S3, Multimodal Production Planning Execution and Tracking Steps: Based on the work order type, execution management is carried out using a precise mode with a fixed cycle and a coarse mode with a non-fixed cycle, with real-time monitoring and dynamic adjustment.

[0045] This step adopts a differentiated execution mode based on the different nature of the work order, and implements dynamic monitoring and adjustment throughout the entire process.

[0046] The precise management model for the sampling and solidification cycle includes: specifying the time interval for work orders down to the day and assigning personnel precisely to individuals; establishing a strict quality control system and multi-node inspection mechanism; and creating a detailed database for tracking legacy work orders for refined management. This precise model is suitable for planned tasks with strict cycle requirements (such as daily, weekly, or monthly inspections) or high precision requirements. In this model, the time interval for work orders is specified down to the day, and executors are precisely assigned to individuals. A strict quality control process is established, and quality inspection nodes are set at key processes. For "legacy work orders" that are not completed on time, a detailed tracking database is established, recording their content, reasons for being left unfinished, responsible persons, and estimated resolution time for closed-loop management.

[0047] A rough, non-fixed-cycle approach is adopted for execution management, including: dividing time intervals into weekly or monthly units, employing phased planning and flexible adjustment strategies; evaluating and adjusting progress through regular meetings and reports; and classifying and handling legacy work orders differently. Under this rough, non-fixed-cycle approach, time intervals are roughly planned on a weekly or monthly basis, employing phased planning and flexible adjustment strategies. During implementation, the specific work arrangements within each week are allowed to be flexibly adjusted based on actual conditions such as weather and on-site construction difficulties. Regular progress meetings and reports are used to evaluate and adjust the work progress throughout the entire non-fixed-cycle, comparing actual progress with planned progress, analyzing the reasons for deviations, and taking timely measures to ensure the project is completed within the stipulated monthly timeframe. Legacy work orders are classified according to their impact on the overall project schedule (e.g., important and urgent, important but not urgent, not important but urgent, not important but not urgent), and handled with differentiated strategies. Important and urgent legacy work orders are prioritized with concentrated resources, while not important and not urgent legacy work orders can be handled when resources are relatively abundant.

[0048] In addition, real-time monitoring and feedback can be carried out during the execution of work orders, and the execution status can be dynamically tracked. When the actual situation deviates from the plan, the production plan and resource scheduling scheme can be dynamically adjusted according to the execution status of the work order.

[0049] Based on the same inventive concept, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the urban rail transit cross-professional multimodal production and maintenance planning and scheduling method as described above.

[0050] Based on the same inventive concept, this embodiment also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the urban rail transit cross-professional multimodal production and maintenance planning and scheduling method as described above.

[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0052] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A multi-modal production and maintenance planning and scheduling method for urban rail transit, characterized in that, Includes the following steps: S1, Dynamic generation steps of cross-professional production plan: By establishing a unified data interface and data standards, data from different professional systems are aggregated and integrated, the production and maintenance needs of each professional are analyzed, and a preliminary cross-professional production and maintenance plan is generated by combining existing plans with real-time construction information. S2, Cross-disciplinary production scheduling steps: Based on the preliminary production and maintenance plan, perform preliminary scheduling of three resources: personnel, time, and tools, and then generate a globally optimized production and maintenance plan and resource scheduling scheme based on the improved genetic algorithm; S3, Multimodal Production Planning Execution and Tracking Steps: Based on the work order type, execution management is carried out using a precise mode with a fixed cycle and a coarse mode with a non-fixed cycle, with real-time monitoring and dynamic adjustment.

2. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 1, characterized in that, In step S1, the process of establishing a unified data interface and data standards to aggregate and integrate data from different professional systems includes: Build a unified data interface based on RESTful APIs; Construct a unified data model covering the core business entities of various specialties; A unified coding rule is adopted to assign a unique identifier to equipment in each specialty; It aggregates equipment status data, maintenance record data, and testing and monitoring data from different professional systems into a central data pool.

3. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 2, characterized in that, In step S1, the analysis of the production and maintenance needs of each specialty includes: Based on the equipment status data, maintenance record data, and testing and monitoring data of each specialty in the central data pool, data analysis algorithms are used to analyze the production and maintenance needs of each specialty. The production and maintenance needs include the target equipment, specialty, demand type, priority, prediction window, and estimated resources.

4. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 3, characterized in that, In step S1, a preliminary cross-disciplinary production and maintenance plan is generated by combining existing plans with real-time construction information, including: Automatically extract existing annual and monthly production and maintenance plans from various professional systems, and combine them with the production and maintenance needs of each profession to form a cross-professional integrated plan library; By combining the cross-disciplinary integrated planning library with real-time construction information obtained from the construction system, a preliminary cross-disciplinary production and maintenance plan is generated.

5. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 1, characterized in that, In step S2, preliminary scheduling of personnel is performed, including: Access the personnel skills database, which records the employees' skill levels and professional qualifications; Based on the employees' skill levels and professional qualifications, and considering the complementary skills of the employees in multi-professional collaborative operations in the preliminary production and maintenance plan, a preliminary personnel scheduling plan is formulated.

6. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 1, characterized in that, In step S2, a preliminary time schedule is performed, including: Prioritize the production and maintenance tasks in the preliminary production and maintenance plan, and formulate a preliminary time schedule based on the work characteristics and resource needs of each specialty.

7. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 1, characterized in that, In step S2, preliminary scheduling of tools and equipment is performed, including: The tool and equipment resource library is invoked, and a preliminary scheduling plan for tool and equipment resources is formulated based on the needs of production and maintenance tasks in the preliminary production and maintenance plan, the frequency of tool and equipment use, the possibility of sharing tools and equipment across disciplines, and the tool and equipment upgrade and replacement period.

8. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method as described in claim 1, characterized in that, In step S2, the step of generating a globally optimized production plan and resource scheduling scheme based on the improved genetic algorithm includes: The preliminary production and maintenance plan, as well as the preliminary scheduling scheme for personnel, time, and tools, are encoded using a four-dimensional chromosome coding structure that includes a spatiotemporal layer, a personnel layer, a priority layer, and a resource layer. Construct a multi-objective fitness function that takes into account scheduling efficiency, personnel optimization, priority satisfaction, and resource utilization; During iterative optimization, cross operators including spatiotemporal-resource block cross, priority-guided cross, and team collaboration cross are used, as well as mutation operators including scheduling mutation, personnel mutation, and resource mutation. A Pareto optimal solution set search is performed using a multi-objective optimization mechanism based on the improved NSGA-II algorithm to obtain a globally optimized production plan and resource scheduling scheme.

9. The urban rail transit cross-professional multimodal production and maintenance planning and scheduling method according to claim 1, characterized in that, In step S3, the precise mode of the solidification cycle includes: refining the time interval of work orders to the day and accurately assigning personnel to individuals; establishing a strict quality control system and a multi-node inspection mechanism; and establishing a detailed database for tracking legacy work orders for refined tracking management.

10. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method according to claim 1, characterized in that, In step S3, the rough model of the non-fixed cycle includes: dividing the time interval into weekly or monthly units, adopting a phased planning and flexible adjustment strategy; evaluating and adjusting progress through regular meetings and reports; and classifying and differentiating legacy work orders.

11. The urban rail transit cross-disciplinary multimodal production and maintenance planning and scheduling method according to claim 1, characterized in that, The aforementioned real-time monitoring and dynamic adjustment include: During the execution of a work order, the execution status is dynamically tracked. When the actual situation deviates from the plan, the production plan and resource scheduling scheme are dynamically adjusted based on the execution status of the work order.

12. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, implements the method of any one of claims 1 to 11.

13. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 11.

Images