451 results about "Network identification" patented technology

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

The invention provides an intelligent detection system for forging defects of a forge piece product, and relates to the technical field of industrial intelligent detection.The intelligent detection system comprises the steps that multi-angle images of a to-be-detected workpiece are collected and then spliced, and a complete surface expansion view of the product is generated; obtaining defect types of the defect candidate regions through region coordinates and region sizes of the defect candidates; obtaining a confidence value of a corresponding defect type judgment result; a lightweight deep network recognition model is called for secondary judgment, new sample data are generated in a manner of supporting manual annotation, and an equipment end is connected with a programmable controller for intelligent defect detection of the workpiece to be detected. According to the invention, the problems of incomplete defect coverage, failure to realize high-precision automatic identification of multiple types of defects and influence on the detection accuracy and the production efficiency caused by diversified and complex forging surface defects and limited image acquisition angles in the prior art can be solved, comprehensive acquisition and splicing of multi-angle images are realized, and the detection accuracy and the production efficiency are improved. The technical effect of improving the defect detection accuracy of the forge piece product is achieved.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention provides a thermal runaway prevention and heat dissipation optimization method and system based on a capacitor module, and relates to the technical field of capacitor module thermal management, and the method comprises the steps: extracting the multi-source operation data features of a single capacitor through an improved layered visual attention network, carrying out the data complementation through combining a bidirectional probability diffusion model, and obtaining a multi-source operation data feature of the single capacitor; and discovering a causal relationship between network identification data by using a causal structure, generating a performance analysis report, constructing a spatio-temporal dynamic graph neural network to predict temperature field distribution, performing fault diagnosis and uncertainty quantification in combination with a multi-task learning framework, further constructing a layered early warning mechanism, and performing early warning based on a layered reinforcement learning framework. And a dynamic prediction map and a performance analysis report are combined, a heat dissipation strategy is optimized, cooperative control of the heat dissipation units is realized, an optimal heat dissipation scheme is finally obtained, thermal runaway is effectively prevented, and the safety and reliability of the capacitor module are improved.

The invention relates to the technical field of e-commerce supervision and prediction, in particular to a cross-border e-commerce retail risk supervision method and system. Comprising the following steps: acquiring cross-border e-commerce multi-source e-commerce data in real time by using the Internet of Things and an API interface; identifying heterogeneous risk factors by using a deep belief migration network; constructing a risk evolution model and generating a dynamic graph based on the spatial state transition type Markov convolutional network; adopting an abnormal subspace clustering algorithm to carry out self-adaptive clustering and real-time grading early warning on the risk modes; and finally, combining a reinforcement learning algorithm, an intelligent decision and a dynamic optimization risk intervention strategy with resource allocation, and outputting a whole-process risk management and control scheme. The intelligent, automatic and precise levels of cross-border e-commerce retail risk monitoring, early warning and response are improved, risk disposal lag and resource waste are effectively reduced, and the industry supervision safety guarantee capability is enhanced.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention discloses a network security malicious traffic tracing method based on a generative adversarial network, which comprises the following steps: collecting network traffic data, selecting a target traffic sample, extracting a protocol behavior, a communication structure and a time evolution characteristic of the target traffic sample, and carrying out semantic coding; mapping the multiple types of features to a unified embedding space through a multi-view fusion mode; constructing a manifold set based on known malicious samples, and generating an intermediate state sample sequence; constructing an attack context and path disturbance information in combination with the target sample, and generating a guide vector; inputting the guide vector and the random variable into a generator, and outputting a potential malicious sample representation; identifying the authenticity of the sample and evaluating the path similarity by using a discrimination network; and finally, constructing an attack path graph according to the similarity and the spatial relationship, and calculating a shortest path sequence to a known malicious sample to realize traceability identification of potential malicious behaviors. According to the invention, tracking analysis of malicious traffic sources is realized by using the generative adversarial network.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments disclose methods for managing a condition in a UE. The method includes initiating a timer for disaster wait range; in case that the UE is switched off while the timer is running, and that the UE is switched on after the UE is switched off, wherein a universal subscriber identity module (USIM) in the UE remains as same, and the UE selects a public land mobile network (PLMN) for a disaster roaming: identifying a first time remaining for the timer for timeout at switch off, identifying a second time related to a time between switching off and switching on, and determining whether to restart the timer based on the first time and the second time.

The invention discloses a bearing defect intelligent detection method and system based on deep learning, and relates to the field of bearing defect detection. Multi-modal data such as bearing vibration, acoustics, thermal imaging and the like are acquired by using various sensors, and a four-dimensional feature tensor is constructed through preprocessing such as noise reduction and feature extraction; features are fused through a reconfigurable multi-branch convolutional neural network, and defects are identified and a development trend is predicted in combination with a meta-learning twin network; a decision threshold is optimized by adopting a quantum heuristic algorithm, and multi-level early warning is realized; and continuous evolution of the model is completed through edge-cloud collaboration and federated learning, the functions of data calibration compensation, model dynamic optimization and the like are achieved, and efficient and accurate detection of bearing defects is achieved. The detection time is remarkably shortened, and the positioning precision is high; the novel defect response speed is high, and faults can be predicted in advance; system energy consumption is reduced, model updating is improved, stable operation of equipment is effectively guaranteed, and cost reduction and efficiency improvement of industrial intelligent operation and maintenance are facilitated.

The invention belongs to the crossing field of computer technology and business management, and discloses a purchase return risk early warning and management method and system based on multi-mode intelligent auditing, electronic equipment and a computer readable storage medium. The method comprises the following steps: segmenting a PDF invoice image through an improved Apache PDFBox analyzer; a CLIP model and an OCR technology are combined, a cross-modal attention layer is used for correcting an OCR text, a multi-modal consistency difference value is generated, and invoice authenticity is judged; when a false invoice is detected, generating a standardized risk event, and extracting supplier, purchaser and transaction edge data from the graph database to construct a time sequence sub-graph; and identifying an abnormal mode based on the graph neural network, verifying the risk by using a time sequence risk scoring model, and executing a treatment strategy. According to the method, through multi-modal fusion, dynamic graph analysis and time sequence modeling, the problems of low false invoice detection precision, long abnormal fund tracing time consumption, risk prediction lag and the like in traditional auditing are solved.

The invention provides a service session control method for a multi-dimensional identification system, which comprises the following steps of: dynamically generating unique multi-dimensional identification information including an equipment identification EID, a service identification SID and a network identification NID when a user terminal accesses for the first time or accesses a network again; encrypting the multi-dimensional identification information by adopting a Hash algorithm, performing digital signature on the encrypted multi-dimensional identification information, sending the encrypted multi-dimensional identification information to a multi-dimensional identification server to complete registration, and performing associative storage on the multi-dimensional identification information and the equipment capability information by the multi-dimensional identification server; establishing an end-to-end communication link; the dynamic management mechanism continuously maintains the session state, and tracks and updates the multi-dimensional identification information of the user terminal in real time; when the equipment signal intensity is lower than the safety threshold or the electric quantity is lower than the threshold, the user can complete equipment migration without perception; terminating the session and releasing resources; according to the method, the safety, the efficiency, the consistency, the flexibility and the expansibility are remarkably improved, and the complex and changeable communication requirements can be better met.

The invention discloses a GIS combined electric appliance operation on-line detection method, and relates to the technical field, and the method comprises the following steps: obtaining a partial discharge original time domain signal, carrying out the signal enhancement processing of the original time domain signal, extracting a weak discharge pulse signal, and generating an enhanced discharge pulse signal; constructing a holographic time-frequency matrix atlas according to the enhanced discharge pulse signals; the holographic time-frequency matrix atlas is used as an input item, and a weak discharge type is identified based on a weak discharge neural network identification model; based on the identification result and the sensor array data, acquiring space coordinates of a discharge source, and outputting a discharge type and a positioning result to the monitoring terminal; according to the method, the holographic time-frequency multi-scale analysis algorithm and the weak discharge neural network recognition mechanism are fused, so that the recognition capability, the anti-noise robustness and the positioning precision of the GIS combined electric appliance online detection system on weak discharge are remarkably improved.

An unmanned aerial vehicle (UAV) identification method based on blind source separation and deep learning is disclosed, in the method, firstly, the one-dimensional radar cross section millimeter wave data set of the UAV is acquired, and the mixed signal is obtained by mixing, and the improved FastICA algorithm is used to separate it. Secondly, the separated signal is converted into a two-dimensional image by data transformation, and the two-dimensional image is augmented, the obtained data set is divided into training set, validation set and test set. Thirdly, establish a UAV classification model based on Improved ResNet18 and train this model on the training set to achieve UAV classification. In the present invention, the training time of the network is not greatly increased while the network identification accuracy is improved, so that it can well complete the UAV type identification, and the design is more reasonable and effective.

The invention discloses a commercial password application security assessment evidence identification method based on image identification, relates to the technical field of image identification, and aims to assess employee advanced engineer certificates, extract information from multi-modal documents by using a Qwen-VL visual language model, generate vectors, label the vectors, convert the vectors into structured evidence nodes and store the structured evidence nodes in a graph database. Establishing various edges according to different associations, and constructing a preliminary evidence network; identifying certificate nodes, and clustering to form a certificate entity group; a correlation score is calculated by searching the support evidence through forward traceability, a correlation score is calculated by searching the contradiction evidence through reverse traceability, the credibility of the certificate entity group is dynamically adjusted, and the influence is propagated according to the updated credibility; and screening abnormal certificates lower than a threshold value, analyzing causes and contradictory points, listing abnormal information, visually displaying key evidences, the contradictory points and associated paths, and generating text description.

The invention discloses a smelting process monitoring system of a medium-frequency electric furnace and a process guidance method of the smelting process monitoring system. The system comprises an edge acquisition terminal and a cloud platform, the edge acquisition terminal comprises an intermediate frequency voltage transmitter used for acquiring a voltage signal at the output side of the intermediate frequency electric control cabinet and converting the voltage signal into a first analog signal; the intermediate-frequency current transmitter is used for collecting a current signal at the output side of the intermediate-frequency electric control cabinet and converting the current signal into a second analog signal; the analog quantity conversion module is used for converting the first analog signal and the second analog signal into digital signals; the intelligent thermodetector is used for measuring the temperature of the molten metal in real time and sending temperature data; the cloud platform is used for receiving and processing the digital signal and the temperature data and storing high-frequency sampling data; identifying a smelting stage through an LSTM network; power control is optimized on the basis of a Transform model and a GAN model; and integrating an expert knowledge base and providing process guidance. According to the system, comprehensive digitization and real-time accurate sensing of the smelting process are achieved; and the timeliness and scientificity of decision making are improved.

The invention relates to the field of artificial intelligence, and discloses a brain-like computing-driven visual information instant analysis method and system, which comprises the following steps of: firstly obtaining real-time perception data and a neural pulse signal, analyzing a signal perception curve to construct a brain-like computing network, then identifying visual information characteristics through the network, analyzing a preset model computing architecture, and obtaining a brain-like computing network; optimizing a neuron mapping relation to obtain an optimized mapping network, analyzing a path based on the analysis information, determining a time sequence distribution state of target data, calculating an analysis efficiency value, determining an analysis optimization direction, generating an instant analysis strategy, calculating an analysis attenuation rate, determining an analysis state according to the attenuation rate, analyzing an analysis dimension, and finally constructing an optimization analysis scheme. According to the invention, the intelligent analysis efficiency of the visual information can be improved.

The invention belongs to the technical field of network security, and particularly relates to a threat intelligence automatic extension method based on network surveying and mapping, which comprises the steps of automatically collecting and preprocessing multi-source threat intelligence by a large model, extracting key network identification information, generating standardized network asset characteristics, and obtaining a multi-dimensional asset characteristic matrix based on a network space surveying and mapping technology. Constructing a network asset fingerprint database, carrying out correlation verification, generating a set of verifiable network asset fingerprints, carrying out feature vectorization on the verifiable network asset fingerprints, carrying out homologous asset analysis through a weighted clustering algorithm, and outputting a threat entity topological graph comprising an IP address cluster, a domain name correlation group and a C2 infrastructure. According to the method, potential threats which are difficult to cover by traditional manual analysis can be automatically identified, an integrated multi-source threat intelligence platform and a network surveying and mapping tool are supported, diversified intelligence formats and feature requirements can be processed, the automation degree is high, threat paths can be rapidly expanded, and response time can be shortened.

The invention discloses a test case detection method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: analyzing a user operation behavior based on a behavior log and an interface call log, and obtaining a block behavior relationship network according to an association relationship among functional blocks and interfaces; identifying a code updating position of the current test case version, analyzing an influence range of the code updating position based on the block behavior relation network, and determining a target interface set contained in the influence range; selecting test cases corresponding to the target interface set from the current test case version and performing expansion processing to obtain a plurality of target test cases, and performing importance level evaluation on the plurality of target test cases from a plurality of dimensions to obtain importance levels of the plurality of target test cases; and determining a detection sequence of the plurality of target test cases according to the importance levels from high to low, and performing fault detection on the plurality of target test cases in sequence based on the detection sequence. The detection efficiency can be improved.

The invention provides an aluminum melting furnace door flue gas automatic control dust removal air volume system, and relates to the field of aluminum melting furnace flue gas purification treatment, and the system comprises a data acquisition and processing unit which is used for acquiring flue gas state data and furnace door state images at an aluminum melting furnace door, and identifying the current flue gas state grade and furnace door opening and closing degree of the aluminum melting furnace; determining a target concentration control threshold value; the air volume distribution unit is used for generating the total air volume adjustment amount by using an increment inversion and gain scheduling algorithm based on the flue gas state grade and the target concentration control threshold value, and performing air volume distribution processing on the multiple execution mechanisms by using a redistribution scaling pseudo-inverse method; and the air volume control unit is used for generating control instructions of the multiple execution mechanisms based on the air volume distribution result, sequentially transmitting the control instructions to all the execution mechanisms and executing air volume control. According to the invention, through combination of deep learning network identification and dynamic adjustment, real-time matching of the dedusting air volume and the flue gas volume is realized, and the operation cost of an enterprise is remarkably reduced.

The invention relates to the technical field of evolution modeling, in particular to a seabed erosion and deposition evolution analysis method and system.The method comprises the steps that the multi-dimensional adjacency relation between nodes is recognized through a graph attention network, joint judgment is conducted on the gradient direction included angle, the elevation trend and the flow direction consistency index, and the influence degree is quantized; the expression capability of complex boundary direction influence factors is improved, direction judgment is not performed according to a fixed rule, and based on quantitative comparison of flow direction and gradient components, scouring dominant path nodes are screened and direction tracks of the scouring dominant path nodes are rearranged, so that sequential sequence expression has dynamic reconstruction capability; performing clustering screening on the angle abrupt change section by means of a greedy algorithm to form a node set representing an abrupt change trend, so as to enhance the extraction stability of the local extreme scouring behavior; and the connection logic expression of the physical state between the nodes, the abnormal extraction efficiency of local evolution change, the expression continuity of the erosion and deposition trend result and the overall identification stability are effectively improved.

The invention discloses a protection pressing plate on-off state detection method for identifying a pressing plate name based on an OCR model, and the method comprises the steps: obtaining a protection pressing plate image, constructing a sample set, and dividing the sample set into a training sample set and a test sample set; performing image processing on the obtained sample set, generating a target candidate box, calculating a candidate box score value S1, a color feature score S2 and a spatial feature score S3, and determining a commissioning and quitting score S; according to the obtained on-off score S, adopting an XGBoost model to obtain a threshold value Sth of the on-off score, comparing S with Sth, and judging according to a comparison result to obtain the on-off state of the pressing plate; identifying the name of the protection pressing plate by using an OCR neural network; and taking the obtained on-off state of the protection pressing plate and the obtained name of the protection pressing plate as a final detection result of the on-off state of the protection pressing plate. According to the invention, intelligent detection of the state of the protection pressing plate is realized, the pressing plate detection efficiency is improved, stable operation of a power system is guaranteed, and the problems of low efficiency and error proneness of manual checking of switching of the relay protection pressing plate are solved.

The invention discloses a multi-scale azimuth perception feature enhancement network SAR target identification method and system, and belongs to the technical field of image processing. The method comprises the following steps: obtaining a feature map of an SAR image; generating a channel parameter and a dynamic convolution kernel according to a target azimuth angle of the SAR image, and performing channel and spatial modulation on the feature map by adopting the channel parameter and the dynamic convolution kernel to obtain an enhanced feature map; fusing the enhanced feature maps of different scales to obtain a final feature map; in the training process, a difference azimuth angle feature consistency loss function, a dynamic angle interval loss function and a cross entropy loss function are adopted to calculate an overall loss function of the network, and iterative training is carried out to obtain a trained multi-scale azimuth angle perception feature enhancement network; identifying a target in the SAR image according to the trained multi-scale azimuth perception feature enhancement network; the method can adapt to feature changes caused by changes of azimuth angles of different targets in radar image recognition, and the robustness of SAR target recognition in a complex environment is improved.

The invention discloses an intelligent data quality repairing method based on dynamic rule evolution, which belongs to the technical field of data quality management, and comprises the following steps: constructing a knowledge graph based on physical storage structure information and service logic of structured data; performing deep learning on the knowledge graph by using a graph neural network, and dynamically generating a global topology view based on a learning result; in combination with a historical damage mode and a global topology view, an optimal structured data scanning path is generated by utilizing reinforcement learning, and association anomalies of abnormal partitions in an optimal path scanning result are identified based on a graph neural network; the method comprises the following steps: constructing a multi-modal association sub-graph based on association anomaly, repairing structural defects in the multi-modal association sub-graph through a correct physical structure reversely deduced by a graph neural network, and carrying out credibility scoring on a repairing result to form a structured data management closed loop. The method can adapt to continuously evolved data modes and novel anomalies, continuously improves the robustness and autonomy of the system to deal with complex data problems, and reduces the long-term operation and maintenance cost.

The invention discloses a chromatographic data optimization processing method for a liquid chromatograph, and relates to the technical field, and the method comprises the following steps: collecting an original signal flow, carrying out preliminary peak detection, and generating an original peak list; extracting modal exclusive characteristics of each chromatographic peak; converting the modal exclusive features into a unified multi-dimensional semantic feature vector; calculating a time interval weight, and calculating a semantic relevancy weight based on the multi-dimensional semantic feature vector; correcting the time interval weight, generating a network edge weight, and constructing a peak correlation topology network by taking the multi-dimensional semantic feature vector as a node; identifying node clusters connected with high edge weights, and aggregating the node clusters into candidate compound entities; tracking a node signal intensity change track, and adding a conflict mark; conflict resolution arbitration is carried out on the candidate compound entities with the added conflict marks, dominant detector evidence is output, and an analysis report is generated. According to the method, the problems of poor data collaboration of multiple detectors, inaccurate peak identification and association and isolated data processing flow are effectively solved.

The invention provides a sheep abnormal behavior real-time monitoring method and system based on the Internet of Things, and relates to the technical field of abnormal monitoring, and the method comprises the steps: obtaining multi-source data information of sheep, including a position coordinate sequence, motion sensor data, a gait rhythm sequence, a colony house environment parameter sequence and a group topological relation; the influence of environmental constraints on group movement is quantified by constructing a group behavior field intensity model; based on an individual physiological energy consumption backstepping mechanism, an energy metabolism imbalance index is identified by using a deep residual network; through Granger causal relationship and conditional independence analysis, behavior causal separation of true and false anomalies is realized; and performing cross-cycle abnormal evolution path prediction in combination with historical behavior data, and generating a graded early warning conclusion. According to the invention, the false anomaly and the real health anomaly caused by environmental factors can be effectively distinguished, and the monitoring accuracy and the early warning timeliness are remarkably improved.

This invention discloses a method for acoustic emission signal recognition, comprising the following steps: collecting acoustic emission signal data generated during axle operation; preprocessing the collected signal data; importing the preprocessed signal data into a network recognition module, and using a pre-trained improved one-dimensional CNN network model in the network module to identify and analyze the imported signal data; and outputting the recognition result. This invention also discloses a model training method for training the improved one-dimensional CNN network model. This invention is applicable to the field of train fault diagnosis, enabling the identification of acoustic emission signals from train axles after acquisition and real-time monitoring during axle operation. It can extract higher-level signal features from the original acoustic emission signals through a deep network architecture and possesses good fault identification capabilities.

The invention discloses a CPE anti-recognition privacy protection system based on a lightweight homomorphic encryption security large model. Relates to the cross technical field of network security and privacy calculation. Comprising a lightweight homomorphic encryption module, a scattering invariance neural network identification module and a quantitative perception training module. The lightweight homomorphic encryption module processes the obtained original CPE data into a ciphertext, and inputs the ciphertext into the scattering invariance neural network identification module; the scattering invariance neural network identification module extracts and identifies the ciphertext and inputs the ciphertext as feature data to the quantitative perception training module; and the quantitative perception training module performs quantitative processing on the feature data, performs analog quantization and training optimization according to the ciphertext in the lightweight homomorphic encryption module, and adjusts parameters and a quantization strategy of the model. According to the method, organic unification of privacy security, anti-attack capability and operation efficiency can be realized, and a systematic solution is provided for security deployment of CPE identification.