1838 results about "Online learning" patented technology

The invention provides an industrial environment monitoring and accident prediction method fusing multi-modal data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic collection and causal association preprocessing of multi-modal heterogeneous data collected in real time through constructing a dynamic industrial knowledge graph; a customized deep learning model is adopted to extract deep abstract features of each mode, and weak signals and potential risks are accurately represented and uncertainty is quantified; a high-fidelity digital twin model is utilized to drive a deep reinforcement learning algorithm, and dynamic optimization and verification are performed to generate a multi-level and multi-target preventive intervention strategy combination; an intervention strategy is executed through an edge-end-cloud three-layer collaborative intelligent architecture, and online learning and system sustainable evolution are realized by using a closed-loop data feedback mechanism. According to the method, the sensing and early warning capability of the early weak and complex abnormal state of the industrial environment can be remarkably improved, the accident evolution path is accurately predicted, and credible explanation is provided.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention discloses an edge computing resource allocation optimization method and system based on reinforcement learning, and relates to the technical field of edge computing resource management.The method comprises the steps that computing load data and network state data are collected, a historical load sequence is formed, a state space matrix is constructed, the historical load sequence is mapped into a discrete state value, and the discrete state value is obtained; establishing a deep neural network model; inputting the state space matrix into a deep neural network model, and generating a resource allocation strategy set according to the priority and time delay requirements of the calculation tasks; iteratively training the resource allocation strategy set by adopting a double Q learning algorithm, setting a reward function based on task completion time and a resource utilization rate, and selecting an optimal resource allocation strategy; and adjusting computing resource allocation of the edge nodes according to the optimal resource allocation strategy, and meanwhile, feeding back an execution result to the deep neural network model for online learning updating. According to the method, intelligent optimization of edge computing resource allocation is realized by introducing a deep reinforcement learning method.

The invention provides a shield tunneling machine fault detection method and system based on edge computing, and relates to the technical field of industrial Internet, and the method comprises the steps: deploying edge computing nodes on a shield tunneling machine, and carrying out the time-space alignment and feature level fusion of data collected by different sensors, and obtaining unified feature data; inputting the fused feature data into an online learning model pre-deployed in an edge computing node to obtain a fault detection result; the edge computing node uploads the fault detection result and feature data related to the fault to the cloud server, generates a fault analysis report and feeds back the fault analysis report to the edge node and the user terminal; and the edge node periodically pulls the updated online learning model and knowledge base from the cloud to realize cloud-edge collaborative model updating and knowledge evolution.

The invention provides a Dores front-end node-based service proxy method and system, and relates to the technical field of database access request processing, and the method comprises the steps: collecting front-end node performance indexes, extracting node performance characteristics through employing multi-head recursive attention and causal convolution solution, extracting inter-node correlation characteristics through combining graph attention convolution, and obtaining a database access request; the method comprises the following steps: constructing a node state vector, optimizing a multi-layer connection pool parameter by utilizing an online learning algorithm, establishing a node incidence matrix based on comparative learning, constructing a node performance probability graph evolution model through variational reasoning and Monte Carlo sampling, predicting a node performance change trend, and receiving a database access request; and generating a query plan by adopting an edge attention message passing mechanism, carrying out resource evaluation in combination with performance trend prediction, carrying out scheduling decision making by utilizing multi-agent reinforcement learning and Monte Carlo tree search, carrying out fault detection and migration according to node performance deviation, and updating a probability graph evolution model.

The invention relates to the technical field of artificial intelligence, and discloses a bidding document generation method based on retrieval enhancement generation and a large language model, and the method comprises the steps: analyzing technical parameters, legal terms and score weights in a bidding demand document; retrieving matched historical bidding document fragments and technical specifications from the industry knowledge base, and generating a retrieval enhanced data set; fusing the data through a dynamic weight distribution algorithm and generating an initial bidding document draft; checking conflict terms based on the legal semantic knowledge graph and marking correction suggestions; and optimizing the bidding document structure and the key content according to the score weight. According to the method, an online learning mechanism driven by sentence vector matching, timeliness weight calculation and user feedback is adopted, so that the problems of inaccurate technical parameter extraction, low legal conflict detection efficiency and non-optimized scoring rules in the bidding document generation process are solved.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention discloses a power failure monitoring system, and relates to the technical field of failure monitoring. During operation of the system, multi-dimensional data acquisition is carried out on key equipment of a transformer substation and a power distribution room, the operation state of the equipment is comprehensively covered through real-time acquisition of data of various sensors, and data acquired by a data acquisition module is preprocessed; the intelligent gateway equipment is deployed, real-time data analysis and preliminary fault diagnosis are carried out locally, the running state of the equipment is monitored in real time through set rules and threshold values, potential faults are detected, preliminary alarm is given out, and the system automatically judges the severity of the faults and decides whether to take automatic control measures including load switching or equipment power failure or not. Meanwhile, alarm information is generated and sent to operation and maintenance personnel, data, fault diagnosis results and automatic control measures in system operation are fed back, and a deep learning model and an edge calculation strategy are periodically updated through online learning and model updating.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention discloses a memory access optimization method based on intelligent cache management, and relates to the technical field of computer storage. According to the method, spatial-temporal characteristics and semantic association data of memory access requests are collected in real time, a dynamic heat matrix is constructed, and a multi-dimensional access rule is fused to improve modeling precision. And inputting the dynamic popularity matrix into a hybrid prediction model, predicting a future access probability by using a time convolutional network, analyzing a competition relationship between data blocks through a graph attention network, generating a conflict pre-judgment weight and a corrected popularity ranking, and effectively reducing the cache jitter risk. On the basis of popularity ranking and conflict weight, a fragmented reinforcement learning algorithm is adopted to divide logic sub-regions, a differential reward function is designed to dynamically decide cache operation, and performance and energy efficiency requirements are balanced; and finally, through an online learning mechanism, combining real-time feedback to dynamically adjust a prediction model weight and strategy parameters, forming a closed-loop optimization link, and realizing adaptive stability under long-term load fluctuation.

The invention provides a thermal power plant boiler combustion visual monitoring and early warning method and system, a storage medium and electronic equipment. According to the method, obtained multi-measuring-point temperature data are input into a pre-constructed temperature field reconstruction model, the model comprehensively considers the geometric structure and combustion thermodynamic characteristic factors of a hearth, model parameters are dynamically adjusted through a particle swarm optimization algorithm, the temperature field reconstruction precision is continuously improved, and a three-dimensional real-time temperature distribution diagram of the hearth is generated; fusing and presenting the diagnosed combustion abnormal information and the visual image of the hearth temperature field, generating a visual in-furnace combustion state monitoring interface, and meanwhile, pushing the abnormal information to a mobile terminal to realize remote monitoring and early warning; the combustion state data in the long-period operation process of the boiler are mined and analyzed, typical combustion modes are extracted, a combustion mode library is continuously enriched and optimized, an online learning algorithm is used for adaptively updating a combustion abnormity diagnosis model, and the intelligent level of combustion state sensing and abnormity early warning is continuously improved.

The invention relates to a traffic flow prediction optimization method based on an intelligent optimization algorithm, which is applied to the technical field of traffic flow prediction. The system comprises a multi-modal data acquisition and fusion module, a hybrid deep learning prediction module, an intelligent parameter optimization module, an integrated learning module, a multi-scale error correction module, a prediction result analysis and visualization module, a distributed learning and privacy protection module and the like. Wherein the hybrid deep learning prediction module adopts an attention mechanism to fuse models such as CNN, Transform and LSTM, and introduces an adaptive error correction mechanism, so that the prediction precision is remarkably improved; the intelligent parameter optimization module accelerates the model search efficiency by using technologies such as quantum computing and swarm intelligence optimization; the integrated learning module and the adversarial training strategy enhance the robustness and generalization of the model; and the multi-scale error correction module adopts a time sequence decomposition and self-adaptive online learning method to improve the accuracy of long-term and short-term prediction.

The invention discloses a multi-model-based intermittent reaction kettle intelligent control method and system. The method comprises the steps of collecting operation data such as temperature, pressure and reactant concentration of a reaction kettle in real time through a sensor, constructing a soft measurement model by utilizing a machine learning algorithm, and performing training based on historical data so as to predict key operation parameters; when operation conditions change, the system updates model parameters in real time through an online learning model by using a recursive least square method or an online gradient descent method; besides, based on a reinforcement learning algorithm, the system can generate and optimize a control strategy, adjust operation parameters such as the opening degree of a cooling water valve and the power of a heater, and finally execute an optimal control instruction through an actual controller to realize high-precision control of the reaction kettle; according to the method, the control precision, the system response speed and the adaptive capacity are improved, the energy utilization is optimized, and the production efficiency is improved.

The invention relates to the technical field of artificial intelligence, in particular to a road surface automatic inspection robot path planning method and device based on artificial intelligence, and the method comprises the steps: collecting road surface environment data in real time through various sensors, carrying out the feature extraction through a convolutional neural network based on the road surface environment data, and constructing a dynamic environment model through a graph convolutional network. According to the method, the change trend of potential road surface damage areas and obstacles is predicted, path planning and optimization are performed by using a deep reinforcement learning algorithm, after path planning is completed, the robot autonomously travels according to a planned path, and real-time adaptive adjustment is performed on the path through an online learning method so as to cope with environmental changes. Collected data and path planning information are fed back to the central control unit in real time to be stored, and a road surface health condition report is generated. According to the invention, the efficiency and the precision of the inspection task are improved, and the method has high environmental adaptability and is suitable for complex and dynamically changing road environments.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention discloses an interdisciplinary scientific research potential assessment method based on a dynamic multi-modal knowledge graph, relates to the technical field of scientific research management and intelligence analysis, and aims at the dynamic requirements of college scientific research ecology by relying on an incremental assessment framework. Comprising the steps of multi-source data acquisition and incremental knowledge graph construction, time sequence trend identification, online and transfer learning optimization, scene simulation and multi-scene strategy verification and multi-modal data fusion. The map structure is updated in real time through concept drift detection and ontology extension, and emerging hotspots are identified by combining time sequence difference indexes such as citation acceleration and a cooperative network; and the accuracy and continuous iteration capability of the evaluation model are kept in multiple environments by means of online learning and a cross-domain migration strategy. Finally, potential evolution is quantified through scene simulation, strategy suggestions are output, the sensitivity to the cross domain is further enhanced with the assistance of multi-mode embedding, a closed-loop evaluation system from data to decision is formed, and college subject cross layout and resource allocation are assisted.

The invention discloses an electrical equipment life prediction system and method based on deep learning, and belongs to the field of electrical equipment life prediction, and the method comprises the steps: constructing a data set, and carrying out the preprocessing; performing convolution operation on the time sequence data in the data set, extracting local features to obtain a convolutional neural network, capturing a long and short term dependency relationship of the time sequence data in the data set by using LSTM to obtain a recurrent neural network, and fusing output features of the convolutional neural network and the recurrent neural network to obtain comprehensive feature representation; constructing a deep learning model based on the fusion of a convolutional neural network and a recurrent neural network, adding an attention mechanism, adopting a mean square error as a loss function, optimizing model parameters, carrying out model training by utilizing a training data set, evaluating model performance by adopting a verification set, and adjusting hyper-parameters; and inputting real-time data into the trained model, predicting the residual life of the electrical equipment, and designing an online learning mechanism. According to the invention, the accuracy and real-time performance of prediction are improved.

The invention relates to the technical field of power transformer discharge fault diagnosis in a power supply system, in particular to a power transformer discharge fault diagnosis method based on a neural network, and the method comprises the steps: collecting a partial discharge signal, characteristic gas data in oil and power supply system operation parameters in real time through a sensor, and unifying timestamps; statistical aggregation is performed on the partial discharge signals, and standardization processing is performed on the electrical quantity parameters and the discharge characteristic parameters to generate fusion characteristic vectors; constructing an expansion mapping relation library containing laboratory simulation data and actual operation data; training a diagnosis model by adopting a mixed structure of a convolutional neural network and a long-short-term memory network; and deploying the model in an edge computing device, and dynamically verifying and optimizing the performance of the model through online learning. According to the method, the problems of low multi-source data fusion efficiency and insufficient generalization ability of a neural network model to a complex discharge mode are solved, and the operation stability of a power supply system is improved.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention relates to an unmanned aerial vehicle knowledge graph construction method based on multi-modal large model recognition, and the method comprises the steps: carrying out the data fusion of texts, images, videos and sensor data related to an unmanned aerial vehicle, and constructing an unmanned aerial vehicle multi-modal data set; constructing a multi-modal large model integrated with a Transform architecture and a self-attention mechanism for extracting structured information from input data, and training the multi-modal large model by using a multi-modal data set of the unmanned aerial vehicle; structured information is extracted from unmanned aerial vehicle data input in real time by using the trained multi-modal large model to construct a knowledge graph, knowledge graph data is updated regularly through an online learning mechanism, and the quality and consistency of the knowledge graph are ensured by adopting an automatic verification and error correction mechanism. According to the method, the defects of low accuracy and low efficiency in unmanned aerial vehicle knowledge graph construction in the prior art are overcome, and the construction quality of the knowledge graph is improved.

The invention relates to the technical field of data processing, in particular to a standardized detection result calibration method based on multi-modal fusion, which comprises the following steps of: performing high-precision space-time synchronization and standardization on different modal data, performing dynamic compensation by utilizing timestamp alignment, a cross-correlation function and an IMU (Inertial Measurement Unit), and performing dynamic parameter adjustment by adopting a local abnormal factor algorithm. Carrying out cross validation by utilizing inherent relevance of multi-modal data, constructing and continuously optimizing a high-confidence system state representation model, and realizing real-time identification and self-adaptive calibration of model parameters by adopting a Bayesian online learning framework; the method further comprises the steps that closed-loop recalibration is conducted through multi-sensor cross validation and residual analysis, an optimal action sequence is generated through a reinforcement learning agent, accurate prediction of key indexes of a monitored object is achieved, instant calibration and situational prediction can be conducted according to a specific event, and the intelligent level and maintenance efficiency of system monitoring are comprehensively improved.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

The invention discloses a knowledge tracking cold start optimization method and system based on a large language model, and belongs to the field of knowledge tracking. The method comprises the following steps: firstly, acquiring student answering records of an online learning platform, constructing a prompt template to enable a large language model to output question solving steps, labeling knowledge concepts, and generating a binary pairing relationship between the question solving steps and the knowledge concepts; secondly, taking the problem and the knowledge concept as heterogeneous graph nodes, establishing edge connection and generating a semantic embedding vector; then, a heterogeneous graph encoder is used in combination with a graph attention network and a jump knowledge mechanism, and final node embedding of knowledge concepts and problems is output. And finally, predicting the answer of the student to the target question by comparing, learning and training the answer prediction model. According to the method, the performance and accuracy of the knowledge tracking cold start stage on the online learning platform are effectively improved.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention provides a task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention discloses an image recognition system based on machine vision, which comprises a deep learning model, and is characterized in that an image recognition module comprises an image data sensing module for receiving and processing original image data, capturing an image from a camera or an image file, and performing preliminary format conversion and preprocessing; the image feature extraction module is used for converting the image into a form which can be understood by a machine based on a convolutional neural network; the online learning framework is used for dynamically adjusting model parameters according to data input in real time; the environment adaptability adjusting module is used for dynamically adjusting image processing parameters, changing a feature extraction weight, adjusting a learning rate and adjusting an identification strategy according to environment changes; the recognition and classification module is used for executing object recognition and classification tasks based on the extracted features and the learned model; the parameter mapping unit is used for mapping the environment parameters to specific parameter adjustment of the model; and the environment adaptability development unit is used for extracting useful information from the environment data and converting the useful information into an instruction of an adaptability adjustment strategy.