619 results about "Automated technique" patented technology

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

Various automated techniques are disclosed herein for monitoring and assessing real-time and future operational and health statuses of real-world assets (e.g., physical devices, components, equipment, structures, buildings, machines, infrastructure, piping systems, etc.). A monitoring system incorporates the use of intelligent Monitoring Devices for monitoring pipe systems and / or other infrastructure for leaks / issues using IoT devices and machine learning. One or more Monitoring Device(s) are attached to specific physical equipment / structure(s) to be monitored. A Monitoring Device performs comprehensive field data gathering. The collected field data is used to train a customized ML model, which is stored locally in that Monitoring Device. The Monitoring Device monitors current conditions of the pipe using various sensors, and uses its locally stored customized, trained model to perform real-time, edge-based analysis of the monitored data to identify possible issues in real-time and / or to predict future maintenance / service needs without relying on continuous cloud connectivity.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention provides a workshop multi-agent deep reinforcement learning scheduling method based on real-time production conditions, and relates to the technical field of production scheduling and industrial automation, and the method comprises the steps: collecting production data in real time through a distributed sensor network, and constructing a multi-dimensional time sequence matrix; establishing a three-dimensional decision space containing time, resource and task dimensions, and adopting an improved multi-agent depth deterministic strategy gradient algorithm; dynamically calculating the real-time production urgency degree of the production batch, and analyzing cross-unit cooperation to generate a cooperation efficiency factor; the two are combined to optimize a scheduling scheme through a course learning strategy; and verifying the scheme by using a virtual twin environment and feeding back and updating model parameters. The method breaks through the limitations of insufficient dynamic adaptability, low efficiency of multi-target coordination and the like of traditional scheduling, realizes real-time response to dynamic production conditions such as equipment faults and order adjustment, balances time efficiency, energy consumption economy and abnormal fault tolerance through a multi-agent coordination and closed-loop verification mechanism, and improves resource coordination efficiency under complex production constraints.

The invention discloses a furniture processing control system based on artificial intelligence, and relates to the technical field of intelligent manufacturing and industrial automation. According to the system, cutter vibration frequency spectrum, plate texture features and environment temperature and humidity data are collected in real time through a distributed sensor array, multi-source data are fused through a space-time attention mechanism, and a joint feature matrix is generated. And based on the joint feature matrix, utilizing a depth map neural network to deconstruct a topology constraint relation of non-standard customization requirements, and generating an initial processing parameter set. And driving the digital twin model to perform virtual processing according to the initial processing parameter set, and predicting a processing node deformation error and generating a compensation vector in combination with the three-dimensional laser point cloud and infrared thermal imaging data. The compensation vector and real-time working condition data are received, a path cost function is evaluated through Monte Carlo tree search and a time sequence convolutional network, the cutter feeding speed and the cutting depth are corrected, and dynamic regulation and control of cutter path parameters are achieved.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention provides a man-machine cooperation method and system based on robot process automation, and relates to the technical field of automation, and the method comprises the steps: building a data perception processing network, collecting a user operation instruction, a system operation state and task environment data, obtaining a task feature vector through semantic analysis, and matching a business model to generate a task processing strategy. A man-machine cooperative processing framework is constructed based on the strategy, a robot unit processes a structured task, a manual operation unit processes an unstructured task and is connected through an information interaction channel, and transmission of a data analysis result and decision suggestions and updating of processing regulation information when deviation exceeds a threshold value are achieved. Besides, a knowledge migration processing network is constructed, business rules and expert experience data features are extracted, a scene migration model is trained, business feature vectors are mapped to a preset scene feature space, and after validity verification, parameters of the data perception processing network and the man-machine cooperative processing framework are updated.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to the technical field of electric power operation and maintenance management, in particular to a transformer area intelligent operation and maintenance device and method based on a machine learning expert model, and the device comprises a logistic regression machine learning module and a robot process automation module. Real-time perception and data analysis of a business scene are realized by means of artificial intelligence, total data information of a master station measurement domain and an operation and maintenance work task is dynamically collected, incremental data is continuously utilized for data analysis and rule mining, a machine learning expert model is introduced, and the operation and maintenance efficiency is improved. A transformer area intelligent operation and maintenance system integrating human-computer interaction such as operation and maintenance index intelligent monitoring and analysis, intelligent abnormity diagnosis, intelligent operation list generation, intelligent dispatching, intelligent path planning and field operation guidance is constructed, service data is analyzed in real time, meanwhile, a robot process automation technology is integrated, automatic operation of a part of processes is achieved, and operation efficiency is improved. And support is provided for on-site acquisition operation and maintenance work.

The invention relates to an intelligent customs declaration system and method in international trade. The intelligent customs declaration method comprises the following steps: constructing a dynamically updated policy knowledge graph by synchronizing global customs policy data in real time, and solving the problem of response lag of a traditional customs declaration policy; multi-platform heterogeneous customs declaration receipts are integrated, intelligent analysis and standardization processing are carried out, and data compatibility barriers are eliminated; a multi-dimensional customs declaration path considering the tax cost, the timeliness and the compliance risk is generated based on a multi-objective optimization algorithm, and the limitation of a single decision model is broken through; risk factors such as policy mutation and logistics delay are quantified through a dynamic risk assessment model, an optimal recommendation scheme is generated, and full-process declaration and feedback closed loop are realized based on an automation technology. According to the intelligent customs declaration method provided by the invention, the pain points of policy adaptation lagging, low efficiency of data integration and risk pre-judgment missing in cross-border trade are effectively solved, and the customs declaration compliance and decision accuracy are remarkably improved.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention discloses an experimental process instruction arrangement method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining an operation description of a target object, and inputting the operation description into a target large language model; analyzing the operation description into a script instruction based on a target knowledge base by utilizing a target large language model; the target knowledge base comprises a professional knowledge base and a local knowledge base; performing parallel task scheduling on the script instruction to obtain a standardized instruction set; and controlling the experimental equipment to execute the target operation according to the standardized instruction set. According to the method, natural language operation description is analyzed into an executable instruction through the synergistic effect of a large model and a multi-dimensional knowledge base, and efficient semantic programming of an experimental process is achieved; through multi-device parallel task scheduling, the device utilization rate is effectively improved. Through deep fusion of a large model and an automation technology, experimental process instruction arrangement can be efficiently realized, and the method can be widely applied to the technical field of data processing.

The invention relates to the technical field of intelligent industrial manufacturing and automation, in particular to a carton production dynamic adjustment control system and a carton production dynamic adjustment method thereof. The problems of parameter lag, process coupling interference, quality tracing difficulty and the like in traditional carton production are solved.

The invention discloses a motor bearing remaining service life prediction method and system using health indexes, and belongs to the technical field of automation. According to the method, time domain, frequency domain and time-frequency domain characteristic extraction is carried out on a vibration signal sequence, and sensitive characteristics of a degradation process can be accurately reflected through monotonicity and time correlation screening. And performing principal component analysis and dimension reduction on the screened features, and selecting the maximum principal component as a health index. And calculating an initial degradation point of the health index and dividing the data set. When residual service life prediction is carried out, an input signal is decomposed into a trend part and a residual error part, different models are used for feature extraction, and finally a residual service life prediction result is output. Through the steps of health index construction, signal decomposition, feature extraction, residual service life prediction and the like, the degradation stage can be accurately divided, meanwhile, accurate feature extraction and service life prediction can be carried out on data of the degradation stage, and the service life prediction accuracy is improved.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

The invention discloses a PCB component layout method, device and equipment based on a multi-modal large model, and relates to the technical field of electronic design automation, the method comprises the following steps: carrying out quality screening on a plurality of PCB design files which have been laid out to obtain a qualified design file set; constructing a serialized training sample set based on the qualified design file set; each training sample sequence comprises a plurality of training samples arranged according to a layout logic sequence; each training sample comprises multi-modal data representing a current layout state and a real position coordinate of a next component to be laid out; based on the serialized training sample set, training the open-source multi-modal large model to obtain a PCB component layout model; and iteratively generating a layout file of the target PCB based on the netlist file of the target PCB and the PCB component layout model. The method is high in generalization capability and low in cost based on the PCB component layout model trained by the layout data, and can realize rapid layout of components of a new PCB.

The invention belongs to the technical field of industrial automation, and discloses an optimization application method and device for an OPC UA protocol in the industrial automation field, and the method comprises the steps: obtaining the multi-dimensional context information of an industrial site, analyzing the future communication demands and behavior trends of a target OPC UA node through a preset prediction model, obtaining predictive behavior features, and carrying out the optimization application of the OPC UA protocol. And according to the predictive behavior characteristics and the current context information, an OPC UA communication optimization strategy is dynamically generated and adjusted, the OPC UA communication optimization strategy comprises a data sampling period, a release interval, a message queue size, a data coding mode and a security strategy level, and the optimization strategy is applied to a communication session between an OPC UA server and a client. According to the method, OPC UA communication parameters are dynamically optimized through intelligent perception and predictive analysis, the data transmission efficiency can be remarkably improved, the system resource consumption is reduced, the real-time performance and reliability of communication are enhanced, and the method is particularly suitable for a complex and dynamic industrial automation environment.

The invention provides an electronic medical record in-process quality control method based on cooperation of an intelligent agent and a robot process automation technology. The method comprises the steps that each operation event in an electronic medical record system is acquired, an operation sequence is constructed, feature splicing is conducted on the operation sequence, behavior embedding vectors are generated, and a behavior embedding sequence is formed; constructing a causal map; constructing a medical knowledge graph, and performing semantic enhancement on the causal graph according to the medical knowledge graph to generate a semantic enhanced causal graph; screening the intelligent agent with the highest score and satisfactory dependency according to the scheduling score, recording a distribution relation, and generating an intelligent agent task distribution structure according to the distribution relation; and converting the agent task allocation structure into an actual intervention behavior, and executing the corresponding intervention behavior by a robot process automation module to generate an intervention result table. According to the method, an in-event intervention mode which is higher in precision, higher in context perception capability and more supportive to doctor behaviors is realized.

The invention relates to the technical field of Internet of Things and industrial automation, and discloses a multi-device collaborative management method and system based on the Internet of Things, and the method comprises the steps: obtaining industrial device data, and recognizing the industrial device data as an abnormal event marked with a priority; obtaining and classifying the abnormal events, extracting an approval process template and querying a resource schedule, and generating a conflict-free allocation scheme through conflict detection; a collaborative management instruction is obtained based on the conflict-free distribution scheme updating template and is distributed and executed; and obtaining an execution feedback record, and if an energy consumption deviation detection result in the record exceeds a threshold value, judging that the abnormal event is a new abnormal event and marking a priority. According to the method, dynamic analysis and scheduling are carried out on the edge side, so that the problem of insufficient real-time performance of multi-device collaborative management in the prior art is solved.

The invention belongs to the technical field of industrial automation, and discloses a multi-target energy consumption analysis and dynamic optimization system based on deep reinforcement learning. The objective of the invention is to solve the problems of state characterization lagging and insufficient optimization decision of industrial equipment under instantaneous load change. The system accurately characterizes the equipment state change rule by constructing a dynamic state transfer chain; a response priority is scientifically determined by analyzing a state transition path and a load change trend; in combination with dynamic decision logic of deep reinforcement learning, equipment operation parameters are optimized; the energy consumption, the response speed and the stability are balanced through a multi-objective optimization strategy; and finally, high-efficiency operation is realized through real-time regulation and control. According to the invention, the adaptability of the system to complex working conditions, the scientificity of response and the energy utilization efficiency are improved, and the production cost is reduced.

The invention relates to the technical field of mineral processing automation, and discloses a metal dissociation degree regulation-based grinding-flotation collaborative process optimization method which comprises the following steps: acquiring multi-dimensional sensor data in a grinding-flotation production process in real time; establishing a reference prediction model offline based on historical process data; in the online operation stage, the working condition drift state of the current process system is quantified and judged online; when it is judged that significant working condition drifting occurs, a residual compensation model is constructed online; and fusing the prediction result of the reference prediction model and the prediction result of the residual compensation model to generate a final process index prediction value. And based on the final process index predicted value, solving through an optimization algorithm, and generating a cooperative control set value. According to the method, the calculation cost and the data requirement of model maintenance are greatly reduced, and the response speed and the adaptive capacity of the system to the working condition change are remarkably improved, so that the long-term stability and reliability of the collaborative optimization effect are ensured.

The invention provides an intelligent ship decision-making system, and relates to the technical field of ship automation, and the system comprises the following modules: a multi-source data collection module which is provided with a plurality of sensors inside and outside a ship; the data fusion processing module is used for preprocessing, fusing and analyzing the original data acquired by the multi-source data acquisition module; the intelligent decision-making module is used for generating an optimal navigation path from the data input by the data fusion processing module through an A * algorithm, a Dijkstra algorithm, a deep learning algorithm and a collision avoidance algorithm; the execution module is used for transmitting the instruction to each corresponding equipment neutralization module; the man-machine interaction module and the communication module realize information interaction and sharing between the ship and an external related system; through the arrangement of the multi-source data acquisition module, the data fusion processing module and the intelligent decision-making module, various factors in ship operation can be fully considered, the optimal navigation route can be quickly and accurately planned, the collision risk is avoided, and the navigation time and cost are saved.

The invention discloses a government affair complaint dispatching system and method based on a dynamic knowledge graph and a decision engine, and relates to the technical field of government affair automation. The government affair complaint dispatching system and method based on the dynamic knowledge graph and the decision engine comprises the following steps: S1, collecting complaint work order monitoring data, and preprocessing the complaint work order monitoring data; s2, constructing an initial graph structure, calculating weight data of each semantic relation edge according to a period, updating the initial graph structure, and constructing a dynamic knowledge graph; s3, evaluating the risk emergency degree of each work order, and generating an emergency work order identifier based on an evaluation result; and S4, extracting all the emergency work orders, executing scheduling evaluation calculation on the current undertaking department, and judging whether the execution department distributes the work orders again or not based on an evaluation result so as to realize order distribution execution and dynamic scheduling closed loop. The problem that high-risk complaints cannot be recognized and scheduled in time due to the fact that an order dispatching strategy cannot be adjusted in real time according to work order complexity and department bearing capacity in the prior art is solved.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention discloses a mechanical arm simulation system supporting industrial quality inspection, which relates to the technical field of industrial automation and comprises a multi-physics field simulation engine module, a digital twin interface module, a defect sample library module and a cooperative control module. According to the method, traditional entity mechanical arm debugging is replaced by rigid body dynamics simulation based on a physical engine, loss and production line shutdown caused by repeated trial and error of equipment are avoided, rapid construction of the digital twin environment of the production line is achieved by means of an efficient CAD model conversion process and a lightweight loading technology, and high-fidelity optical simulation is achieved through a parameterized defect model library and high-fidelity optical simulation. Various common industrial defects and complex optical effects are covered, the modeling bottleneck of a quality inspection scene is broken through, real-time closed-loop interaction of visual inspection and motion control is realized by using thread separation and an efficient communication technology, and the recognition robustness of a visual algorithm to tiny defects is guaranteed; the reliability of algorithm verification is improved through the quantitative output of algorithm performance indexes and the comprehensive test of extreme working conditions by the simulation system.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.