Multi-line collaborative scheduling method and system for electric tricycle production

Abstract

The present invention discloses a multi-line collaborative scheduling method and system for electric tricycle production, and relates to the technical field of production scheduling. The method includes: obtaining production tasks and establishing production task priorities; obtaining production line data of the production line and performing processing management of the production line; activating the production line monitoring of the production line and establishing a real-time monitoring data set; establishing collaborative scheduling optimization results; performing processing management of the production line after performing production line task corrections through the collaborative scheduling optimization results. The present invention solves the technical problems of insufficient production line adaptation optimization, unreasonable production resource allocation, and low overall production efficiency in the prior art, and achieves the technical effect of realizing adaptation optimization of the production line based on the production task priority, improving the rationality of production resource allocation, and improving the overall production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling, and in particular to a multi-line collaborative scheduling method and system for electric tricycle production. Background Art

[0002] As the market for new energy vehicles continues to heat up, the market demand for electric tricycles is growing rapidly. However, in the production and manufacturing of electric tricycles, the traditional production scheduling model has gradually exposed many drawbacks. At present, most manufacturers still use a relatively extensive management method. When obtaining production tasks, they simply list the task list without comprehensively considering key factors such as complex and changeable production plans, fine and strict process requirements, accurate working time estimates, fluctuating material requirements, and strict quality requirements to establish a scientific production task priority, resulting in urgent and critical orders often being mixed with regular orders, delaying the delivery cycle. At the same time, when facing multiple production lines operating in parallel, there is a lack of in-depth mining and precise application of detailed production line data such as equipment performance differences, production capacity limits, and maintenance cycles for each production line. It is difficult to reasonably adapt and optimize production tasks based on this information, resulting in a waste of resources. Furthermore, there is a lack of a comprehensive and real-time production line monitoring system in the production process, and it is impossible to dynamically establish a real-time status of equipment operation, and it is impossible to flexibly allocate production tasks and production lines based on real-time conditions, which ultimately leads to low overall production efficiency.

[0003] The existing technology has technical problems such as insufficient adaptation and optimization of production lines, unreasonable allocation of production resources and low overall production efficiency. Summary of the invention

[0004] The present application provides a multi-line collaborative scheduling method and system for electric tricycle production, which is used to solve the technical problems in the prior art of insufficient production line adaptation and optimization, unreasonable allocation of production resources, and low overall production efficiency.

[0005] In view of the above problems, the present application provides a multi-line collaborative scheduling method and system for electric tricycle production.

[0006] In a first aspect of the present application, a multi-line collaborative scheduling method for electric tricycle production is provided, the method comprising: Acquire a production task, wherein the production task includes a production plan, process requirements, working time estimation, material requirements, and quality requirements, input the production task into a priority evaluation model, and establish a production task priority; acquire production line data of the production line, perform adaptive optimization of the production task according to the production line data and the production task priority, establish a production line-task mapping, and perform processing management of the production line according to the production line-task mapping; activate production line monitoring of the production line, and establish a real-time monitoring data set, wherein the real-time monitoring data set includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set; perform collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority, and establish a collaborative scheduling optimization result; perform production line-task correction based on the collaborative scheduling optimization result, and then execute processing management of the production line.

[0007] The second aspect of the present application provides a multi-line collaborative scheduling system for electric tricycle production, the system comprising: A production task priority establishment module is used to obtain production tasks, which include production plans, process requirements, working time estimates, material requirements, and quality requirements, and input the production tasks into a priority evaluation model to establish production task priorities; a production line-task mapping establishment module is used to obtain production line data of the production line, perform adaptive optimization of the production tasks based on the production line data and the production task priorities, establish production line-task mapping, and perform processing management of the production line based on the production line-task mapping; a real-time monitoring data set establishment module is used to activate production line monitoring of the production line and establish a real-time monitoring data set, which includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set; a collaborative scheduling optimization result establishment module is used to perform collaborative scheduling optimization based on the real-time monitoring data set according to the production task priorities and establish collaborative scheduling optimization results; a processing management module is used to perform processing management of the production line after performing production line-task corrections through the collaborative scheduling optimization results.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Acquire production tasks, input the production tasks into the priority evaluation model, and establish production task priorities; acquire production line data of the production line, establish production line-task mapping, and perform processing management of the production line according to the production line-task mapping; activate production line monitoring of the production line and establish a real-time monitoring data set; perform collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority and establish collaborative scheduling optimization results; perform production line-task corrections based on the collaborative scheduling optimization results and execute processing management of the production line. The technical effect of achieving adaptive optimization of production lines based on production task priorities, improving the rationality of production resource allocation and overall production efficiency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a multi-line collaborative scheduling method for electric tricycle production provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a multi-line collaborative scheduling system for electric tricycle production provided in an embodiment of the present application.

[0012] Explanation of the reference numerals: production task priority establishing module 10 , production line-task mapping establishing module 20 , real-time monitoring data set establishing module 30 , collaborative scheduling optimization result establishing module 40 , and processing management module 50 . DETAILED DESCRIPTION

[0013] This application provides a multi-line collaborative scheduling method and system for electric tricycle production, which is used to solve the technical problems in the prior art of insufficient production line adaptation and optimization, unreasonable production resource allocation and low overall production efficiency.

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a multi-line collaborative scheduling method for electric tricycle production, the method comprising: Step S100: Obtain a production task, which includes a production plan, process requirements, man-hour estimates, material requirements, and quality requirements. Input the production task into a priority evaluation model to establish a production task priority.

[0016] Specifically, it is necessary to obtain comprehensive and detailed production task information for electric tricycles, which covers several key aspects. The production plan clarifies the overall arrangement and goals of production, including the quantity, batches and time nodes of production, and provides a macro planning framework for the entire production activity. The process requirements specify in detail the technical processes, processing methods and quality standards followed in the product manufacturing process to ensure the consistency and high quality of the products. The man-hour estimate is based on past experience, process complexity, equipment capacity and other factors to make a reasonable estimate of the time required for each production link, which helps to reasonably arrange human resources and production schedule. Material requirements accurately list the types, quantities and specifications of various raw materials, parts and auxiliary materials required for production to ensure sufficient supply of materials during the production process. Quality requirements set strict product quality acceptance standards, from appearance to performance, from safety to durability, to ensure that the final product meets market expectations and industry specifications. After obtaining the production task information, the complete production task information is then input into the priority evaluation model. The model conducts a comprehensive analysis and weighs multiple factors, including the urgency of the production plan, the complexity of the process requirements, the rationality of the working time estimate, the urgency of the material requirements, and the strictness of the quality requirements. It uses complex calculation logic and data processing procedures to ultimately determine the precise production task priority, laying a solid foundation for the subsequent orderly production activities and ensuring the rational allocation of resources and efficient production.

[0017] Step S200: Acquire production line data of the production line, perform adaptive optimization of the production task according to the production line data and the production task priority, establish a production line-task mapping, and perform processing management of the production line according to the production line-task mapping.

[0018] Specifically, the first task is to comprehensively collect all kinds of detailed data of the production line. These data cover equipment-level information, such as equipment model, performance parameters, current operating status (including operating time, whether it is in a fault or maintenance state, etc.), production capacity (maximum output per unit time, etc.) and historical maintenance records of the equipment; it also includes human resources, such as the skill level, work efficiency, current work saturation and staff scheduling of workers in various positions; in addition, it also involves the layout information of the production line, such as the connection sequence between each process, the flow path of materials on the production line and the impact of the spatial layout of the workshop on the production process. After obtaining the production line data, the production task adaptation and optimization process is carried out in combination with the determined production task priority. A deep matching analysis is carried out on production tasks and production line resources. Priority is given to allocating high-priority production tasks to production lines with strong production capacity, stable equipment operation and skilled personnel, while fully considering the convenience of material supply and the smoothness of process connection. For example, for production tasks with complex processes and high quality requirements, production lines with high-precision processing equipment and experienced technicians are prioritized; while for tasks with tight working hours, production lines with high equipment efficiency and sufficient staffing are prioritized. Through such an adaptation and optimization process, a production line-task mapping relationship is successfully established. This relationship clarifies the most suitable production line corresponding to each production task, as well as the execution order and resource allocation plan on the production line. Based on this production line-task mapping, the production management team can accurately direct the production line to carry out processing management work. Starting from the input of raw materials, strictly follow the process determined by the mapping relationship, and carry out the processing operations of each process in sequence to ensure that each task is completed efficiently by the right people and equipment at the right time and place. During the processing process, the production progress is monitored in real time, resource allocation is flexibly adjusted according to the actual situation, and possible equipment failures, material shortages or personnel deployment problems are handled in a timely manner to ensure that production activities are carried out stably, orderly and efficiently, and to maximize the utilization of production resources and optimize production efficiency.

[0019] Step S300: activating the production line monitoring of the production line, and establishing a real-time monitoring data set, wherein the real-time monitoring data set includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set.

[0020] Specifically, in order to ensure the efficiency and controllability of the production process of electric tricycles, the monitoring system of the production line needs to be fully activated. First, in view of the equipment operation status, by installing high-precision sensors such as temperature sensors, pressure sensors, vibration sensors and smart meters on various production equipment, the equipment operation parameters are collected in real time, including the equipment's real-time temperature, operating pressure, vibration frequency, energy consumption and other key data, to form an equipment operation data set. These data can intuitively reflect the working status of the equipment. Once the equipment is abnormal, such as excessive temperature, abnormal pressure fluctuations or a sharp increase in energy consumption, the system can issue an early warning signal in time so that maintenance personnel can quickly take measures to maintain and avoid equipment failures from causing serious impact on production. At the same time, a progress tracking mechanism is established to form a progress tracking data set. With the help of advanced production management software and Internet of Things technology, data collection points are set at each production process node to accurately record the completion progress of production tasks. From the online launch of raw materials to the processing and assembly of parts and components, and then to the offline launch of finished products, the progress information of the production task can be obtained in real time, and presented in intuitive forms such as percentages and estimated completion times. This enables production managers to keep track of production rhythm at any time, discover delays in the production process in a timely manner, and adjust production plans or allocate resources in a targeted manner to ensure that production tasks are completed on time. In addition, monitoring of personnel and materials is also crucial. By deploying personnel positioning systems, material inventory management systems, and material transportation tracking systems at the production site, the real-time location information of personnel, work status (such as whether they are busy, whether they are resting or off-duty, etc.), and the inventory quantity, inventory location, material in and out records, transportation routes and distribution progress on the production line, and other information are collected to build a personnel and material monitoring data set. This can ensure the reasonable allocation of human and material resources, avoid the occurrence of problems such as idle personnel or material backlogs and shortages, and ensure the smooth progress of production activities. By activating the production line monitoring system and integrating the equipment operation data set, progress tracking data set, and personnel and material monitoring data set, a comprehensive and real-time monitoring data set is formed, which provides accurate and timely data support for subsequent collaborative scheduling and optimization, and helps to achieve refined management and optimized decision-making in the production process, thereby improving the overall efficiency of electric tricycle production.

[0021] Step S400: performing collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority, and establishing a collaborative scheduling optimization result.

[0022] Specifically, first, based on the determined production task priority, a series of constraints for collaborative scheduling optimization are constructed. Task priority constraints ensure that high-priority tasks enjoy absolute priority in terms of resource allocation, execution order, etc., and always keep the production rhythm and task importance matched; resource constraints fully consider the actual availability and allocation of current human, equipment, materials and other resources to avoid excessive resource occupation or idle waste, and ensure the stable progress of production activities; equipment adaptability constraints are based on the performance characteristics, maintenance status and process adaptability of the equipment, accurately matching the most suitable equipment combination for each production task to maximize the equipment efficiency; task dependency constraints deeply sort out the logical relationship between production tasks, ensuring that the subsequent tasks are carried out in an orderly manner after the previous tasks are successfully completed, and maintaining the continuity of the production process; time window constraints strictly limit the start and end time range of each production task, so that the task can be completed efficiently within the specified time and prevent production delays. Next, an optimization fitness function is established, which cleverly combines load balancing characteristics and task evaluation characteristics. When constructing the load balancing feature, we first set a preset collection window, use the window workload analysis technology, accurately count the workload of each production line or equipment in this time period, and establish the first load balancing function to measure the distribution balance of production resources in the short term. At the same time, we deeply explore the cumulative measurement task characteristics, such as the cumulative processing volume and cumulative time of the task, as well as the continuous work task characteristics, such as the continuous operation duration and the continuous operation task type, and comprehensively analyze these characteristics to construct the second load balancing function, which is used to evaluate the load stability in the long-term production process. The two load balancing functions are organically combined to form a complete load balancing function, which is then included in the optimization fitness function as a load balancing feature. Finally, with the constructed fitness function and the set constraints, the genetic algorithm is used to deeply mine and analyze the real-time monitoring data set. Through the algorithm, iterative optimization is repeatedly performed in a huge data space, and multiple factors such as production task priority, resource status, equipment status, and production progress are comprehensively considered to continuously optimize the task allocation plan and resource scheduling strategy, and finally establish a collaborative scheduling optimization result. This result clarifies how to arrange production tasks and allocate resources in the best way under current production conditions to achieve the comprehensive goals of maximizing production efficiency, minimizing costs and timely delivery. It provides a scientific and accurate basis for subsequent production decisions and effectively promotes the efficient and orderly production of electric tricycles.

[0023] Step S500: After the production line-task correction is performed through the collaborative scheduling optimization result, the processing management of the production line is executed.

[0024] Specifically, according to the optimization scheme provided in the collaborative scheduling optimization results, the current production line-task arrangement is comprehensively and carefully revised. If the optimization results show that a production line has low equipment utilization when processing a specific task, such as too long equipment idle time or unbalanced equipment load, the task allocation will be readjusted to reasonably transfer some tasks to other production lines with idle equipment or light load to achieve full utilization of equipment resources. At the same time, if it is found that the connection between some tasks is not smooth enough, there are problems such as too long waiting time or process conflict, the task sequence will be replanned according to the optimization results to ensure close cooperation and efficient flow between the processes. After completing the production line-task correction, the processing management of the production line is carried out in strict accordance with the optimized and adjusted plan. Starting from the input of raw materials, along the revised production process, each production line strictly follows the task allocation and process sequence for processing operations. During the processing process, pay close attention to the production progress, and timely discover and solve possible problems such as sudden equipment failure, material supply shortage or personnel operation errors through the real-time feedback information of the real-time monitoring system. At the same time, according to the actual production situation, resources are flexibly deployed to ensure that each production link can proceed smoothly according to the predetermined plan. Through such a rigorous execution process, production activities can be carried out efficiently, stably and orderly, and production efficiency can be maximized, the production quality of electric tricycles can be guaranteed, and the optimized management and precise control of the entire production process can be achieved.

[0025] In a possible implementation, step S100 further includes: Step S110: extracting the production tasks through the feature extraction layer of the priority evaluation model to establish a basic data set, wherein the basic data set includes delivery time, task order type, resource requirements, task process, and task history data weight.

[0026] Step S120: Synchronize the basic data set to the importance calculation layer, perform multi-feature task importance calculation of production tasks, and establish task importance calculation results, which include task urgency importance, task type importance, resource requirement importance, task complexity importance, and historical data importance. The importance calculation layer is the calculation processing layer of the priority evaluation model.

[0027] Step S130: After configuring adaptive weights for the task importance calculation results, weighted calculation is performed, and the production task priority is output according to the weighted calculation results.

[0028] Specifically, the feature extraction layer of the priority evaluation model begins to conduct a comprehensive analysis of the electric tricycle production task. It focuses on multiple key dimensions of the production task. First, the delivery time is accurately locked. This factor clarifies the specific deadline by which the product must be delivered to the customer, and its urgency directly affects the priority arrangement of the task. The type of task order is also an important consideration. For example, different types such as regular orders, urgent additional orders, or customized special specification orders have different priority weights in production arrangements. In terms of resource requirements, the number of required manpower, skill requirements, equipment types, quantity and performance parameters, and material types, specifications and quantities are sorted out in detail. This information provides a key basis for subsequent resource allocation. The task process deeply explores the process flow, technical points, key links of quality control, etc. in the product manufacturing process. Its complexity and uniqueness are of great significance to the difficulty and importance assessment of the task. Finally, the extraction of task historical data weights is also indispensable. By reviewing the execution of similar tasks in the past, including production cycle, quality performance, resource consumption and other data, historical experience reference is provided for the current task priority assessment. After meticulous work at the feature extraction layer, we successfully built a basic data set containing core elements such as delivery time, task order type, resource requirements, task process, and task historical data weight, laying a solid foundation for the subsequent accurate assessment of production task priorities.

[0029] The basic data set is transferred to the importance calculation layer, which quickly starts the comprehensive calculation of the importance of multi-feature tasks of production tasks. For the urgency importance of tasks, it calculates the time difference between the delivery time and the current moment, and combines the urgency attribute contained in the task order type to use intelligent algorithms to determine whether the task is urgent. The result will directly reflect the urgency of the task on the production progress. The calculation of the importance of task types depends on the task value system pre-set by the enterprise. This system accurately evaluates the status and value of the current task in the entire production system based on the importance of different task order types in terms of enterprise strategic layout, market demand satisfaction and profit contribution. The process of determining the importance of resource requirements comprehensively considers the scarcity, difficulty of obtaining and the degree of restriction on the smoothness and efficiency of the production process of resource requirements such as manpower, equipment and materials in the basic data set. Scarce and difficult to obtain key resources will significantly increase the importance of tasks in this regard. The calculation of task complexity and importance is the most complicated. It quantifies the complexity of technical requirements by combining a specific formula with industry benchmark values ​​and weight coefficients. At the same time, the complexity of the process flow is analyzed, involving multiple dimensions such as the total number of process steps, the complexity and dependency scores of each step, the frequency of process adjustments, and the possibility of changes. In addition, the complexity of collaborative work is evaluated, considering factors such as the number of collaborative departments, the complexity of collaborative tools, the need for task collaboration, and the frequency of communication. The complexity and importance of tasks are derived by combining these three aspects. Finally, the calculation of historical data importance provides valuable historical references for the current task importance assessment by reviewing the efficiency, quality stability, and impact on production benefits of similar tasks in the past. After this series of rigorous and complex calculation processes, a comprehensive and accurate task importance calculation result was successfully established, providing solid data support for the subsequent determination of production task priorities.

[0030] In the face of the obtained task importance calculation results, according to the current actual production situation, strategic goals, market dynamics and other factors of the enterprise, adaptive weights are configured for task urgency importance, task type importance, resource demand importance, task complexity importance and historical data importance. For example, if the enterprise is in the peak period of orders and the delivery time is tight, the weight of task urgency importance may be increased accordingly; for enterprises committed to technological innovation, the weight of technical requirements complexity in task complexity importance may be increased; if the supply of certain key resources is tight, the weight of resource demand importance will also be emphasized. After completing the adaptive weight configuration, perform weighted calculation, multiply each task importance by its corresponding adaptive weight, and then add these products to obtain a comprehensive value, which is the priority quantitative index of the production task. According to the size of this index, a clear production task priority is finally output, so as to provide accurate and scientific decision-making basis for subsequent production arrangements, resource allocation and collaborative scheduling, so as to ensure that the production process of electric tricycles can be carried out efficiently and orderly, and maximize production benefits.

[0031] In a possible implementation, step S120 further includes: Calculate the complexity of the technical requirements of the production task as follows: ; in, Characterization techniques require complexity, Characterization technology accuracy requirement score, Characterize the technical innovation score, Characterize the technical environment adaptability score, Characterize the technology maturity score, , , , Represent the industry benchmark values, , , Characterize the weight coefficient.

[0032] Calculate the process complexity of the production task as follows: ; in, Characterize the complexity of the process flow, Characterize the total number of process steps for the current production task, , is the process step index, Characterization The complexity score of the process step, Characterize the weight of process step complexity, Characterization Dependency score of the step process, Characterize the process dependency weights, Characterize the process adjustment frequency, Characterize the possibility of process change, The weight that characterizes the frequency and possibility of process adjustment, Characterize the total number of benchmark process steps, Characterize the frequency of baseline process adjustments, Characterize the possibility of changes to the baseline process.

[0033] Calculate the collaborative complexity of production tasks as follows: ; in, Characterize the complexity of collaborative work, Represents the number of collaborative departments, is the weight of the number of collaborative departments, Characterizing the complexity of collaborative tools, To meet the needs of task collaboration, The weight that characterizes the complexity of collaborative tools and the collaborative requirements of tasks, Characterizes the frequency of communication, Characterizes the communication frequency weight, , , , They are the benchmark values ​​for the number of collaborative departments, complexity of collaborative tools, task collaboration requirements, and communication frequency.

[0034] The importance of task complexity is obtained according to the complexity of the technical requirements, the complexity of the process flow and the complexity of the collaborative work.

[0035] Specifically, a carefully designed formula was used to calculate the complexity of the technical requirements for the production of electric tricycles. As a quantitative representation of the complexity of technical requirements, its numerical value directly reflects the complexity of the production task at the technical level. Represents the technical accuracy requirement score. For example, in the frame welding process of an electric tricycle, the accuracy requirement for the welding points may be extremely high. If the deviation is too large, it will affect the strength of the frame and the overall structural stability, which requires a higher score in the calculation. Indicates the technical innovation score. If the production task involves the use of new battery technology to improve the endurance of electric tricycles, or the introduction of advanced braking system control technology, such innovative technology applications will significantly improve the score. The score that represents the environmental adaptability of the technology takes into account that electric tricycles may be used in different geographical environments (such as mountainous areas, plains, humid areas, etc.) and climatic conditions (high temperature, low temperature, humidity, etc.). Its production technology needs to have good environmental adaptability. For example, the performance of the motor's heat dissipation technology in a high temperature environment, or the performance stability of the battery management system in a low temperature environment, etc., will affect the score. It represents the technology maturity score. A technology with high maturity means that it has been fully verified in actual production and has lower risks. For example, the traditional electric tricycle frame manufacturing process is relatively mature and its score will be higher. However, the newly developed lightweight material application technology has a relatively low maturity score because it has not yet been tested in large-scale production practice. , , , As industry benchmark values, they are like technical yardsticks for the industry. Taking technical accuracy requirements as an example, if the industry generally believes that the machining accuracy error of a certain component of an electric tricycle should be controlled within the range of ±0.1mm, and the current production task requires a higher accuracy, such as ±0.05mm, then when calculating the technical accuracy requirement score, a quantitative assessment will be made based on the difference from the industry benchmark value. , , It is set according to the company's strategic focus and product characteristics.

[0036] The formula for calculating process complexity is: Used to accurately characterize process complexity. Indicates the total number of process steps in the current production task, which is a basic indicator to measure the complexity of the process flow. For example, the production of an electric tricycle may involve multiple major process steps such as frame processing, parts assembly, circuit system installation, and painting. Each major step contains several sub-steps. The more steps there are, the more complex the process. , As a process step index, it is used to traverse and analyze the characteristics of each process step in detail. Characterization The complexity score of the step process, such as the motor assembly process of an electric tricycle, may be high because it involves high-precision parts assembly and complex electrical connections; while the complexity score of some simple appearance parts installation processes is relatively low. The weight of the complexity of the process step determines the importance of the complexity score of each process step in the calculation of the overall process complexity. If the enterprise believes that the complexity of the key process step has a huge impact on the overall process, the weight will be increased accordingly. Characterization The dependency score of the first process, taking the battery installation process of the electric tricycle as an example, it depends on the integrity of the frame structure and the preliminary layout of the circuit system. If there are problems with these preliminary processes, the battery installation will not be able to proceed smoothly. This high degree of dependency will lead to a higher dependency score. is the process dependency weight, which is used to adjust the influence of the process dependency score in the calculation. It indicates the frequency of process adjustment. For example, in the production process, in order to optimize the performance of the electric tricycle, it may be necessary to frequently adjust the motor debugging process. This high adjustment frequency will increase the complexity of the process. It represents the possibility of process changes. If the market demand for the appearance color of electric tricycles changes frequently, the coating process may need to be changed, which will also increase the complexity of the process. It is the weight of process adjustment frequency and change possibility, which is set according to the actual production situation of the enterprise. is the total number of benchmark process steps, Indicates the frequency of benchmark process adjustment, Representing the possibility of baseline process changes, these baseline values ​​provide a reference standard for comparing the process complexity of current production tasks, making the calculation results more objective and comparable.

[0037] The calculation formula for the complexity of collaborative work is: Used to quantify the complexity of collaborative work. Indicates the number of collaborative departments. In the production of electric tricycles, the collaborative work of multiple departments such as R&D, procurement, production, quality inspection, and sales is involved. The more departments there are, the more difficult it is to communicate, coordinate, and allocate tasks. For example, in the new product development stage, the R&D department needs to work closely with the procurement department to determine the procurement needs of new materials, work with the production department to develop feasible production processes, and communicate with the sales department about market demand and product positioning. The collaborative relationship between departments is intricate and complex. It is the weight of the number of collaborative departments, which is set according to the company's organizational structure and management model. If the company emphasizes close collaboration between departments, the weight may be higher. Characterizing the complexity of collaborative tools, for example, enterprises use advanced project management software to coordinate the work of various departments. The software functions are complex and requires employees to have certain operating skills, which increases the complexity of collaborative work. For task collaboration needs, for example, in the large-scale production of electric tricycles, in order to ensure on-time delivery, the task collaboration needs between the production department and other departments are very high, which requires precise production plan coordination, material supply guarantee and quality inspection synchronization, etc. It is the weight of the complexity of collaborative tools and the collaborative requirements of tasks, and is used to balance the roles of these two factors in the calculation of collaborative work complexity. It indicates the frequency of communication. Departments need to frequently communicate with each other about information such as production progress, quality issues, and material supply. The higher the frequency of communication, the greater the complexity of collaborative work. It is the communication frequency weight, which is set according to the enterprise communication management strategy. , , , They are the benchmark values ​​for the number of collaborative departments, complexity of collaborative tools, task collaboration requirements, and communication frequency. They provide a reference standard for evaluating the complexity of collaborative work in current production tasks, enabling accurate judgment of the difficulty level of collaborative work, and reasonable arrangement of resources and optimization of management processes accordingly.

[0038] The complexity of technical requirements calculated above , Process complexity and collaboration complexity , to finally determine the complexity and importance of the task. This task complexity and importance occupies an important position in the entire production task priority determination system, and is interrelated and mutually influenced by other factors such as task urgency importance, task type importance, resource demand importance and historical data importance. For example, when evaluating an electric tricycle production task with high technical innovation requirements (high technical requirements complexity), complex process flow and high coordination of multiple departments, its task complexity and importance are relatively high, which will work together with other factors in the subsequent priority calculation to determine the order and resource allocation weight of the task in the entire production plan, thereby realizing multi-line collaborative scheduling optimization in the production process of electric tricycles, improving production efficiency, reducing costs, ensuring product quality, and ensuring the efficient and orderly production and operation of the enterprise.

[0039] In a possible implementation, step S400 further includes: Step S410: Establishing constraints for collaborative scheduling optimization according to the production task priority, wherein the constraints include task priority constraints, resource constraints, equipment adaptability constraints, task dependency constraints, and time window constraints.

[0040] Step S420: Establishing an optimization fitness function, wherein the optimization fitness function includes load balancing characteristics and task evaluation characteristics.

[0041] Step S430: performing collaborative scheduling optimization based on the real-time monitoring data set according to the fitness function and the constraint conditions, and establishing the collaborative scheduling optimization result.

[0042] Specifically, the constraints of collaborative scheduling optimization are constructed based on the priority of production tasks. Task priority constraints ensure that high-priority tasks have absolute priority in resource allocation, equipment use and production sequence arrangement to ensure the smooth progress of overall production goals and key tasks; resource constraints fully consider the existing inventory, supply capacity and allocation status of human resources, equipment, materials and other resources to avoid excessive resource occupation or shortage affecting production; equipment adaptability constraints require accurate matching of the most suitable equipment according to the characteristics of production tasks, so that equipment performance is highly consistent with task requirements, thereby improving production efficiency and product quality; task dependency constraints clarify the logical order between various production tasks, ensure that subsequent tasks are carried out after the completion of the previous tasks, and maintain the continuity and rationality of the production process; time window constraints set a reasonable start and end time interval for each task, so as to prompt the task to be completed efficiently within the specified time and prevent the entire production progress from being out of control due to delays in individual tasks. These constraints lay a solid foundation for collaborative scheduling optimization.

[0043] Establishing the optimization fitness function is a key link in collaborative scheduling optimization. First, for the load balancing characteristics, by setting a specific preset acquisition window, the workload of each part of the production system in the window is carefully analyzed, and the equipment running time, task processing volume and other factors are comprehensively considered to construct the first load balancing function that can accurately reflect the short-term load status. At the same time, the cumulative measurement task characteristics such as the cumulative resource consumption of the task and the continuous working task characteristics such as the continuous working time are obtained. These characteristics are deeply analyzed to establish the second load balancing function for evaluating the long-term load stability, and the two are integrated to form a complete load balancing function. For the task evaluation characteristics, the key elements such as the punctuality of task completion, the quality stability during the execution process, and the resource utilization efficiency are comprehensively covered. Through accurate monitoring and scientific analysis of relevant data, the task execution effect is quantitatively evaluated. Finally, the load balancing function is used as the load balancing feature, and it is constructed together with the task evaluation feature to form the optimization fitness function, which provides a scientific and effective evaluation standard for the subsequent collaborative scheduling optimization, so as to achieve the optimized operation of the production system.

[0044] Genetic algorithm is used to carry out collaborative scheduling optimization based on real-time monitoring data set to establish the collaborative scheduling optimization result. First, the production tasks and production lines are encoded, and the initial population is randomly generated. Each individual represents a task allocation scheme. Then, the fitness of individuals in the population is evaluated based on the fitness function combined with real-time monitoring data. The fitness function comprehensively considers the load balancing and task evaluation characteristics, such as calculating the load balancing situation based on equipment operation and task progress data, and evaluating the task evaluation situation based on task completion time, quality indicators and resource utilization. Then, the roulette selection method is used to select individuals with high fitness as parents, and crossover operations are performed. Part of the genes of the parent individuals are exchanged with a certain probability to generate offspring, introduce new task allocation combinations, and perform mutation operations with a small probability at the same time. The genes of the offspring individuals are randomly changed to avoid falling into local optimality. The evaluation, selection, crossover and mutation processes are repeated continuously, and iterated until the termination conditions are met, such as reaching the maximum number of iterations or the fitness of the optimal individual has not been significantly improved for multiple generations. Finally, the output optimal individual is the collaborative scheduling optimization result, which determines the optimal allocation method and execution order of electric tricycle production tasks in each production line, and achieves efficient collaborative production scheduling.

[0045] In a possible implementation, step S420 further includes: Step S421: establishing a preset collection window, using the preset collection window to perform window workload analysis, and establishing a first load balancing function.

[0046] Step S422: Acquire cumulative metering task characteristics and continuous working task characteristics, perform task analysis according to the cumulative metering task characteristics and the continuous working task characteristics, and establish a second load balancing function.

[0047] Step S423: establishing a load balancing function according to the first load balancing function and the second load balancing function, and constructing the optimization fitness function by using the load balancing function as a load balancing feature.

[0048] Specifically, the preset collection window is flexibly set according to the complexity of the production task and the process requirements, for example, data is collected every 5 minutes, 10 minutes or longer. In the window, the following data is collected and recorded: Real-time task allocation status of the production line: including the type, quantity and execution progress of the task. Resource utilization: statistics on the actual use and idle ratio of equipment, personnel and materials. Production line performance indicators: such as equipment utilization rate, failure rate, task completion time, etc. This window design needs to take into account the real-time nature of the data and system performance, ensuring that the collection window is small enough to capture dynamic changes, while not bringing additional burden to the system due to too frequent collection. In each collection window, the workload of the production line is analyzed. Focus on the following indicators: Load intensity, calculate the task density of each production line in the current collection window, and reflect the compactness of task allocation. Resource distribution, analyze the distribution of resource occupancy among different production lines, and evaluate whether there is uneven resource allocation. Task execution efficiency, compare the task plan with the actual completion status, and identify delayed or over-completed production lines. Through these analyses, the load status of each production line can be grasped in real time, providing data support for the scheduling strategy. Based on the results of the window workload analysis, the first load balancing function is constructed to quantify the load balance of different production lines. The goal of the load balancing function is to minimize the load difference between production lines. The calculation formula is as follows: balance1 = ,in Indicates The workload of the production line, is the average workload of all production lines, is the number of production lines, by minimizing f balance1 It can effectively balance the work intensity of each production line to avoid overloading or idleness of some production lines. Through this step, the load status of each production line can be obtained and analyzed in real time, providing a basis for subsequent dynamic scheduling, ensuring the overall efficiency and resource utilization of multi-line collaborative scheduling.

[0049] In multi-line collaborative scheduling, in order to further optimize load balancing, it is necessary to conduct in-depth analysis of task characteristics. This step mainly includes the extraction and analysis of two key features: cumulative metering task features and continuous work task features. The extraction of cumulative metering task features refers to the cumulative number of tasks completed by each production line and its corresponding resource consumption within a specified time window. For example, the cumulative number of tasks completed, the working hours used, the resource occupation (such as equipment, personnel and materials) and the number of task delays of each production line are counted. By integrating these data, the overall workload and resource utilization efficiency of each production line can be evaluated. For production lines with high loads, task allocation can be adjusted to reduce pressure and ensure the balance of overall production. The continuous work task characteristics reflect the continuity and stability of each production line during task execution, focusing on the frequency of task switching and the impact of unplanned downtime. Including: task switching frequency, counting the number of task switching (line change or task rescheduling) within the acquisition window to evaluate the efficiency loss caused by task switching. Unplanned downtime, analyzing task interruptions caused by equipment failure or insufficient materials and their scope of impact. The task continuity coefficient measures the proportion of continuous work tasks of each production line and reflects whether the task allocation of the production line is stable. The second load balancing function is established. Based on the characteristics of the cumulative measurement tasks and the continuous work tasks, the second load balancing function is constructed by weighting. This function comprehensively considers the impact of task accumulation and continuity to balance the imbalance of task allocation between production lines. The specific formula is as follows: balance2 =α×accumulated task weight+β×task continuity weight, where α and β are the weight coefficients of accumulated tasks and task continuity, respectively, and are adjusted according to actual production needs. By establishing the second load balancing function, a more comprehensive optimization goal is provided for task allocation, ensuring that tasks can be completed in a short time and maintain a stable and efficient production state over a long period of time.

[0050] According to the first load balancing function and the second load balancing function, combined with their respective analysis results, a comprehensive load balancing function is established. The first load balancing function usually distributes the workload based on the preset collection window of the production task, mainly considering the distribution and load balancing of the task within the time window. The second load balancing function is based on the analysis of the cumulative task characteristics and the characteristics of the continuous work task, focusing on the continuity of the task throughout the production cycle and the stability of resource allocation. By combining these two load balancing functions, it will be possible to fully consider the load balancing of the task, from the comprehensive impact of short-term task allocation to long-term resource scheduling. Ultimately, this comprehensive load balancing function will serve as the load balancing feature in the optimization fitness function and participate in the optimization process of collaborative scheduling. The construction of the load balancing function ensures the reasonable distribution of production tasks among multiple production lines, avoids excessive load or idle resources of some production lines, thereby improving production efficiency and providing a scientific basis for subsequent task scheduling decisions.

[0051] In a possible implementation, step S400 further includes: Step S440: Perform a completion prediction of the production task based on the collaborative scheduling optimization result and establish a completion prediction result.

[0052] Step S450: Perform abnormal trigger analysis based on the completed prediction results and establish abnormal warning.

[0053] Step S460: reporting the abnormal warning as an abnormality.

[0054] Specifically, a neural network model suitable for this task is constructed, which adopts a multi-layer perceptron structure and includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer is determined according to the task-related parameters in the collaborative scheduling optimization results, such as task type, resource allocation, expected execution time, production line busyness and other key factors, and these factors are input into the neural network as input feature vectors. A number of neurons are set in the hidden layer, and the connection weights and thresholds between neurons are continuously adjusted through training to learn the complex nonlinear relationship between input features and output results. The output layer outputs the predicted task completion time. In the training stage, a large amount of historical production task data is collected, including the actual task completion time and the corresponding input feature data, and divided into a training set, a validation set, and a test set. The neural network is trained using the training set, and the model parameters are continuously adjusted through the back propagation algorithm to minimize the prediction error of the model on the validation set. After multiple iterations of training, the model reaches the preset convergence standard. After the training is completed, the task-related parameters in the collaborative scheduling optimization results are input into the trained neural network model, and the model will output the predicted task completion time, thereby establishing a completion prediction result. Neural network algorithms can automatically learn patterns from large amounts of historical data, have good fitting capabilities for complex production task relationships, and can provide relatively accurate completion time predictions, providing an important reference for subsequent production arrangements.

[0055] Through the established completion prediction results, abnormal trigger analysis is performed to establish an abnormal warning mechanism. The predicted completion time of each task is compared with the pre-set production plan schedule to check whether there is any deviation. At the same time, refer to the real-time monitoring data set to analyze the actual resource utilization, equipment operation status, and personnel work efficiency in the current production process to determine whether these factors may affect the completion of the task on time. For example, if a key component assembly task is predicted to be delayed by 2 hours, and the current production line equipment has frequent fault alarms and personnel deployment is tight, then it can be determined that the task has an abnormal risk. Once it is discovered that the task may not be completed on time or other abnormal situations occur, immediately establish corresponding abnormal warning information according to the preset abnormal level standards (such as the level of delay time, the degree of impact on subsequent processes, etc.), clarify the abnormal type (such as time delay, resource shortage, quality risk, etc.) and severity, and provide accurate information support for timely response measures.

[0056] The established abnormal warning information will be reported in time. Through the enterprise's internal information management system, such as production management software, electronic kanban system or SMS notification platform, the abnormal warning information will be accurately conveyed to relevant departments and personnel, including production management personnel, quality control personnel, equipment maintenance personnel and supply chain departments. The abnormal warning information is presented in a clear and unambiguous manner, such as highlighting the name, number, expected delay time, abnormal cause and recommended measures of the abnormal task on the electronic kanban. At the same time, the system can automatically adjust the notification method according to the severity of the warning, such as direct notification of serious abnormalities to the relevant person in charge through SMS to ensure that the information can be received and processed in time. Such an abnormal reporting mechanism can prompt various departments of the enterprise to respond quickly, adjust production plans, allocate resources or take corrective measures in a timely manner, minimize the negative impact of abnormal situations on production progress, product quality and corporate benefits, and ensure the smooth production of electric tricycles.

[0057] In a possible implementation, step S500 further includes: Step S510: Perform production adaptation fitting according to the production line data and the production task, and establish a production adaptation fitting result.

[0058] Step S520: determining the production bottleneck through the production adaptation fitting result, and performing production optimization management based on the production bottleneck.

[0059] Specifically, when starting the production adaptation and fitting work, a genetic algorithm is selected. First, the production line data is collected and analyzed in all aspects, including the processing speed, accuracy, failure rate, maintenance cycle, material supply rate, inventory capacity, number of personnel configuration and skill proficiency of each production line equipment. At the same time, the production tasks are carefully disassembled according to the type, quantity and process requirements. Subsequently, the production line data is encoded into chromosome form, the gene corresponds to various parameters, and the production task is used as a constraint. Initialize the population, randomly generate a batch of production line combination schemes, calculate the fitness of each scheme (chromosome), and this fitness comprehensively considers resource utilization efficiency, time consumption, task completion quality, etc. Next, select some chromosomes to enter the next generation, and simulate gene recombination and mutation through crossover and mutation to generate new combinations, and repeat this cycle. After a large number of calculations, the chromosome with the best fitness is finally found, and the production adaptation and fitting results are successfully established, which clearly presents the allocation of production tasks to the optimal production line and the resource allocation plan.

[0060] Use the production adaptation fitting results as a guide to determine the production bottleneck. Carefully review the data in the fitting results, focusing on those links where resource shortages, time delays or efficiency drops sharply. For example, when a production line is performing a specific task, the equipment processing speed cannot keep up with the task requirements, resulting in material accumulation and waiting for subsequent processes. In this case, the equipment here may be the production bottleneck; or the frequent interruption of material supply causes intermittent production stagnation, and the material supply link becomes the bottleneck. Once the production bottleneck is accurately located, immediately carry out production optimization management based on it. On the one hand, carry out targeted upgrades to bottleneck resources, such as replacing more efficient equipment and optimizing material distribution processes; on the other hand, readjust the distribution of production tasks, and reasonably transfer some tasks to other idle or underutilized production lines to improve the overall production synergy, comprehensively ensure the smooth and efficient production process, and promote the smooth progress of production.

[0061] Embodiment 2 is based on the same inventive concept as the multi-line collaborative scheduling method for electric tricycle production in the above embodiment. Figure 2 As shown, the present application provides a multi-line collaborative scheduling system for electric tricycle production, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The production task priority establishment module 10 is used to obtain production tasks, which include production plans, process requirements, labor time estimates, material requirements, and quality requirements, input the production tasks into a priority evaluation model, and establish production task priorities.

[0062] The production line-task mapping establishment module 20 is used to obtain the production line data of the production line, adapt and optimize the production task according to the production line data and the production task priority, establish the production line-task mapping, and perform processing management of the production line according to the production line-task mapping.

[0063] The real-time monitoring data set establishing module 30 is used to activate the production line monitoring of the production line and establish a real-time monitoring data set, which includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set.

[0064] The collaborative scheduling optimization result establishing module 40 is used to perform collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority and establish the collaborative scheduling optimization result.

[0065] The processing management module 50 is used to perform processing management of the production line after the production line-task correction is performed through the collaborative scheduling optimization result.

[0066] Furthermore, the production task priority establishing module 10 also includes: The basic data set establishment unit is used to extract the production tasks through the feature extraction layer of the priority evaluation model to establish a basic data set, and the basic data set includes delivery time, task order type, resource requirements, task process, and task historical data weight.

[0067] The task importance calculation result establishing unit is used to synchronize the basic data set to the importance calculation layer, perform multi-feature task importance calculation of production tasks, and establish task importance calculation results, wherein the task importance calculation results include task urgency importance, task type importance, resource requirement importance, task complexity importance, and historical data importance, wherein the importance calculation layer is the calculation processing layer of the priority evaluation model.

[0068] The weighted calculation unit is used to configure adaptive weights for the task importance calculation results, perform weighted calculations, and output production task priorities according to the weighted calculation results.

[0069] Furthermore, the task importance calculation result establishing unit further includes: Calculate the complexity of the technical requirements of the production task as follows: ; in, Characterization techniques require complexity, Characterization technology accuracy requirement score, Characterize the technical innovation score, Characterize the technical environment adaptability score, Characterize the technology maturity score, , , , Represent the industry benchmark values, , , Characterize the weight coefficient.

[0070] Calculate the process complexity of the production task as follows: ; in, Characterize the complexity of the process flow, Characterize the total number of process steps for the current production task, , is the process step index, Characterization The complexity score of the process step, Characterize the weight of process step complexity, Characterization Dependency score of the step process, Characterize the process dependency weights, Characterize the process adjustment frequency, Characterize the possibility of process change, The weight that characterizes the frequency and possibility of process adjustment, Characterize the total number of benchmark process steps, Characterize the frequency of baseline process adjustments, Characterize the possibility of changes to the baseline process.

[0071] Calculate the collaborative complexity of production tasks as follows: ; in, Characterize the complexity of collaborative work, Represents the number of collaborative departments, is the weight of the number of collaborative departments, Characterizing the complexity of collaborative tools, To meet the needs of task collaboration, The weight that characterizes the complexity of collaborative tools and the collaborative requirements of tasks, Characterizes the frequency of communication, Characterizes the communication frequency weight, , , , They are the benchmark values ​​for the number of collaborative departments, complexity of collaborative tools, task collaboration requirements, and communication frequency.

[0072] The importance of task complexity is obtained according to the complexity of the technical requirements, the complexity of the process flow and the complexity of the collaborative work.

[0073] Furthermore, the collaborative scheduling optimization result establishing module 40 also includes: The constraint condition establishment unit is used to establish the constraint conditions for collaborative scheduling optimization according to the production task priority, and the constraint conditions include task priority constraints, resource constraints, equipment adaptability constraints, task dependency constraints, and time window constraints.

[0074] The optimization fitness function establishing unit is used to establish an optimization fitness function, wherein the optimization fitness function includes a load balancing feature and a task evaluation feature.

[0075] The collaborative scheduling optimization unit is used to perform collaborative scheduling optimization based on the real-time monitoring data set according to the fitness function and the constraint conditions, and establish the collaborative scheduling optimization result.

[0076] Furthermore, the optimization fitness function establishing unit also includes: The first load balancing function establishing unit is used to establish a preset collection window, use the preset collection window to perform window workload analysis, and establish a first load balancing function.

[0077] The second load balancing function establishing unit is used to obtain the cumulative metering task characteristics and the continuous working task characteristics, perform task analysis according to the cumulative metering task characteristics and the continuous working task characteristics, and establish a second load balancing function.

[0078] A load balancing function establishing unit is used to establish a load balancing function according to the first load balancing function and the second load balancing function, and to construct the optimization fitness function by using the load balancing function as a load balancing feature.

[0079] Furthermore, the collaborative scheduling optimization result establishing module 40 also includes: The prediction result establishing unit is used to make a completion prediction of the production task according to the collaborative scheduling optimization result and establish a completion prediction result.

[0080] The abnormal warning establishment unit is used to perform abnormal trigger analysis based on the completed prediction results and establish abnormal warning.

[0081] The abnormality reporting unit is used to report the abnormal warning as an abnormality.

[0082] Furthermore, the processing management module 50 also includes: The production adaptation fitting result establishing unit is used to perform production adaptation fitting according to the production line data and the production task, and establish the production adaptation fitting result.

[0083] The production optimization management unit is used to determine the production bottleneck through the production adaptation fitting result, and perform production optimization management based on the production bottleneck.

[0084] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0086] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A multi-line collaborative scheduling method for electric tricycle production, characterized in that: The method comprises: Obtaining a production task, wherein the production task includes a production plan, process requirements, labor time estimate, material requirements, and quality requirements, and inputting the production task into a priority evaluation model to establish a production task priority; Acquire production line data of the production line, perform adaptive optimization of the production task according to the production line data and the production task priority, establish a production line-task mapping, and perform processing management of the production line according to the production line-task mapping; Activate the production line monitoring of the production line and establish a real-time monitoring data set, wherein the real-time monitoring data set includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set; According to the production task priority, collaborative scheduling optimization is performed based on the real-time monitoring data set, and a collaborative scheduling optimization result is established; After the production line-task correction is performed through the collaborative scheduling optimization result, the processing management of the production line is executed.

2. The multi-line collaborative scheduling method for electric tricycle production according to claim 1, characterized in that: The step of inputting the production task into a priority evaluation model to establish the production task priority includes: Extract the production tasks through the feature extraction layer of the priority evaluation model to establish a basic data set, wherein the basic data set includes delivery time, task order type, resource requirements, task process, and task history data weight; Synchronize the basic data set to the importance calculation layer, perform multi-feature task importance calculation of production tasks, and establish task importance calculation results, which include task urgency importance, task type importance, resource requirement importance, task complexity importance, and historical data importance, wherein the importance calculation layer is the calculation processing layer of the priority evaluation model; After configuring adaptive weights for the task importance calculation results, weighted calculation is performed, and the production task priority is output according to the weighted calculation results.

3. The multi-line collaborative scheduling method for electric tricycle production as claimed in claim 2, characterized in that: The task complexity importance is calculated as follows: Calculate the complexity of the technical requirements of the production task as follows: ； in, Characterization techniques require complexity, Characterization technology accuracy requirement score, Characterize the technical innovation score, Characterize the technical environment adaptability score, Characterize the technology maturity score, , , , Represent the industry benchmark values, , , Characterize the weight coefficient; Calculate the process complexity of the production task as follows: ； in, Characterize the complexity of the process flow, Characterize the total number of process steps for the current production task, , is the process step index, Characterization The complexity score of the process step, Characterize the weight of process step complexity, Characterization Dependency score of the step process, Characterize the process dependency weights, Characterize the process adjustment frequency, Characterize the possibility of process change, The weight that characterizes the frequency and possibility of process adjustment, Characterize the total number of benchmark process steps, Characterize the frequency of benchmark process adjustments, Characterize the possibility of changes to the baseline process; Calculate the collaborative complexity of production tasks as follows: ； in, Characterize the complexity of collaborative work, Represents the number of collaborative departments, is the weight of the number of collaborative departments, Characterizing the complexity of collaborative tools, To meet the needs of task collaboration, The weight that characterizes the complexity of collaborative tools and the collaborative requirements of tasks, Characterizes the frequency of communication, Characterize the communication frequency weight, , , , They are the benchmark values ​​for the number of collaborative departments, complexity of collaborative tools, task collaboration requirements, and communication frequency; The importance of task complexity is obtained according to the complexity of the technical requirements, the complexity of the process flow and the complexity of the collaborative work.

4. The multi-line collaborative scheduling method for electric tricycle production according to claim 1, characterized in that: The step of performing collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority and establishing a collaborative scheduling optimization result includes: Establishing collaborative scheduling optimization constraint conditions according to the production task priority, wherein the constraint conditions include task priority constraint, resource constraint, equipment adaptability constraint, task dependency constraint, and time window constraint; Establishing an optimization fitness function, wherein the optimization fitness function includes a load balancing feature and a task evaluation feature; According to the fitness function and the constraint conditions, collaborative scheduling optimization is performed based on the real-time monitoring data set to establish the collaborative scheduling optimization result.

5. The multi-line collaborative scheduling method for electric tricycle production as claimed in claim 4, characterized in that: The establishment of the optimization fitness function comprises: Establishing a preset collection window, performing window workload analysis using the preset collection window, and establishing a first load balancing function; Acquire cumulative metering task characteristics and continuous working task characteristics, perform task analysis according to the cumulative metering task characteristics and the continuous working task characteristics, and establish a second load balancing function; A load balancing function is established according to the first load balancing function and the second load balancing function, and the optimization fitness function is constructed by using the load balancing function as a load balancing feature.

6. The multi-line collaborative scheduling method for electric tricycle production according to claim 1, characterized in that: The method further comprises: Performing a completion prediction of the production task according to the collaborative scheduling optimization result, and establishing a completion prediction result; Perform abnormal trigger analysis based on the completed prediction results and establish abnormal warning; The abnormal warning is reported as an abnormality.

7. The multi-line collaborative scheduling method for electric tricycle production according to claim 1, characterized in that: The method further comprises: Perform production adaptation fitting according to the production line data and the production task, and establish a production adaptation fitting result; The production bottleneck is determined through the production adaptation fitting result, and production optimization management is performed based on the production bottleneck.

8. The multi-line collaborative scheduling system for electric tricycle production is characterized by: The system is used to implement the multi-line collaborative scheduling method for electric tricycle production according to any one of claims 1 to 7, and the system comprises: A production task priority establishment module is used to obtain production tasks, which include production plans, process requirements, labor time estimates, material requirements, and quality requirements, input the production tasks into a priority evaluation model, and establish production task priorities; A production line-task mapping establishment module is used to obtain production line data of the production line, perform adaptation and optimization of the production task according to the production line data and the production task priority, establish a production line-task mapping, and perform processing management of the production line according to the production line-task mapping; A real-time monitoring data set establishment module, used to activate the production line monitoring of the production line and establish a real-time monitoring data set, wherein the real-time monitoring data set includes an equipment operation data set, a progress tracking data set, and a personnel and material monitoring data set; A collaborative scheduling optimization result establishment module is used to perform collaborative scheduling optimization based on the real-time monitoring data set according to the production task priority and establish a collaborative scheduling optimization result; The processing management module is used to perform processing management of the production line after the production line-task correction is performed through the collaborative scheduling optimization result.

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