748 results about "Process optimization" patented technology

The invention provides a coal dressing whole process monitoring decision method and system based on Internet of Things sensing, and the method comprises the steps: firstly obtaining a real-time sensing data set of a plurality of monitoring nodes of a coal dressing process, covering equipment vibration, medium density and process flow data, and then carrying out the feature extraction, the method comprises the following steps of: acquiring equipment state, medium dynamic and flow stability characteristics of each monitoring node, performing abnormal correlation analysis on the characteristics based on a pre-trained monitoring decision model, generating abnormal probability distribution and flow adjustment parameters, determining a priority processing queue according to the abnormal probability distribution, and processing the flow according to the priority processing queue. And finally, the coal dressing process optimization strategy is issued to the edge execution terminal, coal dressing process adjustment operation is triggered, effective monitoring and decision optimization of the whole coal dressing process are achieved, and the stability and efficiency of the coal dressing process are improved.

The invention relates to a part surface defect detection and process optimization method and system, and solves the problems that defect detection has defects, missing detection and erroneous judgment are easy to occur, and subsequent process improvement faces huge challenges even if defects are detected, and the method comprises the following steps: inputting a feature set into a double-branch fusion deep learning model, the first branch identifies defect types and quantization parameters by fusing three-dimensional features and two-dimensional features, and the second branch calculates the correlation degree between the defect features and each process through association rule mining and a random forest algorithm; when the three-dimensional features and the two-dimensional features both meet a preset defect threshold value and the association degree of a certain process exceeds a preset value, determining that the process is a root process; and analyzing a deviation value between the key parameter of the source process and the defect quantization parameter, and correcting the parameter through a dynamic adjustment mechanism according to the deviation degree. The method has the advantages that the defects of the part are accurately detected, the procedure is traced, parameters are dynamically adjusted, closed-loop optimization is formed, and the quality of the part is improved.

The invention provides a wafer-level test yield prediction and process optimization method and system based on big data, and relates to the technical field of wafer-level tests.The wafer-level test yield prediction and process optimization method comprises the steps that wafer manufacturing process data is obtained, a time-sequence-sensitive multi-modal data fusion network is constructed, a LSTM and GNN combined mixed architecture is adopted, and a multi-modal data fusion network is constructed; self-adaptive weight distribution is carried out through an attention mechanism to predict the yield; and when the predicted yield is lower than a threshold value, triggering a layered progressive process optimization system, and adjusting process parameters through an equipment parameter optimization layer and a process level optimization layer. According to the invention, the accurate prediction of the wafer test yield and the automatic optimization of the process parameters are realized, the production efficiency is improved, and the manufacturing cost is reduced.

The invention discloses a digital twinning-based wastewater and dirty salt treatment process optimization method and system, and relates to the technical field of intelligent wastewater and dirty salt treatment. The method comprises the steps that a digital twinborn model synchronously mapped with a processing system is established, and the digital twinborn model integrates the technological parameter incidence relation of an evaporation section, a crystallization section and a filtering section; collecting component data and flow data of the polluted salt wastewater corresponding to each process section in real time; correcting model characteristic parameters of each process section in the digital twin model based on the component data and the flow data collected in real time; according to the corrected digital twin model and the optimization target, generating a process optimization strategy corresponding to each process section; and monitoring the component data of the treated discharge water, reuse water, industrial salt and solid waste in real time, and if the data does not reach the standard, triggering model parameter correction, optimization strategy iteration or cross-section collaborative logic recalculation. According to the invention, intelligent cooperation of wastewater and dirty salt treatment is realized.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention relates to the technical field of process optimization control, and discloses a silk-covered enameled winding process optimization control method and system based on intelligent analysis. The method comprises the steps that multi-area production parameters are collected in real time and preprocessed; constructing parameter and product quality mapping through principal component dimensionality reduction, grey correlation and support vector regression; training an Actor-Critic structure by using a depth deterministic strategy gradient network to obtain an adjustment strategy; a non-dominated sorting genetic algorithm is combined with analytic hierarchy process to carry out multi-objective optimization, and a control scheme for balancing mass, efficiency and energy consumption is realized. According to the application, on the basis of considering the complex coupling relationship among the process parameters, multi-objective dynamic balance optimization of product quality, production efficiency and energy consumption is realized, the process parameters can be adaptively adjusted according to the production state and the task demand, and the stability and the optimization degree of the silk-covered enameled winding production process are improved.

The invention relates to the technical field of process optimization, in particular to a knowledge graph-driven manufacturing process optimization system, which comprises a graph node generation module, a dependency relationship construction module, a semantic entity matching module, a process path screening module and a trend-driven early warning module. According to the method, semantic implicit relation recognition is achieved through sequential alignment and numerical difference comparison, quantitative screening between the machining size and the tolerance grade is introduced in path construction, and it is ensured that a path connection structure is optimized under the condition that the size precision constraint is met; in combination with the time sequence trend splitting and continuous fluctuation consistency discrimination mechanism of the equipment operation state, the dynamic marking and early warning annotation set generation of the process deviation trend can be completed at the node path level, and the closed-loop processing from knowledge modeling, path screening to trend pre-judgment is realized. And the process path adaptability, the parameter configuration precision and the abnormality identification advancement are effectively improved.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention relates to the technical field of process optimization, and discloses a hot work die steel heat treatment process optimization method and system.The method comprises the steps that hot work die steel samples are collected under multiple sets of different process conditions, and a performance basic data set is obtained; constructing a multi-target coordination optimization model based on the performance basic data set; performing phase change detection on the hot work die steel sample to obtain phase change monitoring data; performing prediction in combination with the phase change monitoring data to obtain a performance prediction result; and solving an optimal process parameter combination based on the performance prediction result and the multi-target coordinated optimization model, and realizing simultaneous optimization and coordinated balance of a plurality of performance indexes in the heat treatment process of the hot work die steel by making full use of associated information among different performance indexes.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention provides a continuous casting quality control method based on a meta-cognitive coordination architecture agent cluster, and relates to the technical field of ferrous metallurgy intelligent manufacturing. The continuous casting quality control method comprises the steps of data input and standardization, center coordination and intelligent agent cluster operation and maintenance. In the central coordination process, the meta-cognitive coordination agent serves as a core to coordinate six kinds of functional agents including a semantic analysis agent, a data perception agent, a defect prediction agent, a root cause analysis agent, a process optimization agent and a digital twinborn agent, and task scheduling and state monitoring of the whole system are achieved. Three key functions of task decomposition, data scheduling and closed-loop management and control are completed; in the closed-loop management and control step, risk early warning, defect prediction, root cause analysis, process optimization and digital twinborn verification and feedback are carried out for continuous casting quality. Compared with a traditional scheme, optimization is carried out in the aspects of whole process, multiple modes, intelligence, collaboration and the like, the management efficiency is improved, and economic benefits can also be increased.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention provides a fertilizer production energy consumption real-time optimization method, and particularly relates to the technical field of industrial process optimization control based on artificial intelligence, and the method comprises the steps: constructing a mechanism sub-model based on a chemical reaction kinetic equation, and outputting a theoretical energy consumption constraint value; and meanwhile, a data driving model is used for receiving temperature, pressure and raw material ratio data collected by a real-time sensor and outputting a real-time energy consumption predicted value. And inputting the theoretical energy consumption constraint value and the real-time energy consumption predicted value into a dynamic weight distribution module to generate a fused energy consumption predicted value. And generating a production parameter adjustment instruction through a rolling optimization algorithm according to the fused energy consumption predicted value, and sending the production parameter adjustment instruction to a production execution system. And if the deviation between the data acquired after the adjustment instruction is executed and the fused energy consumption predicted value exceeds a preset threshold value, performing online fine adjustment on the data-driven model parameters. The technical problems that real-time energy consumption optimization lags behind due to multivariable dynamic coupling in the fertilizer production process, and an optimal production parameter combination cannot be rapidly generated are solved, and the fertilizer production energy consumption is remarkably reduced.

The invention provides a production energy efficiency optimization method and system based on industrial big data, and the method comprises the steps: generating an industrial production data set and creating an industrial knowledge graph according to multi-dimensional operation parameters, energy consumption state data and production line constraint information generated by a target factory, mining a causal association relationship among the multi-dimensional operation parameters through ontology reasoning analysis to generate a causal association path; executing a sequential association rule mining operation on the energy consumption state data to obtain association rule mining information of energy consumption fluctuation and operation parameter change, and matching and fusing the association rule mining information and a causal association path to generate a candidate root cause set of energy efficiency abnormality; inputting the candidate root cause set into a bidirectional long short-term memory network, positioning root cause information of energy efficiency abnormity through time dimension relevance modeling and spatial dimension feature reinforcement, and finally generating energy efficiency optimization guidance containing parameter adjustment priority and process optimization suggestions. The accuracy of energy efficiency anomaly root cause positioning and the pertinence of optimization measures are improved.

The invention relates to the technical field of die parameter optimization, and discloses a process optimization method and system for a multi-part adaptive self-adjusting stamping die, and the method comprises the following steps: generating a structured process input parameter set through input parameters, and storing the structured process input parameter set in a central database; searching related cases and rules from the knowledge graph according to the input parameters; and calculating the similarity between the current parameter and a historical case, using a hybrid algorithm, carrying out weighted averaging, explaining the weight of each parameter, processing special constraints, and finally carrying out normalization to obtain a comprehensive similarity index. Real-time monitoring and dynamic adjustment are achieved through an intelligent control system, self-adaptive mold design optimization and centralized material characteristic database management are combined, an integrated quality detection system automatically recognizes and classifies defects through image recognition and machine learning technologies, and the problems that key parameters cannot be adjusted in time according to actual working conditions, and the working efficiency is high are effectively solved. The production efficiency is low; and the product quality is unstable.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

The invention relates to the technical field of industrial information and data processing, in particular to a cutting fluid multi-parameter self-adaptive monitoring and early warning method and system based on a dynamic threshold value, and aims to obtain and standardize multi-modal data of a cutting fluid in real time, construct a coupling attenuation prediction model in combination with processing working condition parameters, and predict a joint attenuation trend under a specific working condition. A cutting fluid performance time sequence is modeled through a Gaussian process, a confidence interval boundary is dynamically adjusted, and a self-adaptive single parameter dynamic threshold value is calculated. And a Copula function is used to learn a non-linear dependence structure among multiple parameters, a combined overrun risk is identified, and a multi-parameter combination threshold value is dynamically adjusted according to a preset rule. The system monitors data in real time and triggers early warning when abnormity is found, a digital computer is used for intelligent decision support processing, a maintenance knowledge base and a process optimization rule are combined, and accurate maintenance suggestions are generated. In addition, the edge intelligent unit ensures the independent operation capability of the system when the communication is interrupted.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention discloses a die stamping process optimization method and system, and relates to the related field of mechanical manufacturing, and the method comprises the steps: obtaining a data set of a workpiece, and extracting the features of the workpiece; matching simulation of the workpiece and the matching part is carried out, and a restrictive space of the workpiece is established; inputting the workpiece features and the product requirements of the workpiece into a structural feature analysis model to execute structural analysis, and establishing a structural sensitive identifier; performing mold optimization fitting, and establishing a mold reinforcing rib scheme set; performing stamping key parameter optimization based on the scheme set, and establishing a control parameter optimization set; and performing evaluation analysis on a mapping result through a multi-dimensional fitness evaluation function to generate a process optimization scheme. The technical problem that in an existing die stamping process optimization method, die design and process parameter adjustment are carried out separately, integrity and collaboration are lacked, and the optimization effect is limited is solved, and the technical effect that die design and process optimization are closely combined, and comprehensive optimization from die design to process parameters is achieved is achieved.

A forging process optimization system and optimization method based on digital twin, which system and method belong to the technical field of forging. The forging process optimization system comprises a physical entity collection module and a digital twin system, wherein the physical entity collection module comprises a data collection module, and the data collection module is used for collecting information data of the physical entity collection module during operation. A digital twin technique is integrated into a forging process, so as to facilitate the formation of intelligent and networked production, facilitate manufacturing resource configuration and production technique sharing, and facilitate offline operation training and online production guidance. Thus, the yield of parts is increased, resource waste is reduced, and a relatively high economic benefit is achieved; and the blindness and the subjectivity of when a conventional optimization method is used are reduced, and the time for searching for an optimal process parameter combination is shortened, thereby effectively improving the process design efficiency and reducing the sample development cost.

The invention provides a metallurgical process optimization method and system for a low-oxygen low-nitrogen aluminum-vanadium alloy, and relates to the technical field of process optimizing.The method comprises the steps that correlation characteristics among process parameters are extracted through a three-layer gating map attention network, and a parameter coupling state vector is constructed in combination with a time sequence attention module; constructing a process prediction model based on the state vector, and fusing a thermodynamic equilibrium equation and a long-short term memory network to predict the oxygen and nitrogen content; a deep reinforcement learning network of a Soft Actor-Critic algorithm is adopted, the parameter coupling state vector and the oxygen and nitrogen content prediction value serve as state input, and the technological parameter adjustment amount is output; and planning and iteratively training a future adjustment strategy through Monte Carlo tree search to finally obtain an optimal process parameter combination meeting oxygen and nitrogen content constraints, thereby realizing intelligent optimization control of the metallurgical process.

The invention discloses a control system and method for an automatic beer production line, relates to the technical field of automatic control, is used for solving the problems that state judgment is lagged and abnormal trend is difficult to identify in time, identifies electronic tags of key equipment through an RFID reader-writer, imports models and serial numbers into a database, and automatically associates operation parameters. The distributed sensors collect temperature, pressure, flow and vibration data in real time, the analysis unit calculates the operation deviation and the health degree, a multi-condition control strategy is configured through the programmable logic controller based on the health degree, a time window and a task priority algorithm are combined, real-time data and historical data are compared to recognize abnormity, and the real-time data and the historical data are analyzed. And a fuzzy control algorithm is fused to generate an adjustment instruction, the equipment state is automatically adjusted, time sequence modeling is utilized to analyze whole-process data, a start-stop strategy is formulated, energy distribution and process optimization are performed, and intelligent scheduling and self-adaptive optimization of the beer production line are realized.

The invention provides an artificial intelligence-based business platform process optimization method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining an electric power process data set generated by the operation of an electric power business platform, and enabling the electric power process data set to comprise a plurality of process instances composed of business operation nodes and node execution logs; performing flow feature extraction on the electric power flow data set to obtain node behavior features and node association features, calling a pre-constructed electric power flow intelligent optimization model to perform collaborative optimization reasoning, and generating an optimization constraint condition set; based on the optimization constraint condition set, generating an electric power flow optimization strategy including node execution time sequence adjustment and a node dependency relationship reconstruction scheme, deploying the electric power flow optimization strategy to a flow execution engine, then collecting flow operation feedback data after implementation, and iteratively updating model reasoning parameters, so as to obtain an electric power flow optimization strategy; therefore, intelligent optimization of the process is realized based on actual data, the process execution efficiency is improved, and a dynamic intelligent power business process management system is constructed.

The invention belongs to the technical field of petrochemical process simulation and intelligent optimization control, and particularly relates to a method for constructing and optimizing a full-fraction oil catalytic cracking reaction kinetic model. The construction method comprises the steps of raw material matrix construction, reaction network construction, product property calculation, model parameter optimization and process prediction calculation. The optimization method comprises the following steps: constructing a process optimization index system comprehensively reflecting environmental and economic targets; systematic optimization of reaction conditions and product distribution is realized; intelligent regulation and efficient prediction of the catalytic cracking process are realized; and optimal reaction operation conditions meeting multiple requirements are obtained. The method is high in adaptability, process parameters can be adjusted according to the properties of the raw materials, and efficient conversion is achieved. By optimizing the catalytic cracking process, the yield of aromatic hydrocarbon can be remarkably improved. By predicting and optimizing reaction conditions through the kinetic model, the energy consumption is reduced, the reaction efficiency is improved, meanwhile, the equipment investment and operation cost can be reduced, and the production efficiency and economic benefits are improved.

The invention discloses a high-precision intelligent quality inspection system for diamond grains, and relates to the technical field of defect detection and contour measurement, and the system comprises a multi-mode sensing device which is used for obtaining a two-dimensional image, a three-dimensional shape and spectral data of the grains; the module is used for receiving manufacturing process parameters and constructing a theoretical three-dimensional model; the interface is used for acquiring actual manufacturing process data; the unit is used for processing the fused measured data and comparing the fused measured data with a theoretical model to generate deviation data; the hierarchical artificial intelligence analysis engine comprises a data analysis and coordination control AI model based on multi-head attention, the AI model is coupled with an attribution analysis layer, and the engine fuses and analyzes measured data, deviation data, process parameters and actual manufacturing process data. And performing deep cross-modal association, trend analysis, manufacturing data association analysis and accurate attribution by using an attention mechanism, and finally generating a quality evaluation result containing application performance prediction and process optimization suggestions with attribution information.

The invention provides a PCB manufacturability intelligent analysis and early warning method and system based on artificial intelligence, and the method comprises the steps: collecting and marking the multi-source time sequence process parameter data in the PCB design and manufacturing process under working conditions, building a dynamic causal graph model with direction and time lag marks through a sliding window and standardization processing by applying a causal discovery algorithm, and carrying out the calculation of the dynamic causal graph model. Dynamic expression of causal relationships among process variables is realized; when manufacturing abnormity is detected, abnormity attribution is carried out by combining a Bayesian back propagation algorithm, high-contribution-degree root dependent variables are screened, the effectiveness of root causes is verified through virtual intervention simulation and statistical test, and finally verification results and a causal mode are stored in a knowledge base to support subsequent rapid matching and reasoning. According to the method, the accuracy, efficiency and interpretability of PCB manufacturing abnormity attribution are improved, and process optimization and preventive intervention are facilitated.

The invention provides a digital operation inspection system and method for electric power operation inspection, relates to the technical field of electric power system operation and maintenance, and aims at constructing an electric power operation inspection knowledge graph based on electric power equipment data and historical defect data, reasoning potential abnormal risks through a multi-mode perception and reasoning integrated mechanism, and obtaining a power system operation inspection result. The method comprises the following steps: performing local process optimization and global optimization by using cloud side end hierarchical collaborative decision, dynamically adjusting a reasoning sequence in real time according to a dynamic optimization mechanism of an operation process reasoning link, combining a dynamic optimization module with a completed power operation inspection knowledge graph, perfecting a power operation inspection knowledge system, supporting adaptive updating of operation process reasoning and decision, and improving the power operation inspection knowledge system. And a reinforcement learning training mechanism is introduced to realize continuous evolution and performance optimization of the power operation and maintenance expert model. According to the method, the problems of insufficient real-time sensing capability, lack of dynamic optimization, difficulty in continuous updating and disjunction of decision response in the existing power operation and maintenance operation are solved, and the reasoning accuracy and intelligent decision-making capability of the system can be continuously improved.

The invention relates to the technical field of process optimization control, in particular to a single crystal furnace crystal pulling process control method and system based on an intelligent optimization algorithm. Identifying to obtain equipment operation parameters and process state variables; obtaining a state estimation value of the controlled medium based on the equipment operation parameter and the process state variable; determining target operation parameters of the controlled equipment according to the technological process and the equipment operation parameters; on the basis of the target operation parameters, expected changes of process state variables are estimated in combination with system dynamic response characteristics, and predicted state variables are obtained; calculating a medium distribution parameter according to the equipment operation parameter, the process state variable and the state estimation value; generating a state prediction value of the controlled medium based on the medium distribution parameter, the target operating parameter and the predicted state variable; and integrating the state estimation value and the state prediction value, and outputting a control instruction for adjusting the execution structure according to a preset adjustment rule. The closed-loop regulation of the controlled medium is realized through the control instruction.