120 results about "Augmented learning" patented technology

The invention discloses a weak supervision video anomaly detection method based on prompt learning knowledge enhancement, and belongs to the technical field of video intelligent analysis. A video side gives a section of abnormal scene video, video sequence features and audio sequence features are obtained through a feature extraction network, then a trained and complete feature aggregation network is input to carry out multi-modal feature aggregation, an abnormal score is obtained through a score prediction network, and text representation is carried out based on prompt learning. A prompt template is constructed for abnormal video tags through a knowledge graph, semantic expansion is performed on normal tags through a plurality of learnable parameters, cross-modal alignment is performed on the normal tags and a video side, so that features of the video side are close to different normal semantics, knowledge enhancement is performed by introducing external information, positive abnormal boundaries of the video are learned, and the detection performance is improved. And finally, multi-task joint optimization is carried out through different loss functions, and abnormal video clip positioning is carried out.

The invention relates to the technical field of reinforcement learning intelligent scheduling, in particular to an intelligent scheduling management method for prefabricated part production resources based on reinforcement learning. The specific implementation process comprises the steps of collecting order demands, material distribution and production pedestals in real time, and mapping the order demands, the material distribution and the production pedestals into production mold load tensors; when a scheduling request is triggered, inputting the production modulo tensor into a resource arrangement network, and performing search and reasoning by using a scheduling strategy based on a multi-head attention mechanism to generate a resource scheduling matching instruction; calculating a state difference tensor, and outputting an efficiency reward signal in combination with a delivery constraint and a cost constraint; and packaging the production modulo tensor, the resource scheduling matching instruction and the reward signal into semantic interaction experience, storing the semantic interaction experience into a scheduling experience playback pool for gradient modulation, and iteratively optimizing a scheduling strategy. According to the method, learning can be carried out from a large amount of historical data by utilizing reinforcement learning, iterative optimization of the scheduling strategy is realized, the calculation time consumption of scheduling instruction generation is reduced, and the flexibility of production scheduling is improved.

The invention discloses a laboratory detection equipment intelligent management system based on edge calculation and reinforcement learning, and relates to the technical field of equipment intelligent management, the system comprises a multi-dimensional data acquisition module, a model training and feature library construction module, a deviation degree research and judgment module, a model optimization module and an interaction and execution module; according to the invention, through integration of the multi-dimensional data acquisition module and the model training and feature library construction module, all-directional precision monitoring of the operation state of laboratory detection equipment is realized, and various feature data in the operation process of the equipment can be captured in real time through the multi-dimensional sensor array; the model training and feature library construction module uses a deep learning model of a CNN-LSTM mixed structure, combines historical operation data and fault cases of equipment, calculates each feature weight, and dynamically constructs and updates a health state feature vector library of the equipment, thereby improving the accuracy and timeliness of equipment state evaluation, and improving the reliability of equipment state evaluation. Therefore, managers can find potential faults in advance and take preventive maintenance measures.

The invention relates to the technical field of electromagnetic compatibility and signal processing, and discloses a multi-dimensional feature fusion-based electromagnetic leakage signal classification and identification method, which comprises the following steps of: preprocessing an original electromagnetic signal to obtain standardized data, extracting time domain, frequency domain and space domain features to construct a nine-dimensional feature vector, and extracting a three-dimensional feature vector; constructing an initial feature library and completing the initial feature library through a self-supervision verification mechanism, training the initial feature library through a CNN-LSTM fusion model with an attention mechanism to obtain a classification model, classifying signals to be identified, starting a self-supervision reinforcement learning mechanism optimization model according to an F1 score, and dynamically optimizing the feature library each month; the method solves the defects of the traditional technology, improves the recognition accuracy and the system adaptability, and can be used for cable information safety leakage protection.

The invention relates to the field of quality detection of mechanical parts, in particular to an intelligent recognition system for surface defects of mechanical parts based on deep learning. Comprising a data acquisition module, a differential geometric feature extraction module, a small sample learning module, an automatic labeling module, a data reinforcement learning module, a neural network training module, a defect identification module and a defect quantitative evaluation module. Extracting geometric features including curvature features and differential invariant features; small sample learning is adopted to solve the problem of sample scarcity; the efficiency is improved by utilizing automatic labeling; simultaneously capturing microcosmic details and macroscopic morphology through a multi-scale differential analysis framework; organic fusion of surface image information and depth geometric information is realized; according to the method, the problems of insufficient samples, difficulty in labeling, inaccurate defect evaluation and the like in industrial production are effectively solved.

The invention relates to the technical field of e-commerce, and discloses a cross-border e-commerce abnormal order processing method and system based on reinforcement learning, and the method comprises the steps: collecting historical transaction records and real-time behavior records of a user, and obtaining an original data set; performing format unification according to the original data set to obtain a structured data set, and performing multi-dimensional feature extraction to obtain multi-dimensional behavior features; according to the multi-dimensional behavior characteristics, abnormal sequence analysis and risk level classification are carried out, and risk control adjustment parameters are matched; according to the risk control adjustment parameters and the structured data set, environment grouping, risk order identification and abnormal feature extraction are carried out to obtain abnormal distribution features; according to the abnormal distribution characteristics, comprehensive risk analysis is carried out through a pre-constructed neural network model, and a comprehensive risk score is obtained; and performing abnormal order judgment and risk control processing according to the comprehensive risk score and the historical transaction record, and optimizing a neural network model. The method improves the accuracy of abnormal order detection.

The application relates to the technical field of data processing, in particular to a kiln data monitoring and early warning method based on reinforcement learning, which comprises the following steps: acquiring intelligent gas meter readings at each collection time in each cycle during the operation of a rotary kiln and temperatures at each collection time at each monitoring point; calculating heat space diffusivity at each monitoring point at each collection time in each cycle, calculating heat time dissipation and heat propagation index at each monitoring point at each collection time in each cycle; constructing heat abnormality degree at each monitoring point; calculating energy consumption abnormality degree of the rotary kiln; calculating heat preservation degree at each collection time in each cycle; constructing comprehensive abnormality degree of the rotary kiln; constructing a prediction autoregressive term number according to the comprehensive abnormality degree of the rotary kiln, acquiring a predicted energy consumption of the rotary kiln, and monitoring abnormal conditions of rotary kiln operation energy consumption in combination with a preset energy consumption. The application aims to improve the reliability of rotary kiln operation energy consumption abnormality monitoring.

The invention provides an aluminum substrate drilling tool wear prediction method based on reinforcement learning, and the method comprises the steps: collecting working condition parameters, such as main shaft rotation speed, feeding speed, cutting depth, material type and historical vibration energy, through the deployment of an industrial sensor network, and achieving data standardization through feature extraction and normalization processing; a dynamic cognitive map with process meaning is constructed by adopting semantic analysis and a map generation strategy, key semantic nodes are identified by utilizing a map attention network, reinforcement learning is driven through a structured reward function, and self-adaptive optimization of a wear prediction strategy is realized; according to the method, abnormal detection and working condition abrupt change increment updating are supported, map consistency optimization is achieved by fusing expert experience, the tool wear prediction precision and the environment adaptability can be improved, and the intelligent sensing and dynamic response capacity to the complex manufacturing process can be enhanced.

The invention discloses a multi-satellite in-orbit collaborative scheduling method based on deep reinforcement learning and heuristic rule fusion, and relates to the field of multi-satellite in-orbit intelligent scheduling. The method comprises the following steps: establishing a collaborative decision model based on a multi-agent depth deterministic strategy gradient algorithm; a prior experience playback mechanism is introduced to enhance the learning efficiency; designing a heuristic rule for guiding agent decision making; self-adaptive fusion of a reinforcement learning strategy and a heuristic rule is realized through a dynamic mixing coefficient; the agent learning convergence is accelerated by adopting a reward shaping technology; and dynamically adjusting a scheduling scheme according to satellite resource constraints and task priorities. According to the method, intelligent collaborative scheduling of multiple satellite tasks can be realized under complex constraint conditions, the task completion rate and the resource utilization efficiency are improved, and the method has important significance in improving the autonomous decision-making capability of a satellite system and the robustness of dealing with emergencies.

The invention discloses a colorectal early-stage tumor data analysis early-warning method based on reinforcement learning, and the method comprises the steps: achieving the semantic alignment and weighted fusion through the unified expression of different modal features, and employing a differentiable attention unit; dynamic strategy optimization is carried out in combination with a reinforcement learning model, and the generalization ability and early warning accuracy of heterogeneous data are improved; a dynamic weight adjustment and online learning mechanism is introduced, continuous self-adaptive updating of the model and compensation of a data missing scene are realized, the accuracy and stability of colorectal tumor risk early warning are improved, and the method has high clinical application and popularization value.

The invention provides an online course MOOC learning prediction method based on heterogeneous feature fusion, and belongs to the field of computer-aided intelligent education. The method comprises the following steps: acquiring an MOOC data set, and preprocessing the data set to obtain a test set; constructing an IHFNet network comprising a multi-agent adaptive static feature selector module, a hierarchical time sequence feature extractor module and a heterogeneous feature fusion module; a multi-agent adaptive static feature selector module screens key static features; the hierarchical time sequence feature extractor module extracts behavior time sequence features with high discrimination ability; the heterogeneous feature fusion module carries out adaptive fusion on the key static features and the behavior time sequence features and carries out classification prediction; an IHFNet network is trained; and collecting MOOC data of a to-be-predicted learner, and inputting the MOOC data to the trained IHFNet network for learning risk prediction. According to the invention, modeling is carried out by fusing the static features of the learner and the behavior time sequence features, the feature representation ability of the learner is enhanced, and the learning risk prediction effect is improved.

The invention discloses an intelligent food storage tank internal environment adaptive control system and control method, and belongs to the technical field of artificial intelligence and Internet of Things control. The system specifically comprises a multi-mode sensing module, a feature extraction and preprocessing module, a reinforcement learning module, an instruction adaptive control module and a digital simulation module. The multi-modal sensing module deploys a sensor and an environment adjusting device in an array mode; the feature extraction and preprocessing module carries out filtering, standardization and feature extraction on the data; the reinforcement learning module generates an optimization control strategy by using an algorithm; the instruction self-adaptive control module converts the strategy into an instruction and accurately adjusts parameters of the environment adjusting device; the digital simulation module constructs a digital model for simulation learning and evolution of a strategy agent. Compared with a traditional monitoring system, the method has the technical advantage of generating an optimization strategy, solves the problem of insufficient adaptive control capability caused by a fixed strategy of the traditional monitoring system, and provides a more efficient monitoring service.

The invention discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, and relates to the technical field of reinforcement learning, and the method comprises the steps: inputting an initial data packet into a dynamic interaction model, carrying out explicit association mining through a semantic enhancement layer, optimizing a rule matching path through a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topological structure derivation on the dynamic commodity knowledge graph to generate a graph topological analysis report and a metadata list, and performing knowledge reasoning integration on the graph topological analysis report and the metadata list to generate an intelligent navigation engine; calling an intelligent navigation engine to execute multi-path semantic query and rule verification on the dynamic knowledge graph to generate a candidate classification scheme set; and performing multi-target collaborative optimization on the candidate classification scheme set to generate a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. According to the invention, through the dynamic interaction model and multi-target collaborative optimization, the rule adaptation efficiency in a complex scene is improved.

The invention relates to the technical field of hydroelectric generating set intelligent optimization control, in particular to a hydroelectric generating set optimization system and method based on the reinforcement learning technology, and the system comprises a multi-source sensing fusion unit, a reinforcement learning decision unit and an instruction execution unit. A unified time sequence feature tensor is generated through dynamic time warping time sequence alignment, Kalman filtering noise reduction, gradient normalization and feature cascade fusion, a reinforcement learning decision unit extracts three types of features by using a multi-scale time convolution network, and the coupling strength is quantified through a multi-head attention mechanism. The double branches respectively generate a start-stop sequence carrying start-stop loss punishment and a load distribution proportion of a coupling flow power function, a triple optimization mechanism and staged course learning are combined, an optimal matching scheme is output, an instruction execution unit converts the scheme into a control instruction, and the control instruction is output to a unit PLC control system after verification and compliance.

The invention discloses a method for generating a controlled file template based on deep learning, and relates to the technical field of intelligent document processing, and the method comprises the steps: collecting document data, obtaining a multi-modal data set through preprocessing, and generating a domain knowledge graph; based on an optimization learning strategy, optimizing the template generation and content evaluation task through a reinforcement learning algorithm to obtain a file template; performing format specification evaluation and content rationality evaluation on the file template by adopting a self-supervised learning method, and adjusting a template layout structure and content terms and details to generate an optimized file template; according to the real-time feedback data, feedback learning and template adjustment are conducted on the optimized file template through an incremental learning method, and a controlled file template is generated. The template quality is improved, manual intervention is reduced, and a basis is provided for continuous optimization.

The invention discloses a fire-fighting emergency evacuation guiding system driven by reinforcement learning, relates to the technical field of emergency evacuation guiding, and provides the following scheme that the fire-fighting emergency evacuation guiding system comprises the steps of gridding a monitoring area and calculating a comprehensive danger value of each grid by collecting temperature, smoke concentration, personnel density and a standard path in a building in real time; after the high-risk grids are removed, the remaining grid risk values are input into a reinforcement learning decision model to generate candidate paths; a safety score is obtained by calculating the matching degree of a path and a standard path, and the score is dynamically adjusted in combination with the danger value of each grid and the personnel density; meanwhile, obtaining an efficiency score based on a path length ratio and performing corresponding optimization; and finally, determining an optimal evacuation path from the candidate paths by adopting a multi-objective optimization algorithm, and driving an intelligent indicating device to guide evacuation. According to the invention, the limitation of a fixed evacuation scheme in a dynamic fire scene is solved, intelligent emergency evacuation command is realized, and the safety and efficiency of the evacuation process are improved.

The present invention relates to the field of radio navigation system state evaluation. Provided is a radio navigation system state evaluation method based on multi-dimensional data-augmented learning. The method comprises: acquiring function operation monitoring data of a radio navigation system, and selecting data associated with system health; performing sliding window processing on the selected data, so as to obtain sample data including time information; dividing the sample data into a training set, a first test set and a second test set, and constructing positive and negative samples; establishing and training a contrastive learning model; establishing and training a deep one-class support vector machine; and using output results of the contrastive learning model and deep one-class support vector machine for the test sets to complete evaluation. In the present invention, deep features of measured data are learned by means of contrastive learning, and the deep one-class support vector machine is used to construct a state evaluation model. The present invention has the advantages of high detection precision and interpretability, has important theoretical and engineering practice significance, and realizes precise quantitative evaluation of the health state of an airborne radio navigation system.

The invention relates to the technical field of artificial intelligence and reinforcement learning, and discloses a cross-border trade intelligent decision optimization method based on reinforcement learning. The method comprises the following steps: constructing a composite state representation fusing tax, logistics, exchange rate, demand and competitive behavior; high-dimensional state compression is realized through an auto-encoder; designing a hierarchical action architecture to decouple a macroscopic strategy and a microscopic operation; a Nash equilibrium guided reward shaping function is introduced, and an equilibrium income deviation is estimated in combination with anti-factual reasoning; carrying out stable training by adopting a double-delay depth deterministic strategy gradient algorithm with state transition consistency constraint; an online fine tuning mechanism is deployed to adapt to a real business environment. According to the method, the strategy convergence speed, the annual profit rate and the responsiveness to policy mutation are remarkably improved.

The invention belongs to the technical field of clinical medicine learning, and particularly relates to a clinical medicine learning path recommendation system and method. According to the method, personal habits and requirements are precisely fit through multi-dimensional feature extraction, portrait modeling and path recommendation, the learning efficiency and enthusiasm are remarkably improved, quantitative indexes are adopted for the knowledge graph, the difficulty coefficient, the growth weight and the ability gap, a clear basis is provided for learning paths and reconstruction decisions, understanding and supervision of students and tutors are facilitated, and the learning efficiency and enthusiasm are improved. A real-time monitoring and multi-round path reconstruction mechanism can respond to learning progress and problems in time, a closed loop is formed from preview, learning, evaluation, reconstruction, reevaluation and guarantee that each learning node can be adjusted and fed back in time, deep mastering of knowledge and skills is finally realized, and a capability short board report is presented in a chart mode, so that students can clearly know own weak links at a glance, and the learning efficiency is improved. And targeted suggestions are supplemented, so that the pertinence and operability of learning decisions are enhanced.

This application provides a multi-agent collaborative intelligent orchestration method and apparatus based on incremental reinforcement learning, relating to the field of reinforcement learning. The method includes: constructing a task graph model based on initial state information and outputting task hierarchical dependencies; constructing a hierarchical incremental reinforcement learning model based on the task hierarchical dependencies and outputting a first agent policy and a first global policy; constructing a local reward mechanism based on the local task completion progress of each unmanned agent and incrementally updating the first agent policy to output a second agent policy; aggregating the update information of the second agent policy and modifying the first global policy to a second global policy for collaborative intelligent orchestration. This application solves the problem that issuing fixed sets of instructions to each unmanned agent in a cluster based on a pre-set task template leads to an imbalance in task load among agents, resulting in decreased collaboration efficiency and a significant reduction in unmanned search and rescue efficiency.

The invention provides a self-adaptive energy-saving control method for a cooling tower group based on reinforcement learning, and the method comprises the steps: collecting the multi-dimensional operation parameters of a cooling tower unit in real time in a distributed manner, eliminating data noise and loss through a normalization and abnormal interpolation algorithm, and extracting the three features of energy efficiency, dynamic stability and safety constraint, thereby achieving the self-adaptive energy-saving control of the cooling tower group. Realizing multi-target weight distribution and dynamic fusion by using subspace embedding and an attention mechanism; the control strategy combines a current system state and a historical decision track, outputs fan frequency, water pump control and alternate actions based on a reinforcement learning network, dynamically updates weight distribution to adapt to working condition changes, and triggers an amplitude limiting mechanism to guarantee safety when a safety boundary risk is detected. According to the method, the energy efficiency, stability and operation safety of the cooling tower group are effectively improved, and high adaptability and robustness are achieved.

The application discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, relates to the technical field of reinforcement learning, and comprises the following steps: inputting initial data packets into a dynamic interaction model, performing explicit association mining in a semantic enhancement layer, optimizing rule matching paths in a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topology structure derivation on the dynamic commodity knowledge graph, generating a graph topology analysis report and a metadata list, integrating knowledge reasoning on the graph topology analysis report and the metadata list, and generating an intelligent navigation engine; calling the intelligent navigation engine to perform multi-path semantic query and rule verification on the dynamic knowledge graph, generating a candidate classification scheme set; performing multi-objective collaborative optimization on the candidate classification scheme set, generating a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. The application improves the rule adaptation efficiency in complex scenarios through the dynamic interaction model and multi-objective collaborative optimization.

The invention provides a wind power deviation correction method, and relates to the field of wind power deviation correction, and the method comprises the following steps: obtaining five-element microclimate operation data of a wind power plant, and constructing a five-element feature vector; a microclimate state vector is generated through a microclimate state encoder, and the fusion weight of the LSTM and the Self-Attention is dynamically calculated; respectively processing short-term, medium-term and long-term time sequence dependency relationships, and generating a global dependency feature vector; embedding a wind energy conversion physical law into a loss function, and constructing a physical constraint reinforcement learning framework; dynamically adjusting the weight of each microclimate factor to generate a weighted feature vector; and generating a final wind power deviation correction result through the decoder network. According to the method, through microclimate adaptive model fusion, multi-scale time sequence modeling, physical constraint enhancement and dynamic feature optimization, the accuracy of wind power deviation correction and the adaptability to different meteorological conditions are improved.

The invention discloses an intelligent fire-fighting big data analysis method based on reinforcement learning, and the method comprises the following steps: S1, collecting multi-source fire-fighting monitoring data of an intelligent fire-fighting system, and generating a fire-fighting time sequence data set; s2, marking time sequence breakpoints, identifying key breakpoints, and generating a breakpoint marking data set; s3, constructing an improved PatchTST model, introducing a breakpoint reconstruction layer, and generating an anti-fact trajectory candidate set; s4, constructing a reinforcement learning state vector, setting a reinforcement learning action space, and constructing a reinforcement learning environment; s5, constructing a reinforcement learning reward function, executing strategy updating, and generating a fire-fighting decision result; and S6, executing stability analysis and retraining operation, and forming an updated fire-fighting decision result through incremental strategy updating operation. According to the invention, the analysis capability and disposal efficiency of the intelligent fire-fighting system on the complex fire behavior evolution process are improved.

This invention provides an information processing device, an information processing method, and an information processing program that can enhance motivation during learning. [Solution] This information processing device comprises: a first progress unit that determines a first progress level of the game based on the number of correct answers to questions given to a first user; a second progress unit that determines a second progress level of the game based on the number of correct answers to questions given to a second user different from the first user; and a calculation unit that calculates the first user's contribution to the game's progress based on the first and second progress levels, and outputs the calculated contribution.

The present application relates to the technical field of data transmission, and is a multi-path underwater acoustic signal transmission time calculation method based on reinforcement learning, specifically comprising: collecting a wideband M sequence burst signal, and performing discrete transformation and signal pre-enhancement processing on the collected multi-path carrier original data, and performing multi-dimensional feature extraction on waveform regularity features, energy clustering nonlinear mutation features and sound field geometric evolution features; calculating the propagation clock drift rate of the current signal transmission path, cross-medium correcting the propagation clock drift rate based on an environmental feedback model to obtain the arrival deviation of the actual underwater acoustic signal; based on the arrival deviation, dynamically updating the receiving decision window length of the data transmission link, and feeding back to the communication carrier demodulation engine in real time for time step synchronization. The present application solves the problem of poor reliability of sound signal analysis under high dynamics and extreme conditions in the prior art when the hydrological conditions change dramatically for a long time.

The invention discloses a desktop self-balancing education programming robot based on AI interaction. The system comprises the following modules: A, a visual perception module; b, a voice module; d, data transmission; e, an attitude sensing unit; f, a balance control unit; and G, a motion execution unit. The method has the advantages that natural language instructions, visual programming card recognition and automatic logic generation are fused, human-like communication type programming interaction is achieved, the learning threshold is remarkably lowered, and the method is suitable for being used by all-age users, especially low-age users; through real-time visual identification of a specific programming card and dynamic combination with a voice instruction / programming task, a physical-digital fusion teaching mode which can be flexibly configured and is highly situational is created, and immersion and interactivity of learning are greatly enhanced.

The invention discloses a kernel code review method and device fusing reinforcement learning and prompt word tuning, and relates to the technical field of code review, and the method comprises the steps: extracting patch meta-information, building a code review task, extracting a code difference item and a necessary context thereof to form a to-be-reviewed target code segment, and carrying out the review of the to-be-reviewed target code segment; and forming a primary review prompt template, calling the code review large language model to perform primary code review, summarizing primary review results, forming a filled expert-level review prompt template in combination with the patch meta-information and the kernel knowledge base, and performing expert-level code review to generate a comprehensive review result. A code review process is constructed by applying a Boosting thought in machine learning, the code review efficiency is improved, it is ensured that single input content is located in a context window of a large language model, the integrity of code semantic understanding of the model is guaranteed, a preorder model is corrected by using a postorder model, and the code review efficiency is improved. And the discovery capability of complex logic defects and cross-file problems is effectively improved.

The invention provides a coal mine drill arm bit pressure control strategy optimization method and system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the method comprises the steps: obtaining a real-time multi-source original signal in the operation process of a coal mine drill arm; performing noise reduction processing to obtain a real-time state vector; inputting a pre-trained reinforcement learning control strategy network, and outputting a real-time bit pressure action instruction; in the coal mine drilling process, the returned real-time vibration spectrum characteristics and real-time drilling camera pictures are received, and the multi-source original signals are updated; coal seam lithology feature analysis is carried out, and real-time lithology features are output; working condition adaptability judgment is conducted on the real-time bit pressure action instruction, and a working condition adaptability quantized value is output; and a safe switching decision of the real-time bit pressure action instruction is made. The technical problems that in the prior art, coal mine drill arm bit pressure control generally adopts a fixed strategy, dynamic adaptation cannot be conducted according to the actual working environment, bit pressure control cannot be effectively optimized, and the drilling efficiency and safety are reduced are solved.

The invention discloses a laboratory personnel safety early warning method based on migration reinforcement learning. The method specifically comprises the following steps: S1, preprocessing an original data set based on a data enhancement module; s2, learning an original task domain based on a target detection network module; s3, in the face of a new task, on the basis of the transfer learning module, performing fine adjustment on the original task domain model on the basis of the original network weight; and S4, sequentially detecting the human body parts and the protective equipment based on the monitoring and early warning module, and controlling a buzzer to sound when an early warning condition is met to remind an experimenter to do safety protection. According to the method, migration of the network from the original task domain to the new task domain is realized in a data enhancement and migration learning mode, and the general generalization and the detection precision of the model are improved.