1123 results about "Similarity matching" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention relates to a power adapter charging protocol identification and compatibility self-learning optimization method. After the adapter establishes physical connection with a target terminal, a voltage signal output by the target terminal is collected, a handshake data sequence is formed, feature extraction is performed on the handshake data sequence, and a first protocol feature vector is generated. And performing similarity matching on the feature vectors in a protocol feature library to determine a historical protocol category and extract a corresponding historical charging parameter template. And during voltage and current step-by-step adjustment, collecting target terminal load impedance change data, extracting impedance spectrum features, and fusing the impedance spectrum features with the first protocol feature vector to form an enhanced protocol feature vector. And based on the enhancement protocol feature vector and the historical parameter template, predicting negotiation parameters supported by the target terminal, generating a predicted negotiation parameter set, and applying the predicted negotiation parameter set to a charging negotiation process, thereby realizing stable charging output with the target terminal. According to the method, the compatibility and the self-adaptive capability of the power adapter to various fast charging protocols can be improved.

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

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 a content push optimization method and system for analyzing user interests, and relates to the technical field of artificial intelligence and personalized recommendation, and the method comprises the following steps: obtaining user behavior data, constructing user interest vectors, and carrying out the weighted fusion to generate a current interest portrait; vectorizing the to-be-pushed content, and performing similarity matching on the to-be-pushed content and the current interested portrait to obtain a recommendation sorting score; according to the recommendation sorting score, in combination with the user activeness and the push saturation, determining the push content and sequence; extracting user behavior path clustering and content similarity according to the user feedback data, outputting a response estimation result, and dynamically adjusting a content recommendation priority according to the response estimation result; according to the method, the problem of inaccurate content recommendation is solved through the multi-modal fusion and feedback optimization method.

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 discloses a video monitoring equipment fault real-time detection, root cause diagnosis and closed loop processing method and system. According to the method, by collecting multi-dimensional operation data of equipment, a dynamic weighted health degree model is constructed to realize early-stage accurate discovery of a fault; performing intelligent alarm grading by combining time sequence prediction and health degree change; realizing automatic root cause diagnosis by utilizing topological correlation analysis, log semantic analysis and case similarity matching; and closed loop processing and model self-optimization are realized through a work order system. According to the method, the problems of lagging fault discovery, difficulty in positioning, slow repair and disjunction in assessment in the prior art are effectively solved, and the operation and maintenance efficiency and the service quality are remarkably improved. The abstract drawing is Figure 1.

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 a charging pile gun state intelligent detection method and system, and the method comprises the steps: collecting a charging gun head electromagnetic signal, a gun handle temperature gradient and an interface contact surface mechanical strain signal in real time based on a multi-mode sensor array, constructing a space-time coupling data cube, separating the independent components of an electromagnetic field, a temperature field and a strain field, and obtaining a space-time coupling data cube; the multi-physical field coupling interference is eliminated; designing a dynamic cross compensation mechanism to correct mutual influence among sensor data; extracting multi-domain features and carrying out adversarial weight fusion to generate a comprehensive state representation vector; state scores are generated in combination with similarity matching of the dynamic reference library, and abnormal grade classification is achieved; according to the method, the problems of multi-field coupling interference separation, dynamic drift compensation and multi-dimensional feature joint modeling can be solved, the accuracy and real-time performance of poor contact of the charging gun, overheating early warning and mechanical deformation detection are improved, and a guarantee is provided for safe operation of charging facilities.

The invention relates to the fields of artificial intelligence, finance and medical treatment, and discloses an intelligent question and answer accuracy improving method and device, computer equipment and a storage medium, and the method comprises the steps: converting structured supervision fine tuning training data into a high-dimensional vector; constructing a multi-dimensional joint index for the high-dimensional vector, and storing the indexed high-dimensional vector in a distributed vector database; receiving a current dialogue text input by a user, and retrieving an associated question set from the distributed vector database through a similarity matching algorithm; dynamically splicing the retrieved associated questions, the corresponding answers and preset template instructions to generate enhanced cue words containing knowledge contexts; inputting the enhanced cue word into a large language model, and generating N candidate answers in parallel; and carrying out quantitative evaluation on the N candidate answers by adopting a multi-dimensional reward model, and selecting the answer with the highest score and returning the answer to the user. According to the scheme, the answering accuracy of the dialogue robot can be improved, so that the user experience is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a segmentation and matching method, device, equipment and medium for an image background region. Multi-scale features are obtained through cavity convolution branch, fusion features are generated through weighted fusion, the fusion features are input into a decoder network to obtain a background segmentation mask, a background area image is obtained by combining the preprocessed image, and a visual converter model is input to extract background feature vectors; and performing similarity matching with a target feature vector in a preset feature database to retrieve a target image. According to the method, the segmentation accuracy and robustness are improved through combination of multi-scale feature fusion and the visual converter, meanwhile, the retrieval discrimination and reliability are enhanced through feature matching, the problems that an existing method is insufficient in segmentation precision and limited in retrieval capacity are solved, and precise segmentation and efficient retrieval of the background in a complex scene are achieved.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention discloses a bidder hypergraph association analysis method and system based on bid behavior recognition, and relates to the technical field of bid behavior detection. The method comprises the following steps: S1, analyzing a bidding document, constructing a bidding document structure tree according to key structure information, and carrying out document structure fingerprint similarity matching; s2, bidding behavior data are collected, bidding behavior similarity judgment is carried out, and corresponding measures are taken through threshold comparison; s3, constructing a high-order relation structure hypergraph, and performing hyperedge importance evaluation; s4, evaluating the bid enclosing probability of the bidding unit, and taking graded early warning measures; and S5, constructing a risk tracking and feedback curve according to a bid enclosing probability evaluation result, and generating an evidence report. The method mainly solves the problem that bidding behaviors cannot be effectively recognized due to the fact that bidding units share technical scheme templates and avoid detection through typesetting and camouflage in the urban rail transit design bidding process.

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 discloses a costume design assisting method and system based on artificial intelligence, and belongs to the field of costume intelligent design, and the method comprises the steps: extracting multi-dimensional style feature information from a pre-constructed costume image data set and a fashion trend database through employing a convolutional neural network, and constructing a style vector space model; vector similarity matching is carried out, and a costume design sketch is generated by adopting a graph neural network; based on the design sketch, generating a structured process data packet for proofing; the design sketch and the structural data are input into a three-dimensional human body modeling and simulation module, and wearing effect simulation of virtual clothes is realized based on human body motion capture and physical cloth simulation technologies; and constructing a human-computer interaction interface, and quickly responding to a modification request based on the generative adversarial network model. Structured information such as the garment pattern structure, the sewing sequence, the material specification and the process route is automatically generated through the reinforcement learning algorithm, and the garment proofing efficiency and accuracy are remarkably improved.

The invention relates to the technical field of nondestructive testing, and discloses a crack detection device and method for precise automobile parts. The method comprises the following steps: carrying out ultrasonic multi-dimensional pre-scanning and acoustic impedance abnormal region identification on a to-be-detected automobile part, carrying out influence range calculation and sensitive threshold classification based on abnormal region distribution data, and obtaining stress-acoustic response time sequence data through partition grabbing force distribution and stress loading; three-dimensional stress field reconstruction and stress concentration area identification are realized; performing dual-frequency stress modulation excitation and multi-physics field synchronous detection on the stress concentration area to obtain a multi-physics field response signal, performing multi-parameter fusion calculation and multi-scale wavelet decomposition of acoustic components on the multi-physics field response signal, and performing feature dimension splicing to obtain crack feature classification data; and calculating and generating a crack detection result based on feature similarity matching and extension prediction. The precision and reliability of crack detection of the automobile parts are remarkably improved, and the damage risk in the detection process is avoided.

The invention provides an indoor semantic mapping and navigation method and system based on a multi-modal model, and relates to the technical field of target navigation. The method comprises the following steps: controlling a robot provided with a depth camera to move in an indoor environment, acquiring indoor environment data by using the depth camera, generating a pixel-level semantic map, an image-level semantic map and a two-dimensional barrier map, acquiring and analyzing a navigation instruction input by a user, obtaining a navigation target sequence to be executed, and executing the navigation target sequence to execute the navigation target sequence. Performing similarity matching on the navigation target sequence by using the pixel-level semantic map and the image-level semantic map to generate a navigation target; the shortest path is generated according to the position of the robot and the position of the navigation target, so that the robot moves to the target point according to the shortest path, comprehensive modeling of an open indoor scene is realized, and the understanding ability of the robot to a complex unstructured scene is greatly improved.

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The invention provides a DRG intelligent grouping method and system based on a large model, and relates to the technical field of information processing, and the method comprises the steps: obtaining a medical record text of a patient, extracting medical information through a pre-training language large model, generating a structured disease abstract, executing multi-modal matching to generate a standard ICD code, and solving ambiguous contents through similar matching. And forming a complete coding feature, generating an initial grouping path based on a preset rule, updating model parameters through a near-end strategy optimization algorithm, and outputting a final DRG grouping result. According to the invention, the grouping accuracy is improved, the manual auditing burden is reduced, and intelligent medical grouping is realized.

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 discloses an aircraft braking system evaluation and prediction method based on fault simulation and geometric features, and belongs to the technical field of electric data processing. The method comprises the following steps: constructing a simulation model of an aircraft brake system, and injecting faults into the simulation model according to different degrees of fault injection control parameters to generate a degradation monitoring data set of the whole life cycle of the system; dividing the degradation monitoring data set into a training data set and a test data set; training the GoogLeNet model by using the training data set to obtain a health assessment model; evaluating the health degree of the aircraft brake system by using the test data set; constructing a topological graph according to the multi-rack operation data and the health degree of the aircraft braking system; nodes in the topological graph are clustered and divided into K clusters; establishing a residual life prediction model for each cluster; and performing similarity matching on the sample and the K clusters, and selecting the best matched residual life prediction model to perform residual life prediction on the system. The method can accurately predict the remaining life of the aircraft brake system.

A system and a method for generating an optimized power converter design based on one or more multimodal large language models (MLLMs) are disclosed. The system obtains structured design data, one or more user queries, one or more design parameters, and real-time power converter design data. The system performs a similarity matching between one or more embeddings associated with the structured design data, and the one or more user queries and the one or more design parameters to compute an utmost similarity score. The system retrieves one or more similar document chunks. The system processes the one or more user queries and the one or more design parameters to extract one or more parameters. The system determines one or more design workflows based on the one or more parameters. The system executes at least one design workflow. The system visualizes design outcomes, simulation results, and prediction results to a user.

The invention discloses a network traffic abnormal behavior detection method and device. The method comprises the steps of performing normalization processing on traffic feature data, operation log data and running state data to obtain a multi-dimensional feature sequence; adjusting a first transmission rate peak value corresponding to the network flow data to obtain a second transmission rate peak value; under the condition that the average transmission rate of the network flow data in the target time window is greater than a second transmission rate peak value, performing similarity matching on the multi-dimensional feature sequence and a standard feature sequence to obtain a matching distance; and under the condition that the matching distance is greater than a first preset threshold value, if the plurality of preset features do not meet the constraint condition, determining that the network flow data has an abnormal behavior. According to the method and the device, the technical problem that normal work of a service system is affected due to the fact that misjudgment possibly exists in a related network flow abnormal behavior detection method is solved.

The invention provides an abnormity early warning security system and method based on cross-modal semantic retrieval, and relates to the technical field of public security, and the system comprises a video collection and frame sampling module which is used for extracting key frame images from a real-time monitoring video stream according to a preset frame interval and storing the key frame images to an object storage system; the target detection module adopts a YOLO model to execute instance-level target detection on the extraction frame, and generates a structured detection result containing a target position, a category and confidence; the semantic vector storage and structured warehousing module packages the standardized metadata of the image frame and the semantic vector generated by the CLIP into structured data, and stores the structured data into a high-performance vector database; and the semantic retrieval and reverse query module receives a natural language query instruction to generate a semantic vector, and realizes cross-modal retrieval of historical monitoring images through vector similarity matching. According to the invention, a set of exception security system with real-time perception capability, semantic understanding capability and cross-modal retrieval capability is constructed.

The invention discloses an intelligent flow sensor test analysis method and system, and relates to the technical field of sensor testing, and the method comprises the steps: applying a stepped flow excitation signal to a tested sensor through a standard flow generation device, and synchronously collecting the dynamic response data of the sensor; extracting a feature parameter set from the response data; performing similarity matching calculation to obtain a matching degree evaluation value of each error type; quantifying the induction relation strength among different error types; generating a composite weight coefficient in combination with the matching degree evaluation value and the evoked relationship strength; and making a multi-stage compensation strategy based on the composite weight coefficient. The method has the advantages that by combining error correlation network modeling driven by historical data, induction mechanisms and interaction relations of different error types can be analyzed, the pertinence of a compensation strategy is greatly improved, a multi-stage error suppression mechanism is formed, stable control over comprehensive errors is achieved under complex working conditions, and meanwhile the system maintenance requirement is lowered.

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 a diagnostic information-driven multi-modal medical record similarity retrieval system, which comprises a data acquisition module, a data processing module and a similarity retrieval module, the feature extraction module is used for performing feature extraction on the electronic medical record data; the multi-modal semantic generation module is used for generating multi-modal semantic representation; the diagnosis code processing module is used for constructing a tree structure based on diagnosis codes in medical records, performing low-dimensional representation learning on the diagnosis codes by utilizing a Poincare embedding method, and realizing similarity measurement among diagnosis sets in combination with self-supervised contrast learning; the migration module is used for migrating the similarity knowledge of the diagnosis set to the learning process of the multi-modal semantic representation, and optimizing the multi-modal semantic representation to form medical record similarity representation driven by diagnosis information; and the retrieval module is used for performing similarity matching among the medical records in the shared semantic space and completing a retrieval task of the multi-modal medical records. According to the method, the problems of multi-mode isomerism and annotation data scarcity of the electronic medical record are effectively solved.

The invention relates to the technical field of power distribution automation of a power system, and particularly discloses a general operation and maintenance method and system supporting multiple types of power distribution terminals. According to the method, millisecond-level automatic accurate identification of the power distribution terminal is realized through a multi-layer protocol feature fingerprint extraction technology and a weighted dynamic similarity matching algorithm. On the basis of a double-library cooperation mechanism, after equipment identification succeeds, communication parameters, point table mapping relations and operation and maintenance instructions which conform to international / industrial general standards are automatically triggered, loaded and issued. The plug-and-play and zero-configuration deployment of the power distribution terminal is realized, and the point table configuration error rate is reduced to below 1%. And an unidentified equipment self-learning closed-loop process is designed, so that the system can quickly adapt to a power distribution terminal of a new manufacturer, a new number or a new firmware version. And an automatic parameter read-back and verification comparison mechanism after configuration execution forms an operation and maintenance operation closed loop, so that the accuracy of configuration issuing each time and the controllability of the equipment operation state are ensured.