Flexible reconstruction method for part assembly line for flexible capacity matching

Abstract

The invention relates to the technical field of part assembly line flexible reconstruction, and discloses a part assembly line flexible reconstruction method aiming at capacity flexible matching, which comprises the following steps: inputting a product assembly process, station characteristics, material size, weight, carrying equipment and worker skills into a process station optimization strategy model, and carrying out layout on modular stations; inputting the original production plan, the order information and the real-time production data into a schedule change strategy model to obtain an updated production schedule; inputting skills, experiences and availability of workers, complexity of each process, required skill levels, production plans and target yield into a personnel allocation strategy model to obtain personnel allocation schemes of each station, working plans of the workers and shift arrangements; based on a task-driven cooperative control mechanism, dynamically allocating current available resources, and optimizing and adjusting a production process and a task scheduling plan; according to the invention, rapid productivity adjustment of the assembly line under different seasonal requirements is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible reconstruction of parts assembly lines, and more specifically to a flexible reconstruction method of parts assembly lines for flexible matching of production capacity. Background Art

[0002] In today's rapidly developing industrial field, the parts manufacturing industry, as the foundation of the entire industrial production, is facing unprecedented challenges and opportunities. With the growing demand for product diversification and customization, the traditional large-scale, single production model can no longer meet market demand. Therefore, the parts manufacturing industry urgently needs to improve production flexibility to quickly respond to market changes and customer needs; however, the existing assembly lines have many limitations: (1) Insufficient production flexibility: Traditional assembly lines usually adopt a fixed layout and are optimized for specific products, which makes it difficult to adapt to changing production needs; (2) Production efficiency bottleneck: In a multi-variety, small-batch production environment, the existing assembly lines need to frequently change the production line configuration, resulting in low production efficiency; (3) Low resource allocation efficiency: The lack of an effective resource allocation strategy leads to waste of material, human and equipment resources; (4) Slow response to market changes: When facing seasonal fluctuations in market demand, the existing production lines have a long adjustment cycle and cannot quickly adapt to the increase or decrease in production volume; (5) High cost of technology update: With the acceleration of product updates, the cost of technology update and transformation of existing assembly lines is high.

[0003] The limitations currently faced by the parts manufacturing industry highlight an urgent need: a more efficient and flexible production system must be developed; as consumers' demand for personalized products grows, the manufacturing industry is rapidly transforming to diversified production; this market trend leads to orders tending towards small batches and multiple varieties, which not only requires production lines to be highly flexible to adapt to the production of different products, but also requires the ability to quickly adjust production strategies to meet increasingly shortened fast delivery requirements; the intensification of market competition has further exacerbated this demand, and customers expect to receive customized products in a shorter time. Summary of the invention

[0004] The purpose of the present invention is to provide a flexible reconstruction method for a parts assembly line with flexible matching of production capacity, which can quickly adjust the production line capacity according to changes in production demand to meet the needs of different industries for multi-variety variable batch production and cope with seasonal production capacity changes and market fluctuations.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A flexible reconstruction method for a parts assembly line for flexible capacity matching includes the following steps:

[0007] S1. Input product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills into the process and workstation optimization strategy model to layout modular workstations;

[0008] S2, input the original production plan, order information and real-time production data into the scheduling change strategy model to obtain the updated production plan, production schedule and resource reallocation plan;

[0009] S3. Input the workers’ skills, experience, availability, complexity of each process, required skill level, production plan and target output into the staffing strategy model to obtain the staffing plan for each workstation, the workers’ work plan and shift arrangement;

[0010] S4. Based on the task-driven collaborative control mechanism, dynamically allocate currently available resources, monitor the production line in real time, and optimize and adjust the production process and task scheduling plan;

[0011] S5. Use the real-time monitoring and feedback system to collect, monitor and analyze the real-time data and production status of the production line, and continuously optimize the production process and task plan based on the analysis results;

[0012] The product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills are input into the process workstation optimization strategy model to layout the modular workstations, specifically:

[0013] The simulated annealing algorithm is used to make the initial layout of modular workstations;

[0014] Use Floyd's shortest path algorithm to rearrange the distances between modular workstations;

[0015] The original production plan, order information and real-time production data are input into the scheduling change strategy model to obtain the updated production plan, production schedule and resource reallocation plan, specifically:

[0016] Using the particle swarm optimization algorithm, based on production experience and historical data, and according to the current resource status and production demand, the original production plan, order information and real-time production data are calculated to obtain the updated production plan, production schedule and resource reallocation plan.

[0017] Furthermore, the real-time production data includes: completion status, delays and resource availability.

[0018] Furthermore, in step S3, the worker's skills, experience, availability, complexity of each process, required skill level, production plan and target output are input into the personnel allocation strategy model to obtain the personnel allocation plan for each workstation, the worker's work plan and shift arrangement, specifically:

[0019] Through genetic algorithms, workers' skills, experience, availability, complexity of each process, required skill level, production plan and target output are encoded to form an initial population; a group of particles are randomly generated in the initial population, each particle represents a worker configuration plan; the fitness of each particle is evaluated, and the position and speed of the particle are updated to obtain the personnel allocation plan for each modular workstation, the workers' work plan and shift arrangement.

[0020] Further, the fitness of each particle is evaluated, including: production efficiency, product quality and worker satisfaction;

[0021] The updating of the particle position and speed includes: adjusting the search direction according to the individual and global optimal solutions.

[0022] Furthermore, in step S4, based on the task-driven collaborative control mechanism, workstations, personnel and equipment are dynamically allocated, and the production line is monitored in real time to optimize and adjust the production process and task scheduling plan, specifically:

[0023] Using the task-driven collaborative control mechanism, the production management system receives production tasks and order information and decomposes them into specific subtasks and operation sequences;

[0024] Based on specific subtasks and operation sequences, and according to task requirements and current resource status, evaluate the current available resources and generate a preliminary task execution plan;

[0025] Use optimization algorithms to optimize the preliminary task execution plan and deploy it to each modular workstation on the production line;

[0026] Monitor the task execution process in real time and provide feedback on the task execution results.

[0027] Furthermore, in step S5, the real-time monitoring and feedback system is used to collect and monitor the real-time data and production status of the production line, and analyze them, and the production process and task plan are continuously optimized based on the analysis results, specifically:

[0028] According to the execution results of the task and market changes, the task plan is updated and issued to the production line to make adjustments by adjusting the production volume, changing the production sequence or reallocating resources; the adjusted execution results are monitored to verify whether they meet the expected goals and form a closed-loop control; based on the continuous feedback loop, the production process and task plan are continuously optimized.

[0029] Furthermore, the currently available resources include: workstations, personnel, materials and equipment status.

[0030] Furthermore, the production status includes: equipment operation status, material flow and worker operation status.

[0031] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0032] The present invention arranges modular workstations through a process and workstation optimization strategy model; obtains updated production plans, production schedules and resource reallocation plans through a scheduling change strategy model; obtains personnel allocation plans for each workstation, workers' work plans and shift arrangements through a personnel allocation strategy model, which helps to improve the adaptability of the assembly line to multi-variety variable batch production and reduce downtime caused by switching production tasks; and quickly and effectively responds to changes in production plans, minimizes production interruptions and resource waste, realizes rapid capacity adjustment of assembly lines under different seasonal demands, optimizes resource allocation, and reduces production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to the present invention is further described below in conjunction with the accompanying drawings;

[0035] Figure 1 It is a schematic diagram of the overall process of the flexible reconstruction method of the parts assembly line for flexible matching of production capacity provided by the present invention;

[0036] Figure 2 It is a flexible assembly production line layout diagram in the flexible reconstruction method of the parts assembly line for flexible production capacity matching provided by the present invention;

[0037] Figure 3 It is a modular workstation layout diagram of a flexible assembly line in a flexible reconstruction method of a parts assembly line for flexible capacity matching provided by the present invention;

[0038] Figure 4 It is a distance matrix created in the flexible reconstruction method of the parts assembly line for flexible matching of production capacity provided by the present invention. DETAILED DESCRIPTION

[0039] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0040] In order to better understand the purpose, structure and function of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0041] like Figure 1 and Figure 2 As shown, the present invention provides a flexible reconstruction method for a parts assembly line for flexible matching of production capacity, comprising the following steps:

[0042] S1. Input product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills into the process and workstation optimization strategy model to layout modular workstations;

[0043] S2, input the original production plan, order information and real-time production data into the scheduling change strategy model to obtain the updated production plan, production schedule and resource reallocation plan;

[0044] S3. Input the workers’ skills, experience, availability, complexity of each process, required skill level, production plan and target output into the staffing strategy model to obtain the staffing plan for each workstation, the workers’ work plan and shift arrangement;

[0045] It should be noted that: the data such as product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills in step S1 of the present invention; the data such as original production plan, order information and real-time production in step S2; the data such as workers' skills, experience, availability, complexity of each process, required skill level, production plan and target output in step S3 are all realized through sensors and automation equipment, barcode or RFID scanning, MES and ERP system integration and data mining algorithms. Since the means of acquiring data are conventional means in the field, and the focus of the present invention is on how to use various data for layout and allocation through models, the method of acquiring data is not the protection point of the present invention, so it will not be described in detail.

[0046] S4. Based on the task-driven collaborative control mechanism, dynamically allocate currently available resources, monitor the production line in real time, and optimize and adjust the production process and task scheduling plan;

[0047] S5. Use the real-time monitoring and feedback system to collect, monitor and analyze the real-time data and production status of the production line, and continuously optimize the production process and task plan based on the analysis results. Specifically:

[0048] According to the execution results of the task and market changes, the task plan is updated and issued to the production line to make adjustments by adjusting the production volume, changing the production sequence or reallocating resources; the adjusted execution results are monitored to verify whether they meet the expected goals and form a closed-loop control; based on the continuous feedback loop, the production process and task plan are continuously optimized.

[0049] In step S1, the product assembly process, station characteristics, material size, weight, handling equipment and worker skills are input into the process station optimization strategy model to layout the modular stations, specifically:

[0050] The simulated annealing algorithm is used to make the initial layout of modular workstations;

[0051] The distances between modular workstations are rearranged using Floyd's shortest path algorithm.

[0052] It should be noted that after collecting data on product assembly processes, workstation characteristics, material dimensions, weight, handling equipment, and worker skill levels, the simulated annealing algorithm was used to design the initial layout of modular workstations by considering the physical dimensions of the workstations, working space, and material handling paths.

[0053] The modular workstations in the present invention adopt a parallel layout and are equipped with a highly compatible modular tool and fixture replacement system, which can improve the scalability and flexibility of the production line and adapt to the assembly requirements of different types of parts; and every two workstations correspond to an assembly operation process, and the corresponding number of workstations can be activated according to the process time and number of processes of the parts. The assembly parts completed in the previous operation process are transferred to the next process through RGV cross-flow, which helps to reduce the process conversion time and allows local adjustments without stopping the entire production line.

[0054] In order to improve the flexibility and adaptability of the production line, the present invention combines the reconfigurable conveying system used in combination with modular workstations, including diversified conveying equipment such as the mother-and-child trolley RGV, the hidden jacking AGV, the material box robot CTU, and KBK to adapt to parts of different weights and sizes, and can adjust the conveying path and speed according to real-time production data; the mother-and-child trolley RGV is used for the transportation of products between various workstations, with high positioning accuracy and good reliability; the hidden jacking AGV is used for the logistics of steel standard material frames of heavy and large parts carriers from the vertical warehouse to the line-side warehouse; the material box robot CTU is used for the entry and exit of standard material boxes of light and small parts carriers in the line-side warehouse; KBK is used to assist in the completion of the loading and unloading and assembly of heavy parts; the conveying system has autonomous navigation and obstacle avoidance functions, which helps to achieve highly automated and intelligent conveying path planning.

[0055] Use Floyd's shortest path algorithm to rearrange the distances between modular workstations. Specifically, apply Floyd's shortest path algorithm: define a graph model, where workstations are vertices of the graph, material or personnel flows between workstations are edges, and edge weights are moving time, distance, or cost, etc. Initialize the distance matrix and create a distance matrix that represents the initial distance or cost between all pairs of workstations.

[0056] It should be noted that the process of creating the distance matrix is ​​as follows:

[0057] 1. Determine the number of workstations: First, determine the total number of workstations on the production line; let the number of workstations be n.

[0058] 2. Initialize the matrix: Create an n×n matrix, called the distance matrix D, where D[i][j] represents the distance or cost from workstation i to workstation j.

[0059] 3. Collect data: Collect the distance or cost data between each pair of workstations.

[0060] 4. Fill in the matrix: Analyze the path between each pair of stations (i, j); if there is a direct material flow path between stations i and j, fill in the actual measured distance or cost into D[i][j];

[0061] If there is no direct path between two workstations, a larger weight value can be assigned, or a special "unreachable" mark can be used to indicate that the two workstations are not directly connected in the current layout.

[0062] 5. Symmetric processing: If the material flow is bidirectional, that is, the distance from station i to station j and the distance from station j to station i are the same, then D[i][j] and D[j][i] should be equal; for asymmetric cases, it is necessary to determine the values ​​of D[i][j] and D[j][i] respectively according to the actual situation.

[0063] 6. Diagonal processing: For the diagonal element D[i][i], the distance from the workstation to itself is set to 0.

[0064] 7. Verify data: After completing the matrix filling, perform data verification to ensure the accuracy and rationality of all data.

[0065] If there is no direct material flow between two workstations, assign a larger weight value or a specific "unreachable" mark; iterate for each edge by considering all vertices and update the shortest path estimate in the distance matrix; for each vertex k in the graph, perform the following steps: for each pair of vertices i and j, if the path from i to j through vertex k is shorter than the currently recorded path from i to j, update the shortest path estimate from i to j and pass Figure 4 The initial distance matrix in is used as an example:

[0066] After applying Floyd’s algorithm, the updated distance matrix is ​​obtained;

[0067] It should be noted that: for each vertex k( Figure 4 (a) is vertex B), check all vertex pairs i and j ( Figure 4(a) is the path between vertex A and vertex C. If the path from A to C via vertex B is shorter than the currently recorded path, the shortest path estimate from vertex A to C is updated.

[0068] Iterate over each edge by considering all vertices, for example:

[0069] The shortest path from A to D transits through B, and the path length is 2+3=5, which is shorter than the initial direct path length of 8.

[0070] The shortest path from B to C is the direct path, which has a length of 5 units, which is the same as the initial path length, so there is no change.

[0071] The distance matrix from Figure 4 (a) is updated accordingly Figure 4 (b).

[0072] Figure 4 The initial distance matrix calculated based on Floyd's shortest path algorithm is shown. Each element in the matrix represents the initial distance or cost between the corresponding workstation pairs. By applying Floyd's algorithm, we iteratively update the distance matrix to find the shortest path between all workstation pairs. For example, Figure 4 As shown in (a), the shortest path from station A to station D passes through station B, and the path length is 5 units. The updated distance matrix is ​​as follows: Figure 4 (b) shows that this is Figure 4 The initial distance shown in (a) is 8 units short. The final distance matrix contains the shortest path distances between all pairs of workstations and is used to optimize the workstation layout and reduce material flow time and cost.

[0073] Identify paths with higher weights in the distance matrix and re-arrange workstations to reduce material flow distances; analyze flow patterns between workstations and place frequently interacting workstations closer together to reduce material handling time and costs.

[0074] In step S2, the original production plan, order information and real-time production data are input into the schedule change strategy model to obtain an updated production schedule, specifically:

[0075] Using the particle swarm optimization algorithm, based on production experience and historical data, and according to the current resource status and production demand, the original production plan, order information and real-time production data are calculated to obtain the updated production plan, production schedule and resource reallocation plan.

[0076] Real-time production data including completion status, delays and resource availability.

[0077] In step S3, the workers' skills, experience, availability, complexity of each process, required skill level, production plan and target output are collected and input into the personnel allocation strategy model to obtain the personnel allocation plan for each workstation, the workers' work plan and shift arrangement, specifically:

[0078] Through genetic algorithms, workers' skills, experience, availability, complexity of each process, required skill level, production plan and target output are encoded to form an initial population; a group of particles are randomly generated in the initial population, each particle represents a worker configuration plan; the fitness of each particle is evaluated, and the position and speed of the particle are updated to obtain the personnel allocation plan for each modular workstation, the workers' work plan and shift arrangement.

[0079] In step S4, based on the task-driven collaborative control mechanism, workstations, personnel and equipment are dynamically allocated, and the production line is monitored in real time to optimize and adjust the production process and task scheduling plan, specifically:

[0080] Using the task-driven collaborative control mechanism, the production tasks and order information sent by the production management system are evaluated based on specific subtasks and operation sequences, and according to task requirements and current resource status, the current available resources are evaluated to generate a preliminary task execution plan;

[0081] Use optimization algorithms to optimize the preliminary task execution plan and deploy it to each modular workstation on the production line;

[0082] Monitor the task execution process in real time and provide feedback on the task execution results.

[0083] It should be noted that: through the task-driven collaborative control mechanism, the production tasks and order information sent by the production management system are received and decomposed into specific subtasks and operation sequences according to task requirements and current resource status; Figure 3As shown in the figure, it dynamically allocates workstations, personnel and equipment; monitors the status of the production line in real time, collects data and provides feedback for decision support; detects resource conflicts and bottlenecks, and proposes solutions to optimize the production process; creates and adjusts task scheduling plans to maximize production efficiency and meet delivery deadlines; receives production tasks and order information from the production management system, analyzes task requirements, determines required resources and expected goals, evaluates currently available resources, including workstations, personnel, materials and equipment status, generates preliminary task execution plans, including workstation allocation, personnel arrangement and schedule, uses optimization algorithms to adjust plans to improve resource utilization and production efficiency, deploys optimized plans to each workstation of the production line, monitors the task execution process, and collects production line data in real time. The task-driven collaborative control mechanism is integrated with the workstation location optimization strategy to help ensure that task allocation is coordinated with the workstation layout optimization; the task-driven collaborative control mechanism is integrated with the schedule change strategy, which can quickly adjust the schedule to minimize the impact when the task changes. The task-driven collaborative control mechanism is integrated with the personnel allocation strategy, which can dynamically adjust the personnel allocation according to the task requirements and personnel skills; the task-driven collaborative control mechanism is integrated with the real-time monitoring system in the following ways: by integrating sensors and data acquisition systems, it helps to achieve real-time data-driven decision-making; the task-driven collaborative control mechanism is integrated with the user interaction interface, which helps to provide an intuitive user interface for operators to monitor and interact and coordinate the operation of various parts.

[0084] Monitor the task execution process in real time and provide feedback on the task execution results. Specifically, it can identify problems and bottlenecks in the production process based on the monitoring and feedback results. If the execution results do not meet the expected goals, feedback will be provided to adjust the task plan, including: increasing or decreasing the production volume of certain products; adjusting the configuration of workstations or the allocation of personnel; optimizing the material flow path and storage strategy; updating equipment maintenance and overhaul plans; after implementing the adjustments, continue to monitor the production line's response and new task execution results. When an abnormal situation is detected, the exception handling process will be automatically or manually triggered, and the task plan will be updated based on the execution results and market changes to form a closed-loop control and continuously optimize the production process.

[0085] The currently available resources include: workstations, personnel, materials and equipment status;

[0086] The production status includes: equipment operation status, material flow and worker operation status;

[0087] The method of the present invention also includes simulation and visualization tools to assist in the planning, optimization and troubleshooting of production lines. It can predict and evaluate the impact of strategy changes by simulating different production scenarios. It can also support customized production and quickly adapt to: customer personalized needs and small batch diversified production, and achieve rapid product changes and production adjustments through modular design and flexible configuration.

[0088] The method of the present invention also supports integration with upper-level enterprise resource planning (ERP) systems to achieve real-time data interaction between production planning, inventory management, material requirements, etc. and the production line, thereby improving the transparency and coordination of the entire production system.

[0089] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A flexible reconstruction method for a parts assembly line for flexible matching of production capacity, characterized in that: The following steps are involved: S1. Input product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills into the process and workstation optimization strategy model to layout modular workstations; S2, input the original production plan, order information and real-time production data into the scheduling change strategy model to obtain the updated production plan, production schedule and resource reallocation plan; S3. Input the workers’ skills, experience, availability, complexity of each process, required skill level, production plan and target output into the staffing strategy model to obtain the staffing plan for each workstation, the workers’ work plan and shift arrangement; S4. Based on the task-driven collaborative control mechanism, dynamically allocate currently available resources, monitor the production line in real time, and optimize and adjust the production process and task scheduling plan; S5. Use the real-time monitoring and feedback system to collect, monitor and analyze the real-time data and production status of the production line, and continuously optimize the production process and task plan based on the analysis results; The product assembly process, workstation characteristics, material size, weight, handling equipment and worker skills are input into the process workstation optimization strategy model to layout the modular workstations, specifically: The simulated annealing algorithm is used to make the initial layout of modular workstations; Use Floyd's shortest path algorithm to rearrange the distances between modular workstations; The original production plan, order information and real-time production data are input into the scheduling change strategy model to obtain the updated production plan, production schedule and resource reallocation plan, specifically: Using the particle swarm optimization algorithm, based on production experience and historical data, and according to the current resource status and production demand, the original production plan, order information and real-time production data are calculated to obtain the updated production plan, production schedule and resource reallocation plan.

2. The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to claim 1 is characterized in that: Real-time production data including completion status, delays and resource availability.

3. The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to claim 1 is characterized in that: In step S3, the worker's skills, experience, availability, complexity of each process, required skill level, production plan and target output are input into the personnel allocation strategy model to obtain the personnel allocation plan for each workstation, the worker's work plan and shift arrangement, specifically: Through genetic algorithms, workers' skills, experience, availability, complexity of each process, required skill level, production plan and target output are encoded to form an initial population; a group of particles are randomly generated in the initial population, each particle represents a worker configuration plan; the fitness of each particle is evaluated, and the position and speed of the particle are updated to obtain the personnel allocation plan for each modular workstation, the workers' work plan and shift arrangement.

4. The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to claim 3 is characterized in that: The fitness of each particle is evaluated, including: production efficiency, product quality and worker satisfaction; The updating of the particle position and speed includes: adjusting the search direction according to the individual and global optimal solutions.

5. The flexible reconstruction method of parts assembly line for flexible matching of production capacity according to claim 1 is characterized in that: In step S4, based on the task-driven collaborative control mechanism, workstations, personnel and equipment are dynamically allocated, and the production line is monitored in real time to optimize and adjust the production process and task scheduling plan, specifically: Using the task-driven collaborative control mechanism, the production management system receives production tasks and order information and decomposes them into specific subtasks and operation sequences; Based on specific subtasks and operation sequences, and according to task requirements and current resource status, evaluate the current available resources and generate a preliminary task execution plan; Use optimization algorithms to optimize the preliminary task execution plan and deploy it to each modular workstation on the production line; Monitor the task execution process in real time and provide feedback on the task execution results.

6. The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to claim 1 is characterized in that: In step S5, the real-time monitoring and feedback system is used to collect and monitor the real-time data and production status of the production line, and analyze them, and the production process and task plan are continuously optimized based on the analysis results, specifically: According to the execution results of the task and market changes, the task plan is updated and issued to the production line to make adjustments by adjusting the production volume, changing the production sequence or reallocating resources; the adjusted execution results are monitored to verify whether they meet the expected goals and form a closed-loop control; based on the continuous feedback loop, the production process and task plan are continuously optimized.

7. The flexible reconstruction method of the parts assembly line for flexible matching of production capacity according to claim 6 is characterized in that: The currently available resources include: workstations, personnel, materials, and equipment status.

8. The flexible reconstruction method of parts assembly line for flexible matching of production capacity according to claim 1 is characterized in that: The production status includes: equipment operation status, material flow and worker operation status.

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