407 results about "Path recognition" patented technology

The invention relates to the technical field of defect detection, in particular to a special equipment defect automatic identification method and system, and the method comprises the following steps: marking a continuous path based on an image gray scale gradient and adjacent differences, identifying the pixel connection intensity to obtain a contour image layer, extracting continuous pixels, analyzing gray scale and gradient features, and expanding a texture direction to generate a defect image block. And marking and connecting the small regions to obtain a closed layer, analyzing texture and edge differences, matching classification tags, and clustering and coding to generate a defect identification tag set. According to the method, boundary structure recognition is enhanced through gray gradient and direction continuity analysis, a texture aggregation area is expanded in combination with gray consistency and edge direction stability, brightness abrupt change points are removed, contour extraction precision is improved, micro crack continuity is recovered, and pseudo defects are eliminated; and multi-dimensional image attributes are fused to identify key region feature differences, so that the classification precision and the spatial mapping consistency are improved, and efficient and accurate defect identification and stable classification are realized.

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention discloses an autonomous collision avoidance method and system for an unmanned ship in a mixed traffic mode, and the method comprises the following steps: obtaining the current position coordinate information, course information and surrounding environment information of the unmanned ship in real time through a ship positioning system and a ship recognition system on the unmanned ship; combining the target point, constructing a path plan and calculating the energy consumption of a navigation path, selecting a first navigation path, identifying and classifying static obstacles and dynamic obstacles, monitoring the static obstacles in real time, calculating and evaluating a detour difference, adjusting the path according to a collision avoidance rule, and generating a second navigation path; predicting the movement direction of the surrounding dynamic obstacles, and judging whether a collision risk exists or not; predicting a future position and adjusting the course to generate a third navigation path; and respectively evaluating the electric quantity threshold, the energy consumption threshold, the battery electric quantity and the energy consumption value, and adjusting an energy use strategy in real time. According to the invention, the accuracy of the navigation path is improved, and the safety of the unmanned ship in a complex environment is improved.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the technical field of welding automation and robot visual perception, and discloses a robot intelligent welding method and system.The method comprises the steps that a three-dimensional point cloud image is constructed through a laser triangulation method; filtering and enhancing the point cloud image, and extracting a weld point cloud set by adopting a U-Net semantic segmentation model; carrying out space path identification and attitude regression based on the set to obtain a track element of a position-attitude pair; fitting a path by adopting a cubic B-spline curve and interpolating to generate a continuous welding track; and in the welding process, visual feedback is combined, and proportional differential control logic is introduced for closed-loop track error compensation. Compared with the technical problem that in the prior art, weld joint path recognition and accurate tracking cannot be stably achieved under complex structures such as curved surfaces and variable cross sections, a perception control closed loop based on visual servo is constructed, high-precision automatic welding of the complex weld joint structures is achieved, and the track fitting precision is improved.

The invention relates to the technical field of unmanned aerial vehicle remote identification, in particular to an unmanned aerial vehicle flight path detection method and system, and the method comprises the following steps: obtaining an airspace broadcast signal, detecting a newly added signal, correcting a terminal time difference, recognizing an information source position, extracting a position sequence, reconstructing an unmanned aerial vehicle flight path, comparing a declaration path, and recognizing the identity of an unmanned aerial vehicle. And detecting an abnormal unmanned aerial vehicle and an abnormal flight path, extracting a broadcast time sequence, identifying a time overlapping interval, detecting a remote control end, and obtaining a control identification result. According to the invention, by monitoring the airspace signal in real time and dynamically detecting the newly added signal source, the monitoring precision of the signal source is improved, the time difference correction and space cross reconstruction of a plurality of monitoring terminals are combined, the positioning precision of the unmanned aerial vehicle position is optimized, and a track reconstruction method is adopted, so that the real-time monitoring capability of abnormal behaviors is enhanced; by identifying the information source of the control end and extracting the position information, the airspace safety management and illegal flight prevention capability is improved.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention relates to the technical field of structure health monitoring, in particular to an immovable cultural relic structure health detection system based on multi-source sensor fusion, which comprises a multi-source texture response acquisition module, a curved surface drift path identification module, a crack region extraction module, a stress response trend offset module and a health level region output module. According to the method, the continuous coding of the structure texture is combined with the micro-strain response characteristics, so that the accurate judgment of the synchronous drift of the surface texture of the cultural relic structure is realized, the identification sensitivity of potential micro deformation is improved, a multi-period track set is constructed according to the center deviation trend in a time sequence, and the evolution tracking capability of space behaviors is enhanced; the crack linkage area is subjected to consistency screening through image gray abrupt change and strain direction, the accuracy of crack boundary identification is improved, the safety of an immovable cultural relic structure is guaranteed, and meanwhile collaborative improvement of real-time monitoring, the response speed and the automation level is considered.

The invention discloses a dominant path identification method and system in a multi-source scene based on a perturbation response model, and relates to the technical field of computers. According to the method, the influence of the link state change on the steady state of the system is dynamically analyzed based on a perturbation response mode instead of depending on a static index, the method is closer to a real interaction mechanism of the network, and the mechanism modeling precision is improved. By traversing the link state, the actual contribution of each link to the response of the target node is quantified, and the complex interaction of weak connection and multi-link coordination is prevented from being ignored. Through the process that the system is evolved to the steady state again after disturbance, flow distribution and competition at the position of a shared node are naturally modeled instead of depending on local optimal addressing, the global reasonability of dominant path recognition is improved, and the problem of multi-source target resource competition is solved. The method is suitable for various networks, and has a core support effect on construction of a propagation risk early warning model based on network science, implementation of a key node intervention strategy and optimization of an intelligent governance mechanism of a social information system.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention relates to the technical field of liquid cooling energy storage control, in particular to an efficient liquid cooling energy storage system and a control method thereof, and the system comprises a path monitoring module, a path recognition module, an abnormality marking module, a switching execution module and a path reinspection module. According to the method, through combined calculation of the main channel pressure change rate and the outlet pressure deviation value, early recognition of the cooling efficiency attenuation trend is achieved, the channel abnormity sensing precision is improved, the temperature recovery rate is introduced to serve as a heat release accumulation criterion, the dynamic judgment capacity for heat retention is enhanced, and the cooling efficiency is improved through a double abnormity parallel judgment mode. Misjudgment is avoided, early warning accuracy is improved, main and standby channel switching is implemented, an independent cooling path structure is formed, pressure and temperature fluctuation is dynamically rechecked after a standby path is started, stable switching is ensured, the continuous monitoring capacity is achieved, and a parameter trend recognition, path reconstruction and state verification closed-loop mechanism is integrally constructed. And the operation stability and the thermal control reliability of the energy storage system are improved.

The invention relates to the field of e-commerce recommendation, and discloses a user behavior data analysis system and method based on a time series, and the method comprises the steps: continuously capturing user operation events, extracting a short-term interest fluctuation factor, a category offset index and an interaction strength parameter, and forming dynamic weight mapping for constructing a transient preference map; implicit association detection of a cross-category event chain is executed on the transient preference map, and a multi-dimensional influence gradient vector reflecting the potential interest evolution trend is generated; tracking an offset path of a key interest node through a hierarchical interest-driven algorithm by utilizing a multi-dimensional influence gradient vector, and identifying candidate behavior fragments causing recommendation delay; performing priority label reconstruction on the candidate behavior fragments to form a local behavior network with a weight gradient; based on a local behavior network, key nodes with unbalanced interest distribution in a specific time window are detected, and an evolution path for real-time adjustment of a recommendation engine is generated. The method has the advantage of improving the relevance and instantaneity of the recommended content.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention relates to the technical field of transaction verification, in particular to a block chain financial transaction tracing method and system, and the method comprises the following steps: calculating a node interaction proportion, recognizing illegal chain segments and marking nodes, analyzing a field combination sequence matching path structure, judging numbering jump and behavior path pairing, and extracting a field corresponding relation and a matching difference. And identifying a node state offset and a structure jump trend, and generating a state chain segment backtracking section record. According to the method, the interaction frequency and the behavior type proportion are introduced to serve as the node state recognition basis, dynamic credible judgment of boundary nodes is achieved, the recognition precision of chain segment structure interruption is enhanced through the path field combination sequence and the serial number continuity, the serial number jumping behavior and node action offset are combined, the chain jumping path recognition capacity is optimized, and the reliability of the system is improved. The field consistency verification effect is improved by adopting field logic pairing and value domain offset comparison, and the backtracking positioning capability of the broken link node is enhanced by using a path state offset and backtracking pointer association strategy.

The invention relates to the technical field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, and the method comprises the following steps: obtaining a jump segment direction vector, classifying label nodes with consistent directions, splitting a path lacking a mapping relation to form an interruption segment, recognizing a node with continuous directions as a bridge segment entrance, and obtaining a bridge segment direction vector; according to the method, by means of classification according to the node sequence and the channel number in the segment hopping direction, direction aggregation boundary determination and semantic field combination path position interruption recognition, the node sequence structure positioning precision and the paragraph division reasonability are improved, and the focus analysis set is generated by combining the path segments. Bridge segment connection is screened based on direction consistency, path continuation logic, behavior tracks and aggregation numbers are kept in sequence and hierarchy regularization, a behavior aggregation sequence of a semantic chain is constructed, and the continuity of path recognition in a channel aggregation area and the stability of node attribution are enhanced.

The invention provides an intelligent mine management and control method and system based on the Internet of Things technology, and relates to the technical field of mine management and control, and the method comprises the steps: obtaining the roadway feature data and mining progress data of a mine in a mine; the influence degree on sensor signal transmission is calculated according to geological stress change data and equipment electromagnetic radiation intensity, and the distribution positions of the sensors are planned by combining roadway distribution data. And generating a sensor migration path plan, adjusting a point distribution position to obtain an optimized point distribution position, simulating a signal propagation path by adopting a Monte Carlo algorithm, and identifying a key area of which signal attenuation exceeds a preset threshold value. And adjusting the optimized point distribution position of the key area to obtain key area distribution points, and formulating a sensor distribution and control scheme in combination with the optimized point distribution position. According to the invention, the distribution positions of the sensors are optimized, the monitoring efficiency and the stability of signal transmission are improved, and the real-time monitoring and dynamic adjustment of the mine environment are realized.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention relates to a dynamic path identification method and device based on multi-sensor fusion and an ETC system, and the method comprises the steps: carrying out the data collection through multiple sensors, and obtaining multi-mode original data; processing the multi-modal original data to obtain multi-modal input data; performing quality evaluation and calculation to obtain a quality evaluation value and a quality evaluation result of each modal; based on the quality evaluation value and the quality evaluation result of each modal, carrying out dynamic weight distribution on the multi-modal input data by adopting a dynamic weight distribution algorithm to obtain weighted multi-source observation data; and performing fusion and prediction according to the multi-source observation data, the spatial-temporal characteristic data and the environmental parameters to obtain a target motion track of the target vehicle. Through a multi-sensor dynamic fusion mechanism, the weight of each sensor is adjusted, and the problems of poor environmental sensitivity, insufficient spatial resolution, data processing bottleneck and multi-source data missing are solved.

The invention relates to the technical field of image recognition, in particular to a blind sidewalk recognition method based on image processing. The method comprises the following steps: collecting a ground scene image, and carrying out noise suppression and illumination normalization processing to obtain a ground scene image to be processed; inputting the to-be-processed ground scene image into a pre-constructed convolutional neural network model to identify feature textures of the blind sidewalk bricks; dividing a blind sidewalk area in the ground scene image to be processed by using the blind sidewalk brick feature texture, and determining a direction gradient feature of the blind sidewalk area; executing blind sidewalk direction consistency constraint based on the direction gradient features to collect blind sidewalk structured path segments; recognizing a blind sidewalk fracture area based on directional gradient features; and performing cross-frame target tracking according to the structured path segment of the blind sidewalk, and outputting a continuous blind sidewalk trajectory. The automatic recognition rate of the blind sidewalk area is improved based on the image recognition technology, and the continuous recognition capacity of the blind sidewalk path in the complex illumination and shielding environment is enhanced.

The invention discloses a clock domain crossing circuit timing sequence closed-loop optimization method and system, and relates to the technical field of integrated circuit design, and the method comprises the steps: reading a circuit netlist and timing sequence constraints, recognizing a converged path, and carrying out the clustering analysis to form a plurality of path groups; establishing a time sequence analysis model, and dynamically calculating the maximum delay constraint of the data signal in the path group; matching and executing at least one time sequence optimization strategy for each path group; and carrying out closed-loop time sequence verification, retaining a legacy path set which does not meet the constraint, and repeatedly executing the time sequence optimization strategy until a convergence condition is reached or the maximum preset number of iterations is reached. According to the method disclosed by the invention, the cross-domain path needing to be optimized in the DMUX structure can be accurately positioned without manual intervention, so that the path identification efficiency is improved, constraint omission or grouping deviation caused by manual operation is avoided, design defects are reduced from the source, and the design reliability of a chip is improved.

The invention relates to the technical field of dispensing equipment, and discloses double-platform five-axis dispensing equipment which comprises a peripheral dispensing movement adjusting mechanism and a filling dispensing movement adjusting mechanism which are arranged in parallel in the X-axis direction, a USC cleaning mechanism, a peripheral dispensing mechanism, a curing lamp, a 2D detection mechanism, a 3D detection mechanism and a carrying mechanism are sequentially arranged above the peripheral dispensing movement adjusting mechanism in parallel in the Y-axis direction, and a filling dispensing mechanism is arranged above the filling dispensing movement adjusting mechanism. The filling and dispensing mechanism and the peripheral dispensing mechanism are arranged in parallel in the X-axis direction, and a peripheral visual alignment mechanism and a filling visual alignment mechanism are correspondingly mounted on the peripheral dispensing mechanism and the filling and dispensing mechanism respectively; the dispensing working efficiency of the equipment is effectively improved, the technical problems that a gluing path is difficult to recognize and dispensing is difficult to complete are effectively solved, the dispensing accuracy is improved, and the accuracy of the dispensing position and the dispensing uniformity are guaranteed.

According to the passive underwater cooperative positioning system and method provided by the invention, an intelligent signal processing and analysis module in the system receives a target underwater sound backscattering signal and a target environment parameter output by an environment adaptive feedback module, and then a path identification result and a positioning result are obtained by combining an identification model; obtaining a sound velocity correction value and a propagation time correction value based on the target environment parameters and a sound velocity correction model so as to obtain the moving speed of the target object; the cooperative positioning calculation module predicts the positioning information of the next moment based on the historical positioning information and the positioning information of the current moment; the environment self-adaptive feedback module receives the underwater sound backscattering signals generated by the multi-mode signal acquisition module and the acquired environment parameters, and transmits the underwater sound backscattering signals and the acquired environment parameters to the intelligent signal processing and analysis module as target underwater sound backscattering signals and target environment parameters; and the dynamic adjustment module is also used for dynamically adjusting the state parameters of the underwater acoustic backscattering signals and the environmental parameters so as to obtain new target underwater acoustic backscattering signals and target environmental parameters.

The invention provides a power grid fault propagation path identification method and system based on a graph neural network, and relates to the technical field of power grid fault identification, and the method comprises the steps: obtaining a power grid topological structure and fault observation data, constructing a direction perception graph neural network, achieving the one-way message passing based on potential energy difference, and obtaining a node potential energy value and embedded representation; edges are screened to generate a directed graph, and candidate paths are generated in the potential energy gradient direction; and verifying the satisfaction degree of the graph neural network evaluation path to the physical constraint through an invariant, and finally selecting the path with the minimum topological entropy. According to the invention, the accuracy and reliability of fault propagation path identification are improved.

The invention belongs to the technical field of power transmission and transformation project construction, and provides an ecological environment protection method and system for a power transmission and transformation project, which can construct a frequency portrait of a bird flapping behavior in real time by fusing multiple technical means such as high-frame-rate visual perception, target tracking, individual flapping frequency extraction and frequency domain behavior modeling. The operation state of the rotating device is adaptively adjusted according to a frequency interference similarity principle, so that high-frequency phase dislocation disturbance with the most interference effect is triggered at the moment when birds enter a key critical area, the flight rhythm and the path recognition capability of the birds are effectively damaged, and the birds are promoted to deviate from a flight path of a power transmission equipment area; birds can be prevented from approaching a lead area in time, and the risks of short-circuit tripping, biological casualties and other events caused by the fact that the birds strike the lead are remarkably reduced.

The invention relates to the technical field of intelligent traffic, in particular to a large-scale road network inter-region hot spot path identification method, which comprises the following steps: dividing a target road network into a plurality of regions, obtaining license plate identification data of a period on the target road network, extracting a single vehicle OD travel according to the license plate identification data, and screening out a cross-region single vehicle OD travel as to-be-analyzed data; grouping the to-be-analyzed data, wherein each group of data comprises a cross-regional single-vehicle OD travel in the same starting and ending region and in the same preset time interval; performing simplification and similarity calculation on each group of data to obtain a representative path of the data to be analyzed; aggregating the representative paths of the to-be-analyzed data to obtain a representative path list; and according to the representative path list, obtaining an occurrence frequency of each representative path, and according to the occurrence frequency, determining an inter-region hotspot path in the target road network. According to the method and the device, the hot spot paths among the regions can be efficiently, correctly and intelligently mined.

The invention relates to the technical field of acoustic analysis, in particular to a self-adaptive acoustic analysis method and system, and the method comprises the following steps: obtaining a sound pressure amplitude screening channel, extracting a main sound point time index, judging a fluctuation direction to generate a path identifier, and completing channel alignment and path binding to generate an analysis result. According to the invention, by acquiring the maximum sound pressure amplitude of each channel and dynamically screening the effective channel, the capability of capturing significant sound in a complex sound source environment is improved, and by combining directional extraction and fluctuation trend judgment of main sound point time index differences in a continuous period, dynamic perception and track identification of sound source feature changes are realized. The stability of path identification is enhanced by using direction consistency and time sequence alignment operation, accurate binding and real-time adaptive sound source tracking of multiple channels are completed, the identification accuracy and path continuity in a sound source dynamic change scene are effectively improved, meanwhile, redundant channel interference is avoided, and the accuracy of path identification is improved. And the processing efficiency and the adaptive capacity of the acoustic model are enhanced.

The invention discloses an anomaly recognition method for realizing intelligent security and protection based on deep learning. The anomaly recognition method comprises the following steps: S1, acquiring video data; s2, the edge cloud preprocesses the video data; s3, performing real-time video analysis; s4, performing regional warning; s5, target feature extraction of the warning area; s6, identifying a target path; s7, target residence time judgment; s8, the edge cloud uploads the calculation data to the platform; the invention discloses an anomaly recognition system for realizing intelligent security based on deep learning. The anomaly recognition system comprises an intelligent security camera, a communication module, an edge cloud and a security platform. According to the invention, the collected security and protection video data are preprocessed through the edge cloud; the abnormal path is judged through three-level warning, so that the judgment accuracy of the abnormal path is improved; the security and protection platform performs judgment through three times of warning, so that the judgment accuracy of path abnormity is improved, the calculation pressure of the security and protection platform is reduced, the processing efficiency is improved, and the response rate of judgment reaction can be improved.

The invention is applicable to the technical field of file archiving, and provides an intelligent electronic file sorting and archiving method and system based on an AI large model, and the method comprises the following steps: receiving electronic text information, and carrying out title recognition and paragraph recognition on the electronic text information to obtain title information and paragraph texts; all paragraph texts are input into an AI large model for extraction description, and an extraction statement of each paragraph text is obtained; performing semantic matching on the title information and the extracted statement with classification tags, and determining classification tags corresponding to the title information and each paragraph text; the weight value of each classification label is calculated according to the title information weight and the paragraph text weight, classification label arrangement information is obtained, and N labels are arranged in the classification label arrangement information in sequence; and inputting the classification label arrangement information into the classification path identification model to obtain an archiving category and a storage path. According to the method, all contents of the paragraph text do not need to be processed, so that the consumption of computing resources is greatly reduced.

The invention relates to the technical field of network security, in particular to a network security operation and maintenance automatic analysis and response system based on artificial intelligence, which comprises an operation sensing module for acquiring a server state, extracting a security log, monitoring a network protocol and a transmission direction, identifying a performance bottleneck, sorting event data and generating a sensing summary sheet, the path offset identification module tracks a cross-domain path, identifies an abnormal path and generates an offset list; the behavior chain construction module sorts an event sequence and generates a behavior chain graph; the response processing module arranges processing steps, records a response process, generates an operation and maintenance execution sequence, synthesizes an output module to complete a process and generates an analysis result. According to the invention, by comparing the server resources with the historical reference, the abnormity is identified, and the event behavior basis is provided. And tracking cross-domain path lag and abnormity, and positioning threats. Arranging event data, and constructing an attack chain. The automatic response improves the speed and accuracy, shortens the response time, and enhances the attack handling capability.

The present application relates to the technical field of supply chain management, and in particular to a supply chain resilience path identification method based on fuzzy set qualitative comparative analysis. The method comprises: acquiring historical operation data of nodes in a supply chain and response data under a sudden incident; on the basis of the similarity of the historical operation data between the nodes, obtaining the degree of operational correlation between the nodes; grouping all the nodes to obtain a plurality of node group clusters; on the basis of the response consistency of the nodes under the sudden incident, screening out a key node group cluster; on the basis of the distribution characteristics of the response data of different nodes, obtaining the response priority of each node; on the basis of the change difference of the response data between each node in the key node group cluster and other nodes in the same batch, obtaining the degree of external interference of each node; and on the basis of the degree of external interference, correcting the response priority to obtain a corrected response priority. The present invention can improve the evaluation efficiency and decision support capability of supply chain resilience.