28130 results about "Automation" patented technology

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention provides an MES-based power distribution cabinet quality inspection analysis and process regulation and control method and system, and the method comprises the steps: converting a quality inspection standard into an executable rule, generating a standardized quality inspection template, evaluating the quality of a single project and a batch, monitoring the mean value and fluctuation of a production process based on a statistical process control theory, and calculating a process capability index. A dynamic threshold value formula is combined to adjust a control limit in real time, process stability is evaluated, equipment data is standardized, production batches are associated, data consistency is verified and ensured, graded early warning is performed according to the deviation degree, a machine learning model is combined to realize predictive maintenance, process parameters are dynamically corrected, historical data are analyzed through machine learning, and the process quality is improved. According to the method, high-order feature quantification influence factors are extracted, abnormal parameter combination is detected in combination with an isolated forest, a process strategy library is updated, closed-loop optimization is formed, full-process automation from data acquisition, real-time analysis and parameter adaptive adjustment to predictive maintenance is realized, and quality inspection efficiency and process stability are improved.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention relates to the technical field of defect detection, in particular to a curtain wall defect detection system based on computer vision, which comprises an image acquisition module, a texture analysis module, a dynamic defect tracking module, a defect positioning module and a defect identification module. According to the invention, a multi-angle curtain wall data set is collected through the arranged fixed camera and the angle-adjustable camera, the coverage range of the whole and details of the building curtain wall is increased, the accurate adjustment of synchronous shooting parameters and the deep analysis of each frame of image enable the data collection to be more complete, and the high-quality image data is ensured. The system is allowed to perform local texture feature analysis and dynamic trajectory tracking, so that the automation degree of defect detection is improved, the manual intervention requirement is reduced, the detection efficiency and accuracy are improved, an abnormal motion area is automatically marked and analyzed, the system can find potential structural safety problems in time, and the safety of the system is improved. Accurate data support is provided for maintenance and repair, and maintenance cost and building safety risks are reduced.

The invention belongs to the technical field of intelligent navigation, and discloses a navigation obstacle avoidance method and system in a low-confidence and feature similar environment. The method comprises the following steps: acquiring multi-modal sensor data of obstacles in a detection area in a preset low-confidence environment by using a sensor; preprocessing the data of the multi-modal sensor; introducing a generative adversarial network to carry out image enhancement processing on the denoised image; performing cross-domain fusion on the data by using a converter architecture; obtaining an obstacle position, an obstacle type and a confidence degree distribution value based on a deep neural network, and constructing and obtaining an obstacle confidence degree map; dynamically planning an obstacle avoidance path by combining a deep reinforcement learning algorithm based on an obstacle confidence map obtained in real time, and performing auxiliary planning of the obstacle avoidance path based on an MPC model; the method can effectively cope with high challenge scenes such as tunnels and is widely applied to the fields of industrial automation, intelligent transportation and the like.

The invention provides a teaching-free welding robot path planning system based on visual autonomous learning, and relates to the technical field of industrial welding, and the teaching-free welding robot path planning system comprises a pose compensation module which is used for arranging three infrared reflective mark points on the surface of a clamp base in an L shape, collecting original point clouds of the mark points and a workpiece by using a stereo camera, and preprocessing the point clouds; generating a pose compensation matrix based on the offset of the actual coordinate and the theoretical coordinate of the mark point, and outputting a de-noising point cloud data set and a compensation matrix; and the pose correction module is used for fitting a workpiece plane equation by adopting a random sampling consistency algorithm on the basis of the de-noised point cloud data set, acting a pose compensation matrix on the workpiece plane equation, correcting a pose error caused by clamp drifting, calculating a welding seam track end point coordinate and outputting a corrected welding seam pose parameter and a scanning pose instruction. According to the method, clamp drift can be compensated, weld joint poses can be corrected, three-dimensional features are extracted, a collision-free path is planned, and the welding precision, efficiency and automation degree are improved.

The invention provides an intelligent mechanical arm control system scheme based on multi-model cooperation, and belongs to the field of automation and intelligent control. The system is divided into four parts of man-machine interaction, detection and perception, task decoupling planning and program generation, and robot execution operation. Man-machine interaction is carried out, a user inputs an instruction in a text or voice format, and instruction processing is carried out through voice recognition and a natural language translation model; detecting and sensing, and identifying and positioning a target captured by the camera by using a multi-modal large model; task decoupling planning and program generation: analyzing the user instruction and the perception information into an executable program by applying a task planning model and a program translation model; and checking and correcting the program by using the program checking model, and driving the mechanical arm to complete a task. According to the system scheme, variable environments can be dealt with, complex language instructions can be understood, intelligent control over the mechanical arm is achieved, and the multi-task execution capacity and the intelligent level of the mechanical arm are remarkably improved.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

The invention relates to the technical field of information management, and comprises a distribution automation operation and maintenance work log intelligent recording platform based on a CS architecture, and the platform comprises a log field analysis module, a task chain construction module, a task logic verification module, and a log storage tracing module. According to the method, missing and abnormal fields are effectively marked and corrected through meticulous analysis and comparison of uploaded log records, the accuracy and integrity of data are improved, task scheduling is optimized and the operation and maintenance management efficiency is improved through association of device numbers and task types and adjustment of execution time in sorting and grouping of task sequences, and the task scheduling efficiency is improved. Task chains are recombined to solve conflicts and adjust priorities, smooth execution of operation and maintenance tasks is guaranteed, delay is reduced, logic verification enhances task reliability through rule verification and error marking, unassociated tasks are deduced, analyzed and corrected, the exception processing capacity is enhanced, the data tracing and storage efficiency is improved, and the tracing capacity of data management is enhanced.

The invention discloses a dispatching automation fault intelligent processing system based on knowledge reasoning navigation. The dispatching automation fault intelligent processing system comprises a dispatching automation system environment sensing module which obtains machine room environment multi-source data in real time to serve as a data source; the intelligent analysis module performs preprocessing and labeling on the image type data and the text type data, then performs knowledge extraction to obtain entities and relationships among the entities, performs data fusion on the obtained entities, constructs an automatic duty knowledge graph, forms a scheduling automation system fault research and judgment mechanism reasoning model, and performs fault diagnosis on the scheduling automation system. A fault alarm event is generated, a knowledge reasoning environment is constructed, and a fault research and judgment conclusion is obtained through reasoning of a knowledge reasoning algorithm; an auxiliary decision-making module performs reasoning matching on the fault alarm event and the automatic duty knowledge graph through an RE2 model to obtain a fault handling auxiliary decision-making opinion; and constructing a fault exception handling flow navigation chart, and generating a fault handling coping strategy. The operation and maintenance management level of the dispatching automation machine room is improved.

The invention relates to the technical field of industrial automation control, and discloses a high-precision and quick-response industrial thermostat temperature regulation system which comprises a temperature acquisition module, a fluctuation analysis module, a dynamic calibration module, a gradient optimization module, a threshold management module and a regulation execution module. The temperature acquisition module acquires real-time temperature and environment thermal inertia parameters, and sets a temperature adjustment interval; the fluctuation analysis module divides temperature nodes and generates fluctuation feature vectors; the dynamic calibration module extracts a temperature deviation index and establishes an adjustment compensation rule; the gradient optimization module identifies a temperature change grade and calculates a fluctuation density difference value; the threshold value management module deduces an optimal adjustment threshold value and generates a threshold value deviation sequence; and the adjustment execution module is integrated to form a temperature adjustment execution scheme. The system realizes deep analysis and dynamic compensation of temperature fluctuation through cooperation of multiple modules, improves the adjustment precision and the response speed, and is suitable for high-precision temperature control industrial scenes such as chemical engineering and mechanical manufacturing.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention belongs to the technical field of automation control, and discloses an inspection robot control system and method based on inspection information, and the inspection robot control system based on the inspection information comprises a path planning module, an obstacle detection module, a region evaluation module, a path reconstruction module and a state recovery module. By acquiring the coordinate point set of the inspection map and the start-stop position of the robot and analyzing the path steering change and distance accumulation, the navigation precision of the inspection robot in a complex environment is optimized, roundabout and repeated movement caused by inaccurate path planning is reduced, the real-time distance between an obstacle and the path is measured and judged, and the navigation precision of the inspection robot is improved. And the response speed and accuracy of dealing with sudden obstacles are improved, when the path is reconstructed, through analysis of connection curve data, the smoothness of the path and the movement continuity of the inspection robot are ensured, the operation efficiency of the inspection robot is improved, the manpower monitoring demand is reduced, and the resource configuration is optimized.

The invention relates to the technical field of intelligent maintenance, and discloses a fault diagnosis positioning technology, which comprises a data acquisition and alarm module for acquiring equipment operation state data and generating a dynamic alarm signal; the integrated analysis processing module is used for analyzing, processing and identifying the abnormal state of the system; the multi-source fault positioning module is used for analyzing the identified abnormal state, extracting fault features through an optimization algorithm and positioning parts which have faults and possibly have faults in combination with a deep learning model; and the three-dimensional fault visualization module is used for marking the position and the reason of the fault component in the model. Through cooperative work of the data acquisition and alarm module, the integrated analysis processing module, the multi-source fault positioning module and the three-dimensional fault visualization module, automation of the whole process of fault diagnosis is realized, and the diagnosis efficiency is greatly improved. Compared with the existing problems of dependence on manual checking and simple alarm and difficulty in accurate positioning, the method effectively solves the defects of low positioning efficiency and slow response of a traditional method.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention provides a valve actuator dispatching control method and system based on AI intelligence, and relates to the technical field of industrial automation control, and the method comprises the steps: collecting the running state parameters of a steam turbine in real time, determining the relation between the steam inlet amount and the valve opening degree under the current working condition to obtain a preliminary valve opening degree estimated value, and carrying out dynamic response compensation; the method comprises the following steps: carrying out comprehensive coupling analysis on a theoretical valve opening value and mechanical parameters such as response time, inertial parameters and an opening limit threshold value of a valve actuator, calculating a dynamic adjustment weight and carrying out boundary constraint processing to obtain a processed parameter, and calling a preset valve opening-silicon controlled trigger angle nonlinear mapping model in a single chip microcomputer based on the parameter; and calculating a final silicon controlled trigger angle by inquiring adjacent data points and a linear interpolation method. By fusing intelligent prediction, multi-dimensional adjustment and a closed-loop feedback mechanism, the valve control precision and response speed are improved, and efficient and intelligent operation of industrial automatic control is achieved.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention relates to the technical field of intelligent warehousing and logistics automation, in particular to a forklift dynamic path planning method based on deep reinforcement learning, and the method comprises the steps: deploying multi-view vision, geomagnetism and other multi-source sensors, achieving data calibration and fusion through employing an insect compound eye-imitating vision model, and constructing a high-dimensional state space vector; heuristic models such as migrant bird navigation and biological stress response are adopted, deep reinforcement learning is combined, decision instructions are generated from the three aspects of path planning, dynamic obstacle avoidance and energy efficiency management, actions are executed through a control system, and deviation is fed back; a reward function is used for evaluating the decision effect, rewards are formed by weighting path efficiency, obstacle avoidance success and energy consumption penalty, the weight can be updated in a self-adaptive mode, and therefore the deep reinforcement learning model is optimized. According to the method, the accuracy, safety and efficiency of forklift path planning are effectively improved, the method can adapt to complex dynamic environments, and the requirements of intelligent logistics and industrial automation for forklift intelligent operation are met.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

Disclosed is a method and system for quickly identifying and responding weak point-associated power flow based on a sensitivity factor, relating to the technical field of dispatching automation of power system. The method includes: parameter data is acquired, and a transfer factor (SF) matrix is established; consumption and accommodation of new energy of a power distribution network is evaluated, and a line with power flow exceeding a limit is evaluated; and flexible load in the power distribution network is dispatched based on the sensitivity factor, and safety verification is carried out on the result. The present invention identifies a blocking line of the power distribution network, the situation of out of limit of the line is eliminated, so that the safety of the power gird is ensured, and the new energy is consumed and accommodated.

The invention discloses an intelligent operation and maintenance method and system based on an MCP protocol, and belongs to the crossing field of artificial intelligence and operation and maintenance automation. The system comprises an AI Agent module, a central intelligent analysis engine module, a man-machine interaction module and a safety control module. The method comprises the following steps: collecting system operation data in real time through an AI Agent module, and packaging the system operation data into an MCP protocol format; the central intelligent analysis engine module predicts the load based on an LSTM model, and generates operation and maintenance decision suggestions in combination with log anomaly recognition and reinforcement learning; the man-machine interaction module realizes understanding of user input through a natural language large model, automatically converts the user input into an MCP instruction, and supports multiple rounds of interaction confirmation and instruction verification; the safety control module provides an MCP operation sandbox and semantic instruction verification, and the operation safety is guaranteed. The system can dynamically schedule resources and automatically dispose faults, realizes that non-technical personnel can execute complex operation and maintenance operations, and is suitable for application scenes with high reliability requirements, such as industrial data acquisition and chemical engineering.

According to the multi-parameter monitoring method and device for the material stress corrosion test, the flow rate, temperature and ion concentration of corrosive liquid are adjusted in real time through closed-loop control, and the real service environment of a material is simulated; in combination with a constant strain rate and constant load mixed loading mode, loading parameters are dynamically adjusted according to material failure and environment change, and the behavior of deviating from real failure is avoided. Electrochemical, mechanical and corrosion environment data are synchronously collected, a ridge regression algorithm is utilized to fuse multi-modal features, a Granger causal test and mutual information entropy are combined to analyze a corrosion-mechanical-environment interaction effect, and early-stage tiny damage is captured. And calculating the number of residual cycles, generating a three-dimensional probability cloud picture to quantify a failure risk level, and realizing dynamic regulation and control through a grading strategy instead of traditional fixed threshold judgment. According to the method, the real-time performance, the reliability and the automation level of stress corrosion testing are remarkably improved, and a systematic solution is provided for deep exploration of a material failure mechanism in a complex service environment.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention discloses an AI-based data asset quality assessment and automatic repair method and system, and the method comprises the steps: receiving a to-be-processed target data asset, inputting the target data asset into a multi-dimensional joint quality scoring engine, and generating a comprehensive quality assessment report containing an integrity defect map, a distribution abnormality thermodynamic diagram and a semantic conflict link; based on the comprehensive quality evaluation report, utilizing a rule and learning fused strategy generator to analyze repair requirements and constraint conditions contained in a defect map, an abnormal thermodynamic diagram and a conflict link, and dynamically generating repair operation instruction sets and priority sequences for different types of defects; and inputting the target data assets, the repair operation instruction set and the priority sequence into a multi-expert collaborative repair execution module, repairing defects in the target data assets according to the priority sequence, and outputting the repaired data assets. According to the embodiment of the invention, comprehensive and accurate data quality evaluation can be realized, and the automation level and accuracy of data restoration are improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.