281 results about "Production control system" patented technology

The invention discloses an accurate fertilizer proportioning production control system based on data analysis, and relates to the technical field of intelligent agriculture, the system collects soil and crop related data in real time through a sensor group and monitoring equipment, and after preprocessing, a crop growth environment data set is formed; summarizing and calculating a soil microorganism activity index, a root respiration rate index and a photosynthesis efficiency index; the indexes and the soil environment data are combined to determine the specific feeding demand quantity of nitrogen, phosphorus and potassium, the specific feeding demand quantity of nitrogen, phosphorus and potassium is summarized, a comprehensive fertilizer proportion index is calculated, evaluation is performed according to a preset first fertilizer demand threshold value and a preset second fertilizer demand threshold value, and when an evaluation result triggers a proportion adjustment mechanism, the fertilizer is adjusted. And the fertilizer ratio regulation and control module dynamically adjusts the fertilizer ratio according to the actual crop and soil conditions, and optimizes the ratio effect through a feedback mechanism. According to the system, the nutrient supply precision of crops is improved, fertilizer waste can be effectively reduced, and sustainable agricultural development is promoted.

The invention discloses a hard alloy production control method and system based on digital twinning, and relates to the technical field of digital twinning. A hard alloy production control system based on digital twinning comprises a twinning processing model building module and a hard alloy production control module. According to the method, the digital twinborn model is constructed for the full-process alloy production equipment, the historical production data and the equipment loss data are introduced for calibration regulation and control, the virtual model can truly map the equipment operation state and the process behavior, and the model prediction precision and adaptability are remarkably improved; on the premise that the quality target and the cost constraint are met, the candidate process paths are generated in the multiple equipment clusters, and screening is performed in combination with the raw material feature prediction portrait and the historical database, so that the scientificity and operability of path planning are improved, and the blindness and resource waste of process path selection are effectively reduced.

The invention provides a coal bed gas productivity prediction method and system combined with artificial intelligence, and the method comprises the steps: firstly obtaining a multi-source coal bed gas productivity data set, which comprises geological structure data, reservoir physical property data and historical production dynamic data, of a target block, and then carrying out the time-space alignment processing of the multi-source coal bed gas productivity data set, the method comprises the following steps: obtaining a standardized coal bed gas productivity characteristic set, then performing characteristic extraction on the standardized coal bed gas productivity characteristic set to generate a prediction characteristic set containing geological attribute characteristic and dynamic attribute characteristic dual codes, and then performing iterative prediction on the prediction characteristic set based on a reinforcement learning strategy network. Finally, an optimization strategy set is generated according to the target coalbed methane productivity prediction result and error distribution of historical production dynamic data and fed back to a coalbed methane production control system to trigger parameter adjustment operation, and therefore the accuracy of coalbed methane productivity prediction can be improved, and optimization control over the production process is achieved.

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

The invention relates to the technical field of program control, and discloses a high-activity selenium yeast intelligent production control system based on gradient feeding monitoring. The system comprises a data acquisition module, a control parameter acquisition module, a gradient calculation module, an intelligent control module and a material supplementing control module. The system collects process production data, extracts key control parameters and weights, calculates real-time gradient values of the parameters, forms six-dimensional gradient vectors, generates control gradient indexes in combination with the parameter weights and time sequence attenuation weights, and then forms an addition control decision. And according to the decision and the process state dynamic data, the material supplementing opportunity is identified, an active adding instruction is generated, programmed control and adjustment of the raw materials and the additives are achieved, and the control precision and the production automation level are improved.

The invention discloses a new material production whole process monitoring method, system and device and a medium. The method comprises the following steps: collecting monitoring data of a plurality of process links in a new material production process and preprocessing the monitoring data; performing correlation analysis on the monitoring data of different process links, identifying a coupling relationship between the previous and later process links, and establishing a correlation model between the process stages; fusing the monitoring data of the plurality of process links to generate a dynamic key monitoring index under the current production condition; performing anomaly detection on the real-time monitoring data based on the association model and the dynamic key monitoring index, and judging whether abnormal data deviating from an expected range exists in the production process or not; and pushing the detected abnormity or potential risk to an operator, and / or generating a parameter optimization and adjustment suggestion of the production process, and sending the parameter optimization and adjustment suggestion to a production control system and the operator. According to the monitoring method provided by the invention, the monitoring precision can be enhanced, and the fine management level of the production process is improved.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention relates to the technical field of control systems, and discloses a thick broad-bean sauce production control system and method, which are used for solving the problems of quality fluctuation and pollution risk caused by insufficient precision control in the existing thick broad-bean sauce fermentation process. The method comprises the following steps: acquiring temperature, humidity, pH value and microbial activity indexes in a fermentation process in real time to form a dynamic parameter data set; analyzing the deviation degree of the data set, calculating the matching degree with a standard fermentation curve, and identifying potential unstable factors; generating a preliminary adjustment instruction based on the deviation result, and performing automatic fine adjustment on the temperature and humidity to stabilize the environment; monitoring the activity change of the adjusted microorganisms, feeding back and verifying a control effect through a sensor, and calculating an activity recovery rate; optimizing fermentation model parameters, and iteratively adjusting a control algorithm according to a recovery rate to realize self-adaptive precision improvement; and outputting a final control report, and feeding back optimized parameters to the system to realize closed-loop precision control.

The invention provides a puree production line microbial pollution risk real-time monitoring and early warning method, which comprises the following steps: deploying multiple types of sensors, an industrial camera and an operation log interface, collecting environmental temperature and humidity, pH value, dissolved oxygen, image and operation behavior multi-modal data in a fermentation tank in real time, and carrying out edge calculation and standardized preprocessing to obtain the microbial pollution risk of a puree production line. Data missing, abnormity and time sequence difference are eliminated; multi-modal features and a dynamic knowledge graph constructed based on a production process and a pollution event are fused, and a deep learning model and a graph neural network are utilized to realize microbial pollution risk probability intelligent prediction; risk grading and a dynamic weighting algorithm are introduced, grading early warning signals and disposal suggestions are automatically generated, and a production control system is linked to execute response measures; according to the subsequent production state, continuous feedback is carried out, the model and the early warning threshold are adjusted in a self-adaptive mode, and the accuracy, response efficiency and production safety of pollution early warning are remarkably improved.

The invention relates to the technical field of hazardous chemical substance production, and provides a hazardous chemical substance safety production management and control system, in the aspect of data acquisition, all-around acquisition of equipment operation, environment and personnel operation data is realized, a high-precision sensor ensures data accuracy and far exceeds traditional finite dimension and precision acquisition, and during data analysis and processing, edge calculation is combined with deep learning, so that the safety production management and control of hazardous chemical substances is realized. A risk assessment model is dynamically adjusted, the limitation of a traditional fixed model is broken through, production change and risk early warning can be adapted in real time, risk levels are dynamically adjusted, multi-element early warning and visual display are achieved, compared with single early warning and simple display, risk perception is more timely and accurate, accurate reasons are found through a unique accident analysis model, and the risk assessment efficiency is improved. According to the method, the production management and operation improvement can be promoted, each module of the system can be optimized, a closed-loop lifting mechanism is formed, the safety production management and control level of the hazardous chemical substances is comprehensively improved from data acquisition, analysis, early warning and optimization, the safety risk is greatly reduced, and the production safety and stability are guaranteed.

The invention discloses an intelligent injection molding product quality prediction method, which comprises the steps of collecting product structure information, mold parameters and injection molding equipment operation parameters, and constructing a scientific mold testing process window based on simulation analysis; designing an orthogonal experiment scheme to generate a simulation data set, and forming a multi-source data training sample in combination with field test model data; training a quality prediction model by using a distributed gradient boosting tree model, inputting process parameters, and outputting quality indexes such as product gram weight, volume shrinkage and buckling deformation; the model interpretability is realized through a shop value analysis method, and the influence weight of the process parameters on the quality indexes is evaluated; the model is deployed in a production control system or a cloud platform, the product quality is predicted in real time, a dynamic feedback mechanism is established, and when the error between actual detection data and a predicted value exceeds a threshold value, model retraining and parameter correction are automatically triggered to form closed-loop control. The method effectively improves the accuracy, transparency and engineering adaptability of quality prediction of the injection molding product, and can be popularized.

The invention relates to an abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention discloses a rapid batch online detection method and system for miniature bearings, and relates to the technical field of bearing relevance, and the method comprises the steps: carrying out the cooperative collection of miniature bearings, and generating a multi-dimensional detection data set; traversing the key detection area to carry out dynamic space registration, and positioning the key detection area of the miniature bearing; traversing the key detection area to carry out defect feature extraction, generating a defect probability distribution diagram, and carrying out dynamic segmentation on the defect probability distribution diagram to generate a defect type label group; and on-line sorting is conducted on the miniature bearings based on the defect type label group, a sorting instruction set is obtained, and a detection quality report is generated and fed back to a production control system. The technical problems that in the prior art, the miniature bearing detection efficiency is low, the sampling inspection precision is insufficient, and the quality of the bearing is difficult to detect and control online in real time are solved, rapid batch online detection of the miniature bearing is achieved, and the technical effect of improving the miniature bearing detection efficiency and precision is achieved.

A production control system for a hydrogen storage tank includes: an operating unit, a design terminal, and a processor. The processor may be communicatively connected to the operating unit and the design terminal, respectively. The design terminal may be configured to operate a finite element simulation command stored in a storage unit. The processor may be configured to obtain the target laying parameter from the design terminal, generate an operation command based on the target laying parameter, send the operation command to the winding mechanism; and determine whether to generate an optimization command. In response to a determination of generating the optimization command, the optimization command may be generating and sent to at least one of the operating unit, the monitoring unit, and the design terminal.

The invention provides a cloud factory production control system for a carbon-based microbial fertilizer, and the system comprises the steps: obtaining associated data for producing the carbon-based microbial fertilizer, carrying out the feature extraction of the associated data, and constructing a time sequence association matrix; inputting the time sequence incidence matrix into a long short-term memory network model for dynamic proportion prediction to obtain an optimal raw material proportion scheme; performing real-time simulation verification on the optimal raw material proportioning scheme, and generating an equipment control instruction after the verification is passed; wherein the equipment control instruction comprises production process parameters; executing the equipment control instruction in a distributed manner based on edge computing nodes so as to produce the carbon-based microbial fertilizer; and performing causal analysis on the quality data and the production process parameters of the produced finished product, and identifying key influence factors to generate a quality optimization strategy. According to the method, the defects that the standard production requirement is difficult to meet and the root of the quality problem is difficult to quickly position at present are overcome.

The invention relates to the technical field of industrial Internet of Things intelligent production control, and discloses an industrial Internet of Things cloud edge collaboration-based equipment intelligent operation method and system, and the method comprises the steps: extracting production flow information and production demands in each product industrial production order, analyzing the elastic production flow related information of the product industrial production order, and carrying out the cloud edge collaboration of the product industrial production order; querying artificial production resources and industrial production auxiliary equipment resources, solving a product industrial production order allocation sub-strategy and an industrial production auxiliary equipment configuration sub-strategy, and issuing the sub-strategies to each production line production edge management terminal and each production line production edge execution terminal; and a production executor is driven to control the production processing equipment to execute the production operation of independent manual production or equipment-assisted production. According to the invention, seamless application and connection of the production auxiliary equipment under the industrial Internet of Things intelligent production control system are realized, and the overall energy efficiency of industrial production is improved while the distribution rationality and scientificity of the production processing orders and the production auxiliary equipment are improved.

The invention relates to the technical field of equipment control, in particular to a feeding and discharging machining production control system and method based on a PLC, and the system comprises a multi-mode data sensing module which collects a three-dimensional point cloud and image sequence of a working scene through a laser radar and a visual sensor, and combines a vibration signal and a current signal of a machining main shaft; and after feature extraction, outputting a comprehensive state vector including the workpiece pose and the equipment health state. And the digital twinborn collaborative planning module receives the comprehensive state vector, drives a digital twinborn model to be synchronized with a physical environment in a virtual space, performs Monte Carlo tree search by applying a reinforcement learning algorithm, and generates a flexible control strategy with a time sequence mark. And the digital twinborn collaborative planning module adjusts a task allocation strategy according to the early warning signal, and corrects digital twinborn model parameters based on the execution log. According to the operation process corresponding to the modules, collaborative optimization of the feeding and discharging system and the machining rhythm is achieved, and the equipment utilization rate and the production efficiency are improved.

The application discloses a rapid batch online detection method and system for micro bearings, and relates to the technical field of bearings.The method comprises the following steps: cooperatively collecting micro bearings to generate a multidimensional detection data set; performing dynamic space registration on key detection areas to locate the key detection areas of the micro bearings; performing defect feature extraction on the key detection areas to generate a defect probability distribution graph, performing dynamic segmentation on the defect probability distribution graph, and generating a defect type label group; performing online sorting on the micro bearings based on the defect type label group to obtain a sorting instruction set, generating a detection quality report, and feeding back the detection quality report to a production control system.The application solves the technical problems of low detection efficiency, insufficient sampling accuracy, and difficulty in real-time online detection and quality control of the micro bearings in the prior art, realizes rapid batch online detection of the micro bearings, and improves the detection efficiency and accuracy of the micro bearings.

The invention belongs to the field of automation control, particularly relates to an ampoule bottle production control system based on a PLC, and aims to solve the problems of lack of a feed-forward adjusting mechanism and control lagging during ampoule bottle production control in the prior art. The system comprises a sensing unit which is configured to obtain raw material characteristic parameters used for representing raw material component fluctuation and process state parameters used for representing product quality in the ampoule bottle production process; the processing unit is in communication connection with the sensing unit so as to receive the raw material characteristic parameters and the process state parameters; the control execution unit is in communication connection with the processing unit, and the control execution unit is configured to receive a global optimization instruction; and analyzing the global optimization instruction into a control instruction for at least one station execution mechanism, and driving the at least one station execution mechanism to execute corresponding operation based on the control instruction. According to the invention, the process parameters of the downstream station can be adjusted in advance through a feed-forward adjustment mechanism.

The invention relates to the technical field of control systems, in particular to a plate production control system with a heating function, which comprises a cross-domain data fusion module for acquiring deformation data, energy transfer data, electrical signal data, acoustic emission signals and stress distribution images through a distributed sensor network; generating a characteristic matrix containing a mapping relation of thermal resistance distribution, stress gradient and current density based on a physical field coupling model; the multi-parameter coupling control module synchronously outputs a temperature gradient adjustment signal, a pressure distribution control signal and a current density modulation signal according to the characteristic matrix; the heat flow coordinated regulation and control module combines conductive layer power data and temperature field characteristics; the digital twinning optimization module constructs a thermodynamic electric coupling virtual model, optimizes a weight coefficient through a genetic algorithm and returns the weight coefficient to the control module. Through cross-domain data association and multi-target cooperative control, heat conduction path stability and interface stress dynamic digestion are realized.

The invention discloses a cross-linking agent production control system and method based on the Internet of Things, and relates to the technical field of chemical production control, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module for obtaining spectral data in a reaction kettle and obtaining temperature data and pressure data of different positions on the wall of the reaction kettle; the data processing and control module is used for converting the spectral data into reaction data of a cross-linking agent intermediate product based on a meta-calibration model; on the basis of the temperature data and the pressure data of different positions, state data, including a local overheating area and a pressure abnormal area, of a cross-linking agent intermediate product are identified by adopting an image identification and mode analysis algorithm; and the decision execution module is used for establishing a dynamic model fusing the production characteristics of the cross-linking agent based on the reaction data and the state data, training an ML model by using historical data and real-time IoT data based on the dynamic model, and automatically generating and executing an optimal control strategy under security constraints.

The invention relates to the technical field of data acquisition, in particular to a data acquisition system based on edge computing, which comprises a sensor module, an edge computing module and a cloud server module, the sensor module is composed of various types of sensors, and the edge computing module comprises a data preprocessing unit, a data analysis unit and a data caching unit. The data analysis unit analyzes the preprocessed data in real time based on a preset analysis model, data feature extraction and pattern recognition are carried out, and the data caching unit is used for temporarily storing the analyzed key data and analysis results. According to the data acquisition system based on edge computing, real-time preprocessing, feature extraction, mode recognition and other operations are performed on the acquired data at the edge computing node, a simple decision task can be quickly judged, and once the equipment temperature exceeds a preset threshold value, the data can be quickly acquired. The edge computing node can immediately perceive and notify the production control system to take cooling measures, so that delay caused by data transmission to the cloud for processing is avoided.

The invention provides an anomaly analysis method and system applied to a diborane production control system, and the method comprises the steps: firstly obtaining a multi-source production data set which is generated in the operation of the diborane production control system and comprises data with timestamp marks, such as equipment states, raw material conveying and reaction parameters, and then constructing a production association knowledge graph; the method comprises the following steps: carrying out physical connection between production equipment or parameters corresponding to production nodes and between equipment or logic association between parameters corresponding to production edges, then carrying out graph feature extraction on a production association knowledge graph, calling a pre-training graph neural network model to carry out graph structure analysis, and generating an anomaly detection result containing different anomaly type detection confidence degrees; and determining an abnormal event type and distribution characteristic information in the knowledge graph according to an anomaly detection result, and finally generating a production early warning instruction containing an event positioning identifier and sending the production early warning instruction to the production control terminal to trigger an anomaly response operation, thereby comprehensively and accurately analyzing the anomaly in diborane production, and improving the production safety and stability.

The invention provides a coal dressing decision-making method and system based on offline and online data fusion, and relates to the technical field of coal washing and dressing processing. According to the method, firstly, a dynamic mapping relation between an offline basic data set and an online real-time data set in the coal dressing process is established; then, a collaborative constraint condition across data types is generated based on the relationship. And then a decision influence evaluation model of the coal dressing process parameters is constructed through the conditions. And generating a coal preparation process adjustment scheme according to a model output result. And finally, the scheme is sent to a coal dressing production control system to be executed, the dynamic mapping relation and the collaborative constraint conditions are optimized according to execution result data, and coal dressing decision optimization is achieved.

The invention provides a ring forge piece production control system and method for wind power generation, and relates to the technical field of production control systems.The system comprises a sensing module used for collecting the thickness change rate, the forge piece temperature and the vibration acceleration of a ring forge piece in real time; the edge calculation module is used for receiving data collected by the sensing module, executing rolling force dynamic compensation calculation and outputting dynamic compensation force to the control execution module; a cloud optimization module; and a control execution module. The thickness change rate, the forging temperature and the vibration acceleration of the ring forging are collected in real time through the sensing module, then rolling force dynamic compensation calculation is conducted through the edge calculation module, real-time dynamic compensation of the rolling force is achieved, and the response time of dynamic disturbance during high-speed rolling can be effectively shortened from 30 ms to 8 ms.

The invention provides a heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity drainage, and belongs to the field of oil-gas field development. The experimental device comprises a three-dimensional oil reservoir simulation system, a microwave-assisted heating system, a horizontal well injection-production regulation and control system and a multi-field monitoring system; the microwave auxiliary heating system comprises a microwave generation regulation and control system, a microwave transmission system and a shaft integrated microwave radiator, the microwave generation regulation and control system comprises an industrial grade microwave generator, a power regulator and a PLC, and the microwave transmission system comprises a microwave coaxial cable and a microwave waveguide tube bundle; and the microwave generator is continuously connected with the microwave waveguide tube bundle and the shaft integrated microwave radiator. According to the heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage, the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce a'thermal-flow-force 'multi-field coupling environment, the microwave heating adaptability is poor, and the experimental scale and the field deviation is large are solved. The heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage solve the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce the'thermal-flow-force' multi-field coupling environment.

The invention discloses a welding wire production control system and method based on visual feedback, and belongs to the technical field of precision machining. The system comprises a multi-mode intelligent sensing subsystem, a self-adaptive model prediction controller and a safety and exception handling subsystem, and a two-stage control architecture of a quality closed loop and an execution closed loop is constructed. According to the method, the microcosmic roughness, macroscopic geometry, defects and other multi-dimensional quality characteristics of the surface of the welding wire are obtained in real time through multi-modal sensing and fused into a comprehensive quality index; carrying out rolling optimization by adopting a model prediction control algorithm for updating parameters on line, and generating a control instruction; meanwhile, trend early warning based on statistical process control and sudden fault diagnosis based on model residual analysis are operated in parallel, and active safety protection is achieved. According to the method, the problems of unstable quality, control lag and passive safety caused by supplied material fluctuation and tool time variation in the welding wire flaking machining process are solved, and self-adaptive accurate control and intelligent safety protection in the machining process are achieved.

The invention discloses an aviation fastener quality control method and system, belongs to the technical field of production control systems, and aims to solve the problems that multiple tests are needed before the existing aviation fastener is produced, the test steps are tedious, and the test period is long. Based on comprehensive collection, preprocessing, hierarchical analysis and decision support of data and effective storage and management of the data, key data in the production process are collected in real time, and after cleaning and arrangement, the system adopts an analytic hierarchy process to decompose a quality control target into a plurality of hierarchies, including a target layer, a criterion layer and a sub-criterion layer, so that the quality of the product is controlled. A clear quality control framework is formed, through expert consultation and statistical analysis, a pairwise comparison judgment matrix is constructed, the relative importance between elements of all levels is quantified, a weight vector is calculated, consistency check is conducted on the judgment matrix, and a reasonable threshold range is set through a statistical method. Scientific basis is provided for optimization of technological parameters and adjustment of threshold values, and test efficiency and product quality are improved.

The invention relates to the technical field of industrial internet, in particular to a control system and method for stirrer production. Comprising an edge data preprocessing unit, a flexible production instruction distribution unit and a working condition diagnosis and protection control unit. By constructing an integrated control parameter mapping model, an industrial operation signal is converted into a collaborative sensing digital primitive; the production task is compiled into an intelligent production execution process cluster based on a resource scheduling algorithm, and equipment is driven to execute differentiated production; and performing situation assessment on the digital primitives by using the anomaly detection model, and generating a regulation and control instruction to execute process logic recombination and safety protection. According to the invention, the problems of low production information integration level and tight data in each link of a production control system are solved, and flexible scheduling and active security defense in the production process are realized.

The application provides a rotating electrode atomization powder production control system and a control method, a multivariate machine learning prediction model is established between an electrode rotating speed, a plasma arc power, a protective gas flow and powder particle size and sphericity, according to target powder particle size and sphericity, the multivariate machine learning prediction model is used to calculate optimal electrode rotating speed, plasma arc power and protective gas flow, the optimal process parameters are applied to a powder production process, and a powder production system is operated, the electrode rotating speed, the arc power and the gas pressure are monitored in real time in the atomization process, and the process parameters are dynamically adjusted to compensate for fluctuations, an image acquisition unit is used to obtain images of powder particles in flight, AI vision model is used to analyze particle size distribution and sphericity in real time, and the electrode rotating speed, the plasma arc power and the protective gas flow are adjusted according to the analysis results.