6620 results about "Feature matrix" patented technology

The invention discloses a dam safety monitoring system and method based on digital twinning, and belongs to the technical field of data processing, and the method comprises the steps: collecting dam surface acoustic emission signals and internal infrared thermal image data in real time through a multi-source sensor, and generating a multi-dimensional damage feature matrix; constructing a disaster node topology network in combination with density peak clustering and dynamic weight; on the basis of gravitational field intensity calculation and improved Monte Carlo simulation, a damage chain reaction path is accurately predicted; generating a dynamic risk thermodynamic diagram through space-time folding mapping and a bidirectional diffraction equation; and extracting hidden danger indexes by adopting multi-axis domain characteristic distillation, dynamically comparing a safety envelope function, and generating a flood discharge scheduling and reinforcing scheme on line. According to the method, cross-modal fusion, a gravitational field dynamic topology network and a time-space folding mapping technology are adopted, deep fusion of accurate simulation of a damage energy conduction path, carbonization-deformation bidirectional coupling prediction and intelligent risk hidden danger disposal decision can be realized, and the accuracy and initiative of dam safety early warning are improved.

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

The invention provides an operation and maintenance alarm processing method and system based on a knowledge graph enhanced large model, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting multi-source operation and maintenance alarm historical data, carrying out the adaptive hierarchical structured processing of the data, and constructing a space-time semantic multi-dimensional feature matrix; constructing a multi-dimensional alarm association network based on a dynamic weight fusion algorithm and forming a hierarchical knowledge graph; constructing a root cause inference rule base in combination with time sequence evolution, topology propagation and business dependence characteristics of the alarm; and when a real-time alarm is received, a diagnostic reasoning system is started to analyze a propagation link, a root cause is identified, and a processing suggestion is generated. The method can accurately identify the alarm root cause, improves the operation and maintenance efficiency, and reduces the false alarm rate.

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The invention provides an employee post matching and deploying method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an optimized employee capability feature vector based on employee historical work data, extracting an employee capability development trend, and combining team cooperation data to obtain an optimized employee capability feature vector; a cross-modal contrast learning network is utilized to obtain an employee comprehensive ability vector, a development potential index is calculated, a post skill knowledge graph is constructed based on post instruction data and historical recruitment data, and a post skill feature matrix and a team structure feature vector are generated through a dynamic heterogeneous graph neural network and an adaptive graph attention network. Calculating an employee-post matching degree score matrix in combination with an employee comprehensive ability vector, inputting the matching degree score matrix and a development potential index into a distributed multi-agent reinforcement learning model, constructing a reward function, generating and evaluating a deployment scheme through hierarchical task decomposition and Monte Carlo tree search, and finally outputting a deployment execution scheme.

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention provides a network traffic anomaly detection model training method and device and a readable storage medium, and the method comprises the steps: extracting a traffic statistical feature vector according to original network traffic data, and generating an initial mixed data set; generating a confrontation disturbance sample output enhanced feature matrix based on the initial mixed data set; constructing a self-adaptive feature fusion rule based on the enhanced feature matrix, embedding asset association degree parameters into an attention calculation layer of a feature encoder, and outputting encoding features fusing threat intelligence; inputting the coding features fused with the threat intelligence into a pre-constructed initial detection model, generating false report and missing report correction labels based on the suspicious traffic fragments, and outputting an adversarial sample correction data set; and performing adversarial training on the initial detection model through the adversarial sample correction data set to obtain an incremental detection model for network traffic anomaly detection. According to the invention, the detection precision, the anti-interference capability and the real-time defense response capability of the detection model to novel attacks can be improved.

The invention belongs to the technical field of equipment health monitoring and anomaly detection, and discloses an industrial time series data learning fusion and anomaly detection method, which comprises the steps of constructing an IP-PLC mapping relation table, collecting multi-modal data, constructing a physical constraint parameter list, and generating structured data and a storage index. Time domain features and frequency domain features are extracted, a spatial topological graph is constructed, node spatial feature vectors and edge association strength are extracted, spatial association feature vectors are generated, a constraint rule base is constructed, and an enhanced feature set is formed; aggregating the enhanced feature set and the constraint rule base, generating a multi-dimensional feature matrix and a global reference parameter table, further constructing a global reference system, obtaining an equipment-level anomaly probability matrix, and generating a working condition-level anomaly probability matrix; hierarchical optimization is carried out through hierarchical modeling, and an optimization parameter set is generated; constructing an alarm response mechanism, and performing reverse updating to form closed-loop iteration; and an interpretable and extensible solution is provided for equipment health management in a complex industrial scene.

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention provides a real-time deviation analysis method and system for grid-connected operation data of a new energy station, and relates to the technical field of data deviation analysis, and the method comprises the steps: collecting the real-time grid-connected operation data of the station, and carrying out the adaptive decomposition based on a data fluctuation trend, and obtaining a plurality of groups of original feature sequences; screening features through a recursive support degree model and information entropy, and generating a reconstructed feature matrix by using a manifold embedded network; the double-branch verification network analyzes the features from the time sequence and the frequency domain, and generates time sequence feature vectors and frequency domain feature vectors; the topological continuous coherence analyzer extracts the topological relation between the features; the dynamic coupler constructs a multi-dimensional feature space, a deviation source is tracked, analyzed and positioned through a feature propagation link, and a propagation path is established; constructing a fractional order differential operator based on a propagation path, depicting a long-range dependency relationship among parameters, generating a dependency intensity matrix, performing causality test, constructing a dynamic causality graph, quantifying contribution degrees of propagation nodes, analyzing a deviation evolution mechanism, and outputting a deviation analysis result.

The invention provides an underwater multi-modal identification method and system for hidden micro defects of a high dam, and the method comprises the following steps: obtaining displacement monitoring data, stress monitoring data and temperature monitoring data, generating a multi-source monitoring data matrix through time synchronization processing, extracting abnormal features through employing a self-adaptive weight method, and carrying out the recognition of the abnormal features. Obtaining a suspicious area list with priority; planning a sonar scanning path according to the suspicious area list, obtaining edge feature data through multi-stage signal enhancement processing, and determining an accurate defect area; acoustic data and optical data are collected, and a fusion feature matrix is obtained through space-time registration and feature fusion; and carrying out feature discrimination on the fusion feature matrix, determining defect types and levels, obtaining a positioning result and a reliability evaluation result through multi-level analysis, and finally generating a defect identification report. According to the invention, the accuracy and reliability of underwater hidden micro defect identification are improved.

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The embodiment of the invention discloses an outer wall thermal insulation defect diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an infrared thermal imaging and visible light image sequence of a target building outer wall, the former comprising continuous temperature distribution data, and the latter comprising textural feature data in time-space alignment with the latter; performing dynamic temperature gradient analysis on the infrared thermal imaging image sequence to generate a three-dimensional heat conduction abnormal map, extracting surface deformation characteristics from the visible light image sequence to generate a structure deformation distribution map, and performing multi-modal characteristic fusion on the two to obtain a joint defect characteristic matrix; performing defect type classification and region positioning on the matrix based on a pre-trained deep residual neural network model, outputting a defect type identifier and a corresponding region boundary coordinate, and finally generating a diagnosis report containing a repair priority score and a material matching suggestion according to the defect type identifier and the corresponding region boundary coordinate, and sending the diagnosis report to a user terminal for visual display. And efficient and accurate external wall thermal insulation defect diagnosis is realized.

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention provides an unmanned aerial vehicle accessory intelligent identification and access method and system based on a sensor, and relates to the technical field of unmanned aerial vehicle accessory management, and the method comprises the steps: collecting the three-dimensional contour information of unmanned aerial vehicle accessories, generating standard point cloud data, carrying out the feature extraction, and carrying out the feature alignment and interaction, generating a high-dimensional fusion feature matrix of the target unmanned aerial vehicle accessories; based on the feature pyramid network, constructing multi-scale feature representation, generating hierarchical feature descriptors, performing similarity calculation, generating a similarity score matrix, performing optimal matching, establishing an adaptive verification rule base and performing accessory verification; calculating an optimal action sequence of an access path based on model information, mapping the optimal action sequence into a motion track parameter of a mechanical arm, collecting multi-point stress data, predicting the optimal clamping force, establishing an accessory service life prediction model, performing prediction in combination with historical data, and generating an accessory state evaluation report and a maintenance suggestion.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.