7055 results about "Adaptive control" patented technology

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The invention discloses a swivel bridge attitude monitoring system based on dynamic feedback control, and belongs to the technical field of bridge construction. Comprising the following modules: a multi-source sensor integration module for constructing an omnibearing attitude monitoring network and realizing high-precision real-time monitoring of key parts of a bridge; the edge calculation and data fusion module realizes real-time on-site processing and fusion of sensor data, effectively reduces data transmission delay and improves monitoring stability; the multi-physics field coupling modeling module is used for constructing a multi-physics field coupling model comprising structural mechanics, a temperature field, contact friction and fluid action, and realizing accurate prediction of the attitude change of the swivel bridge; the self-adaptive control strategy module is used for realizing high-precision self-adaptive control on the attitude of the swivel bridge; the grading early warning and cooperative control module automatically matches a corresponding control strategy and realizes risk grading response and intelligent cooperative control; and the digital twinning interaction verification module is used for constructing a high-precision digital twinning model of the swivel bridge.

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

The invention discloses a heterogeneous double-mechanical-arm tail end pose mapping method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining the angular velocity, position and tail end pose information of each joint of a main mechanical arm, and calculating the spatial position and pose of the tail end of the main mechanical arm; s2, motion trail data in the teaching process of the main mechanical arm is collected, a long-short-term memory network model is established, the pose of the future time step length is predicted, and the motion trail of the auxiliary mechanical arm is planned; s3, data information is transmitted, and the problem of communication delay is solved; s4, the pose information of the tail end of the main mechanical arm is solved, the joint angle is adjusted, and the error is adjusted in combination with proportional-integral-differential control; s5, the position and speed signals are filtered, a mechanical arm dynamic model is established, an adaptive control law is designed, and a trajectory smoothing algorithm is adopted; and S6, establishing a virtual obstacle map, and limiting a safe working space. According to the method, comprehensive and accurate mapping and prediction of the tail end poses of the heterogeneous double mechanical arms are achieved.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention discloses a three-degree-of-freedom parallel robot trajectory tracking method and system, and relates to the technical field of industry-university-research, and the method comprises the steps: obtaining motion data related to the execution trajectory of a three-degree-of-freedom parallel robot, and carrying out the data preprocessing; based on historical operation data, an intelligent prediction model is constructed to predict trajectory errors and is used for optimizing feedforward compensation of trajectory tracking control parameters; acquiring actual execution track information of the robot based on a visual sensor, and performing error calculation on the actual execution track information and an expected track to perform real-time track correction; dynamically adjusting control parameters in combination with error prediction data and visual feedback data; in the operation process of the robot, the control strategy is continuously optimized, and the intelligent prediction model is updated by using real-time data. Through the intelligent prediction model, visual feedback and adaptive control optimization, high-precision trajectory tracking, rapid error correction, energy consumption reduction and environmental adaptability enhancement are realized.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

The invention discloses an intelligent illumination control system and method based on a multi-modal sensor, and particularly relates to the field of data analysis, and the system comprises a multi-modal data collection module, a feature calculation module, a space-time coupling analysis module, a multi-modal verification module, a biological rhythm compensation module and an intelligent decision control module. According to the invention, through cooperative acquisition of the multi-modal sensor and space-time coupling modeling, accurate environment perception is realized, and the photo-thermal interaction effect is quantified to eliminate control deviation; an innovative biological rhythm compensation mechanism is fused with the ultraviolet dose and the human body activity data, and a dynamic safety protection boundary is established; and an LSTM-Transform hybrid architecture is combined with a rolling time domain optimization strategy, so that 200ms-level quick response and complex scene self-adaptive control are realized, and the dynamic performance and the security of the system are remarkably improved.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

An intelligent real-time adaptive control air-blowing-type condenser and a usage method therefor, belonging to the technical field of condensers. The intelligent real-time adaptive control air-blowing-type condenser comprises: a condenser body (1) arranged on a mounting frame (101), an axial flow fan (2) being arranged on the top of the condenser body (1); a filter screen (3), which is mounted on the side wall of the condenser body (1) by means of bolts; a cleaning mechanism (4), which is arranged on the outer side of the filter screen (3) and is configured to remove impurities from the filter screen (3); and a moving mechanism (5), which is arranged on the mounting frame (101) and is configured to drive the cleaning mechanism (4) to move, wherein the cleaning mechanism (4) comprises a dust collection shell (401), a cleaning assembly arranged in the dust collection shell (401) and configured to sweep the impurities away from the outer side of the filter screen (3), and a dust settling assembly arranged in the dust collection shell (401) and configured for dust settling. The filter screen (3) on the outer side of the condenser can be cleaned without shutdown of the condenser.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

The invention discloses an electric power cabinet comprehensive monitoring method and system based on multiple sensors and a power distribution cabinet, and relates to the technical field of sensor monitoring. During operation of the system, various sensor data are collected from an electric power cabinet and the surrounding environment of the electric power cabinet, and electromagnetic environments, equipment states and environmental parameters inside and outside the electric power cabinet are monitored in real time; the method comprises the following steps: preprocessing original sensor data through a signal filtering algorithm, extracting feature information related to electromagnetic interference (EMI) from the processed data, calculating and obtaining an EMIindex through a machine learning algorithm, performing state evaluation and fault prediction on equipment in the power cabinet, providing early warning of fault types and giving maintenance suggestions, when the electromagnetic interference intensity exceeds the standard or an equipment fault occurs, a real-time alarm signal is automatically triggered through an intelligent judgment mechanism, a system self-adaptive control mechanism is started, an electromagnetic environment index and an equipment state report are generated, and remote monitoring is carried out on the electric power cabinet.

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The embodiment of the invention provides a humanoid robot self-adaptive control method and system, electronic equipment and a storage medium, and belongs to the technical field of robot intelligent control. The method comprises the steps that electroencephalogram signals and electromyographic signals of a target object are collected; determining a first time-frequency domain feature of the electroencephalogram signal according to the electroencephalogram signal, and determining a second time-frequency domain feature of a muscle activation state according to the electromyographic signal; fusing the first time-frequency domain feature and the second time-frequency domain feature by adopting a dynamic weighting algorithm to obtain a multi-modal feature; inputting the multi-modal features into an action intention recognition model to obtain a predicted action intention; and determining a robot control instruction according to the predicted action intention and the current state of the robot so as to control the humanoid robot. The precision of self-adaptive control of the humanoid robot can be improved.

The invention provides a machine room control method and system based on communication between an air conditioner terminal and a water chilling unit, and the method comprises the steps: obtaining three-dimensional data and equipment attribute data of a machine room and accessory equipment of the machine room through a server, and building a digital twinborn model of the machine room; the server establishes a dynamic load prediction model and sends the dynamic load prediction model to a first controller of the water chilling unit; the intelligent sensor collects environment data and equipment data; a second controller of the air conditioner terminal obtains the environment data and sends the environment data to the first controller; the first controller predicts the cold load change according to the dynamic load prediction model and the historical data and adjusts the operation strategy of the air conditioning system; the server simulates a machine room environment and an equipment operation condition in real time by using a digital twin model, generates a control strategy in combination with a self-adaptive control strategy after evaluating a result, and sends the control strategy to the first controller; and the first controller dynamically adjusts operation parameters of the air conditioning system according to the strategy. By means of the scheme, efficient, intelligent and accurate real-time monitoring and control over the energy efficiency of the air conditioning system can be achieved.

The invention provides a heat input regulation and control method and system for electric arc additive continuous forming, and relates to the technical field of additive manufacturing, and the method comprises the following steps: collecting process parameters in real time, inputting pretreatment process data into a self-adaptive heat input quantity calculation model, and constructing and calculating to obtain a real-time heat input quantity based on a thermodynamic principle and a heat transfer theory; starting the multispectral imaging equipment, performing real-time temperature field scanning, recording temperature field scanning data, extracting temperature gradient and cooling rate information, performing correlation calculation with real-time heat input quantity, and determining an influence relation evaluation result; generating a heat input sequence according to the influence relation evaluation result; and inputting the heat input sequence into a self-adaptive control system constructed based on a reinforcement learning algorithm, calculating an optimal parameter scheme according to a preset target sedimentary layer performance index and a current temperature field state, adjusting corresponding spatial-temporal distribution of control energy input according to the optimal parameter scheme, and performing control heat input on different positions and different layers of the sedimentary layer.

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.