742 results about "Parameter control" patented technology

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

The invention provides a parameter control method and system in the friction stir welding process, and relates to the technical field of welding equipment of metal materials. The method comprises the steps that before friction stir welding is started, working condition information of a to-be-welded workpiece is input into a welding process simulation model for virtual test welding; screening out a plurality of groups of standard process parameter combinations meeting preset welding quality requirements; in the friction stir welding process, the change trend of all the technological parameters is determined according to the real-time technological parameter combination within the first preset time period; and if it is determined that the deviation risk exists in the current welding state within the second preset time period in the future according to the change trend, the standard technological parameter combination with the maximum similarity with the real-time technological parameter combination in the standard technological parameter library is determined as the target technological parameter combination to conduct parameter control adjustment on the welding equipment. By implementing the method, the welding defects caused by sudden change of the process window can be overcome, and the stability of the welding process and the consistency of the welding seam quality are improved.

The invention relates to the technical field of marking control, and discloses a control method and system for product marking. The method comprises the steps of scanning the surface of a part to obtain material data; performing three-dimensional positioning according to the material data to obtain coordinate data; marking parameters are screened according to the coordinate data; using the parameters to control the marking device to generate process data; analyzing the process data to evaluate the marking quality; and optimizing parameters by using the quality data and storing the parameters in a database to form a control scheme. According to the invention, accurate identification of the material and shape characteristics of the part can be realized, the optimal marking position and process parameters are automatically determined according to the identification result, real-time monitoring and adjustment are carried out in the marking process, the marking quality is comprehensively evaluated and fed back and optimized after the real-time monitoring and adjustment are completed, and the consistency and reliability of the marking quality are improved.

The invention provides an intelligent comprehensive system and method for steel production process parameter optimization, and the system comprises a data configuration module which is used for collecting quality-related data in a steel production process; the influence factor analysis module is used for processing data related to quality by using a feature engineering technology and identifying key variables; the quality index prediction module is used for constructing a quality prediction model by using a machine learning algorithm and key variables; the process parameter optimization module is used for constructing a process parameter optimization model based on the prediction result and the quality index control target, and analyzing and recommending an optimal process parameter control range by utilizing an intelligent algorithm; and the model monitoring optimization module is used for evaluating and optimizing the operation effects of the quality prediction model and the process parameter optimization model in real time. According to the method, the whole-process intelligent management from data configuration and key factor identification to quality prediction, process parameter optimization and model dynamic monitoring is realized, a closed-loop optimization mechanism is formed, and the production efficiency and the product quality are effectively improved.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention relates to the technical field of plastic processing, and discloses an environment-friendly plastic product extrusion molding intelligent parameter control system and method, and the system comprises a data collection and fusion module, a causal association construction module, a digital twinning and virtual-real interaction verification module, an intelligent control module, a self-adaptive reconstruction module, and a man-machine interaction and visualization module. The method comprises the steps of collecting data in real time, and performing deep fusion to generate features; dynamically constructing a knowledge graph by fusing features, and synchronously updating causal topology; constructing a digital twinborn model, and performing virtual operation prediction; generating an instruction to regulate and control the microstructure; recognizing and classifying disturbances, and reconstructing a causal topology to guide strategy adjustment; the system state is displayed in real time, and operation intervention is supported. On-line microstructure data are acquired and deeply fused in real time through the data acquisition and fusion module, and a deep correlation model is constructed in combination with the microstructure guide reverse deduction unit, so that the problems of large batch difference and unstable physical properties of environment-friendly plastic materials are effectively solved.

The invention relates to the technical field of printed circuit board manufacturing, in particular to a PCB lamination parameter control method and system, and the method comprises the steps: receiving a global pressure adjustment instruction; acquiring real-time temperature data of the to-be-laminated sheet, and constructing a surface temperature distribution diagram of the to-be-laminated sheet; the surface temperature distribution diagram is analyzed, and operation areas, with different thermal response attributes, of the to-be-laminated sheet are divided; according to the thermal response attribute of each operation region and the global pressure adjustment instruction, obtaining a region area weight and a response deviation coefficient, carrying out differential decomposition on the global pressure adjustment instruction, and calculating differential pressure adjustment amounts of the plurality of operation regions; and mapping the differential pressure adjustment amount to a multi-partition hydraulic execution mechanism in the laminating equipment, and controlling a target pressure value output by each operation area. The method has the advantages that refined and differentiated pressure adjustment of operation areas with different thermal response attributes in the plate can be achieved, and local defects caused by uneven material response are effectively avoided.

The invention relates to the technical field of tool control, in particular to a tooth cutting tool parameter control system based on bevel angle cutting, which comprises a tool parameter acquisition module used for acquiring the rotating speed, the axial feeding amount and the cutting depth of a tool in real time; the force-heat prediction model is used for outputting predicted cutting force and predicted cutting tool temperature; the multi-target parameter optimization model is used for receiving constraint conditions set by a user, optimizing the rotating speed, the axial feeding amount and the cutting depth of the cutter by taking the lowest cutting force, the lowest temperature of the cutting cutter and the highest machining efficiency as targets in the constraint conditions, and integrating and outputting a regulation and control strategy; and the cutter parameter control module is used for regulating and controlling the rotating speed, the axial feeding amount and the cutting depth of the cutter based on a regulation and control strategy when the predicted cutting force and the predicted temperature of the cutting cutter exceed threshold values. According to the method, by accurately regulating and controlling the parameters of the tooth cutting tool, the machining efficiency of the tooth cutting tool can be ensured, and meanwhile the abrasion speed of the tooth cutting tool is reduced.

The invention relates to the technical field of spraying control, and discloses a water turbine spraying process parameter real-time monitoring, regulating and controlling method and system.The water turbine spraying process parameter real-time monitoring, regulating and controlling method comprises the following steps that multi-modal data in the spraying process is collected, and a four-dimensional data structure is constructed; tensor decomposition is carried out, and a core feature tensor and a factor matrix are extracted; constructing an enhanced game strategy model, and outputting a process parameter control strategy; lyapunov optimization is introduced, and regulation and control input is generated; the control strategy and the control input are combined to generate a spraying execution action; and performing feedforward compensation based on the disturbance observer to form a final control instruction. According to the method, the multi-modal process data fusion and tensor decomposition technology is adopted, the four-dimensional data tensor is constructed, the core features of the spraying process are extracted, and by means of the method, the system can more comprehensively capture dynamic changes in the spraying process, and an accurate technological parameter control strategy is provided.

The invention discloses a thermal power plant boiler combustion optimization method and system, and the method comprises the steps: obtaining historical combustion data and historical control data of a thermal power plant boiler, and carrying out the analysis to determine control parameters affecting the combustion; extracting features of the control parameters, and constructing a combustion data prediction model based on the features and a preset neural network model for prediction to obtain combustion prediction data; analyzing the combustion prediction data, determining combustion data characteristics, and determining a combustion state coefficient of the thermal power plant boiler based on the combustion data characteristics; an expected combustion state coefficient preset in advance by the thermal power plant boiler is obtained, and the difference value between the combustion state coefficient and the expected combustion state coefficient is determined; and determining a parameter control optimization coefficient according to the difference value to optimize the current control parameter. According to the invention, refined and intelligent monitoring and optimal control of the combustion process can be realized, so that the combustion efficiency of the thermal power plant boiler is improved, the cost is reduced, the emission is reduced, and the stability and the safety of boiler operation are improved.

The invention discloses an operation system and method of a full-automatic coating machine, and relates to the technical field of coating processing control, and the system comprises a data acquisition module which obtains various preset parameters when a current coating machine performs coating operation on a current base material, obtains defect data which easily occurs when the current coating machine performs coating operation on the current base material based on a preset database, and transmits the defect data to a database processing module; the defect data comprises defect positions and defect types; according to the method, a complete parameter regulation and control system is formed through close combination of a risk assessment function and a grading early warning mechanism with technologies of dynamic association judgment, parameter linkage adjustment and the like, and before parameter adjustment, whether linkage adjustment is needed or not is determined through dynamic association judgment; in the adjusting process, the risk is evaluated through a risk evaluation function, and a corresponding adjusting strategy is executed according to the risk level.

The invention provides a cast-in-place pile construction system and method in a soft foundation area. The system comprises a drilling system, a grouting system, a monitoring system and a control system, wherein the drilling system is used for drilling and monitoring and optimizing the drilling condition; the grouting system is used for grouting into the drill hole and optimizing grouting parameters; the monitoring system is used for monitoring conditions in the drill hole and slurry parameters of grouting; the control system is electrically connected with the drilling system, the grouting system and the monitoring system and used for controlling the drilling system to drill holes and the grouting system to grout and adjusting construction parameters of the drilling system and the grouting system according to information monitored by the monitoring system. According to the invention, the drilling system and the grouting system can perform monitoring and optimization, and meanwhile, the monitoring system performs real-time monitoring, so that the control device can conveniently adjust the drilling system and the grouting system, the construction quality of the cast-in-place pile is improved, the construction efficiency can be improved, and even if a problem encounters in the construction process, the problem can be timely monitored and optimized.

The invention discloses an annealing furnace temperature parameter control method and system based on multi-source data, and relates to the technical field of annealing furnace temperature parameter control, and the method comprises the following steps: carrying out the data collection of the operation process of an annealing furnace, and generating an annealing process record; a temperature sensor is arranged in an annealing furnace to collect the temperature in the furnace in real time, the uniformity of a temperature field in the furnace is measured by calculating a temperature equilibrium coefficient, and time parameters used for compensating the heat conduction hysteresis effect are determined according to material characteristics and thickness parameters of a workpiece to be processed; based on the temperature equilibrium state monitored in real time and process requirements, the annealing process is subjected to control stage division; a comprehensive uniformity index is calculated by analyzing the temperature balance degree and the cooling rate uniformity at all moments in the annealing process, whether the control stage of the annealing furnace is abnormal or not is judged according to the condition that the fluctuation amplitude of the index at all moments does not exceed the preset multiple of the average value of historical uniformity indexes of workpieces of the same type, and temperature parameter control over the annealing furnace is achieved.

The invention relates to the technical field of cloth bag processing, and discloses a non-woven fabric bag processing parameter regulation and control method and system based on deep learning, and the method comprises the steps: obtaining the real-time rotation speed and tension data of equipment, carrying out the signal filtering and state calculation, obtaining a cloth traction state, carrying out the fuzzy logic processing, and determining the adjustment amount; obtaining a target rotation speed adjustment amount; according to the target rotation speed adjustment amount, threshold judgment and parameter dynamic updating are carried out, a new tension fluctuation index is obtained, fitness evaluation is carried out, and the product specification fitness is obtained; according to the product specification fitness, a dynamic adjustment decision is made, a parameter regulation and control scheme is obtained, risk quantification is carried out, and a production interruption risk level is obtained; and according to the production interruption risk level, risk threshold verification and optimization algorithm correction are carried out, a defective percentage inhibition value is obtained, tension stable signal analysis and global fusion output are carried out, and final processing equipment parameter configuration is obtained. The method can improve the qualified rate of products.

The invention discloses a control method of a hybrid energy storage system and the hybrid energy storage system, and relates to the technical field of hybrid energy storage systems.The control method comprises the steps that corresponding simulation control parameters of the hybrid energy storage system under different operation conditions are obtained, and the simulation control parameters are parameters used for simulating the characteristics of a battery cluster; and controlling the operation state of each energy storage converter according to the simulation control parameters. By adopting the method, the flywheel can be controlled to simulate the characteristics of the battery to work, the cost of the hybrid energy storage system can be effectively reduced, and the complexity of the control process of the hybrid energy storage system is reduced.

The invention relates to the technical field of precision casting, and discloses a high-precision control method for stainless steel castings, which comprises the following steps: preparing raw materials, recording component characteristics of the raw materials, designing a formula proportion of the raw materials, carrying out pouring process design, and setting a parameter control system in each link of pouring, according to the shape of the needed stainless steel casting, mold design and manufacturing and mold quality control are carried out, grinding tool design parameters are recorded, a stainless steel casting solidification process simulation model is established according to raw material component characteristics, a raw material formula proportion, a designed pouring process and the mold design parameters, and the casting solidification process is simulated in the model. The method comprises the following steps: predicting a defect area in a casting production process, carrying out advanced prevention and optimization treatment on the defect area according to the predicted defect area in the casting production process, strengthening a refining degassing operation in a casting process, further carrying out a metal solution purification step after refining degassing, and demolding the casting, and the mold is cleaned, checked and maintained in time.

The invention provides a multi-parameter regulation and control method and system for a vacuum insulation panel heat insulation layer spraying robot, relates to the technical field of multi-parameter regulation and control, and effectively solves the problems of uneven spraying thickness and poor space consistency by dynamically sensing and regulating and controlling the interference relation and track offset influence among multiple nozzles in the spraying process. Fine adjustment of parameters of the spray heads can be achieved, all the spray heads can maintain the ideal spraying state under different working conditions, and therefore the overall spraying precision and stability are improved. By introducing a multi-parameter combined regulation and control mechanism, spraying defects caused by factors such as attitude deviation and coupling interference can be remarkably reduced, and the rework rate and material waste are reduced.

The invention belongs to the field of production quality traceability, and particularly relates to an abnormity traceability and parameter regulation and control system for a premix production line, which comprises a monitoring module, a reverse mapping module and a forward adjustment module, a monitoring module can acquire whole-course fingerprint information of an abnormal production process in real time by constructing a whole-course tracking chain containing a process-control association relationship; the reverse mapping module performs reverse whole-course reasoning by using a hidden Markov algorithm, performs longitudinal local reasoning in combination with an index mapping relation, accurately locates an abnormal source, and generates a reverse abnormal information fingerprint chain; and the forward adjustment module performs forward processing control parameter adaptive adjustment from the abnormal starting node according to the fingerprint chain and a preset correction strategy until all indexes return to normal. According to the invention, closed-loop intelligent control from abnormity monitoring and source tracing to production self-adjustment is realized, and the quality consistency and tracing efficiency of premix production are remarkably improved.

The invention discloses a control method and device for a loading and unloading clamp based on pneumatic and electromagnetic composite self-locking, and relates to the technical field of logistics automation equipment. The method comprises the steps that size information and material information of goods to be loaded and unloaded are acquired; acquiring initial position information and target position information of goods to be loaded and unloaded; based on the size information, the material information, the initial position information and the target position information, clamping parameters of the loading and unloading clamp are determined, and the clamping parameters are clamping jaw control parameters of the loading and unloading clamp; and controlling the loading and unloading clamp to clamp the to-be-loaded and unloaded goods based on the clamping parameters of the loading and unloading clamp. The technical problem that in the prior art, automatic loading and unloading equipment is poor in adaptability is solved.

The invention relates to the technical field of intelligent decision making, and discloses an intelligent decision making method and system based on a computing network integrated industrial PLC, and the method comprises the steps: carrying out the time slice distribution of control parameters of water supply equipment, and dividing the decision period of the PLC, and obtaining an initial control parameter queue; determining occurrence probability distribution of a normal operation state, an efficient control state and a decision conflict state to obtain a water supply parameter conflict probability table; redistributing the parameter control time resource to obtain a water supply parameter optimization control time sequence; converting the parameter groups which still have conflicts into mutually independent control variables, and respectively executing optimization calculation to obtain a multi-parameter parallel control instruction of the water supply equipment; and carrying out dynamic resource scheduling on the multi-parameter parallel control instruction of the water supply equipment to generate an energy-saving control scheme of the water supply equipment. The method effectively optimizes the dynamic distribution of control resources, remarkably shortens the response time of control parameters, and improves the overall throughput of the system.

The embodiment of the invention provides a washing machine, a control method and device of the washing machine and a storage medium, the control method of the washing machine comprises the steps that clothes information of clothes to be washed in the washing machine and temperature and humidity information in the washing machine are obtained, and the clothes information comprises materials, stain types and weight; predicting washing parameters according to the clothes information and the temperature and humidity information; and controlling the washing machine to operate based on the washing parameters. The washing parameters are predicted according to the clothes information and the temperature and humidity information, that is, the washing parameters can be dynamically adjusted, compared with existing fixed washing parameters, changes of the washing environment can be dynamically responded, the flexibility of the washing machine is improved, and then the energy utilization efficiency is improved.

The invention relates to the field of energy-saving management, in particular to an energy-saving control management system based on a public building, and the system comprises a region monitoring module which is used for periodically responding to a parameter standard condition so as to determine whether each target control parameter meets a control requirement or not; the standard optimization module is used for performing parameter standard optimization on each target control parameter; the area evaluation module is used for periodically responding to an area evaluation condition to determine a parameter estimation strategy of the target management area; the pre-estimation analysis module is used for executing the parameter pre-estimation strategy determined by the region evaluation module so as to determine a pre-estimation index of the target optimization parameter; and the parameter analysis module is used for determining a parameter control strategy of each target optimization parameter in response to the parameter evaluation condition, and adjusting the parameter control coefficient of the target optimization parameter, so that the effectiveness of an energy-saving control result is improved.

The invention provides a heavy duty gas turbine control system which comprises a signal conditioning module and a plurality of parameter control modules, and a rotating speed control module, a load control module, a temperature limit control module and an acceleration control module in the parameter control modules are all used for achieving closed-loop control according to the deviation between a set value and a corresponding observation value; the starting control module is used for acquiring a starting fuel quantity instruction through a designed control strategy; the IGV and VGVs control module obtains an IGV and VGVs angle instruction according to the turbine inlet temperature set value, the turbine outlet temperature set value and the deviation maximum value of the corresponding observation value; the fuel quantity control module obtains a total fuel quantity instruction based on a full working condition range fuel quantity control strategy; the fuel gas distribution control module is used for obtaining an opening degree instruction of each fuel valve according to the total fuel quantity instruction; the adaptability, the reliability and the usability of the control system in application scenes such as autonomous design and technical improvement are improved.

The invention relates to the technical field of injection molding monitoring, and discloses a double-color highlight molding injection mold data real-time processing method and system, and the method comprises the following steps: 1, collecting mold sensing data of two paths of melts, and forming a double-channel time sequence data sequence; step 2, calculating the time delay of the dual-channel time sequence data sequence and performing compensation; 3, extracting a pressure difference feature, a temperature difference feature and a flow difference feature, and fusing to obtain a coupling index; step 4, outputting predicted surface glossiness according to the pre-trained lightweight neural network model; step 5, generating a feedback control quantity and a feed-forward compensation quantity according to the deviation between the predicted surface glossiness and the preset target glossiness, and fusing the feedback control quantity and the feed-forward compensation quantity to obtain a parameter control quantity; and step 6, comparing the coupling index with a preset coupling threshold value, calculating the yield of finished products, and further performing early warning. According to the invention, real-time intelligent closed-loop control of the double-color highlight injection molded part from data acquisition to quality early warning is realized.

The invention relates to the technical field of aluminum alloy preparation methods, in particular to a control method for preparing an aluminum alloy rod, the aluminum alloy rod, electronic equipment and a storage medium. The method comprises the following steps: firstly, acquiring process parameters of a continuous casting machine and quality parameters of an aluminum alloy bar at an outlet, then comparing the quality parameters with corresponding target parameters, when the quality parameters do not accord with the target parameters, inputting the quality parameters and the process parameters into a regulation and control large model, and outputting parameter regulation and control quantity; and finally, controlling the continuous casting machine according to the parameter regulation quantity. The defect condition and the process parameters are obtained in real time, the operation of the large model is regulated and controlled according to the real-time condition, the continuous casting machine is dynamically controlled, and the defects generated by controlling casting of the aluminum alloy bar through static preset parameters are reduced.

The invention provides a large-scale fluid extraction and separation intelligent control method based on a mixing tank structure, and relates to the technical field of intelligent control, and the method comprises the steps: collecting parameter information in a mixing tank through double-cycle sampling, dividing flow field partitions based on three-dimensional numerical characteristics, collecting concentration gradient information of a target object, and building a digital twinborn model; executing a partition self-adaptive control strategy to dynamically adjust stirring parameters, controlling extraction agent supplement, adjusting the opening degree of a partition plate, and monitoring and updating the control strategy in real time. According to the method, precise zone control is realized, the extraction efficiency is improved, the energy consumption is reduced, and the method adapts to complex working condition changes.

The invention provides a task processing method and device for an intelligent capsule fresh extraction beverage machine, and the method comprises the steps: building a double-layer space structure through a differential private space efficiency algorithm based on an initial parameter set containing a capsule basic formula, and generating a task model containing a priority; based on the task model, constructing a task dependency graph and extracting spectral features, and generating a task scheduling strategy based on the spectral features; performing resource demand prediction and conflict resolution based on a task scheduling strategy to form a resource allocation plan; based on the resource allocation plan and the initial parameter set, performing parameter control through an environmental factor compensation mechanism to obtain an optimized parameter set; according to the optimization parameter set and the task scheduling strategy, executing a beverage preparation task; and performing anomaly detection and emergency response processing on the beverage preparation task. The technical problems that an existing capsule beverage machine is not intelligent in resource scheduling and limited in parameter control precision in multi-task processing are solved, and the system resource utilization rate and the beverage preparation quality are improved.

The invention discloses a reinforcement learning-based casting cleaning process parameter adaptive control method, which comprises the following steps of: constructing a reinforcement learning model for updating casting cleaning process parameters; the method comprises the steps that in the process that a casting cleaning system cleans a casting according to a set control period, key process signals in each control period are sequentially collected, and observation vectors are generated; inputting the obtained observation vector into a reinforcement learning model, and processing the observation vector by the reinforcement learning model to obtain a process parameter adjusting quantity, and the casting cleaning process parameters of the current control period are updated according to the process parameter adjusting quantity to obtain updated casting cleaning process parameters, and the updated casting cleaning process parameters are adopted to control a casting cleaning system of the next control period. According to the casting cleaning process parameter self-adaptive control method based on reinforcement learning, continuous and self-adaptive adjustment of the process parameters can be achieved, and the problem that fixed parameter control is insufficient in adaptability under complex working conditions is solved.

The invention discloses a stainless steel pipeline welding control system based on a large model, and relates to the technical field of stainless steel pipeline welding control, and the stainless steel pipeline welding control system comprises a pipeline welding route planning module, a welding parameter control analysis module, a pipeline welding monitoring module, an early warning terminal and a database. On the basis of the corresponding welding form of the stainless steel pipeline, a connecting line corresponding to the stainless steel pipeline is analyzed, planned pipeline welding is carried out, the corresponding welding mode and control parameters of the stainless steel pipeline are analyzed, high-quality welding parameter control over the stainless steel pipeline is carried out, and therefore the welding quality of the stainless steel pipeline is guaranteed; meanwhile, based on control and adjustment of corresponding welding of the stainless steel pipeline, the welding waste rate of the stainless steel pipeline is further reduced, fault tolerance of the welding quality problem of the stainless steel pipeline is avoided, and effective control over the welding quality of the stainless steel pipeline is achieved.

The invention relates to a production environment parameter control method and system based on industrial Internet of Things. The method comprises the following steps: collecting a first real-time production environment parameter and a subsequent planned production task, carrying out production environment parameter prediction, obtaining a prediction time sequence production environment parameter set, and determining a critical time point; acquiring response time characteristics of the environment controller, and determining a control time point for the critical time point; and at the control time point, collecting a second real-time production environment parameter of the target workshop, generating an environment control parameter of the environment controller, and controlling the environment controller to execute production environment control based on the environment control parameter. Intelligent prediction of production environment parameters, critical point identification and optimal opportunity intervention control are realized through an Internet of Things sensing network and a production plan, so that the stability of the production environment is ensured, and meanwhile, the energy consumption is reduced.