692 results about "Similarity matching" patented technology

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention relates to the technical field of battery management, in particular to an intelligent monitoring control method and system for a lithium battery BMS. According to the application, through the multi-mode sensing array integrated on the surface of the inactive correlation structure of the single cell, the limitation of traditional voltage and current monitoring is broken through, and microcosmic parameter signals such as high-frequency impedance, microstrain and characteristic steam spectrum are synchronously acquired; an empirical mode decomposition algorithm is innovatively adopted to separate electrochemical and mechanical process intrinsic modes, and fusion with spectral signal time domain features is carried out, so that deep fusion of multi-physical field information is realized; a channel coupling deep learning network is constructed, a process fingerprint spectrum is constructed through a dynamic updating algorithm, and full-life-cycle state tracking is achieved; based on failure mode recognition of dynamic time warping distance and similarity matching, a target control strategy is generated in combination with multi-physics coupling simulation; according to the application, multi-dimensional microscopic parameter fusion monitoring is realized, and the fault early warning precision and the battery safety protection capability are remarkably improved.

The invention discloses an enamel plate surface defect detection method based on machine vision, and relates to the technical field of enamel plate surface defect detection. The method comprises the following steps: acquiring an original image of an enamel plate, and fusing the original image through a point-by-point optimal gray fusion method to obtain a real-time reflection suppression image; a defect-free reflection suppression image of the enamel plate is collected, matching analysis is conducted on the defect-free reflection suppression image and the real-time reflection suppression image through an improved PaDiM algorithm, a difference texture area is obtained, and coordinates of the difference texture area are recorded; in the difference texture area, two types of image features are extracted through a scale attention feature extraction method and fused, a real-time and standard fusion matrix is obtained, and a defect detection result is obtained through similarity matching; and converting the coordinates of the difference region to obtain a defect positioning result, calculating a defect comprehensive score based on a defect detection result, and grading to complete the surface defect detection of the enamel plate.

The invention discloses an intelligent generation method and device for an unmanned aerial vehicle countering strategy, relates to the technical field of unmanned aerial vehicles, and aims to solve the problems of poor countering means matching effect and low efficiency caused by limited applicable scenes, lack of deep semantic support and single means of unmanned aerial vehicle countering in the prior art. The method comprises the following steps: acquiring multi-source sensor data of a target area, and determining environment information of the target area and situation type information of a suspicious unmanned aerial vehicle; extracting to-be-matched features corresponding to the situation type information from the multi-source sensor data, performing similarity matching on the to-be-matched features and the features of the multiple target matching entity nodes, and determining a matching result; based on the matching result and the environment information, obtaining at least one countering means from the unmanned aerial vehicle countering knowledge graph and generating an initial countering strategy set; and processing the initial countering strategy set by adopting a target optimization algorithm to obtain a target countering strategy. The method is used for improving the matching precision and the matching efficiency of the unmanned aerial vehicle countering technology.

The invention discloses an automobile part assembly precision intelligent compensation method and a self-adaptive regulation and control system, and relates to the field of intelligent compensation, and the method comprises the steps: synchronously collecting part geometric parameters, tool poses and environment data, and constructing an associated data matrix; analyzing data by using an improved random forest-attention model, and outputting a deviation factor contribution degree sequence; a compensation calculation model is designed accordingly, initial compensation amounts are generated in a segmented mode in combination with a precision margin threshold value, and correction is conducted through historical data similarity matching; selecting an execution path according to the compensation amount and the core deviation type; an actual precision value is obtained through laser detection after assembly, and a compensation error is calculated; based on an error triggering model optimization mechanism, model parameters are iteratively updated by using a gradient descent algorithm, and the deviation identification and compensation precision is improved. The method has the advantages that the model attribution deviation is improved, the precise compensation amount is calculated in combination with historical data, flexible execution, real-time monitoring and model self-optimization are matched, and the assembly precision and the production efficiency are efficiently improved.

The invention relates to the technical field of medical data processing, in particular to a multi-modal body feeling information processing method and system for physiological state assessment, and the method comprises the steps: collecting multi-modal parameters; constructing a consistency feature; carrying out individual normal behavior zone modeling; compressing and generating a consistent track; extracting deviation path features; carrying out similar matching on physiological trajectories; state type and stage judgment; carrying out self-adaptive correction on the behavior belt; and outputting a health intervention prompt. According to the method, the multi-source physiological signals are introduced at the same time, the time sequence cooperation relation of different types of parameters is utilized, and the cross-modal consistency characteristic matrix is constructed, so that the evaluation process does not depend on isolated change of a single signal any more, and comprehensive judgment is carried out based on linkage evolution of multi-dimensional information; the problems that due to the fact that a single physiological signal and a static evaluation benchmark are relied on, physiological state evaluation is prone to being influenced by individual differences and long-term state changes, and misjudgment and drifting are generated are effectively solved.

The invention discloses a multi-source data fusion indoor positioning method and system based on deep learning, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining a wireless signal, inertial sensor data, a timestamp and an indoor map topology; processing the signal through a deep learning model to obtain a preliminary positioning coordinate, and mapping the preliminary positioning coordinate to a map path edge for topological constraint; calculating a displacement and a time interval in combination with the coordinate sequence and the timestamp, fusing the speed and the steering angle of the inertial data, and constructing a spatial-temporal feature vector; calculating a reconstruction error of the vector by using an auto-encoder model to obtain an abnormal confidence coefficient score; if the score exceeds a threshold value, calculating a moving direction according to sensor data, screening a candidate path edge through direction similarity matching, and projecting the coordinate to the edge for correction; otherwise, receiving the original coordinates; and finally outputting the corrected positioning track or the positioning track formed by the original coordinates. The method can ensure that the positioning track better conforms to the actual motion state, and the self-adaptive capability of the positioning system is enhanced.

The invention provides a sealed self-adaptive soft package lithium battery packaging image detection method and system, relates to the technical field of image detection, and obtains feature information by analyzing and processing image information of a to-be-detected soft package lithium battery packaging area. Based on the feature information, whether material batch switching occurs or not is judged, when material batch switching is detected, an image sample of a current product is collected, local visual features are extracted for statistical analysis, and reference information is constructed. The method comprises the following steps: acquiring current image information of a to-be-detected product, extracting local visual features of the to-be-detected product, comparing the local visual features with reference information, identifying an abnormal region, quantifying the geometric shape, size and strength of the abnormal region, performing similarity matching and multiple verification in combination with a preset defect mode, and judging whether the abnormal region is a real defect or not. The problem that in the prior art, when facing different batches of raw materials, an automatic image detection system cannot adapt to material characteristic changes, so that qualified products are frequently misjudged as defects is effectively solved.

The embodiment of the invention provides an address data matching method and related equipment, and belongs to the technical field of geographic information services. The method comprises the following steps: constructing an address annotation corpus according to input address information data and a preset address database; the method comprises the following steps: generating a geographic information embedding vector according to a preset geographic information knowledge graph, performing address element analysis in combination with an address annotation corpus to obtain an address element sequence so as to construct a dictionary tree, and performing similarity screening through a spatial hierarchical matching algorithm to obtain a similar address set; generating an address embedding vector matrix through a preset word embedding vector model, and performing feature extraction through a preset semantic feature extraction model to obtain semantic-level similar features; according to input address information data, multi-dimensional character similarity matching is carried out to obtain character-level similar features, then weighted fusion is carried out in combination with semantic-level similar features, and target matching address data is determined according to a weighted fusion result. According to the embodiment of the invention, the address data matching accuracy and efficiency can be improved.

The invention provides a geological disaster prediction and early warning method and system combining monitoring data and numerical simulation, and relates to the technical field of geological disaster early warning, and the method comprises the steps: arranging a plurality of sensors in a geological disaster prone region to continuously collect data, carrying out the preprocessing, forming a standardized data matrix, and constructing a catastrophe structure map; each variable is regarded as a map node to calculate an edge weight, threshold screening is carried out, a sparse weighted adjacent matrix is generated, node features are updated through a neural network, probability distribution of a catastrophe stage is output through a full connection layer to identify a current catastrophe state, a coupling strength index is calculated, and similarity matching is carried out; coupling strength and structural similarity are comprehensively evaluated, map migration weight is obtained, then critical variable threshold and time are predicted, critical parameters and neural network recognition results are fused to construct a unified risk index model, early warning information and corresponding measures are issued according to risk grade division, and risk assessment is performed. Structured fusion and dynamic intelligent identification of multi-source data are realized.

The invention relates to the technical field of data security, in particular to a business data security access method based on an intelligent strategy. The method comprises the following steps: constructing a context-aware intelligent permission template library; collecting multi-dimensional context information in real time and normalizing the multi-dimensional context information into feature vectors; authority granting is automatically decided through weighted similarity matching; creating an activated session object and executing dynamic constraint; and continuously monitoring the session risk and dynamically adjusting the authority. According to the method, the tracking identifier is embedded in the data and the data propagation path is monitored, so that the security control on the derivative data is realized, and the problem that the traditional method only controls the original data access and ignores the data diffusion risk is solved. And meanwhile, the session object and the dynamic constraint execution point are activated, so that the whole-process refined management and control of the permission life cycle are realized, and the requirements of service agility and security are balanced.

The invention discloses an isotope-based surface water pollution tracing method, which relates to the technical field of environment monitoring, and comprises the following steps: carrying out spatial aggregation analysis on an isotope residual distribution diagram, obtaining a time evolution characteristic table, executing space-time coding on the time evolution characteristic table, and obtaining an abnormal event sequence; analyzing the time continuity and the space change of the abnormal event sequence, outputting a pollution propagation characteristic table, and performing space-time similarity matching and node association on the pollution propagation characteristic table to generate a pollution propagation association matrix; and deducing a link direction of the pollution propagation incidence matrix, generating a pollution propagation path set, executing reverse tracking and regional assembly on the pollution propagation path set, and outputting a surface water pollution traceability report. According to the method, the isotope background model based on the space-time environment feature set is constructed, and the pollution propagation features are analyzed, so that chain reconstruction and dynamic tracking of the pollution event in time and space dimensions are realized.

The invention relates to a coal machine spare part similarity identification and coding unification system and method and a storage medium. The system comprises a data acquisition module for extracting coal machine spare part data from a plurality of systems; the cleaning and preprocessing module is used for carrying out unified formatting processing on the spare part data; the feature extraction and matching engine is used for performing multi-dimensional feature extraction on the preprocessed data, including text similarity features, semantic features, structural features and attribute features; the similarity calculation and clustering module is used for calculating the comprehensive similarity of spare part data through a deep fusion network according to the multi-dimensional features, and performing automatic clustering according to the similarity; and the master data generation and system docking module is used for generating unified spare part master data for each cluster and synchronizing the spare part master data to a plurality of systems. According to the method, a large model and deep learning are utilized, multi-dimensional features are extracted and fused for multi-source spare part data, spare part similarity matching is carried out, dependence on a spare part coding format is broken through, and the accuracy and robustness of cross-system matching are improved.

The invention discloses an intelligent image processing method and device for an AI chip and a medium, and relates to the technical field of intelligent image processing, and the method comprises the steps: collecting original image data, carrying out K-MEANS filtering and regularization processing, and generating an image data matrix; based on the image data matrix, calculating a similarity score matrix through a query-key-value matrix, extracting an image key region, and generating a key region mask matrix and an attention weight matrix; calculating a quantization precision level parameter according to the attention weight matrix and the key area mask matrix, and performing error compensation in combination with residual learning to obtain a quantization compression feature vector; and based on the quantized compressed feature vector, performing multi-scale similarity matching with a sample library through hierarchical feature fusion, and calculating a confidence score in combination with an attention weight. According to the method, the identification precision is ensured, and the deployment efficiency of the AI chip on the edge equipment is improved.

The invention provides a video retrieval method and server based on semantic embedding and video memory coding, and the method comprises the steps: executing visual content analysis on continuous frames of an input video through a pre-trained visual language large model, and mapping the visual content of each frame into a semantic embedding vector; a video memory coding module is constructed based on the time sequence features of the semantic embedding vectors, and the continuous frame semantic embedding vectors are dynamically coded according to the time sequence to generate a video memory feature sequence containing time sequence association information; performing semantic analysis on the query text based on the visual language large model, and converting the query text into a query semantic embedding vector which is in the same spatial dimension as the semantic embedding vector; taking the query semantic embedding vector as a retrieval basis, performing similarity matching in a video memory feature sequence, and determining an associated video clip candidate set through feature association analysis in a time sequence window; and performing time sequence coherence verification on the candidate set, and outputting a matched video clip in combination with the semantic embedding vector time sequence correlation degree.

The invention discloses an automatic emergency braking system and method based on a graph neural network and retrieval enhancement generation. Comprises: a dangerous scene map construction module for extracting target spatial-temporal characteristics based on an accident video, marking on a high-precision map, and constructing a dangerous scene map; the GNN encoding module encodes the dangerous scene into an embedded vector and stores the embedded vector in a cloud vector database; the real-time positioning and retrieval module is used for calculating a retrieval radius and indexing and downloading nearby scene subsets; the real-time sensing module is used for generating a structured sensing data frame through time-space synchronization and multi-sensor fusion; the real-time GNN coding and similarity matching module is used for carrying out similarity matching on the coded current scene and the subset and calculating a safety evaluation coefficient; and the decision optimization module is used for triggering the active intervention strategy according to the priority based on the safety evaluation coefficient. According to the method, advanced perception and active avoidance of potential risks are realized by retrieving historical dangerous scene experience, and the driving safety in a complex scene is remarkably improved.

The invention belongs to the technical field of intelligent dispatching of power systems, and particularly relates to a power distribution network historical case retrieval and recommendation method based on multi-modal data fusion and depth similarity matching. The method comprises the following steps: constructing a multi-modal case feature library, and storing historical case data including a topological structure, a load curve and a scheduling text; receiving current power distribution network fault or scheduling demand information; calculating the topological similarity between the current scene and the historical case by adopting a graph neural network; calculating load distribution similarity by applying a dynamic time warping algorithm; calculating the semantic similarity of the scheduling text based on a fine-tuned BERT model of the electric power major; dynamically adjusting each feature weight according to the real-time new energy permeability, and generating a comprehensive similarity score; candidate cases conforming to power grid safe operation conditions are screened through a physical constraint verification layer; and outputting the recommendation case and the applicability analysis report thereof.

The invention relates to the field of logistics, and discloses a to-be-collected parcel anomaly identification method, device and equipment and a storage medium, which are used for identifying an abnormal parcel through multi-modal data of a to-be-collected parcel. The method comprises the steps of obtaining multi-modal data of a to-be-collected package, and performing format conversion on the multi-modal data of the to-be-collected package to obtain conversion data; based on the conversion data, obtaining a consistency coefficient of the to-be-collected parcel; when the consistency coefficient of the to-be-collected parcel is greater than a preset coefficient threshold value, performing linear fusion on the extracted image feature vector, text feature vector and physical feature vector according to a preset weight to generate a joint feature vector; inputting the joint feature vector into a classifier, carrying out similarity matching on the joint feature vector and a feature template in a pre-constructed template database, and determining the category of the parcel to be collected and the identification confidence of the parcel; and determining risk levels of the to-be-collected parcels according to the identification confidence of the to-be-collected parcels, and generating corresponding risk reminding schemes according to different risk levels.

The invention belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

The invention discloses a multi-strategy fusion large model retrieval enhancement generation method, and belongs to the field of artificial intelligence large model application. In the data preprocessing stage, a text partitioning strategy and a knowledge labeling mechanism are adopted, deterministic knowledge and dynamic updating knowledge are stored in a question and answer library and a knowledge library respectively, and efficient organization and management of unstructured knowledge are achieved; in the retrieval generation stage, a segmented mixed retrieval mechanism is adopted, vector similarity matching and keyword matching are fused through a linear weighted fusion method, the retrieval accuracy and efficiency are improved, and the retrieval performance is maximized on the premise that it is ensured that the calculation cost is controllable. According to the method, through multi-strategy fusion design, the whole process from unstructured document data storage to retrieval generation is optimized, and the problems of low knowledge retrieval efficiency and insufficient accuracy of a large model in question and answer interaction are solved.

The invention discloses a QMIX framework-based sluice group scheduling method. The method comprises the following steps of: constructing a simulation basin environment by utilizing a historical environment data set; constructing a multi-agent reinforcement learning method for cooperative sluices in the watershed based on a QMIX framework to realize dynamic cooperation and global optimization of the sluices in the watershed; designing a reinforcement learning single-agent DQN model of a single sluice, and supporting autonomous perception and decision of the single sluice; designing a multi-agent global reward function and a single-agent independent reward function, and balancing a group collaboration target and individual local requirements through a layered reward mechanism; a mixed experience playback mechanism based on high-priority experience similarity matching is introduced, and the problems that a traditional experience playback method is low in sample utilization efficiency and weak in key event generalization ability in a complex dynamic environment are solved. According to the method, the water gate intelligent agent can fully explore the scheduling action space in the compliance framework, and the robustness and the control accuracy of the water gate scheduling strategy under the QMIX framework are improved.

The invention relates to a paper archive digital processing method and device, equipment and a medium. The method comprises the following steps: extracting image quality characteristics of a paper archive image, identifying a flaw region based on the image quality characteristics, and classifying flaw types through a pre-trained neural network; according to a preset defect type-image restoration algorithm mapping rule, restoring the defect area by adopting a corresponding algorithm to obtain a finished image; extracting a character sequence from the trimmed image by using an optical character recognition algorithm, encoding the character sequence into a semantic vector, and determining a document theme label in a pre-established archive theme library through vector similarity matching; and a multi-dimensional index structure is generated by combining the original image, the trimmed image and the character sequence, so that efficient retrieval and management of archive contents are realized. According to the method, the accuracy of defect repair and the recognition rate of optical character recognition are improved, and the automation level of digital processing is enhanced.

The invention belongs to the field of big data processing, and particularly relates to an event analysis method, device and equipment and a storage medium, target event information is obtained, the target event information is converted into event feature vectors, the event feature vectors are input into multiple analysis models respectively, and the analysis models output corresponding event types and initial risk levels respectively. Performing similarity matching on the event feature vector and a historical event feature vector of a historical event in a preset knowledge base, screening one or more pieces of historical event information of which the similarity score is greater than a preset threshold, and determining the credibility of each analysis model based on the historical event information and the event type, performing weighted fusion on the initial analysis result based on the credibility of each analysis model, and determining a target risk level corresponding to the target event information; according to the method, by combining multi-model analysis, historical data similarity matching and credibility weighted fusion, the accuracy of risk level judgment of the target event is improved.

The invention discloses a target abnormal movement early-warning method based on eye walking along with a hook, and the method comprises the following steps: collecting an eye movement data stream, constructing a fixation behavior data set, and generating a fixation track sequence; constructing the fixation points as graph nodes, generating graph edges according to a time sequence and spatial proximity, and forming a graph structure sequence; inputting to the improved ST-GCN model, and outputting a target prediction vector; identifying offset candidate segments; performing trajectory morphological analysis on the offset candidate segments, and judging whether formation conditions of a trajectory loopback structure are met or not; if not, calculating an access frequency domain; if the access frequency is greater than a preset frequency threshold, outputting a low early warning signal; and screening the high-weight fixation segment based on the offset candidate segment, carrying out similarity matching, and outputting formal early warning information according to a matching result. According to the invention, multi-level accurate early warning of the abnormal motion state of the target is realized, and the method has the advantages of clear structure, strong real-time performance, good adaptability and the like.

The invention belongs to the technical field of equipment state monitoring, and discloses a relay state monitoring and diagnosis method and system. Vibration time domain signals of all monitoring points in the working process of a relay are collected, a time delay characteristic matrix is constructed through segmented cross-correlation operation, and a near-field sound source candidate area is analyzed and recognized in combination with the spatial divergence of a time delay gradient vector field. A sound-vibration coupling propagation model is introduced, near-field sound source excitation and structure conduction vibration components are distinguished, multi-component signal decoupling is realized through spatial constraint and frequency domain sparsity constraint, and a relay body vibration signal is effectively separated. The method comprises the following steps: performing time-frequency decomposition on a body vibration signal, extracting a transient impact feature of a contact action and a steady-state vibration feature of coil excitation, constructing a multi-dimensional state feature vector, and realizing state diagnosis through similarity matching with a preset feature library. According to the invention, the early fault detection sensitivity and diagnosis accuracy of the relay are improved, and a reliable basis is provided for preventive maintenance.

The invention provides a multi-modal domain data vectorization processing method and system, and relates to the technical field of multi-modal data processing, and the method comprises the steps: executing dynamic multi-modal feature vectorization processing based on the file type of multi-modal military resource domain data, and generating a corresponding modal feature vector and a confidence coefficient parameter; evaluating a vector influence weight of each modal feature vector based on a task target and a confidence coefficient parameter, and performing dynamic alignment on the generated multi-modal feature vectors based on the vector influence weights; based on the vector influence weight and the vector complexity, performing dynamic quantization compression on the multi-modal feature vector after dynamic alignment processing, and storing the multi-modal feature vector after dynamic quantization compression in a vector database; and processing the query request into a request vector, executing similarity matching in the vector database based on the request vector, and dynamically executing processing feedback based on a similarity matching result.