APS scheduling management method of MES system

Abstract

The invention relates to an APS scheduling management method of an MES system, and relates to the technical field of system scheduling, and the method comprises the steps: identifying potential correlation factors between scheduling tasks through a data mining technology; defining and constructing a production element input structure; reversing the latest starting time of each process according to the delivery time of the workpieces; calculating the remaining working hours of the processing procedure in real time, and dynamically adjusting the scheduling based on the resource utilization rate and the processing progress factor; the scheduling principle of batch concentration and batch continuity is adopted, and resource optimization configuration is achieved; a scheduling scheme is optimized by using a particle swarm algorithm, standards of balanced processes, minimum delay and minimum overtime are evaluated, and an optimal scheme is automatically selected. The scheduling is optimized through data mining, the production precision and flexibility are improved, the remaining working hours are calculated in real time and dynamically adjusted, the progress and resources are balanced, the task dependency relationship is recognized, the resources are reasonably distributed, the overall production efficiency is improved, and smooth production is ensured.

Description

Technical Field

[0001] The present application relates to system scheduling technology, and in particular to an APS scheduling management method for an MES system. Background Art

[0002] With the rapid development of the manufacturing industry, companies are facing increasingly complex production scheduling challenges. To improve production efficiency and reduce waiting time, waste, and bottlenecks in the production process, manufacturers are widely adopting a combination of Manufacturing Execution Systems (MES) and Advanced Scheduling Systems (APS) for production scheduling management. APS optimizes production scheduling plans, coordinates production resources, and implements intelligent scheduling, load balancing, and progress control throughout the production process, helping companies improve production efficiency, reduce production costs, and enhance market flexibility.

[0003] Traditional MES system scheduling methods are usually based on experience or simple rules, and lack in-depth exploration and optimization of the relationship between production processes and tasks. This method is difficult to cope with the changing demands, complex resource allocation and high-demand production scheduling in modern production. With the continuous development of data mining technology, the APS scheduling method based on data analysis has emerged and has gradually become an important tool for improving production efficiency. By introducing data mining and optimization algorithms, it can effectively identify the potential correlation factors between tasks in the production process, optimize resource allocation, and improve the intelligence level of production scheduling. Summary of the Invention

[0004] The purpose of this application is to provide an APS scheduling management method for an MES system, including: Identify potential correlation factors between scheduling tasks through data mining technology, analyze and adjust production factors that affect resource utilization efficiency; Define and build the production factor input structure, including predecessor tasks, sequence transfer time, priority, standard working time difference ratio, equipment efficiency ratio, equipment working calendar and task processing status parameters; The latest start time of each process is reversed according to the workpiece delivery date, and a specific scheduling plan is generated based on task decomposition. The accurate planning time across outsourced processes is also calculated. Calculate the remaining working hours of the processing process in real time and dynamically adjust the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process; Adopting the batch concentration and batch continuous scheduling principles to achieve optimal resource allocation, and rationally arranging various production resources through the load balancing principle to maximize overall production efficiency; Use particle swarm optimization to optimize scheduling plans, evaluate process balance, minimize delays, and minimize overtime, automatically select the optimal plan, and provide overtime suggestions to ensure that production tasks are completed on time.

[0005] Preferably, the identifying potential correlation factors between scheduled tasks by using data mining technology and analyzing and adjusting production factors that affect resource utilization efficiency specifically include: Collect data from the MES system and production process, including task processing time, equipment usage, transfer time, and priority, and clean and pre-process the data to remove noise data and fill in missing values; Apply data mining techniques to analyze the potential relationships between scheduled tasks and identify dependencies, order of tasks, and resource sharing characteristics; By analyzing the data, we identify the key factors that affect resource utilization efficiency and use statistical methods to evaluate the impact of each factor on scheduling efficiency. The resource utilization efficiency calculation formula is: Where, E u is the resource utilization efficiency, T u,i is the actual usage time of the i-th task, T a is the available time of the i-th task, and n is the total number of tasks; Based on the identified task relevance and influencing factors, a forecasting model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes, and production factors are adjusted according to the model results; During the scheduling process, production data is collected in real time to monitor the deviation between task execution and expected targets, and data mining algorithms are used to make dynamic adjustments; Based on the results of data mining analysis, an optimized scheduling plan is generated, and task combinations and production factor configurations are recommended.

[0006] Preferably, analyzing data to identify key factors affecting resource utilization efficiency and using statistical methods to evaluate the impact of each factor on scheduling efficiency specifically include: Regression analysis is used to evaluate the impact of the standard working hours variance ratio and equipment efficiency ratio on scheduling efficiency. The regression analysis formula is: E s =α1·P+α2·W+α3·C+∈ Where, E s is the scheduling efficiency, P is the priority, W is the working time difference, C is the equipment efficiency ratio, α1, α2, α3 are the regression coefficients, and ∈ is the error term.

[0007] Preferably, based on the identified task relevance and influencing factors, a prediction model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes. Adjusting production factors according to the model results specifically includes: Based on task relevance and influencing factors, data mining models are used to predict resource utilization and production efficiency under different scheduling schemes; Based on the model, we predict the impact of different scheduling schemes on resource utilization and production efficiency. For each simulated scheduling scheme, we evaluate the indicators of resource utilization, production efficiency and task completion time. By simulating different schemes, we find the optimal scheduling strategy and adjust the resource allocation in the production process according to the results of the prediction model.

[0008] Preferably, the method of using a data mining model to predict resource utilization and production efficiency under different scheduling schemes based on task relevance and influencing factors specifically includes: Among them, the data mining model formula is: E p =β0+β1T t +β2E u +β3D(T i )+β4D f +∈ Where, E p is the predicted resource utilization and production efficiency, which represents the output of the model. Under a given scheduling scheme, the predicted production efficiency and resource utilization are as follows. β0 is a constant term, β2, β3, and β4 are regression coefficients. β1 represents the influence weight of task execution time on the prediction result, β2 represents the influence weight of resource utilization efficiency on the prediction result, β3 represents the influence weight of task dependency on the prediction result, and β4 represents the influence weight of other influencing factors on the prediction result. T t is the execution time of the task, which means the time required for each task, E u is the resource utilization efficiency, D(T i ) is task T i The order of D f are other factors that affect production efficiency, and ∈ is the error term.

[0009] Preferably, the method of reversing the latest start time of each process according to the workpiece delivery date, generating a specific scheduling plan in combination with task decomposition, and calculating the accurate planned time across outsourced processes specifically includes: According to the delivery date of the workpiece, the latest start time of each process is reversed, and the processing time, transfer time and priority of each process are taken into consideration to ensure that all processes can be completed on time; Split the process into smaller tasks, generate a schedule through task decomposition, calculate the accurate planned time for outsourced processes, and coordinate internal and external resources to meet the production plan.

[0010] Preferably, the real-time calculation of the remaining working hours of the processing steps and the dynamic adjustment of the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process specifically include: Monitor the processing progress of each process in real time, and dynamically calculate the remaining working hours of the task by analyzing the actual remaining working hours and resource utilization; Adjust the schedule based on the current progress, resource availability, and equipment usage to ensure flexible response to changes in production and ensure flexibility and scalability in the production process.

[0011] Preferably, the batch concentration and batch continuous scheduling principles are adopted to achieve optimal resource allocation, and various production resources are reasonably arranged through the load balancing principle to maximize overall production efficiency. Specifically, the following steps are included: Adopting the principles of batch concentration and batch continuous scheduling to centralize similar tasks, thus reducing changeover time and improving resource utilization; Through the load balancing principle, various production resources are arranged to avoid excessive load and idleness of certain resources, thereby improving overall production efficiency.

[0012] Preferably, the method of using a particle swarm algorithm to optimize the scheduling scheme, evaluate the standards of process balance, minimum delay and minimum overtime, automatically select the optimal scheme, and provide overtime suggestions to ensure that the production tasks are completed on time specifically includes: using a particle swarm algorithm to optimize the scheduling scheme, evaluate the standards of process balance, minimum delay and minimum overtime under different scheduling schemes; Automatically select the optimal scheduling plan based on the optimization results, and provide overtime suggestions based on production progress and resource allocation to ensure that production tasks can be completed on time.

[0013] Preferably, the particle swarm algorithm formula is: F(x)=w1·j(x)+w2·h(x)+w3·l(x) Where F(x) is the comprehensive evaluation result under a given scheduling plan x, x is the solution vector of the scheduling plan, w1 represents the relative importance of process balance in the evaluation function, w2 represents the relative importance of minimum delay in the evaluation function, and w3 represents the relative importance of minimum overtime in the evaluation function.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. Through data mining technology, it is possible to identify potential correlation factors between scheduling tasks, so as to Analyze and optimize production factors in a targeted manner. This data-based optimization method improves scheduling accuracy and flexibility and can respond to changes in production in real time. 2. The system can calculate the remaining working hours of the processing process in real time and dynamically adjust it according to resource utilization and processing progress. In this way, uncertainties and emergencies in the production process can be handled promptly, ensuring a balance between production progress and resource utilization; 3. By identifying the dependencies, order and resource sharing characteristics between tasks, it is possible to reasonably Arrange production tasks to avoid over-utilization and waste of resources. The system can make adjustments based on the actual situation of the tasks, optimize the production sequence and resource allocation of tasks, and improve overall production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a step flow chart of the present invention; Figure 2 It is a detailed step flow chart of the present invention. DETAILED DESCRIPTION

[0016] The following is combined with Figure 1 -Attached Figure 2 , further details of this application are given.

[0017] Example 1: APS scheduling management method for an MES system, referring to Figure 1 , including: identifying potential correlation factors between scheduling tasks through data mining technology, analyzing and adjusting production factors that affect resource utilization efficiency; Define and build the production factor input structure, including predecessor tasks, sequence transfer time, priority, standard working time difference ratio, equipment efficiency ratio, equipment working calendar and task processing status parameters; The latest start time of each process is reversed according to the workpiece delivery date, and a specific scheduling plan is generated based on task decomposition. The accurate planning time across outsourced processes is also calculated. Calculate the remaining working hours of the processing process in real time and dynamically adjust the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process; Adopting the batch concentration and batch continuous scheduling principles to achieve optimal resource allocation, and rationally arranging various production resources through the load balancing principle to maximize overall production efficiency; Use particle swarm optimization to optimize scheduling plans, evaluate process balance, minimize delays, and minimize overtime, automatically select the optimal plan, and provide overtime suggestions to ensure that production tasks are completed on time.

[0018] Example 2: APS scheduling management method for an MES system, referring to Figure 2 , using data mining technology to identify potential correlation factors between scheduling tasks, and analyze and adjust production factors that affect resource utilization efficiency, including: Collect data from the MES system and production process, including task processing time, equipment usage, transfer time, and priority, and clean and pre-process the data to remove noise data and fill in missing values; Apply data mining techniques to analyze the potential relationships between scheduled tasks and identify dependencies, order of tasks, and resource sharing characteristics; By analyzing the data, we identify the key factors that affect resource utilization efficiency and use statistical methods to evaluate the impact of each factor on scheduling efficiency. The resource utilization efficiency calculation formula is: Where, E u is the resource utilization efficiency, T u,i is the actual usage time of the i-th task, T a is the available time of the i-th task, and n is the total number of tasks; Based on the identified task relevance and influencing factors, a forecasting model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes, and production factors are adjusted according to the model results; During the scheduling process, production data is collected in real time to monitor the deviation between task execution and expected targets, and data mining algorithms are used to make dynamic adjustments; Generate optimized scheduling plans based on data mining analysis results, and recommend task combinations and production factor configurations; By using data mining technology to analyze the potential correlation factors and key influencing factors between scheduling tasks, a complete prediction model was established. This dynamic adjustment based on data makes the scheduling plan more accurate and sustainable, improving the system's automation level and scheduling efficiency.

[0019] By analyzing data, we identify the key factors that affect resource utilization efficiency and use statistical methods to evaluate the impact of each factor on scheduling efficiency. Specifically, the following factors are involved: Regression analysis is used to evaluate the impact of the standard working hours variance ratio and equipment efficiency ratio on scheduling efficiency. The regression analysis formula is: E s =α1·P+α2·W+α3·C+∈ Where, E s is the scheduling efficiency, P is the priority, W is the working time difference, C is the equipment efficiency ratio, α1, α2, α3 are the regression coefficients, and ∈ is the error term.

[0020] Based on the identified task relevance and influencing factors, a forecasting model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes. Production factors are adjusted based on the model results, specifically including: Based on task relevance and influencing factors, data mining models are used to predict resource utilization and production efficiency under different scheduling schemes; Based on the model, we predict the impact of different scheduling schemes on resource utilization and production efficiency. For each simulated scheduling scheme, we evaluate the indicators of resource utilization, production efficiency, and task completion time. By simulating different schemes, we find the optimal scheduling strategy and adjust the resource allocation in the production process based on the results of the prediction model. Using regression analysis to quantify the impact of key production factors on scheduling efficiency breaks through the traditional rules of thumb and further improves the scientific nature and accuracy of scheduling optimization. This method can provide a feasible quantitative basis for scheduling adjustments in actual production.

[0021] Based on task relevance and influencing factors, data mining models are used to predict resource utilization and production efficiency under different scheduling schemes. Specifically, the following are included: Among them, the data mining model formula is: E p =β0+β1T t +β2E u +β3D(T i )+β4D f +∈ Where, E p is the predicted resource utilization and production efficiency, which represents the output of the model. Under a given scheduling scheme, the predicted production efficiency and resource utilization are as follows. β0 is a constant term, β2, β3, and β4 are regression coefficients. β1 represents the influence weight of task execution time on the prediction result, β2 represents the influence weight of resource utilization efficiency on the prediction result, β3 represents the influence weight of task dependency on the prediction result, and β4 represents the influence weight of other influencing factors on the prediction result. T t is the execution time of the task, which means the time required for each task, E u is the resource utilization efficiency, D(T i ) is task T i The order of D f are other factors that affect production efficiency, ∈ is the error term; The data mining model builds a dynamic and comprehensive resource management and production scheduling solution by refining the evaluation of factors such as task execution time, resource utilization efficiency, and task dependencies, further improving the scientific nature of scheduling forecasts and reducing resource waste and production delays.

[0022] The latest start time of each process is reversed according to the workpiece delivery date. Combined with the task decomposition, a specific scheduling plan is generated. The accurate planning time for cross-outsourcing processes is calculated. Specifically, the following are included: According to the delivery date of the workpiece, the latest start time of each process is reversed, and the processing time, transfer time and priority of each process are taken into consideration to ensure that all processes can be completed on time; Split the process into smaller tasks, generate a schedule based on task decomposition, calculate the accurate planned time for outsourced processes, and coordinate internal and external resources to meet the production plan; The latest start time of each process is reversed according to the delivery date of the workpiece, and combined with task decomposition and accurate time calculation across outsourced processes, the precise docking of each production link is ensured. This method improves the accuracy of scheduling and optimizes the execution efficiency of the production plan through the coordination of internal and external resources.

[0023] Calculate the remaining working hours of the processing operation in real time and dynamically adjust the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process. Specifically include: Monitor the processing progress of each process in real time, and dynamically calculate the remaining working hours of the task by analyzing the actual remaining working hours and resource utilization; Adjust the schedule based on the current progress, resource availability, and equipment usage to ensure flexibility in responding to changes in production and ensuring flexibility and scalability in the production process; Real-time calculation of the remaining working hours of the process and dynamic adjustment of the schedule based on the progress provide more flexible allocation space for the production process. This real-time dynamic adjustment mechanism can respond to changes in different production stages and improve resource utilization efficiency and time management in the production process.

[0024] Adopting the batch concentration and batch continuous scheduling principles to achieve optimal resource allocation, and rationally arranging various production resources through the load balancing principle to maximize overall production efficiency, specifically including: Adopting the principles of batch concentration and batch continuous scheduling to centralize similar tasks, thus reducing changeover time and improving resource utilization; Through the load balancing principle, various production resources are arranged to avoid excessive load and idleness of certain resources, thereby improving overall production efficiency.

[0025] Use particle swarm optimization to optimize scheduling plans, evaluate process balance, minimize delays, and minimize overtime, automatically select the best plan, and provide overtime suggestions to ensure that production tasks are completed on time. Specific details include: Use particle swarm optimization to optimize the scheduling scheme and evaluate the process balance, minimum delay and minimum overtime standards under different scheduling schemes; Automatically select the optimal scheduling plan based on the optimization results, and provide overtime suggestions based on production progress and resource allocation to ensure that production tasks can be completed on time.

[0026] The particle swarm algorithm formula is: F(x)=w1·j(x)+w2·h(x)+w3·l(x) Where F(x) is the comprehensive evaluation result under a given scheduling scheme x, x is the solution vector of the scheduling scheme, w1 represents the relative importance of process balance in the evaluation function, w2 represents the relative importance of minimum delay in the evaluation function, and w3 represents the relative importance of minimum overtime in the evaluation function. The particle swarm algorithm is applied to the optimization of scheduling schemes. By comprehensively evaluating the standards of process balance, delay and minimum overtime, it provides automated decision support for production. This method not only improves the accuracy of scheduling, but also effectively reduces overtime costs and optimizes production progress.

[0027] In summary, the advantages of the present invention are: Data mining technology can identify potential correlations between scheduled tasks, enabling targeted analysis and optimization of production factors. This data-based optimization approach improves scheduling accuracy and flexibility, enabling real-time responses to changes in production. The system can calculate the remaining hours of ongoing processing steps in real time and dynamically adjust them based on resource utilization and processing progress. This allows for timely handling of uncertainties and emergencies in the production process, ensuring a balance between production progress and resource utilization. By identifying the dependencies, order, and resource sharing characteristics between tasks, production tasks can be arranged reasonably to avoid over-utilization and waste of resources. The system can adjust according to the actual situation of the tasks, optimize the production sequence and resource allocation of tasks, and improve overall production efficiency. The system uses batch-centralized and batch-continuous scheduling principles to rationally arrange production resources, reduce task switching time, and improve production efficiency. The load balancing principle helps avoid overloading or idle production resources, thereby optimizing the production process. A particle swarm algorithm is used to optimize scheduling plans. By evaluating criteria such as process balance, minimal delays, and minimal overtime, the optimal solution is automatically selected. The algorithm can find the optimal solution among multiple scheduling options and provide overtime recommendations to ensure that production tasks are completed on time. Combining workpiece delivery reverse scheduling and task decomposition, the latest start time for each process can be accurately calculated, and accurate planning time across outsourced processes can be provided. This approach ensures efficient execution of production plans and reduces production delays caused by planning lags or errors. The system establishes a scheduling prediction model through data mining, which can predict resource utilization and production efficiency under different scheduling plans, and adjust production factors according to the prediction results. This greatly improves the foresight and scientific nature of scheduling.

[0028] The examples of this specific embodiment are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. An APS scheduling management method for an MES system, characterized in that: include: Identify potential correlation factors between scheduling tasks through data mining technology, analyze and adjust production factors that affect resource utilization efficiency; Define and build the production factor input structure, including predecessor tasks, sequence transfer time, priority, standard working time difference ratio, equipment efficiency ratio, equipment working calendar and task processing status parameters; The latest start time of each process is reversed according to the workpiece delivery date, and a specific scheduling plan is generated based on task decomposition. The accurate planning time across outsourced processes is also calculated. Calculate the remaining working hours of the processing process in real time and dynamically adjust the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process; Adopting the batch concentration and batch continuous scheduling principles to achieve optimal resource allocation, and rationally arranging various production resources through the load balancing principle to maximize overall production efficiency; Use particle swarm optimization to optimize scheduling plans, evaluate process balance, minimize delays, and minimize overtime, automatically select the optimal plan, and provide overtime suggestions to ensure that production tasks are completed on time.

2. The APS scheduling management method of an MES system according to claim 1, characterized in that: The identification of potential correlation factors between scheduled tasks through data mining technology and the analysis and adjustment of production factors that affect resource utilization efficiency specifically include: Collect data from the MES system and production process, including task processing time, equipment usage, transfer time, and priority, and clean and pre-process the data to remove noise data and fill in missing values; Apply data mining techniques to analyze the potential relationships between scheduled tasks and identify dependencies, order of tasks, and resource sharing characteristics; By analyzing the data, we identify the key factors that affect resource utilization efficiency and use statistical methods to evaluate the impact of each factor on scheduling efficiency. The resource utilization efficiency calculation formula is: Where, E u is the resource utilization efficiency, T u,i is the actual usage time of the i-th task, T a is the available time of the i-th task, and n is the total number of tasks; Based on the identified task relevance and influencing factors, a forecasting model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes, and production factors are adjusted according to the model results; During the scheduling process, production data is collected in real time to monitor the deviation between task execution and expected targets, and data mining algorithms are used to make dynamic adjustments; Based on the results of data mining analysis, an optimized scheduling plan is generated, and task combinations and production factor configurations are recommended.

3. The APS scheduling management method of an MES system according to claim 2, characterized in that: The above mentioned methods of analyzing data, identifying key factors that affect resource utilization efficiency, and using statistical methods to evaluate the impact of each factor on scheduling efficiency include: Regression analysis is used to evaluate the impact of the standard working hours variance ratio and equipment efficiency ratio on scheduling efficiency. The regression analysis formula is: E s =α1·P+α2·W+α3·C+∈ Where, E s is the scheduling efficiency, P is the priority, W is the working time difference, C is the equipment efficiency ratio, α1, α2, α3 are the regression coefficients, and ∈ is the error term.

4. The APS scheduling management method of an MES system according to claim 2, characterized in that: Based on the identified task relevance and influencing factors, a forecasting model for optimized scheduling is constructed to predict resource utilization and production efficiency under different scheduling schemes. Production factors are adjusted based on the model results, specifically including: Based on task relevance and influencing factors, data mining models are used to predict resource utilization and production efficiency under different scheduling schemes; Based on the model, we predict the impact of different scheduling schemes on resource utilization and production efficiency. For each simulated scheduling scheme, we evaluate the indicators of resource utilization, production efficiency and task completion time. By simulating different schemes, we find the optimal scheduling strategy and adjust the resource allocation in the production process according to the results of the prediction model.

5. The APS scheduling management method of an MES system according to claim 4, characterized in that: Based on the task relevance and influencing factors, the data mining model is used to predict the resource utilization and production efficiency under different scheduling schemes. include: Among them, the data mining model formula is: E p =β0+β1T t +β2E u +β3D(T i )+β4D f +∈ Where, E p is the predicted resource utilization and production efficiency, which represents the output of the model. Under a given scheduling scheme, the predicted production efficiency and resource utilization are as follows. β0 is a constant term, β2, β3, and β4 are regression coefficients. β1 represents the influence weight of task execution time on the prediction result, β2 represents the influence weight of resource utilization efficiency on the prediction result, β3 represents the influence weight of task dependency on the prediction result, and β4 represents the influence weight of other influencing factors on the prediction result. T t is the execution time of the task, which means the time required for each task, E u is the resource utilization efficiency, D(T i ) is task T i The order of D f are other factors that affect production efficiency, and ∈ is the error term.

6. The APS scheduling management method of an MES system according to claim 1, characterized in that: The method of reversing the latest start time of each process based on the workpiece delivery date, generating a specific scheduling plan based on task decomposition, and calculating the accurate planned time across outsourced processes specifically includes: According to the delivery date of the workpiece, the latest start time of each process is reversed, and the processing time, transfer time and priority of each process are taken into consideration to ensure that all processes can be completed on time; Split the process into smaller tasks, generate a schedule through task decomposition, calculate the accurate planned time for outsourced processes, and coordinate internal and external resources to meet the production plan.

7. The APS scheduling management method of an MES system according to claim 1, characterized in that: The real-time calculation of the remaining working hours of the processing process and the dynamic adjustment of the schedule based on resource utilization and processing progress factors to ensure the flexibility and allocation space of the production process specifically include: Monitor the processing progress of each process in real time, and dynamically calculate the remaining working hours of the task by analyzing the actual remaining working hours and resource utilization; Adjust the schedule based on the current progress, resource availability, and equipment usage to ensure flexible response to changes in production and ensure flexibility and scalability in the production process.

8. The APS scheduling management method of an MES system according to claim 1, characterized in that: The aforementioned batch concentration and batch continuous scheduling principles are adopted to achieve optimal resource allocation, and various production resources are reasonably arranged through the load balancing principle to maximize overall production efficiency. Specifically, the following are included: Adopting the principles of batch concentration and batch continuous scheduling to centralize similar tasks, thus reducing changeover time and improving resource utilization; Through the load balancing principle, various production resources are arranged to avoid excessive load and idleness of certain resources, thereby improving overall production efficiency.

9. The APS scheduling management method of an MES system according to claim 1, characterized in that: The use of particle swarm optimization to optimize scheduling plans, evaluate process balance, minimize delays, and minimize overtime, automatically select the optimal plan, and provide overtime suggestions to ensure that production tasks are completed on time specifically includes: Use particle swarm optimization to optimize the scheduling scheme and evaluate the process balance, minimum delay and minimum overtime standards under different scheduling schemes; Automatically select the optimal scheduling plan based on the optimization results, and provide overtime suggestions based on production progress and resource allocation to ensure that production tasks can be completed on time.

10. The APS scheduling management method of an MES system according to claim 9, characterized in that: The particle swarm algorithm formula is: F(x)=w1·j(x)+w2·h(x)+w3·l(x) Where F(x) is the comprehensive evaluation result under a given scheduling plan x, x is the solution vector of the scheduling plan, w1 represents the relative importance of process balance in the evaluation function, w2 represents the relative importance of minimum delay in the evaluation function, and w3 represents the relative importance of minimum overtime in the evaluation function.

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