1599 results about "Similarity matching" patented technology

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention provides an AI-based factory abnormal behavior identification monitoring method and system. The method comprises the steps of obtaining continuous video stream data collected by a plurality of monitoring devices in a target factory in real time; performing time dimension segmentation and space dimension preprocessing on the continuous video stream data to obtain a multi-frame behavior feature set corresponding to the dynamic behavior sequence, and inputting the multi-frame behavior feature set into a pre-trained abnormal behavior recognition model, performing cross-modal association fusion on the skeleton key point features, the environment associated object contour features and the action trajectory features to generate a target behavior feature vector, performing similarity matching with a preset abnormal behavior feature library, determining an abnormal behavior category, and triggering a corresponding early warning signal according to the behavior type when it is determined that the target behavior feature vector belongs to the abnormal behavior category. And real-time alarm display is carried out. According to the invention, the problem that the existing monitoring system is insufficient in accuracy and real-time performance can be solved.

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 belongs to the technical field of applets, and discloses an applet abnormal behavior management system fused with artificial intelligence. Comprising the steps of collecting a multi-dimensional data stream in real time, performing space-time slicing on the multi-dimensional data stream, extracting a behavior feature vector in each time window, and constructing a dynamic behavior baseline model bound with a user identity; calculating a deviation degree between the behavior feature vector and a dynamic behavior baseline model, and generating a real-time anomaly confidence coefficient in combination with a similarity matching degree with a preset historical anomaly event library; the hidden relevance of a cross-interface calling link is recognized, then an abnormal propagation probability graph is constructed, the weights of the nodes in the abnormal propagation probability graph are dynamically corrected through a real-time abnormal confidence coefficient, and the corrected weights of the nodes in the abnormal propagation probability graph are obtained; and calculating a comprehensive risk entropy value, and when the comprehensive risk entropy value exceeds a risk threshold value, dynamically triggering a multi-level management and control strategy, so that complex applet abnormal behaviors can be comprehensively detected and managed.

The invention relates to a fermentation process abnormal state early warning method, system and device and a medium, and the early warning method comprises the steps: collecting fermentation process parameters in a fermentation tank in real time, and generating a physical parameter matrix; obtaining Raman spectrum data of the fermentation liquor and extracting metabolic feature vectors; fusing the physical parameter matrix with the metabolic feature vector to obtain a multi-modal data cube; constructing a metabolic network model, inputting the physical parameter matrix as a boundary condition, and generating calibrated metabolic flux distribution data; dynamically adjusting the monitoring weight coefficient of the fermentation process parameter, and generating a parameter sensitivity weight vector; inputting the multi-modal data cube and the parameter sensitivity weight vector into a space-time convolutional network, extracting time sequence features and spatial distribution features, and performing similarity matching with a preset abnormal mode library to obtain a matching result; and determining an abnormity early warning type according to a matching result with abnormity, and generating a corresponding compensation control instruction. The stability of the fermentation process is improved.

The invention discloses an electric power knowledge system construction and intelligent question and answer method based on a lightweight large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electric power field knowledge system, and building a reasoning logic classification system to carry out the classification marking of a knowledge base in the electric power field knowledge system; based on the power domain knowledge system, a multi-dimensional similarity matching strategy is adopted to process a problem input by a user; generating a reasoning link based on a matching result of the multi-dimensional similarity matching strategy; the reasoning link is used for extracting and combining answers from the power domain knowledge system, and the generation process of the reasoning link comprises question decomposition and answer integration. According to the invention, systematic management of electric power professional knowledge is realized, and the matching accuracy of professional problems is improved; by generating the self-adaptive multi-hop reasoning link based on the question type and combining the question decomposition and answer integration technology, the intelligent question and answer ability of the system under the scenes of power equipment fault diagnosis, maintenance scheme recommendation, risk assessment and the like is improved.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is 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 an Agent-based tool combination and task processing method and device, equipment and a medium, and the method comprises the steps: carrying out the natural language processing of a task instruction inputted by a user, and generating a structured semantic vector containing task semantics; performing similarity matching on the structured semantic vector and a predefined tool node semantic tag in a knowledge base, and outputting a candidate tool node set related to the current task; performing dynamic priority ranking on the candidate tool node set based on context parameters to generate an initial thinking chain; converting the thinking chain into a tree flow chart structure, and performing dynamic addition and deletion or parameter adjustment on tool nodes in the flow chart according to a feedback or preset optimization strategy; and updating tool combinational logic according to the adjusted thinking chain, processing the task instruction, and outputting a task processing result. The tool nodes are matched through the Agent agents, and the thinking chain is generated through dynamic sorting, so that the task processing efficiency and accuracy are improved.

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 provides a modeling method and system for a user charging behavior portrait. Wherein the charging time point, the driving mileage and the battery attenuation data of a user are collected, a regional charging demand thermodynamic diagram is generated in combination with a driving track, and the electric energy consumption and the battery attenuation data are fused to form energy consumption characteristics. In an underground parking lot and other satellite signal missing areas, a differential positioning base station and vehicle-mounted inertial navigation are used for compensating positioning, and continuous space-time trajectory coordinates are generated. Energy consumption characteristics and track coordinates are analyzed through deep reinforcement learning, the correlation among the charging time period, the pile distribution density and the power grid load is identified, and a charging demand space-time weight coefficient is generated; and constructing a user charging behavior feature vector based on the coefficient and power grid data, performing similarity matching classification, and finally establishing a user charging behavior portrait model. According to the technical scheme provided by the invention, accurate prediction of the charging demand and space-time collaborative optimization of the power grid load are realized.

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 discloses a surface mine disaster detection method based on an unmanned aerial vehicle, and belongs to the technical field of mine disaster detection, and the method comprises the steps: evaluating terrain complexity and geological stability, obtaining known disaster risk area information, and generating a risk area distribution map based on the known disaster risk area information; a high-risk area is obtained based on terrain complexity, geological stability and the risk area distribution map, and an unmanned aerial vehicle flight path avoiding the high-risk area is generated based on the high-risk area; acquiring high-resolution image data in real time through the unmanned aerial vehicle according to the flight path of the unmanned aerial vehicle to obtain a panoramic image map; detecting the panoramic image by using a deep learning algorithm to obtain attribute information of a suspected collapse area; in combination with known mine collapse feature data, the authenticity of the collapse area is judged by using a similarity matching algorithm, and a detection result is obtained; and uploading a detection result to a management platform, and carrying out real-time display and dynamic updating. According to the invention, the efficiency and accuracy of mine disaster monitoring are improved.

The invention discloses a power supply fault rapid detection system and method based on an intelligent diagnosis engine, and belongs to the technical field of power electronic equipment fault diagnosis, and the system comprises a function region division module which is mainly used for dividing a plurality of function regions according to a power supply topological structure; the real-time data acquisition module is mainly used for acquiring parameter data of each functional area in a power supply operation stage; the current model building module is mainly used for building a current time sequence prediction model; the trend prediction module is mainly used for generating a future current change trend; the abnormity judgment module is mainly used for identifying an abnormal condition by performing similarity matching with real-time data; the fault positioning module is mainly used for matching with a fault mode based on an abnormal current waveform and constructing a directed graph to position a fault position; the method has the advantages that early warning and accurate positioning of the power supply fault are realized through multi-modal data fusion and a deep learning model, and the operation and maintenance efficiency and reliability of the power electronic equipment are remarkably 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 present invention provides a method and system for multi-modal remote sensing image hybrid matching with multi-dimensional oriented self-similarity features. The method comprises: hybrid feature coarse matching is executed by rapidly extracting the image's self-similarity features using the offset mean filtering method and describing the feature points using the directional information of the self-similarity features. Through this coarse matching, an affine transformation model between multi-modal images is estimated, and an initial affine transformation is applied to the matched image. Next, the multi-dimensional oriented self-similarity feature fine matching is performed by constructing multi-dimensional oriented self-similarity template features using the multi-channel self-similarity map obtained from the hybrid feature coarse matching stage. Through a subsampling strategy applied to the constructed template features and convolution enhancement using a three-dimensional Gaussian kernel, the directional self-similarity features are strengthened. Finally, a three-dimensional phase consistency measure is used to identify highly accurate matching homologous points.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention relates to a public data automatic classification and grading method and system, and belongs to the field of data automatic classification and grading. Comprising the following steps: constructing a classification and grading vector knowledge base; for input data to be classified and graded, similarity matching is carried out on the basis of a nearest neighbor search algorithm and the content of the classification and grading vector knowledge base, and matching information of the field to which the data belongs and the grading rule is obtained; carrying out pre-training on the large model, and then carrying out adaptive fine tuning on the pre-trained large model based on the public data classification and grading vector knowledge base; and inputting data to be classified and graded and matching information of the classification and grading vector knowledge base into the large model for automatic classification and grading, and outputting a classification and grading result. According to the method, the problem that the existing classification and classification lack an automatic method and need to depend on a large amount of manual customized services is solved, automatic public data classification and classification are realized, and the generalization and efficiency of classification and classification are improved.

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 discloses a path planning method of a river channel walking device, which realizes the accuracy, intelligence and self-adaptability of path planning in a river channel dredging process through the technical means of multi-dimensional information acquisition, landform image similarity matching, multi-unit path planning and fusion, real-time water flow parameter dynamic adjustment and the like. According to the method, the dredging efficiency can be effectively improved, the operation risk is reduced, the overall performance of river dredging operation is optimized, and river landform features are accurately matched; dynamically adjusting a dredging path; monitoring water flow parameters in real time; intelligent path planning; and the safety and the accuracy of dredging operation are improved.

The invention relates to the technical field of traffic mode recognition, in particular to an urban traffic mode recognition system based on trajectory clustering, which comprises a trajectory credibility rejection module, a trajectory semantic clustering module, a trajectory evolution stage recognition module, a dynamic parking judgment module and an urban traffic mode generation module. According to the method, boundary dispersion rate difference analysis is carried out on traffic trajectory data, continuous cross-grid anomalies are identified, unstable sections in the trajectory are effectively eliminated, the spatial direction distribution concentration degree is taken as a basis, the trajectory coverage rate difference of the clustering center in adjacent time periods is calculated, and the spatial evolution trend of the trajectory is quantified; according to the method, dynamic description of a traffic mode in the time dimension is achieved, the misjudgment risk caused by a single time or space index is avoided in combination with double indexes of the space overlapping degree and the parking persistence between track fragments, and efficient identification of urban area travel function types is achieved through similarity matching between track features and travel features.

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 provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy 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 data processing, and discloses a remote sensing monitoring classification system for seawall ecological features and a sample library construction method. The method comprises the following steps: fusing and processing a multi-source remote sensing image to obtain a standardized data set; extracting seawall section characteristic parameters, and generating preliminary type distribution data; fusing geographic and ecological information to form feature element data; calculating vegetation coverage and material characteristics to obtain quantitative indexes; establishing a three-level sample library structure; and updating the sample through similarity matching, and constructing a classification system. According to the invention, remote sensing monitoring standardized expression and intelligent classification of seawall ecological features are realized.