154 results about "Path diagram" patented technology

The invention discloses an enterprise big data mining method and system based on artificial intelligence, and the method comprises the steps: carrying out the dynamic mode alignment through employing a multi-mode hypergraph neural network according to enterprise multi-source heterogeneous data, and generating a time-space consistent multi-mode joint embedded tensor; inputting the multi-modal joint embedding tensor into an orthogonal adversarial manifold learning module, and generating a low-dimensional compact semantic embedding vector with enhanced category separability; performing space-time causal association mining on the semantic embedding vector, and outputting a space-time causal meta-path map containing the recessive commercial logic; and inputting the space-time causal element path map into a dynamic game adversarial interpretation framework, and finally outputting an enterprise-level intelligent decision map with anti-factual robustness. By utilizing the embodiment of the invention, the multi-modal data can be efficiently integrated and intelligently analyzed, and the accuracy and effectiveness of the mining result are improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention provides an AI-based heart failure risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: recognizing a mutation point of a physiological index time sequence through a recursive segmentation algorithm, extracting a mutation feature matrix, carrying out the time sequence mapping, recognizing a periodic mutation and gradual change mode, and converting into a risk score to generate a risk accumulation curve. Calculating index conduction time delay, drawing a risk conduction path diagram, marking an intervention time window, and finally determining a heart failure risk level and generating an evaluation report. According to the invention, early accurate recognition of the heart failure risk can be realized, and the best opportunity is provided for clinical intervention.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention relates to the technical field of automatic data analysis, in particular to an intelligent data report generation method and system based on an MCP protocol. The method comprises the following steps: constructing a semantic intention vector through a context semantic structure according to an obtained natural language instruction of a user; constructing a semantic task path graph based on the semantic intention vector by utilizing a knowledge graph; according to the constructed semantic task path diagram, performing intention path and tool capability mapping based on an MCP protocol; constructing a task flow based on an MCP protocol and performing task scheduling optimization; carrying out data source unified modeling based on the constructed task flow, and generating a stable and controllable calculation task flow; and visual expression generation from data to graphs is realized through an intelligent recommendation and structural modeling mechanism. According to the method, a semantic-driven MCP plug-in tool chain and a chart capability component index are constructed, so that a full-automatic closed loop from a natural language intention to data analysis and chart generation is realized, and meanwhile, the use threshold of non-technical users is reduced.

The invention provides a geological data driving path optimization and settlement prediction method for pipe jacking construction, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring sparse drilling point location geological data, constructing a geological data set including soil layer types, forming continuous geological feature tensors through multi-dimensional interpolation, and generating a path vector sequence by combining starting and ending points and building distribution to represent crossing tracks of different paths under geological conditions; and constructing a path graph topological structure through a graph coding neural network, performing feature propagation, obtaining a path comprehensive score vector, and screening an optimal path meeting structural integrity and settlement response constraints. Further identifying settlement trend sensitive points by utilizing the settlement risk prediction values and the accessibility scores of the nodes, constructing a settlement trend map and calculating settlement transaction coefficients. And the transaction coefficient is fed back to a path generation link and is used for iteratively optimizing path sampling density and node distribution so as to realize dynamic correction and stable convergence of a path scheme.

The invention discloses a personal obesity risk prediction system and method based on AI of big data, and belongs to the field of medical health, and the system comprises a data collection module, a multi-dimensional feature construction module, a risk label dynamic generation module, a model training and risk prediction module, a credibility evaluation and calibration module and the like. The system collects multi-source heterogeneous data through wearable equipment, a biochemical interface and a health platform API (Application Program Interface), uniformly encodes the multi-source heterogeneous data into a standard time sequence and then constructs behavior-metabolism-environment coupling characteristics. And performing joint modeling on the dynamic features and the labels by adopting a graph neural network in combination with causal factorization, and outputting an individual obesity risk prediction result. The result credibility is improved through Monte Carlo Dropout and a confidence interval calibration mechanism, and calibration information is fed back to a feature construction link to optimize a modeling strategy. Finally, the key risk factors are presented in the form of a visual thermodynamic diagram and a causal path diagram, and an individualized intervention suggestion vector is generated. The method has the beneficial effects of improving prediction accuracy and enhancing individual intervention pertinence.

The invention discloses a network security risk assessment system and method, and particularly relates to the field of network security risk assessment, and the method comprises the steps: receiving control instruction sequences continuously executed by terminal equipment deployed in an edge scene of the Internet of Things, extracting the execution content and the response state of each control instruction sequence, and forming an initial instruction state sequence; and matching the corresponding timestamp, state context and input channel information of each control instruction in the instruction state sequence to generate a behavior node set. By constructing a time-stable window constraint model based on a behavior node graph structure, on the premise of not depending on command content analysis, the time sequence consistency of control instructions is comprehensively judged by utilizing an actual trigger interval between the control instructions, context disturbance and a path graph topological relation; the low-frequency playback control instruction with legitimate semantics and abnormal time logic is recognized, and the problem that an existing system cannot discover'behavior time sequence mismatch type attack 'under the condition that the edge node computing power is limited is effectively solved.

The invention discloses a power grid operation risk assessment method and system considering time period coupling characteristics, and relates to the technical field of power grid operation risk assessment, and the method comprises the following steps: obtaining operation data of a target power grid in a plurality of continuous time periods, and constructing a time-space state tensor based on the operation data; introducing a time period memory kernel function to obtain a cross-time period evolution memory curve of a node state; constructing a risk diffusion path diagram based on the evolutionary memory curve and the power grid power flow equation; establishing a critical energy accumulation function based on the risk diffusion path diagram, and triggering a node risk source mark when accumulation exceeds a preset threshold value; and generating a space-time risk evolution spectrum based on the risk source mark, and outputting a risk trajectory and a threshold breakthrough time point of each node in a continuous time period. According to the method, the cross-time evolution memory curve and the dynamic risk diffusion path diagram are constructed, so that high-risk nodes of continuous-time-period tail events are recognized in advance, and the problem of lack of cross-time-period accumulation risk quantification is solved.

The invention discloses an unmanned aerial vehicle charging path optimization method based on deep learning, and the method comprises the following steps: S1, collecting the flight state data of an unmanned aerial vehicle, and constructing a state input sequence; s2, normalizing the state input sequence to form modeling input data; s3, inputting the modeling input data into the improved closed continuous time state evolution model to generate a state trajectory prediction result; s4, generating a path adjustment instruction according to a prediction result, and constructing path diagram structure data; s5, inputting the path graph structure data into the improved graph pointer network to generate an optimal path instruction; s6, fusing the optimal path instruction and modeling input data, iteratively updating and re-predicting a state trajectory; s7, generating a charging path execution scheme; and S8, forming an unmanned aerial vehicle path optimization control instruction sequence. According to the method, the path planning and charging scheduling efficiency of the unmanned aerial vehicle in a complex environment is improved, and the method has relatively high intellectualization and adaptability.

The invention discloses a network security threat detection system and method based on deep learning, and particularly relates to the field of network security trapping analysis based on a honeynet and a honeypot, and the method comprises the steps: collecting trapping node log data in a network security behavior trapping process; extracting a behavior event set including access time, request action types, parameter structures and node identifiers, organizing the behavior event set according to a timestamp sequence, and constructing a basic behavior node chain; and performing access sequence consistency check on the behavior node chain. A path map structure based on access tension evolution is constructed in a honeynet environment, a deep learning model is fused to execute trend reasoning and behavior disturbance prediction on a high-tension path segment, and a redundant path induction feedback mechanism is combined to realize preposed recognition and active trapping intervention on an attack path. The problem that high-concealment attacks cannot be perceived and responded in advance in a behavior window period is solved.

The invention discloses an airspace engine 3D printing interlayer metallurgical bonding enhancing method which comprises the following steps: S1, slicing an airspace engine 3D model, and constructing a printing path diagram structure; s2, constructing a lattice consistency map of the current printing layer; s3, performing graph structure alignment on the lattice consistency graphs of the current printing layer and the previous printing layer to generate a cross-layer lattice graph sequence; s4, inputting the cross-layer lattice atlas sequence into the graph neural control body model, obtaining a disturbance sensitivity vector and a disturbance score value of each node, and outputting a correction control strategy vector; s5, executing a cross-layer disturbance projection operation based on the corrected control strategy vector, and generating a control intervention instruction; and S6, repeatedly executing the steps S2 to S5 to form a complete lattice atlas evolution record sequence. According to the method, the lattice atlas modeling and the graph neural control body model are utilized to realize the metallurgical enhancement between the printing layers of the airspace engine, and the method has the advantages of closed loop regulation and control, structural continuity and the like.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention relates to the technical field of mechanical fault detection, and discloses a motor fault diagnosis method and system based on a hybrid graph neural network and a path diagram, and the method comprises the steps: S1, obtaining the time series data of a motor, and constructing a path diagram; s2, hierarchical feature extraction is carried out through the mixed graph neural network model; s3, respectively executing dynamic graph pooling operation to perform downsampling on the graph structure, and performing global aggregation on the downsampled graph to form an embedded vector set; s4, fusing the embedded vector set to generate a unified graph-level feature representation; and S5, the classifier outputs a fault diagnosis result of the motor. According to the method, the path diagram of the time sequence data of the motor is constructed, and the characteristics are extracted by adopting the mixed graph neural network, so that the inherent evolution logic and structural dependence of the modeling signal are effectively modeled, the defect of insufficient modeling in the prior art is overcome, and the diagnosis accuracy is improved.

The invention discloses a method and device for automatically designing a gas path diagram. The method comprises the following steps: carrying out element identification on an input three-dimensional model to obtain the element type of each element in the three-dimensional model; based on the element type, selecting elements contained in an action group through interaction setting of a client, and configuring corresponding action instructions for the action group to obtain action group configuration data; performing legend matching on elements contained in the action group to obtain model element legend data; and carrying out legend feature recognition on the model element legend data, and carrying out legend connection and content labeling on the basis of a legend feature recognition result and the action group configuration data so as to generate a gas path diagram. The technical problems that an existing gas circuit diagram is low in design efficiency and prone to errors are solved.

The invention discloses a cooperative control method and system for a seabed intelligent underwater vehicle and a mother boat, and relates to the technical field of underwater intelligent control and multi-boat cooperative navigation, and the method comprises the steps: collecting multi-source marine environment parameters to construct a dynamic environment perception model, and building a propulsion energy consumption estimation map according to three-dimensional space nodes. And constructing a multi-agent state-action structure, and executing path selection and cooperative route planning of the mother boat-underwater vehicle through reinforcement learning. And error judgment is executed according to the propulsion energy consumption data returned by the underwater vehicle, and an online increment mechanism is called to dynamically update the energy consumption model and the path diagram structure. According to the method disclosed by the invention, an underwater cluster control whole-process system from sensing to planning to feedback optimization is completed. Each step has independent value and supports each other, finally, a high-adaptability, high-efficiency and intelligent cooperative seabed operation control system is constructed, the dependence of the prior art on communication, a high-precision model and cluster control capability is broken through, and the method is suitable for deep sea complex application scenes.

The invention belongs to the technical field of operation monitoring of electric energy meters, and discloses an operation data monitoring and analyzing method and system based on an electric red-keyed electric energy meter, and the method comprises the steps: deploying the electric red-keyed electric energy meter in a low-voltage transformer area, collecting multi-dimensional electric energy operation parameters in real time, carrying out the collection and time axis reconstruction of the multi-dimensional electric energy operation parameters according to nodes, and carrying out the monitoring and analysis of the operation data of the electric red-keyed electric energy meter. Generating a load behavior time sequence, identifying a load behavior event by using a feature detection algorithm, and representing the load behavior event as a behavior lexical element; analyzing a hierarchical dependency relationship between a trunk mode and a modification structure of a load behavior event by constructing a syntactic tree structure of a load behavior, identifying a structural abnormal load mode, and generating an abnormal disturbance subgraph; fusing the abnormal disturbance sub-graph with a preset power supply topological structure, constructing a path graph with a body, executing a graph transformation operation group in the path construction process, and generating a multi-disturbance path sub-graph; and the accuracy, timeliness and reliability of load anomaly detection of the power distribution area are improved.

The invention discloses a multi-generation sequencing data quality control management method and system, and relates to the technical field related to data management, and the method comprises the following steps: reading file header data of sequencing data, and loading a quality control rule template; after the sequencing data is imported and converted, feature extraction is carried out under single read granularity, and a feature extraction result is established; performing multi-layer nested quality control analysis according to the quality control rule template, the sequencing data and the feature extraction result, and establishing a sample quality grade identifier; configuring a trigger threshold value, performing trigger analysis on a sample quality grade identifier, executing traceability detection, and establishing an abnormal influence path diagram; and correcting the sample quality grade identifier and generating a quality control analysis result. The technical problems that in the prior art, due to the fact that the sequencing data quality control dimension is single, the dynamic adaptability is poor and the deep anomaly recognition capacity is insufficient, the sequencing data quality control management quality and reliability are poor are solved, and the technical effects that multi-dimensional accurate quality control is achieved, and the sequencing data quality control management quality and reliability are improved are achieved.

The invention discloses a power transmission line maintenance robot dynamic path planning method, and relates to the technical field of robot maintenance, and the method comprises the steps: obtaining a three-dimensional topology model of a power transmission line, and constructing a path diagram; performing initial path planning based on a comprehensive scoring algorithm; surrounding environment data are continuously collected, dynamic obstacle recognition and prediction are carried out, a path is re-planned online, and an optimized path is regenerated; an energy model is introduced, and path-energy consumption joint decision making is carried out; and an abnormal feedback and path rollback mechanism is introduced. The method can achieve efficient planning and dynamic adjustment of the path, has high environmental adaptability and task safety, is suitable for intelligent maintenance operation of the power transmission line under the complex terrain, makes a decision by introducing an energy consumption model to cooperate with path comprehensive scoring, effectively prevents the robot from neglecting energy efficiency in the path planning process, and improves the maintenance efficiency of the power transmission line. The method guarantees the continuity and safety of task execution, and is especially suitable for long-distance, high-fall and energy consumption constraint scenes.

The invention discloses a BIM and block chain-based construction whole process quality tracing method and system, and relates to the technical field of building construction quality management. In a material entering stage, material information is collected through the Internet of Things and is associated with a BIM component identifier, and an initial block is generated by using an intelligent contract; in the process execution stage, BIM 4D / 5D simulation data is associated with a construction log, and a Hash chain algorithm is adopted to generate a process block containing IPFS Hash; in the acceptance inspection stage, the consistency of detection results is verified through zero-knowledge proof, and a new block is generated; checking the block data through a consensus mechanism to obtain a complete data set; constructing a graph database index based on the BIM component tree, and generating a chain path graph; responding to a query request, traversing path diagram nodes, comparing hash values and outputting a tracing result; trusted communication, tamper-proof evidence storage and intelligent tracing of data in the whole construction process are achieved, and the accuracy and reliability of project quality management are remarkably improved.

The invention relates to the technical field of verification and evaluation, in particular to a reusable rocket attitude control system redundancy verification and fault tolerance evaluation method, which comprises the following steps: performing multi-dimensional combination on disturbance units according to disturbance types, action time periods and intensity grades to form a disturbance combination set, constructing a group of induction task profiles covering different attitude control abnormal situations; recording a switching state of an internal control path of the attitude control system, a redundant component intervention time sequence and a function takeover sequence in real time, and constructing a dynamic response path map of a redundant link; and generating a response integrity index and a fault tolerance conflict degree criterion of redundancy configuration as a fault tolerance capability evaluation result of the attitude control system. Compared with an existing method which only verifies individual static disturbance scenes, the method has the advantages that systematic design and composite excitation of disturbance conditions can be realized, scene complexity and coverage of redundancy verification are enhanced, and authenticity and representativeness of fault-tolerant testing are improved.

The invention discloses a big data analysis system and method for a financial guarantee circle, and relates to the technical field of data analysis, and the method comprises the steps: collecting industrial and commercial, judicial and financial data in real time through an API gateway, and constructing a quantization guarantee network model and a quantum annealing parameter configuration file after preprocessing; a quantum annealing machine is used for executing hidden relation detection, and Bayesian confidence coefficient calibration is combined to generate a high-association guarantee ring list and a quantum calculation log; simulating a risk conduction path based on a synaptic plasticity model of the spiking neural network, and outputting a synaptic weight change table and a dynamic conduction diagram; the risk levels are mapped into a hierarchical blocking strategy matrix through a dynamic blocking decision engine, topology reinforcement is carried out by combining historical data and adopting a minimum spanning tree algorithm, and a stable subnet list and a protection path diagram are generated; and finally, performing weighted fusion on the quantum log, the conduction model and the subnet data, constructing a three-dimensional risk map, and optimizing a real-time decision by using a long and short-term memory network and an attention mechanism network.

The invention discloses a spoken dialogue data processing method based on an agent, and belongs to the technical field of artificial intelligence big data processing, and the method comprises the steps: receiving a voice input file sent by a user side, converting a voice signal into text data, extracting an emotion feature, generating an emotion tag and a voice rhythm feature, text and emotion information are combined to form dialogue anchor point information, a path diagram is constructed, and edge weights are calculated; constructing a propagation matrix according to the similarity between a current dialogue anchor point and a historical path node, performing intelligent path reasoning, calculating a path score, ensuring the influence of emotional fluctuation on task path selection, selecting an optimal path by an intelligent agent according to the path score to perform subsequent task processing, and performing real-time correction after user feedback; anchor point information of the current dialogue is adjusted in real time through the sentiment analysis technology, the reasoning process is adjusted according to feedback, and a closed-loop self-adaptive adjustment mechanism is formed, so that the response quality of the system is optimized, and the trust of a user to the system is enhanced.

The invention provides a visualization method and device for coupling place name labels and TreeMap subdivision and electronic equipment, and relates to the field of geographic information science and visualization, and the method comprises the steps: extracting all scenic spots of a city, determining the word sizes of the scenic spots according to the popularity of the scenic spots, and determining the weight of the city according to the number of weighted characters of all the scenic spots and the word sizes of the scenic spots; according to the weight of each city in the complete city list, each city is distributed to the shortest edge direction of a preset canvas in a node mode, and the canvas after layout is obtained; and sequentially placing the scenic spot label corresponding to each scenic spot until the layout of all scenic spots in the rectangular space of the city is completed, traversing all cities until the visual layout of all cities in the canvas after layout is completed, and displaying the visual layout. According to the method, through the Squaried layout algorithm and in combination with the place name label, the places along the line in the space path are mapped to the canvas space according to the weight, and the problem that overall and local information expression in a traditional path diagram is unbalanced is solved.

The invention discloses a dynamic MOEA / D unmanned aerial vehicle flight path optimization method based on a hybrid prediction model. The method comprises the steps that an unmanned aerial vehicle is used for collecting surrounding road environment data through a carried laser radar sensor and preprocessing the surrounding road environment data; constructing a dynamic multi-target path optimization problem, carrying out modeling by using a vector objective function, constructing a feasible path diagram in a flight area, and carrying out unmanned aerial vehicle route planning modeling; an MOEA / D algorithm population is initialized; in the flight process of the unmanned aerial vehicle, continuously detecting whether environmental parameters are changed or not, and if the environment is changed, calling an unmanned aerial vehicle path optimization method based on an improved dynamic MOEA / D algorithm; and if the environment is not changed, path optimization is carried out by using a conventional MOEA / D algorithm. The method has good real-time adaptability, multi-target balance capability and robustness, and is suitable for an intelligent flight path optimization task in a complex environment.

The invention discloses an industrial internet risk analysis system and method based on a knowledge graph, and the method comprises the steps: collecting data, and defining a multi-source original data flow and the knowledge graph; performing data preprocessing, unifying sample data, and performing mapping splicing on each mode in the multi-source original data stream; constructing, storing and updating a knowledge graph; carrying out risk modeling based on attention, and introducing an attention mechanism and a causal relationship model; carrying out risk scoring and inference; and performing risk early warning and visualization, outputting an early warning list, a propagation path diagram and key hub nodes, and performing butt joint with an existing industrial safety platform for display through an analysis report interface. The invention provides an industrial internet risk analysis scheme which not only can fuse the structural modeling capability of the knowledge graph, but also can utilize a self-attention mechanism to improve the key risk feature recognition precision.

The invention provides a large-span beam formwork three-dimensional parameterization deepening design method, and belongs to the technical field of building construction.The method includes the steps that geometric parameters of a large-span beam three-dimensional parameterization model are obtained, and a multi-scale homogenization mechanical analysis model is established to describe nonlinear characteristics of a concrete material; an arc length control method is combined with a Newton-Raphson iterative improved algorithm to carry out large-deformation nonlinear analysis to calculate deformation control parameters, safety performance is evaluated, support structure parameters are adjusted, and a double-layer game optimization model is established to determine optimal template support system parameters. On the basis of a bending-torsion coupling analysis result, a force transmission path diagram is established through a load transmission path generation function, and an optimal force transmission path is determined by adopting a gradient optimization algorithm, so that the problems that the design of a large-span beam formwork supporting system lacks an accurate mechanical calculation basis, and structural deformation and stress distribution cannot be accurately predicted are solved; and the design of the supporting system is not reasonable.

The invention discloses a transformer area high-frequency acquisition data quality improvement method, system and device and a medium, and the method comprises the steps: constructing a multi-level rule field, converting a rule into a continuous potential energy function with directivity and range control, and forming a regular force field in a semantic space; defining a field state function, and generating a plurality of field completion candidate values through rule-driven nonlinear evolution reasoning; a candidate value path diagram is constructed, nodes of the path diagram represent candidate values, and edge weights are determined according to semantic and rule association features among the candidate values; calculating the global path participation degree of each node based on the path diagram, and selecting an optimal complement value according to the global path participation degree; and executing multi-dimensional consistency verification and high-confidence judgment, and marking and supplementing the credibility level according to a verification result, thereby realizing closed-loop improvement of data quality. According to the method, the accuracy, the logic consistency and the high confidence of missing field supplementation of the high-frequency acquisition data of the transformer area in a complex interference environment are remarkably improved.

The invention is suitable for the technical field of bearing fault prediction and artificial intelligence, and provides a bearing residual life prediction method and system, and the method comprises the following steps: obtaining a bearing full-life vibration signal; preprocessing the full-life vibration signal of the bearing, identifying a degradation stage of the performance of the bearing, and constructing a high-dimensional feature space; based on variational mode decomposition and double-graph structure construction, the high-dimensional feature space is decomposed to obtain a trend component and a signal component, a path graph is constructed by the trend component, a radius graph is constructed by the signal component, and spatial-temporal features are mined; performing feature extraction and fusion on the path diagram and the radius diagram to obtain space-time fusion features; and inputting the fused space-time fusion features into a prediction layer, and outputting a bearing residual life prediction result. According to the invention, through a double-graph structure and a spatial-temporal feature fusion mechanism, the over-smoothing problem of the graph convolutional network is effectively relieved, and the feature expression capability and the life prediction precision are improved.