4388 results about "Process control" patented technology

The invention discloses a concrete surface intelligent construction method and system based on multi-modal sensing. The method comprises the following steps: firstly, receiving multi-source sensing data, and generating standardized feature data and defect feature vectors through standardization processing and feature extraction; performing time sequence alignment processing on the sensing data, and generating time sequence alignment data in combination with multi-scale analysis; constructing a state evaluation model based on the dynamic Bayesian network, and generating an environment state vector and a construction difficulty graph; carrying out region segmentation and optimizing task allocation by adopting a spectral clustering algorithm, and generating a task allocation scheme and a collaboration strategy; then, construction parameters are determined through multi-objective optimization, tool configuration is planned, and an optimization parameter set and a tool configuration scheme are generated; and finally, trajectory planning is carried out based on an improved A * algorithm, and a multi-robot cooperative construction trajectory is generated through comprehensive optimization. Full-process control of intelligent construction of the concrete surface is achieved, and the construction quality and efficiency are improved.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts are improved.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention discloses an AI model adaptive tuning method based on deep reinforcement learning, and belongs to the technical field of industrial process control, the method comprises the following steps: firstly, constructing a deep reinforcement learning environment comprising a state space, an action space and a reward function, the state space comprising a process variable measurement value, a controller parameter and a performance index; the action space is a parameter adjustment quantity vector, and the reward function comprehensively considers the stability, rapidity and accuracy of the system; then, based on a deep Q learning network of a dual-network structure, training data are stored and sampled by adopting an experience playback mechanism, and network parameters are iteratively optimized through a deep Q learning algorithm; and finally, realizing online adaptive adjustment of controller parameters by using the optimized deep Q network. The method has the advantages of being high in adaptability, good in optimization performance, high in practicability and the like, intelligent optimization control over an industrial process control system can be achieved, and the overall performance of the system is improved.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention discloses a rolling bearing fault diagnosis method based on an SE-ResNeXt and Informer hybrid network model, and relates to the field of industrial process control, spatial features of time series data are extracted through a plurality of convolution modules in the SE-ResNeXt, an SE module is added for adaptively adjusting the response intensity of each channel, and a fault diagnosis result is obtained. The design of a residual network is combined, so that the feature extraction process is more efficient, and fault diagnosis analysis is performed through an Informer model. In the training process of the model, cross entropy is adopted as a loss function, and optimization is carried out through an Adam optimizer, so that the prediction accuracy and stability are improved. Experimental results show that the method shows excellent fault diagnosis capability on a public data set CWRU, has good robustness and prediction precision, is significantly superior to a traditional deep learning model, and provides a new thought for state monitoring and fault prediction under complex working conditions.

The invention relates to the technical field of process control, in particular to an intelligent environment monitoring and adjusting system for lip fish culture, which comprises an environment parameter monitoring module, a multivariable linkage calculation module, a self-adaptive adjusting module, a fuzzy logic control module and an abnormity emergency response module. According to the method, the trend value is formed through time-phased grouping analysis and relevance calculation of the water quality parameters, dynamic prediction of the breeding environment change is achieved, the cooperative adjustment capacity among the dissolved oxygen, the water temperature and the ammonia nitrogen concentration is optimized through analysis of the multi-parameter linkage relation, the accuracy of environment control is improved, and the method is suitable for large-scale popularization and application. The cooperation efficiency of oxygen pump frequency and temperature control equipment response is improved through real-time matching calculation of equipment operation parameters, the rapid adaptive capacity to water quality changes is enhanced, abnormal fluctuation monitoring is combined with equipment linkage adjustment, rapid response during water quality sudden change is ensured, and the adverse effect of environment fluctuation on fish growth is reduced.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment aeration control method and system.The method comprises the steps that according to collected historical data, abnormal values are removed through intelligent calculation in combination with optimization and correction, an accurate sample data set is obtained and then classified through a clustering model, and all classifications are subjected to training and verification testing; a trained BP neural network model is obtained, a follow-up controller outputs accurate aeration quantity through the BP neural network model according to latest data to control each aeration device, and the increase / decrease of the air quantity of the biochemical pool is automatically adjusted; the blast volume of the biochemical pool is controlled and the air volume matching working condition is controlled according to the inlet water quality and the water volume, so that the control precision of the aeration rate in the sewage treatment process can be effectively improved, the energy consumption waste is reduced, the process control efficiency is improved, the air volume of the biochemical pool under different working conditions is ensured to be at the optimal value, the outlet water quality stability and the standard-reaching rate are ensured, and the power consumption cost is reduced; the method is suitable for sewage treatment plants of various types and scales, and has a wide market application prospect.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The invention relates to the technical field of electric power engineering management and control, and discloses an electric power engineering construction project whole process management and control system. The system comprises an engineering parameter monitoring module, a risk analysis module, a progress control module, a quality evaluation module, an early warning output module and a background supervision terminal. The engineering parameter monitoring module collects multi-dimensional real-time parameters through a sensor array and sends the multi-dimensional real-time parameters to the risk analysis module and the background supervision terminal. The risk analysis module generates a progress control instruction according to the deviation between the real-time parameter and a preset standard parameter and sends the progress control instruction to the progress control module. The progress control module executes progress adjustment operation, and the quality evaluation module monitors the adjustment process and generates a quality signal. The early warning output module receives the quality early warning signal, generates early warning information and sends the early warning information to the background supervision terminal, and the background supervision terminal gives an alarm. According to the system, collaborative operation of engineering parameter real-time monitoring, risk analysis, progress adjustment, quality evaluation and early warning is realized, and a whole-process management and control closed loop is formed.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention discloses a charging and moisture regaining equipment temperature prediction method based on a time sequence fusion network model, and relates to the field of production process control, and the method comprises the steps: collecting time sequence data in a charging and moisture regaining equipment production environment, and carrying out the preprocessing; dividing the data set into a training set, a verification set and a test set, and injecting Gaussian noise into the training set; a prediction model for predicting the outlet temperature is constructed, and the prediction model is a time sequence fusion network model and comprises a residual TCN time sequence convolutional network, an SK-Net multi-scale attention network and a BiLSTM bidirectional circulation network; pre-training the prediction model by using the training set; utilizing the trained prediction model to predict the outlet temperature of the feeding and moisture regaining equipment; and evaluating a prediction result, and if an evaluation index is greater than a threshold value, starting an incremental training process to re-train the prediction model. According to the invention, through a multi-module combined deep learning model, the prediction accuracy of the outlet temperature of the charging and moisture regaining equipment can be improved in a complex and changeable industrial environment.

The invention belongs to the technical field of industrial control, and discloses a powder mixing processing control method, device and equipment and a storage medium, and the method comprises the steps: collecting target data in a powder mixing processing process in real time; inputting the target data into a prediction model to obtain a probability distribution prediction result of the state of the mixed production line in a plurality of time steps in the future, the prediction model being constructed by adopting a dynamic Bayesian network in advance based on the mixed production line; based on the probability distribution prediction result and the target data, a model prediction control algorithm is adopted to optimize a real-time control strategy, and a control instruction is obtained; and controlling the mixed production line according to the control instruction. The powder is uniformly mixed, and the dynamic adaptability is high.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

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 discloses an intelligent thickness control system for calendering a high-brightness edge sealing sheet, and relates to the technical field of intelligent manufacturing and industrial process control. The method sequentially comprises six steps of multi-modal online acquisition, multi-source fusion drift compensation, digital twinning real-time synchronization, model prediction closed-loop control, high-speed execution fault self-diagnosis and reinforcement learning adaptive optimization. Thickness data are synchronously obtained through laser, terahertz, ultrasound and environment quantity, trusted thickness flow is output through a drift model and a confidence matrix, future thickness is predicted through digital twinning rolling to give uncertainty, first-step control quantity is generated through quadratic programming, millisecond-level execution is conducted through a distributed clock, and self-healing is conducted under shadow driving hot standby and cloud diagnosis. And reinforcement learning agent online iteration improves the energy-saving and stable performance, and full-life-cycle closed-loop control with accurate thickness, low energy consumption and long service life of equipment under complex working conditions is realized.

The invention relates to an electronic tag production quality control method in the technical field of information, and the method comprises the steps: carrying out the noise reduction of an image through a multi-scale denoising algorithm in a second image for vibration interference and dust interference, and obtaining a third image; the online detection system judges the printing quality of the electronic tag according to the OCR recognition result, and if abnormality is detected, an abnormal signal is generated and transmitted to the process control system; the process control system receives the abnormal signal, adjusts operation parameters of printing, fitting and cutting processes, ensures smooth process connection, and avoids unbalance of the production rhythm; comparing the adjusted process parameters with a preset product consistency standard, and if the parameter deviation exceeds a preset range, starting an automatic calibration module to ensure the product consistency; the operation state of the whole production process is monitored in real time through a system monitoring module, if the stability of the system is reduced, an early warning mechanism is triggered, and a maintenance signal is generated and transmitted to a maintenance system.

The invention relates to the technical field of grease production control, in particular to a grease product production control method and system based on intelligent digitization. The method comprises the following steps: monitoring temperature, viscosity and turbidity data in a grease crystallization process in real time, and generating grease crystallization state snapshot data; performing crystallization state prediction according to the grease crystallization state snapshot data, and performing reaction kettle cooling rate adjustment to obtain a target control cooling rate; performing real-time stirring rotating speed adjustment fuzzy reasoning on the grease crystallization state snapshot data to generate a corrected grease stirring speed; and performing control instruction conversion on the target control cooling rate and the corrected grease stirring speed, executing a production control instruction, and generating a grease crystallization process control report. By accurately controlling the cooling rate and the stirring speed, intelligent control over the grease crystallization process is achieved, and the quality and stability of grease products are remarkably improved.

The invention belongs to the technical field of flowmeter verification, and discloses a two-phase flow analysis system and method based on a differential pressure flowmeter and an electromagnetic flowmeter. The method comprises the steps that a differential pressure flow meter and an electromagnetic flow meter are arranged on adjacent sections of a pipeline, the electromagnetic flow meter collects voltage signals and analyzes the voltage signals to obtain liquid phase volume flow, and liquid phase mass flow is obtained by combining temperature correction; the differential pressure flowmeter calculates the gas density by using an ideal gas law according to the temperature and the pressure; inputting multi-sensor time sequence data into the weight optimization model, and optimizing the volume gas content and the total mass flow rate; baseline parameters are calibrated during full-pipe single-phase flow to obtain zero drift, and real-time temperature and pressure dynamic correction are combined, so that the metering precision and the working condition adaptability are improved; the problem of verification deviation of a traditional flowmeter is solved, high precision and high reliability of complex gas-liquid two-phase flow metering are achieved, and efficient and reliable technical support is provided for two-phase flow precise metering and process control in the fields of chemical engineering, energy and the like.

The invention relates to the technical field of process control, and particularly discloses an unmanned aerial vehicle real-time control system and method based on 5G network and perception fusion. The system comprises an airborne sensing and communication module, an edge collaborative decision center and a global resource management and task planning platform, and dynamic collaboration of sensing data fusion, real-time decision and resource scheduling is realized through a multi-layer closed-loop control framework, so that the trajectory tracking precision and the system response agility of an unmanned aerial vehicle group are improved, and the system performance is improved. And the utilization efficiency of limited wireless resources is optimized.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention discloses a beneficiation process intelligent optimization control method and system based on a knowledge graph, and relates to the technical field of beneficiation process control, and the method comprises the steps: collecting static attribute data and dynamic operation data, constructing a causal knowledge graph, and obtaining a causal relationship between nodes in the causal knowledge graph and an optimization target constraint; based on a causal relationship between nodes in the causal knowledge graph and optimization target constraints, screening data from a historical database to form a high-value historical data set; according to the high-value historical data set, a reward function model is constructed, a strategy function is trained, and a reinforcement learning control strategy is obtained; running the reinforcement learning control strategy, generating a control instruction, and issuing the control instruction to the target equipment to obtain dynamic optimization control; and performing real-time monitoring on the dynamic optimization control, and performing anti-fact reasoning based on a causal knowledge graph when abnormal deviation of the running state is detected to obtain an anti-fact reasoning result.

The invention relates to the technical field of industrial automation and process control, in particular to a digital visual control system and method for automation equipment. The method comprises the following steps: carrying out image sequence acquisition and preprocessing on the material conveying automation equipment to obtain an enhanced flow image set; performing flow texture and flow velocity distribution analysis on the enhanced flow image set to obtain a texture feature data set and a velocity field distribution diagram; performing flow state feature extraction on the texture feature data set and the velocity field distribution map to obtain a flow state feature vector map; performing three-dimensional point cloud construction on the material to obtain a stacking curved surface model; and carrying out repose angle measurement and partition processing on the accumulation curved surface model to obtain a repose angle distribution diagram. By means of the industrial automation and process control technology, control normal form transformation from passive response to active prevention is achieved, and the stability and accuracy of the powder material conveying and subpackaging process are remarkably improved.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The invention provides an automobile control device foaming optimization control method based on multivariate temperature coupling analysis, and relates to the technical field of manufacturing process control, and the method comprises the following steps: based on supporting frame attribute characteristics and a material component ratio, according to a preset foaming processing stage and a foaming evaluation function, in a simulation foaming space, according to a foaming evaluation function; performing simulation optimization on the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter to generate an optimal temperature control strategy; and according to the preset foaming processing stage and the optimal temperature control strategy, executing foaming control of the target steering wheel. The technical problems that a traditional foaming technology pays more attention to control of single temperature, multiple influences of different temperature changes on foaming reaction cannot be effectively considered, foaming is not uniform, and the foam structure is not stable can be solved; by accurately controlling the four temperature control parameters, the mutual influence and coupling relationship among the temperatures can be fully considered, and uniform temperature distribution and stable foam structure in the foaming process are ensured.

The invention discloses a power grid transient process deduction method based on digital twinning, electronic equipment and a medium, and the method comprises the steps: building a digital twinning simulation model based on a new energy single-machine simulation model and a three-dimensional field station virtual model, and outputting a virtual value by the digital twinning simulation model; collecting core parameters in a monitoring period; calculating the deviation ratio of the physical value and the virtual value of the physical station based on the core parameter; and adjusting the transient process control strategy based on an adjustment object and the deviation rate, wherein the adjustment object comprises a virtual value, a core parameter, a scene parameter and a model control mode. The digital twinborn model can be calibrated in real time through deviation ratio closed-loop correction of a physical value and a virtual value, it can be ensured that simulation of the transient process of the power grid is highly consistent with the physical reality, and the precision and reliability guarantee of a deduction result is improved.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.