MES Cross-Plant Production Capacity Dynamic Coordination Control Method Based on Multi-Objective Optimization

Abstract

The present invention discloses a method for dynamically coordinating and controlling the production capacity of MES across factories based on multi-objective optimization, which relates to the technical field of production capacity regulation and control. First, the Internet of Things is used to collect and integrate the production data of each factory to ensure the accuracy and integrity of the data. Then, algorithms are used to predict demand, allocate orders, and adjust the production plan in real time according to the production capacity assessment. Next, a multi-objective optimization model is constructed, and the optimal solution is obtained through solution for the decision maker to choose. Finally, the production is monitored through a visual interface, and when an abnormality occurs, it automatically feeds back and adjusts to achieve the dynamic coordination and control of the production capacity across factories. The present invention can improve production efficiency, reduce costs, avoid resource waste and risk losses, ensure product quality, ensure the consistency of standards among factories, optimize resource utilization, promote green manufacturing, strengthen supply chain collaboration, improve the overall operation and management level of the enterprise, and enhance market competitiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of production capacity regulation, and particularly to a dynamic coordination control method for MES cross-plant production capacity based on multi-objective optimization. Background Art

[0002] In the current context of increasingly fierce global competition, the business scope of manufacturing enterprises continues to expand, and the cross-plant production mode is becoming more and more common. There are differences in geographical location, resource allocation, production process, etc. among factories. How to achieve efficient coordination control of cross-plant production capacity has become a key problem in enhancing the overall competitiveness of enterprises.

[0003] Traditional Manufacturing Execution Systems (MES) can play a certain role in dealing with single-plant production management, but they expose many deficiencies in cross-plant scenarios. In terms of data management, the data of each factory is independent of each other, lacking an effective integration mechanism. The data formats are not unified, and the phenomenon of data islands is serious, resulting in difficulties for management to obtain comprehensive and accurate production information and make timely and scientific decisions. For example, the storage methods of equipment operation data, inventory data, etc. in different factories are different, making it difficult to directly compare and analyze, so that enterprises cannot accurately grasp the overall production capacity status.

[0004] In terms of production capacity coordination, traditional methods mostly rely on manual experience and simple production scheduling, and cannot respond in real time to changes in market demand and unexpected situations in the production process. When the order volume fluctuates, equipment fails, or raw material supply is delayed, it is difficult to quickly adjust the production tasks and resource allocation of each factory, often resulting in problems such as delivery delays and cost increases. For example, when the order volume of an enterprise surges during the peak season, due to the inability to coordinate the production capacity of each factory in time, the delivery of some orders lags behind, which not only affects customer satisfaction but also causes economic losses.

[0005] With the diversification and personalization of market demand, customers have higher and higher requirements for product quality and delivery time. At the same time, enterprises are also facing multiple challenges such as cost control and optimal utilization of resources. Existing MES systems have limited capabilities in multi-objective optimization and cannot balance multiple objectives such as production cost, product quality, delivery time, and resource utilization rate. In actual production, often one objective is sacrificed to meet another, making it difficult to maximize enterprise benefits. For example, in order to shorten the delivery time, production costs may be increased, product quality may be reduced, or resources may be over-consumed, which is not conducive to the sustainable development of enterprises. Against this background, it is urgent to develop a dynamic coordination control method for MES cross-plant production capacity based on multi-objective optimization. Summary of the Invention

[0006] The dynamic coordination control method for MES cross-plant production capacity based on multi-objective optimization proposed by the present invention is to solve the problems mentioned in the above prior art.

[0007] To achieve the above object, the present invention adopts the following technical solutions: A method for dynamically coordinating and controlling the production capacity of MES across factories based on multi-objective optimization, including:

[0008] Data collection and integration step: Using Internet of Things technology, deploy sensors at production equipment, material inventory points, and quality inspection links in each factory to collect production data in real time, including equipment operation status, production progress, material consumption, and product quality inspection results; transmit the data to the central database through industrial Ethernet or 5G network, and use ExtractTransformLoad technology to clean, transform, and load the data to eliminate data noise and integrate it into a unified format;

[0009] Demand forecasting and order allocation step: Use time series analysis and long short-term memory network LSTM, combined with historical market data, customer order trends, and industry dynamic factors to forecast product demand; construct an order allocation model based on the production capacity, cost, product quality level, and delivery date factors of each factory, and use the Hungarian algorithm to solve it to achieve reasonable allocation of orders among different factories; The objective function of the order allocation model is: , where is the total order allocation cost, is the cost of factory production order , is the delivery time of factory to complete order , is the order allocated to factory quantity, is the number of factories, is the number of orders;

[0010] Production capacity evaluation and dynamic adjustment step: Based on the collected equipment data and production process parameters, combined with the human and material resource allocation of the factory, use the production capacity evaluation model to evaluate the production capacity of each factory in real time; The production capacity evaluation model considers equipment utilization rate, production efficiency, and production bottleneck factors, and the calculation formula is: , where is the production capacity of the factory, is the utilization rate of equipment , is the production efficiency of equipment , is the effective production time of equipment , is the number of equipment in the factory; When the production capacity of the factory fluctuates, dynamically adjust the production plan and resource allocation according to order priority, delivery date, and production cost factors;

[0011] Target optimization steps: Taking production cost, delivery time, product quality, and resource utilization rate as optimization targets, construct a target optimization model; use the non-dominated sorting genetic algorithm NSGA-II to solve the model and obtain the Pareto solution; during the optimization process, by adaptively adjusting the crossover probability and the mutation probability , improve the convergence speed and optimization ability of the algorithm. The adjustment formula is: , where , are the initial maximum and minimum values of the crossover probability, , are the initial maximum and minimum values of the mutation probability, is the fitness value of the current individual, , are the maximum and minimum fitness values in the population; the decision maker selects the optimization plan from the Pareto solution according to actual needs;

[0012] Collaborative monitoring and feedback steps: Through the visualization interface of the MES system, monitor the production progress, order execution status, and production capacity utilization of each factory in real time; when an abnormal situation occurs, the system automatically issues an alarm and feeds back the abnormal information to the management department and the factory; each factory adjusts the production strategy according to the feedback information and at the same time feeds back the adjustment result to the MES system to form a closed-loop control and achieve the stable operation of the production process.

[0013] In the present invention, it further includes:

[0014] Supply chain collaborative optimization steps: By establishing an information sharing platform with suppliers and logistics providers, obtain raw material supply information and logistics distribution capacity data in real time; combine factory production capacity and order demand, and use the theory of constraints TOC to determine the bottleneck links in the supply chain and optimize the supply chain resource allocation; adopt a joint inventory management strategy to establish a collaborative inventory model between suppliers and factories to reduce inventory costs. The collaborative inventory model formula is: , where I is the total inventory cost, is the inventory holding cost per unit product in factory , is the safety inventory of factory , is the shortage cost per unit product in factory , is the demand forecast value of factory .

[0015] Risk assessment and response steps: Establish a risk assessment index system, including market risk, technical risk, supply chain risk, and production risk; use the Analytic Hierarchy Process (AHP) to determine the weights of each risk index, and combine the fuzzy comprehensive evaluation method to evaluate the risks in the cross-factory production process; when the risk assessment result exceeds the preset threshold, initiate the risk response strategy. The risk assessment score calculation formula is: , where is the risk assessment score, is the weight of the th risk index, is the evaluation score of the th risk index, is the number of risk indices.

[0016] In the present invention, in the data collection and integration step, for the collected production data, use distributed ledger technology for encrypted storage, utilize contract technology to automatically execute data verification and sharing rules, and at the same time, adopt a data compression algorithm to compress and store historical data.

[0017] In the present invention, in the demand forecasting and order allocation step, introduce the deep Q-network (DQN) of the reinforcement learning algorithm, and dynamically adjust the order allocation strategy according to the real-time production capacity changes and order completion status of the factory; achieve the adaptation of the order allocation strategy to the production environment through trial and error and learning; the update formula of the DQN algorithm is: , where is the value of taking action in state , α is the learning rate, is the reward obtained by taking action in state , is the discount factor, is the next state.

[0018] In the present invention, in the production capacity assessment and dynamic adjustment step, use digital twin technology to construct a virtual model of the factory production process; through real-time comparative analysis of actual production data and the virtual model, discover production capacity bottlenecks and production problems in advance; adopt a method based on model predictive control (MPC) to optimize the control of the production process and adjust production parameters in advance according to the predicted production situation.

[0019] In the present invention, in the objective optimization step, in addition to production cost, delivery time, product quality, and resource utilization rate, incorporate carbon emissions into the optimization objective, and construct a green manufacturing objective optimization model; use the improved multi-objective particle swarm optimization (MOPSO) algorithm to solve it, and achieve the sustainable development of cross-factory production by optimizing energy consumption and carbon emissions in the production process.

[0020] In the present invention, in the collaborative monitoring and feedback step, big data analysis technology is used to mine the monitoring data; the relationship between various factors in the production process is discovered through the Apriori algorithm of association rule mining, providing information for production decision-making.

[0021] In the present invention, in the supply chain collaborative optimization step, distributed ledger technology is used to build a supply chain information sharing platform; automatic settlement and trust mechanisms for all links of the supply chain are realized through contracts, and the supplier relationship management (SRM) system is adopted to classify and manage suppliers and establish stable cooperative relationships with high-quality suppliers.

[0022] In the present invention, in the risk assessment and response step, machine learning algorithms are used to learn and train historical risk data to build a risk prediction model; by real-time monitoring of risk index data, the occurrence probability and impact degree of risks are predicted in advance; combined with the risk matrix, different risks are classified and managed, and targeted risk response plans are formulated.

[0023] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0024] In terms of production efficiency, by using the Internet of Things to collect and integrate data in real time, enterprises can grasp the production status of each factory in real time, discover and solve production bottlenecks in a timely manner. Accurate demand forecasting and reasonable order allocation can make full use of the production capacity of each factory and avoid uneven production tasks. In case of emergencies, the system can quickly adjust the production plan, reduce downtime, and improve the overall production efficiency.

[0025] In terms of cost control, multi-objective optimization takes into account the production cost, optimizes order allocation and resource configuration, and avoids resource waste and overproduction. Supply chain collaborative optimization can reduce the raw material procurement cost and inventory cost and improve the capital turnover rate. Moreover, the risk assessment and response mechanism can prevent risks in advance and reduce the additional costs caused by risk events.

[0026] In terms of product quality assurance, the production process is monitored in real time, and the quality problem correlation factors are mined by combining big data analysis, so that quality hidden dangers can be discovered in a timely manner and measures can be taken. Each factory produces according to the unified quality standards to ensure the consistency of product quality and improve customer satisfaction.

[0027] At the level of resource utilization, the system dynamically allocates resources such as human, material, and equipment resources to improve resource utilization rate. Incorporating carbon emissions into the optimization goal helps to promote green manufacturing and achieve the sustainable development of enterprises.

[0028] In terms of collaborative management, supply chain collaborative optimization strengthens the cooperation between enterprises and suppliers and logistics providers, establishes stable cooperative relationships, and ensures the smooth supply of raw materials and logistics distribution. The real-time monitoring and feedback mechanism across factories can communicate and coordinate problems in a timely manner, improve the overall operation and management level of enterprises, and enhance the competitiveness of enterprises in the market. Brief Description of the Drawings

[0029] Figure 1 It is a schematic block diagram of the MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization proposed by the present invention;

[0030] Figure 2 It is a schematic block diagram for comparing the production capacity utilization rates of each factory of the MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization of the present invention;

[0031] Figure 3 It is a schematic block diagram for comparing the production costs of different orders of the MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization of the present invention before and after applying this patent. Detailed Embodiment

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0034] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined. In addition, the terms "installed", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0035] Referring to Figures 1 - 3 : A specific implementation method of a MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization

[0036] I. Data collection and integration

[0037] At key nodes in the production workshops of each factory, such as production equipment, material storage areas, quality inspection workstations, etc., various sensors are deployed, including temperature sensors, pressure sensors, displacement sensors, material flow sensors, etc., to achieve real-time data collection by means of the Internet of Things technology. The collected data covers information such as the operating status of equipment (such as rotational speed, temperature, fault alarm signal), production progress (quantity of products, production batches), material consumption (types of materials, consumption quantity), and product quality inspection results (dimensional deviation, performance parameters).

[0038] Through industrial Ethernet or 5G network, the collected data is transmitted to the central database. Using ETL tools, the data is cleaned to remove duplicate, incorrect and incomplete data records. For example, for abnormal data caused by sensor failures, it is screened and eliminated by setting reasonable data thresholds; for data with inconsistent formats, unified conversion is performed, such as unifying the recording formats of equipment operating times in different factories into a standard time format. Then, the processed data is loaded into the database to build a complete and accurate production data warehouse, providing reliable data support for subsequent analysis and decision-making.

[0039] II. Demand forecasting and order allocation

[0040] Collect historical sales data, market trend data, and customer order data from channels such as the enterprise's sales database, market research institutions, and industry reports. Use time series analysis algorithms, such as the ARIMA model, to analyze the historical sales data and capture the trend, seasonality, and periodicity characteristics of the data. At the same time, input these data into a long short-term memory network (LSTM) for training. The LSTM can learn the long-term dependencies in the data, thus more accurately predicting future product demand.

[0041] Construct an order allocation model based on data such as the production capacity, production cost, product quality level, and delivery date of each factory. Assume that the enterprise has 3 factories (n = 3) and currently has 5 orders (m = 5). By collecting the cost of each factory producing each order and the estimated delivery time , substitute them into the objective function of the order allocation model , and use the Hungarian algorithm to solve it to determine which factory each order is allocated to and the quantity allocated , and achieve a reasonable allocation of orders.

[0042] III. Capacity Evaluation and Dynamic Adjustment

[0043] Obtain the operating data of each factory's equipment in real time through sensors and calculate the equipment utilization rate , for example, the ratio of the actual operating time to the planned operating time of equipment k within a shift. Obtain the equipment production efficiency from the production process documents, such as the number of products produced by the equipment per unit time. At the same time, determine the effective production time of the equipment according to the production plan and equipment maintenance arrangements . Substitute these data into the capacity evaluation model to calculate the production capacity of each factory.

[0044] When the production capacity fluctuates due to equipment failures, insufficient raw material supply, etc. in the factory, re-adjust the production plan according to factors such as the priority of the order, delivery date, and production cost by using the dynamic programming algorithm. For example, for orders with urgent delivery dates and high priorities, allocate resources for production first; for orders with higher costs, arrange production in factories with lower costs as much as possible while ensuring other goals.

[0045] IV. Multi-objective Optimization

[0046] Determine the production cost, delivery date, product quality, and resource utilization rate as the optimization objectives, and construct a multi-objective optimization model. Use the non-dominated sorting genetic algorithm (NSGA-II) to solve this model. In the initialization stage of the algorithm, randomly generate a certain number of initial populations. During the iteration process, calculate the fitness value of each individual and generate a new population through selection, crossover, and mutation operations.

[0047] To improve the convergence speed and optimization ability of the algorithm, adaptively adjust the crossover probability and the mutation probability . For example, at the initial stage of the algorithm, to accelerate the search speed, set the crossover probability to 0.8 and the mutation probability to 0.2; as the iteration progresses, when the fitness value of the population gradually stabilizes, to avoid the algorithm falling into local optimality, set the crossover probability to 0.6 and the mutation probability to 0.1. According to the formulas and , adjust the probability values in real time. Finally, generate a set of Pareto optimal solutions, and the decision maker selects the most suitable solution from the Pareto optimal solutions according to the current strategic focus and actual needs of the enterprise.

[0048] V. Collaborative Monitoring and Feedback Develop a visual monitoring interface in the MES system to display information such as the production progress, order execution status, and production capacity utilization of each factory in an intuitive way, such as through charts and graphs. For example, display the production progress of each order in each factory through a Gantt chart and compare the production capacity utilization rates of each factory with a bar chart. When abnormal situations such as production progress delays and quality problems occur, the system automatically issues an alarm according to the preset rules, such as notifying relevant management personnel via text message, email, or system pop-up window.

[0049] After receiving the alarm, the relevant personnel promptly take measures to adjust the production strategy, such as adjusting the production order, increasing the input of manpower or equipment, etc. After the adjustment is completed, feedback the adjustment result to the MES system, and the system continuously monitors the adjusted production situation to form a closed-loop control to ensure that the production process is always in a stable and efficient operating state.

[0050] VI. Data Representation and Interpretation

[0051]

[0052] It can be seen from the data that after the implementation of the patented method, the on-time delivery rate of orders has increased significantly, which benefits from accurate demand forecasting, reasonable order allocation, and dynamic adjustment of production capacity, ensuring that orders can be produced and delivered within the specified time. The production cost has been reduced by 15% because of the consideration of cost in the multi-objective optimization process and the optimization of order allocation and resource configuration, avoiding unnecessary waste. The product unqualified rate has been reduced by half because the production process is monitored in real time, quality problems can be detected and measures can be taken in a timely manner, and the unified quality standards of each factory also ensure the stability of product quality. The resource utilization rate has increased by 20%. Through the dynamic allocation of resources in each factory, resources are more fully utilized, reducing idleness and waste, and comprehensively enhancing the production efficiency and competitiveness of the enterprise.

[0053] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A dynamic coordination control method for MES cross-plant production capacity based on multi-objective optimization, characterized in that, Including: Data collection and integration step: Using Internet of Things technology, deploy sensors at production equipment, material inventory points, and quality inspection links in each factory to collect production data in real time, including equipment operation status, production progress, material consumption, and product quality inspection results; transmit the data to the central database through industrial Ethernet or 5G network, and use ExtractTransformLoad technology to clean, transform, and load the data to eliminate data noise and integrate it into a unified format; Demand forecasting and order allocation steps: Use time series analysis and long short-term memory network (LSTM), combined with historical market data, customer order trends, and industry dynamic factors to forecast product demand; construct an order allocation model based on the production capacity, cost, product quality level, and delivery time factors of each factory, and use the Hungarian algorithm to solve it to achieve reasonable allocation of orders among different factories; the objective function of the order allocation model is: , where is the total order allocation cost, is the cost of factory producing order , is the delivery time of factory completing order , is the quantity of order allocated to factory , is the number of factories, is the number of orders; Capacity assessment and dynamic adjustment steps: Based on the collected equipment data and production process parameters, combined with the human and material resource allocation in the factory, the capacity of each factory is evaluated in real time through a capacity assessment model; the capacity assessment model takes into account equipment utilization rate, production efficiency, and production bottleneck factors, and the calculation formula is: , where is the capacity of the factory, is the utilization rate of equipment , is the production efficiency of equipment , is the effective production time of equipment , is the number of equipment in the factory; when the factory capacity fluctuates, the production plan and resource allocation are dynamically adjusted according to order priority, delivery date, and production cost factors; Target optimization step: Taking production cost, delivery time, product quality, and resource utilization rate as optimization targets, construct a target optimization model; use the non-dominated sorting genetic algorithm NSGA-II to solve the model and obtain the Pareto solution; During the optimization process, by adaptively adjusting the crossover probability and the mutation probability , the convergence speed and optimization ability of the algorithm are improved. The adjustment formula is: , where and are the initial maximum and minimum values of the crossover probability, and are the initial maximum and minimum values of the mutation probability, is the fitness value of the current individual, and are the maximum and minimum fitness values in the population; The decision maker selects the optimization scheme from the Pareto solutions according to the actual needs;​ Supply chain collaborative optimization step: Establish an information sharing platform with suppliers and logistics providers to obtain raw material supply information and logistics distribution capacity data in real time; combine factory production capacity and order demand, and use the theory of constraints TOC to determine the bottleneck links in the supply chain and optimize the supply chain resource allocation; adopt the joint inventory management strategy to establish a collaborative inventory model between suppliers and factories to reduce inventory costs; Collaboration The inventory model formula is as follows: , where I is the total inventory cost, is the inventory holding cost per unit product at the factory , is the safety stock at the factory , is the shortage cost per unit product at the factory , is the demand forecast value at the factory .

2. The method for dynamically coordinating and controlling the MES cross-plant production capacity based on multi-objective optimization according to claim 1, characterized in that It also includes: Collaborative monitoring and feedback step: Through the visualization interface of the MES system, monitor the production progress, order execution status, and production capacity utilization status of each factory in real time; When an abnormal situation occurs, the system automatically issues an alarm and feeds back the abnormal information to the management department and the factory; each factory adjusts the production strategy according to the feedback information and at the same time feeds back the adjustment results to the MES system to form a closed-loop control and achieve the stable operation of the production process.

3. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 1, wherein, It also includes: Risk assessment and response step: Establish a risk assessment index system, including market risk, technology risk, supply chain risk, and production risk aspects; Use the analytic hierarchy process AHP to determine the weights of each risk index, and combine the fuzzy comprehensive evaluation method to evaluate the risks in the cross-factory production process; When the risk assessment result exceeds the preset threshold, the risk response strategy is initiated. The risk assessment score calculation formula is: , where is the risk assessment score, is the weight of the th risk indicator, is the evaluation score of the th risk indicator, is the number of risk indicators.

4. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 1, characterized in that It also includes: In the data collection and integration step, for the collected production data, use distributed ledger technology for encrypted storage, use contract technology to automatically execute data verification and sharing rules, and at the same time, use data compression algorithms to compress and store historical data.

5. The MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization according to claim 1, wherein, It also includes: In the demand forecasting and order allocation step, introduce the reinforcement learning algorithm deep Q network DQN, and dynamically adjust the order allocation strategy according to the real-time production capacity changes and order completion status of the factory; achieve the adaptation of the order allocation strategy to the production environment through trial and error and learning; the update formula of the DQN algorithm is: , where is the value of taking action in state , α is the learning rate, is the value of taking action in state and getting reward obtained, is the discount factor, and is the next state.

6. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 1, characterized in that It also includes: In the production capacity evaluation and dynamic adjustment step, use digital twin technology to build a virtual model of the factory production process; through real-time comparison and analysis of actual production data and the virtual model, discover production capacity bottlenecks and production problems in advance; adopt the method based on model predictive control MPC to optimize the control of the production process and adjust production parameters in advance according to the predicted production situation.

7. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 2, wherein It also includes: In the target optimization step, in addition to production cost, delivery time, product quality, and resource utilization rate, include carbon emissions in the optimization target and construct a green manufacturing target optimization model; Solve it using the improved multi-objective particle swarm optimization algorithm (MOPSO). By optimizing energy consumption and carbon emissions in the production process, achieve the sustainable development of cross-factory production.

8. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 2, wherein It also includes: In the collaborative monitoring and feedback step, use big data analysis technology to mine the monitoring data. Discover the relationships between various factors in the production process through the Apriori association rule mining algorithm to provide information for production decision-making.

9. The MES cross-plant production capacity dynamic coordination control method based on multi-objective optimization according to claim 2, wherein It also includes: In the supply chain collaborative optimization step, use distributed ledger technology to build a supply chain information sharing platform; achieve automatic settlement and trust mechanism for each link of the supply chain through contracts, adopt the supplier relationship management (SRM) system to classify and manage suppliers, and establish stable cooperative relationships with high-quality suppliers.

10. The MES cross-factory production capacity dynamic coordination control method based on multi-objective optimization according to claim 3, characterized in that, It also includes: In the risk assessment and response step, use machine learning algorithms to learn and train historical risk data to build a risk prediction model. Predict the occurrence probability and impact degree of risks in advance by real-time monitoring of risk indicator data. Combine the risk matrix to classify and manage different risks and formulate risk response plans.

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