57 results about "Operation model" patented technology

The invention provides a virtual power plant scheduling method based on risk avoidance and demand response. The method comprises the following steps: S1, respectively constructing a power market price model, a power grid operation model, a power demand model of a virtual power plant and an available resource model; s2, considering market fluctuation and equipment fault risk factors, and establishing a risk avoidance model of the virtual power plant; s3, based on the risk avoidance model, designing an optimization scheduling strategy for minimizing the risk avoidance index; s4, considering interaction between the virtual power plant and the power market and the power grid, and designing an interaction response mechanism; and S5, performing virtual power plant scheduling simulation evaluation by integrating the models, the scheduling strategies, the constraint conditions and the interactive response mechanism, and adjusting model parameters and strategies according to an evaluation result to optimize performance. According to the scheme, the technical problems in virtual power plant scheduling are solved more comprehensively and flexibly, and compared with a traditional method, the method has obvious superiority in performance.

The invention discloses a management method and system of a smart park based on digital twinning, and relates to the technical field of digital modeling, when the system operates, a real-time park operation model is established by using the digital twinning technology, and comprehensive dynamic monitoring and optimization of the park operation state are realized. Through feature extraction and preprocessing, the system can accurately obtain use and demand features of resources, and a more efficient resource scheduling index OSP is generated. And generating and testing a production plan factor PPG according to the optimized resource scheduling strategy, thereby adjusting a production plan and realizing balance and optimization of production tasks. The real-time monitoring and feedback module ensures the real-time monitoring of the execution state, generates a comprehensive optimization index COF through a feedback vector, further adjusts the resource scheduling and production plan, effectively reduces the operation cost, solves the large consumption of human resources, reduces the dependence on the maneuverability of personnel, and improves the production efficiency. And the operation flexibility and the economic benefit of the park are obviously improved.

The invention belongs to the field of intelligent scheduling of an energy system, and provides a coupling market operation optimization method and system based on a multi-agent game, a coupling market operation model is established through a power distribution market operation model and an operator operation model, and operation data among a power distribution market, an operator and a user market can be correlated, so that the operation efficiency of the power distribution market is improved. Furthermore, by obtaining a double-layer game model and a target function set, the electricity purchase cost of the user and operation target data of the active power distribution network and operator operation are correlated, and the target function set can be constrained and solved based on a target constraint function set; and the parameters of the double-layer game model and the coupling market operation model can be optimized according to the solving result of the target function, so that the stable operation of the whole coupling market can be ensured, and the operation requirements among a power distribution market, an operator and a user market can be considered.

The invention discloses an industrial park comprehensive energy system low-carbon planning method considering a production process, and the method comprises the following steps: 1, obtaining the production line condition of an industrial park, and constructing a production line model; 2, determining to-be-planned equipment and parameters, and constructing a lower-layer planning operation model considering short-term uncertainty; 3, determining medium and long term uncertainty parameters, and constructing a medium and long term uncertainty set; step 4, determining a multi-objective optimization model; 5, searching a Pareto leading edge by using multi-target reinforcement learning based on a decomposition technology; and 6, selecting a proper Pareto frontier solution, and substituting the Pareto frontier solution into the lower-layer planning operation model to obtain a corresponding planning result. The invention aims to realize low-carbon transformation of an integrated energy system (IRIES) in an industrial area so as to cope with carbon reduction, emission reduction and challenges in a carbon dense area.

The invention provides an operation optimization method and system for an energy storage participation auxiliary service market, and the method comprises the steps: determining an energy storage performance aging factor, and defining an energy storage aging time function according to the energy storage performance aging factor; the energy storage performance aging factors comprise calendar aging and cycle aging; based on the energy storage aging time function, constructing an intra-day operation model taking energy storage expected life maximization and intra-day benefit maximization as objective functions; and solving the intra-day operation model by adopting a multi-agent algorithm of a non-adversarial game to obtain an optimal strategy of energy storage intra-day operation. According to the invention, the relation between the energy storage aging characteristic and the working characteristic is introduced to carry out the energy storage intra-day operation decision, the economic benefit can be truly evaluated, the energy efficiency of energy storage and the stability of scheduling are considered, and the operation maintenance level is greatly improved.

The invention relates to a power distribution network variable time scale optimization method considering source-load-storage collaborative optimization. The method specifically comprises the following steps: step 1, constructing an excitation type load demand corresponding model, an energy storage comprehensive operation model, a day-ahead scheduling stage model and an intra-day multi-time scale scheduling model; step 2, through day-ahead-day-real-time continuous fine scheduling, fully excavating the adjustment potential of the energy storage device and the flexible load; 3, introducing a deviation reduction rate-cost increase rate dynamic game model through scheduling of a variable time scale, and adaptively adjusting the time scale in a real-time stage; 4, constructing a real-time variable time scale scheduling model; 5, improving a rolling optimization strategy, and classifying various resources to match corresponding time scales; step 6, performing instance analysis; the method has the advantages that the consumption rate is increased, the time scale is adaptively adjusted, the power quality is improved, the dynamic game model is introduced, and cooperative scheduling is realized.

The invention discloses a benefit distribution model construction and collaborative optimization method under multiple operation modes of an industrial software service platform, and the method comprises the following steps: 1, analyzing an optimal decision based on a Stackelberg master-slave game decision model: 11) constructing a pricing model, profit functions and constraint conditions of the industrial software service platform and the industrial software developer are obtained respectively; 12) performing optimal game decision analysis; 121) analyzing optimal decisions of an industrial software developer and an industrial software service platform under an independent operation condition to obtain an optimal price and a maximum benefit; 122) analyzing the optimal decision of the industrial software developer and the industrial software service platform under the cooperative operation condition to obtain the overall maximum benefit; 2, establishing a benefit distribution model based on a Shapley value method; step 3, benefit decision optimization: 31) fitting a relation function between the price and the number of functional modules by using historical data; and 32) adopting a reinforcement learning method to decide an optimal benefit distribution scheme.

Technological improvements are described for automatically generating crew and equipment schedules that are optimized for regulatory compliance and balanced according to goals or policies referred to as balance settings. Example systems and methods include building and maintaining a compliance library of regulations. A machine-learning operations model generates a schedule that complies with regulations and receives input data including a proposed schedule and the priority-weighted balance settings. Then, the operations model, using a set of recursive processes, generates a candidate schedule that is optimized based on the regulations and the balance settings. An auditor confirms the candidate schedule complies with regulations. The auditor evaluates real-time data to flag potential violations and generate compliance notices and compliance reports. The system includes a generative AI model trained using an industry-specific small language model (ISLM) for receiving textual and image data for use by the operations model and for generating responsive messages.

The invention discloses an intelligent production scheduling method and system based on a fixed sequence genetic algorithm. The method comprises the following steps: dividing independent production areas of a workshop; key operation is determined, and modeling is carried out on time of the key operation; generating a process set containing a fixed sequence key job based on a process path, performing crossover variation by using an improved genetic algorithm, and keeping the sequence of internal processes of a task unchanged to avoid an invalid solution; and calculating the shortest processing duration through population iteration, and generating a production scheduling Gantt chart. The system comprises a workshop function area module, a key operation type module, a key operation modeling module and a production scheduling result output module, so as to realize the output of an intelligent production scheduling result. The method improves the production scheduling efficiency and the applicability of the production scheduling result, and is suitable for complex production scenes such as electronic assembly and cabinet debugging.

The present invention discloses a rSOC operation model and a modeling method and system taking into account electric-hydrogen-thermal coupling, belonging to the technical field of energy conversion and storage; a modeling method of a rSOC operation model taking into account electric-hydrogen-thermal coupling includes: establishing a rSOC electrolysis model taking into account electric-hydrogen-thermal coupling; establishing a rSOC power generation model taking into account electric-hydrogen-thermal coupling; based on the rSOC electrolysis model taking into account electric-hydrogen-thermal coupling and the rSOC power generation model taking into account electric-hydrogen-thermal coupling, considering the start-stop and climbing characteristics of rSOC, establishing the rSOC operation model; linearizing the rSOC operation model by a piecewise linearization method and a large M method. The relationship between electric-hydrogen-thermal multi-energy coupling during the operation of rSOC is effectively characterized, the real operation of rSOC is simulated, and the difficulty of solving the rSOC operation model is reduced.

The invention relates to a supply chain modeling method based on a platform operation mode and a carbon transaction policy, and belongs to the technical field of supply chain design. According to the method, carbon emission data of each link of a supply chain is collected in real time through a multi-source sensor, the law of conservation of energy is used as a constraint condition to be embedded into a Transform-XL neural network model, a multi-link nonlinear coupling model is constructed, and a nonlinear coupling relational expression of carbon emission and sales is output. And in combination with a carbon quota distribution mechanism, solving a QUBO matrix by using a quantum annealing algorithm to obtain an optimal scale combination strategy of each link scale of the supply chain, and realizing collaborative optimization of carbon emission intensity reduction and profit improvement. According to the method, the problems of poor adaptability, low calculation efficiency and the like of a traditional supply chain optimization model under a dynamic carbon transaction policy are solved, and an efficient solution is provided for supply chain decision making.

The invention discloses a task scheduling method and device based on intelligent MRP operation, a medium and a program product. The task scheduling method comprises the steps that first real-time production data in the production process is obtained; performing data preprocessing on the first real-time production data to obtain second real-time production data; carrying out stream data analysis processing on the second real-time production data based on an Flink framework to obtain a real-time data analysis result; the real-time data analysis result is input into a trained intelligent MRP operation model, an MRP production scheduling plan is generated by the intelligent MRP operation model according to real-time productivity and user order demands, and the intelligent MRP operation model is constructed based on a machine learning algorithm and is trained by using historical MRP data; and finally, carrying out production task scheduling according to the MRP production scheduling plan. According to the invention, the accuracy of MRP operation is effectively improved, and the instantaneity and responsiveness of generating the MRP production scheduling plan are further improved, so that the production efficiency is improved, and the resource waste is reduced.

The invention relates to a virtual power plant optimization scheduling method and system. The method comprises the following steps: establishing an operation model of a multi-type adjustable resource main body; constructing a master-slave game model of the aggregator and the resource main body, wherein the master-slave game model comprises an upper-layer model taking the aggregator as a leader and a lower-layer model taking the resource main body as a follower; solving a master-slave game model of the aggregator and the resource main body to obtain an optimal compensation price strategy and an optimal response power strategy, and constructing an optimal power scheduling plan according to the optimal response power strategy; the optimal power scheduling plan is used for being issued to each adjustable resource main body for execution so as to realize optimal scheduling of the virtual power plant. According to the method, the participation enthusiasm of the resource main body can be stimulated, the user participation degree can be improved, the scheduling instruction execution rate can be improved, the model accuracy can be improved, the cooperative capability of heterogeneous resources can be improved, and the overall operation economy and stability of the virtual power plant can be improved.

The embodiment of the invention provides a manufacturing industry operation performance indicator management system, and relates to the technical field of performance management. The system comprises a data source management module which is used for managing data sources required by indexes and fusing collected data and KPI indexes; the KPI definition management module is used for performing addition, deletion, modification and checking on the KPI, configuring attribute information and establishing a logic association relationship with the business operation model; the manufacturing industry operation model management module is used for constructing business operation models and associating the operation resource information with the business operation models; and the KPI management module is used for calculating the credibility of the KPI and monitoring and processing the data missing condition and the data conflict condition. According to the embodiment of the invention, the corresponding business operation model is constructed in combination with the enterprise characteristics, the applicability of the performance indicators is ensured, and meanwhile, the efficiency and quality of data acquisition are ensured through data source management, so that the efficiency and accuracy of operation performance management are effectively improved.

The invention provides a heterogeneous virtual power plant cluster marketization transaction optimization method based on decision fusion, and the method comprises the following steps: firstly, constructing a framework and a transaction model of a virtual power plant cluster, and building an output model; carrying out clearing simulation on the spot market, and constructing a scheduling model; performing simulation calculation on the bid-winning electric quantity through the scheduling model; and then, establishing a virtual power plant cluster collaborative operation model, and obtaining transferred electric quantity and unit transaction cost among the virtual power plants through Nash bargaining so as to formulate a transaction plan. The method is simple and low in execution cost, the transaction strategy obtained by constructing the virtual power plant cluster collaborative operation optimization model and carrying out Nash bargaining and information gap robust decision fusion can reflect heterogeneity characteristics among virtual power plants, and the deficiency of calculation efficiency can be made up by accurately modeling strong random resources, so that the method can be applied to the collaborative operation of the virtual power plants. Even if wind and light fluctuation exists in the execution period, the whole cluster can still stably operate and supply power, and the overall power generation cost is reduced.

The present invention relates to a method for constructing digital ship intelligent manufacturing. The construction method comprises the following steps: digital dock construction; digital weight and center of gravity control; hull response monitoring system construction; operation and management integrated digital management platform construction: building a digital overall framework, mainly including a basic layer, an equipment resource layer, an execution basic layer, a coordination business layer, an operation management layer and a decision management layer; digital inspection management platform construction. The advantages of the present invention are: with the help of digital transformation, the components are based on engineering decomposition, with planning management as the command, and an integrated solution for overall design, materials, production, quality and cost management is constructed. Through the construction of the industrial Internet and the integrated application of the information system, a digital shipyard based on digital twins is constructed, facilities, equipment, resources, etc. are interconnected, early warning and analysis are carried out from multiple angles, a big data operation system is explored, a digital operation model is constructed, and finally a new digital production model is formed.

The present application relates to the technical field of retail management, and particularly refers to a lean operation model for clothing retail stores from the perspective of data empowerment. The model comprises a four-dimensional health degree evaluation module, a data report generation module, a store manager interview consensus module and a closed-loop execution review module. The four-dimensional health degree evaluation module constructs a mathematical model based on four dimensions of sales efficiency, human efficiency quality, member value and product efficiency and their subordinate indicators, and outputs an overall health degree score OHS to realize quality diagnosis of store operation. The data report generation module generates five key business reports to convert raw data into visual insights. The store manager interview consensus module realizes the integration of data and experience through a structured eight-step process and generates a SMART rectification plan. The closed-loop execution review module realizes the PDCA cycle through an agile review mechanism. The present application solves problems such as low operation efficiency, decision-making relying on experience and insufficient rectification execution of clothing retail stores through the deep integration of data-driven and lean operation.

The invention discloses an agent use method and device, equipment and a storage medium. The method comprises the steps of obtaining multiple pieces of historical operation data corresponding to a preset game environment and corresponding historical gold coin information; obtaining historical operations corresponding to the game data, wherein the historical operations comprise any one of an equipment purchasing operation, an equipment selling operation, a skill upgrading operation and other operations; inputting the plurality of pieces of historical operation data and the historical gold coin information corresponding to each piece of historical operation data into a to-be-trained operation prediction model, and completing the training of the operation prediction model according to the output prediction operation and the corresponding historical operation; obtaining current equipment information, current skill information and current gold coin information corresponding to the intelligent agent to be used; inputting the current equipment information, the current skill information and the current gold coin information into the prediction operation model, and outputting; the usage information includes one or more of equipment purchase operation, equipment sale operation, skill upgrade operation and other operation.

Technological improvements are described for automatically generating crew and equipment schedules that are optimized for regulatory compliance and balanced according to goals or policies referred to as balance settings. Example systems and methods include building and maintaining a compliance library of regulations. A machine-learning operations model generates a schedule that complies with regulations and receives input data including a proposed schedule and the priority-weighted balance settings. Then, the operations model, using a set of recursive processes, generates a candidate schedule that is optimized based on the regulations and the balance settings. An auditor confirms the candidate schedule complies with regulations. The auditor evaluates real-time data to flag potential violations and generate compliance notices and compliance reports. The system includes a generative AI model trained using an industry-specific small language model (ISLM) for receiving textual and image data for use by the operations model and for generating responsive messages.

The invention provides an electro-thermal coupling virtual power plant cost optimization method based on a stochastic optimization method. The method comprises the steps of obtaining historical operation parameters; constructing a physical simulation model of each component according to the historical operation parameters to obtain a power plant operation model; constructing an operation cost model of each component according to the historical operation parameters to obtain a power plant cost model; determining uncertainty parameters of the virtual power plant according to the historical operation parameters and constructing a random model; constructing a target constraint group based on the power plant operation model; and based on the system uncertainty, risk avoidance solution and opportunity seeking solution are carried out through an IGDT method to obtain first uncertainty and second uncertainty, power plant control parameters are further solved, and operation of the virtual power plant is controlled according to the power plant control parameters. The method solves the problem that in the prior art, a virtual power plant scheduling optimization method integrating a combined heat and power generation unit, carbon capture and electrical conversion equipment and meeting the low-cost and low-carbon requirements is lacked.

The invention discloses an integrated production operation platform based on digital and intelligent twinning, relates to the technical field of production operation, and establishes a digital and intelligent twinning operation model of a physical execution layer and a virtual mapping layer by constructing state characteristic parameters of operation nodes and fusing multi-source time sequence data. A state consistency constraint function is introduced to represent the synchronization deviation between the two layers; on the basis, performing real-time iterative calculation on the target job execution model to obtain a state deviation value of each job node, and checking state consistency based on signal sequential logic and a robust online monitoring algorithm to generate an abnormal state judgment result; further executing instruction delay, path re-planning or state rollback according to the exception type to realize dynamic correction and closed-loop control of the operation process; according to the invention, the accuracy of abnormity identification and the timeliness of processing in complex production operation can be effectively improved, and the stability and the safety of overall operation of the system are enhanced.

The invention discloses a time-varying load dynamic compensation motion control method and system based on UVW kernel alignment, and belongs to the technical field of precision machine.The time-varying load dynamic compensation motion control method based on UVW kernel alignment comprises the following steps that S1, a motion model is established, firstly, the motion model of a UVB platform is established, the motion model comprises motion equations and kinematics equations of all axes, and the motion equations of all the axes are calculated; meanwhile, key parts of the motion model are subjected to structure optimization, a foundation is laid for deformation error analysis and compensation, and the foundation is used for achieving dynamic compensation; s2, load change is monitored, load change information borne by the UVW platform is obtained in real time based on the sensing monitoring technology, and the obtained load change information serves as the basis of dynamic compensation. By optimizing the mechanical structures of key components of the motion model, the structural performance of the UVW platform can be improved, the material use amount is reduced, the machining technology is simplified, and the cost is reduced. The manufacturing cost is reduced, so that the flexibility of an operation model is improved, and deformation errors caused by self-weight are reduced.

The application relates to the technical field of equipment operation and maintenance decision and optimization, and discloses a multi-modal knowledge driven production equipment system multi-objective operation mode optimization method, which comprises the following steps: constructing a production demand change prediction model based on periodical trend decomposition; constructing a multi-modal knowledge graph model; constructing a production equipment system operation quality evaluation index system and determining optimization targets; constructing a data driven equipment operation model based on an extended random flow network model; evaluating each optimization target of the production equipment system; constructing a multi-objective reward function based on a knowledge driven Pareto distance method; constructing an operation and maintenance management and mode optimization joint strategy optimization model; identifying and selecting key optimization targets and operation and maintenance technologies based on multi-modal production demand and production equipment operation data driven multi-modal knowledge graphs; and performing production equipment mode optimization and operation and maintenance management strategy joint optimization by using a deep deterministic policy gradient, so that operation and maintenance strategy dynamic optimization solving is realized.

The invention discloses a multi-agricultural machinery collaborative operation method and system, electronic equipment and a storage medium, and the method comprises the steps: building a field operation model of a target farmland based on the boundary information and geographic information of the target farmland; dividing the field operation model into a plurality of operation sub-regions according to the operation capability parameters of each agricultural machine participating in the collaborative operation, allocating at least one operation sub-region to each agricultural machine, and generating an initial operation path covering all the corresponding operation sub-regions for each agricultural machine; and establishing communication connection among a plurality of agricultural machines participating in collaborative operation, and dynamically scheduling operation sub-region distribution and an initial operation path corresponding to each agricultural machine based on real-time position information and operation state information, shared in real time, of each agricultural machine. According to the method, the problems of unreasonable scheduling, frequent conflict of operation paths and insufficient dynamic adjustment capability in collaborative operation of the multiple agricultural machines are solved, and the collaborative operation efficiency and overall robustness of the multiple agricultural machines are greatly improved.

The invention relates to a capacity configuration method and device of a hydrogen production system, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of energy system optimization, and can improve the operation efficiency of the hydrogen production system. The method comprises the following steps: establishing a system operation model for representing the operation state of the hydrogen production system; obtaining an optimization target of capacity configuration according to operation resources consumed in the operation process of the hydrogen production system; according to the system operation model and the optimization target, determining candidate capacity configuration of each unit; according to the target quantized values of the plurality of candidate capacity configurations relative to the optimization target of the capacity configuration, determining the quality degrees corresponding to the plurality of candidate capacity configurations, and according to the quality degrees, determining the adjustment directions corresponding to the plurality of candidate capacity configurations; and performing configuration iteration adjustment of different adjustment degrees on the plurality of candidate capacity configurations according to each adjustment direction until an iteration ending condition is met, and determining respective target capacity configurations of the units.

The application discloses a microbial early warning and prediction system and method, the system comprises a data integration module, an operation modeling module, a model verification module and a total evaluation module, the data integration module associates public microbial information with customer information, and transmits the associated data to the operation modeling module; the operation modeling module operates and models the associated data, and transmits the result to the model verification module, the model verification module is used for verifying the accuracy and rationality of the individualized customized model, and transmitting the processed information to the total evaluation module, finally, the total evaluation module outputs a risk index, a risk description and a recommended improvement scheme according to the information of the operation modeling module. Due to the adoption of the above technology, the application comprehensively, systematically and multi-dimensionally analyzes various aspects of products and factories, achieves the purposes that prediction and recommended results are more accurate, application fields are more extensive, interpretation modes are more detailed and convenient, and the like.

The present invention discloses a method, terminal and storage medium for predicting effective berth combinations based on neural networks, wherein the method comprises: real-time acquisition of parking data of a parking lot and preprocessing thereof; processing the preprocessed data in three prediction modes to obtain training samples and test samples; inputting the training samples into a neural network model for training to obtain a trained neural network model; inputting the test samples into the trained neural network model for effective berth prediction to obtain three prediction results; constructing a combined prediction model based on the three prediction results to obtain a combined model prediction result; calculating and comparing the performance evaluation indexes of each prediction result, and selecting the prediction result with the lowest prediction error as the final prediction result. The present invention can effectively predict the parking lot vacant berth information through a combined prediction model based on a neural network, which is helpful for the application and implementation of operation modes such as parking guidance and berth sharing in actual scenarios.

The invention relates to a virtual power plant scheduling optimization method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: solving a day-ahead scheduling model according to a random variable generated by an uncertainty parameter model under the limitation of power constraint and energy balance constraint in combination with an operation cost model of a synchronous unit, an income model of a day-ahead market, an income model of a standby market and an operation model of a pumped storage unit, so as to obtain a current scheduling model; and determining the generated power, the adjustment reserve capacity and the expected profit of each component in the virtual power plant when the expected profit of the virtual power plant is maximum during scheduling. By adopting the method, the day-ahead market and the standby market are comprehensively considered, and collaborative scheduling optimization is realized. Therefore, the virtual power plant can reasonably arrange the operation strategy of each component in an uncertain environment so as to realize profit maximization. The method can adapt to a randomly changing market environment, and improves the robustness of an operation strategy and the operation reliability of the virtual power plant.

The invention discloses a robot unstacking and stacking method, and belongs to the technical field of industrial robots. Determining an unstacking area and a stacking area of the materials; according to the size parameters of the materials, an unstacking pile and a stacking pile of the materials are simulated and generated; according to the unstacking pile of the materials and the unstacking mode of the materials, an unstacking model of the materials is constructed in the unstacking area; the unstacking model is used for indicating an unstacking track and an unstacking sequence of the robot for unstacking the materials; according to the stacking pile of the materials, the unstacking mode of the materials and the stacking mode of the materials, a stacking model of the materials is constructed in the stacking area; and according to the unstacking model of the materials and the stacking model of the materials, the robot is controlled to unstack and stack the materials. A user only needs to input the material size, the pile type parameters and the operation mode, a complete operation model and a motion instruction can be automatically generated, efficient configuration of unstacking and stacking tasks is achieved, and the method is particularly suitable for industrial scenes such as flexible manufacturing, intelligent storage and logistics sorting which have high requirements for the automation degree.

The present invention discloses a method and system for adaptively adjusting the operating parameters of a vacuum coating machine, and relates to the technical field of operating parameter control of a vacuum coating machine. The system includes a data collection module, a control strategy generation module, a real-time monitoring and analysis module, and a data interaction module. The data collection module is used to collect the operating parameters of the vacuum coating machine, the control strategy generation module is used to produce optimal parameters, and the real-time monitoring and analysis module is used to monitor the production process. The method analyzes the main operating parameters of the vacuum coating machine through the control strategy generation module combined with the historical data collection unit, and determines the important parameters, constructs the operating model of the vacuum coating machine with the important parameters, sets the output conditions, obtains the optimal collection of important parameters, and outputs the specific indicators of each parameter to the data interaction module, and performs vacuum coating with these parameters, thereby ensuring the uniformity of vacuum coating parameters and the relatively stable coating quality of all products.