356 results about "Nearest neighbor search" patented technology

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention discloses a literature semantic search method and system based on elastic search, and relates to data retrieval. The literature semantic search method comprises the steps that vectorization processing is conducted on a noun phrase list by means of a text2vec-based-multilingual model trained based on a CoSENT method; according to the semantic vector, performing approximate nearest neighbor search in a second retrieval module to obtain first candidate data; inputting the query text data into a first retrieval module, and performing keyword matching through a BM25 algorithm to obtain second candidate data; fusing the first candidate data and the second candidate data to obtain third candidate data; a Sequence Matcher algorithm is adopted to calculate character string similarity between expansion words in the third candidate data, a similarity threshold value is set based on the length of the longest common subsequence, duplicate removal is carried out, and fourth candidate data is obtained; and performing weight distribution based on positions and similarity scores on the fourth candidate data, and enhancing the distinction degree of the extension words by expanding a score interval to obtain extension word recommendation list data. According to the method, the accuracy of document retrieval is remarkably improved.

The invention discloses a document analysis method based on an RAG model and a dynamic knowledge graph, and relates to the technical field of artificial intelligence. The method is combined with an RAG model and a dynamic knowledge graph technology, and is realized by the following steps of: performing entity relationship joint extraction on an input document, generating a structural triple, and constructing a dynamically updatable knowledge graph; based on the knowledge graph, mapping entities and relationships into low-dimensional vectors by adopting a graph embedding model, and constructing a local vector knowledge base with a topological structure; receiving user questions in real time, encoding the user questions into query vectors, executing approximate nearest neighbor search based on the vector knowledge base, and matching related map fragments; and combining the retrieved graph fragments with the large language model, and generating a structured answer through path constraint of the injection knowledge graph. The method is used for solving the problem that in the prior art, a model cannot capture document deep semantics and dynamic relations insufficiently.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention discloses a sparse view angle-oriented three-dimensional Gaussian visual positioning method, which belongs to the technical field of positioning, is used for visual positioning, and comprises the following steps of: extracting global image features and local image features of a sparse image, and constructing a feature database; performing nearest neighbor search on the feature database and the RGB image to obtain a sparseness index, calculating a camera pose of a pseudo view angle, rendering a color image and a depth image under the pseudo view angle by using a training main model, extracting global features and local features and storing the global features and the local features into the feature database, constructing a 2D-3D corresponding relation in combination with a target color image, and performing dynamic inner point screening and pose solving. And obtaining a positioning result. According to the method, visual angle deficiency of a key area is intelligently completed, the balance and coverage of feature expression in a three-dimensional scene are effectively improved, and dynamic updating and mismatching elimination of a 2D-3D matching relation are realized, so that the positioning precision and the system stability are remarkably improved, and the method has higher robustness and actual availability under a sparse training condition.

The invention discloses a few-sample visual defect detection method based on approximate proximity search, and belongs to the technical field of industrial intellectualization. The system mainly comprises an image acquisition module, a feature extraction module, a memory bank construction module, a feature compression module, an approximate nearest neighbor search module and an anomaly detection and result output module. The feature extraction module is used for performing feature extraction on the collected image; the feature compression module is used for reducing the feature data volume and reducing the internal memory and calculation burden; the memory bank construction module is mainly operated during system initialization or model training and is used for constructing a feature memory bank of normal samples; the approximate nearest neighbor search module is responsible for quickly finding normal sample features closest to the input features in the memory bank; and the anomaly detection and result output module is used for converting distance information obtained by neighbor search into anomaly. According to the few-sample visual defect detection method based on the approximate proximity search, the effect of few-sample anomaly detection is achieved.

Provided are systems, methods, and apparatuses for systems and methods of memory efficient multi-vector information retrieval based on embeddings from a storage pipelined network. In one or more examples, the systems, devices, and methods include performing a first portion of a nearest neighbor search on a first subset of a set of media sources; performing a fetch process on the first subset that include identifying a first set of highest matching media sources from the first subset, transferring multi-vector representations of the first set of highest matching media sources from a storage drive to a memory, and performing a second ranking of the first set highest matching media sources. The systems, devices, and methods include performing, in parallel with the fetch process, a second portion of the nearest neighbor search on a second subset of the set of media sources.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

The invention discloses a tunnel surrounding rock multi-source data dynamic fine rapid sensing method, which comprises the following steps: based on a mobile terminal multi-view image sequence, arranging shooting positions according to a preset space interval and an orthogonal angle, generating a sparse point cloud through a motion recovery structure algorithm, and generating a dense point cloud model in combination with multi-view stereo matching optimization; and for the dense point cloud model, delimiting a local neighborhood based on k-nearest neighbor search, resolving a neighborhood point covariance matrix through principal component analysis, extracting a feature vector corresponding to a minimum feature value as a normal vector, and constructing a rock mass surface microscopic geometric feature field. The invention provides a dynamic fine rapid sensing method based on multi-source data, and aims to improve the efficiency, precision and timeliness of tunnel surrounding rock information acquisition and provide accurate surrounding rock information support for tunnel construction and support design.

To accelerate search speeds for approximate nearest neighbor searches of vector databases, compute-in-memory techniques using NAND memory structures are introduced. For each element of the database, a kernel of its M nearest neighbors is determined. For each vector of the database, both the vector and its kernel are programmed in the arrays of a NAND memory based accelerator card, so that the vectors will be written into the memory arrays both as themselves and also in kernels of vectors for which they are a nearest neighbor. Metadata, associating the locations of the kernel members with the correspond vector is also stored in the memory system. After determining the input's nearest neighbor at one level of search, the metadata is then used to locate that nearest neighbor's nearest neighbors and their distances to the input vector are then computed in parallel in a compute-in-memory vector-vector dot product multiplication.

The invention discloses an obstacle avoidance motion planning method and system for redundant drive of an intelligent process of a welding robot and computer equipment. According to the method, firstly, a motion path of a robot is planned based on welding process redundancy, then a sampling strategy that the position and posture of a welding gun are separated is adopted, then the sampled posture is mapped to a planar two-dimensional space, and the relevance between an obstacle collision boundary and the obstacle avoidance posture of the welding gun is constructed; an incremental extreme gradient lifting model is adopted to carry out probability modeling on the collision boundary and obstacle avoidance attitude relevance, nearest neighbor search and new planning node expansion are carried out based on target cost to obtain new planning nodes, collision detection is carried out on the new planning nodes, and the new planning nodes passing the collision detection are used for incremental learning of an obstacle avoidance model; and finally, an optimization strategy of spherical linear interpolation and cosine slow motion time mapping is adopted to ensure continuity and smoothness of a motion planning path trace. The problem that in the prior art, it is difficult to quickly calculate and generate a collision-free welding track is solved.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention discloses an electric power multi-modal corpus construction query method and system based on a sliding window, which is applied to the field of electric power data query, and comprises the following steps: obtaining a structured document according to electric power multi-modal data, segmenting the structured document to obtain a plurality of segmented text blocks, and storing the segmented text blocks into a database; inputting each segmented text block into a large language model to generate a to-be-stored text vector and construct an electric power multi-mode corpus, when a user query request is received, generating a plurality of query variants according to query data, performing nearest neighbor search on each query variant in the electric power multi-mode corpus to obtain a corresponding nearest neighbor search result, and storing the nearest neighbor search result in the electric power multi-mode corpus. And fusing each nearest neighbor search result to generate an electric power related document set comprising a multi-modal association mark. According to the method, the semantic units can be accurately captured, semantic breakage is avoided, the retrieval continuity and coverage rate are improved, the comprehensiveness and context adaptability of retrieval results are improved, and the data retrieval requirement under the complex scene of the power industry is met.

The invention discloses an AIGC-based digital media content recommendation system, which comprises the following steps of: firstly, interpreting a series of behavior events displayed by a user in a current session in real time by using a large language model (LLM) subjected to instruction fine tuning so as to intelligently generate a text capable of accurately describing the current dynamic intention of the user; this enables the system to sensitively capture subtle and instant interest focuses of the user; then, converting the dynamic intention description into a vectorized representation by adopting a text embedding model to obtain an intention vector, further performing efficient approximate nearest neighbor search in a pre-constructed content library vector index by utilizing the intention vector, so as to quickly position and screen out candidate content highly matched with the current intention of the user, and finally, extracting the current intention of the user from the candidate content. And selecting a plurality of most relevant items from the candidate list to obtain a final recommendation list. Therefore, the system can significantly optimize the content recommendation effect in the session scene by deeply utilizing the understanding of the LLM on the dynamic intention of the user and the generation capability of the AIGC, and improves the user experience.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

An online system applies a visual language model and an optical character recognition model to a received image to generate descriptive information about unknown items in the image. The online system prompts a generative model with the descriptive information about unknown items in the image to separate the descriptive information into different bins each corresponding to a different unknown item in the image. For each unknown item detected in the image, the online system generates a target embedding from its descriptive information and performs a nearest neighbor search on an item catalog including embeddings for various items to find a set of candidate embeddings matching the target embedding. The online system retrieves item attributes of candidate items each corresponding to a candidate embedding of the set and prompts the generative model with this information to rank candidate items for the unknown item in the image.

The invention discloses an automatic SCADE test case generation method based on a large language model, which comprises the following steps of: analyzing various SCADE related documents to obtain an analysis result; the analysis result is converted into JSON data; performing key information extraction on the JSON data, and performing block processing on the extracted key information to obtain text blocks; vectorizing the text blocks, storing the vectorized text blocks into a vector database, converting a natural language query requirement of a user into a query vector, and performing approximate nearest neighbor search with the query vector in the vector database by utilizing RAG retrieval to obtain a preliminary retrieval result; based on metadata in the preliminary retrieval result, filtering, timeliness sorting and deduplication fusion processing are carried out, and a retrieved context information set is obtained; adopting a dynamic cue word template to construct an enhanced cue word based on the retrieved context information set and the natural language query of the user; and calling a large language model API, inputting an enhanced cue word, and generating a structured test case.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

The invention discloses a data fusion method, system and device for power transmission and transformation equipment and a medium, and the method comprises the steps: obtaining the data of the power transmission and transformation equipment through a sensor, and extracting a multi-scale visual feature set based on a Gaussian filter; fitting the laser point cloud data through a principal component analysis method to generate a multi-scale point cloud feature set, and performing feature matching through a nearest neighbor search algorithm; and converting the visual image and the laser point cloud data into a common coordinate system, and fusing the laser points and the pixel points according to a preset weight to generate multi-source fusion data. According to the invention, by fusing the visual image and the laser point cloud data, the multi-dimensional information of the power transmission and transformation equipment can be obtained, and a more comprehensive and more accurate digital twinborn model is constructed; the subtle change of the power transmission and transformation equipment can be more accurately captured through multi-source fusion data, and the dynamic sensing of the operation state of the equipment is realized; through the multi-scale fusion method, complementary information in multi-source data can be effectively utilized, and the precision of the digital twin model is improved.

The invention discloses a large-scale vector approximate nearest neighbor search heterogeneous system based on a GPU-NVMe framework, which is characterized in that the system is based on a direct GPU-NVMe framework, and a hybrid index combining a clustering algorithm and a graph algorithm is designed. According to the hybrid index, vectors with good clustering are efficiently processed by utilizing the parallel processing capacity of the GPU, and meanwhile, the search accuracy of noise vectors is improved by taking a graph index as a compensation mechanism. According to the method, collaborative optimization of hardware and software is fully considered, vector index data deployment is implemented at an NVMe driving level, meanwhile, CPU and GPU hardware are cooperatively utilized to carry out a gigabit-level vector retrieval calculation process, and an extensible large-scale retrieval system on a direct GPU-NVMe architecture is constructed. Compared with the prior art, the method has the advantages that the performance bottleneck in the gigabit-level vector data search is effectively solved, the search throughput and accuracy are remarkably improved, and the search delay is reduced.

The invention belongs to the technical field of image matching, and particularly relates to an image matching method for improving RANSAC (Random Sample Consensus) by fusing iterative weighted regression and affine transformation, which comprises the following steps of: S1, inputting a reference image and a target image, and carrying out graying preprocessing on the two images; s2, key feature points are extracted from the grayed reference image and target image by using a feature extraction algorithm, then feature point matching is performed in combination with an FLANN algorithm, the FLANN finds the most similar feature point pair through approximate nearest neighbor search, and a preliminary matching point set is generated as a basis for subsequent mismatching elimination and optimization; through the affine matrix RANSAC method optimized by iterative weight regression, the problem that the matching precision is reduced due to mismatching caused by various transformation influences such as rotation, zooming and non-rigid deformation after preliminary matching of the image can be better solved, and the robustness and reliability of the matching method are enhanced.

The invention relates to the field of unmanned aerial vehicle route analysis, and provides an unmanned aerial vehicle route safety analysis method and system for a Web side. Firstly, cube serialization is carried out on the laser point cloud according to the maximum cutting side length, and an octree index is established; secondly, converting the latitude and longitude of the air route into a self-defined rectangular coordinate system and sampling at equal intervals; screening point cloud sub-blocks intersected with the route buffer bounding box by using a separation axis theorem, executing radius query and nearest neighbor search in the sub-blocks based on KD-Tree, and bidirectionally judging a proximity relationship between route points and obstacle points; and finally, a three-dimensional result with a risk level label is generated and can be rendered in real time at a browser end. According to the method, calculation and memory loads can be greatly reduced, calculation efficiency is improved, centimeter-level real-time safety evaluation can be realized, and the method is suitable for multi-scene unmanned aerial vehicle operation such as power transmission inspection and urban surveying and mapping.

The invention discloses a decision control system and method based on temperature field and atmosphere analysis, and relates to the technical field of intelligent control. A monitoring module in the system obtains a temperature set and a gas concentration set; the trend analysis module constructs a three-dimensional model and establishes a coordinate system, a variation function is combined with a Kriging equation to supplement a temperature set to generate an expanded temperature set, the three-dimensional model is discretized into a plurality of nodes and is matched with points in the expanded temperature set through nearest neighbor search, a heat conduction equation and a convection boundary are set, and a finite element matrix equation set is constructed; solving to obtain a temperature field model and carrying out statistics to generate a temperature trend curve; a decision module compares the temperature trend curve, the gas concentration set and a process standard to obtain an abnormal section number, and analyzes a section temperature difference and a concentration error vector of the abnormal section number by adopting a fuzzy PID control algorithm to generate an adjustment vector; and the execution module performs automatic adjustment based on the adjustment vector, so that intelligent decision adjustment based on temperature field and atmosphere conjoint analysis is realized.

This application discloses a digital media content recommendation system based on AIGC. First, by leveraging a large language model (LLM) fine-tuned with instructions to interpret a series of behavioral events demonstrated by the user in the current session in real time, it can intelligently generate text that can accurately describe the user's current dynamic intention, enabling the system to keenly capture the user's subtle and immediate interest focus. Then, a text embedding model is used to convert the dynamic intention description into a vectorized representation to obtain an intention vector. Further, the intention vector is used to perform an efficient approximate nearest neighbor search in a pre-constructed content library vector index, thereby quickly locating and screening out candidate content that highly matches the user's current intention. Finally, several of the most relevant items are selected from the candidate list to obtain the final recommendation list. In this way, the system can significantly optimize the content recommendation effect in the session scenario and enhance the user experience by deeply leveraging the LLM's understanding of the user's dynamic intention and the generation ability of AIGC.

The invention relates to the technical field of natural language processing, in particular to a retrieval system and method based on retrieval enhancement generation, and the method comprises the steps: obtaining a user query text through a user interaction interface, and carrying out the embedding processing of the user query text to obtain a query vector; inputting the query vector into a vector knowledge base, performing approximate nearest neighbor search on the query vector and a storage vector in the vector knowledge base, and inputting the storage vector meeting a preset semantic distance into a candidate text block sequence; analyzing the semantic association strength between each text block in the candidate text block sequence and the query vector; performing descending sorting on each text block in the candidate text block sequence according to the similarity score vector; and inputting the enhanced prompt text into the large language model for reasoning analysis to obtain a retrieval result. According to the method, the text semantic similarity and the context correlation can be considered at the same time in the retrieval process, and the accuracy of the retrieval result is improved through a dynamic weighting mechanism.

The invention provides a laser radar and camera joint calibration method based on pixel point alignment, and the method comprises the steps: carrying out the preprocessing of point cloud data and an RGB image, and obtaining an optimized point cloud and a corrected image; carrying out edge feature recognition and extraction to obtain an edge pixel point set; projecting the optimized point cloud to a plane of the corrected image, and extracting an intensity gradient salient point set of the virtual intensity image; establishing a mapping relation of the two point sets through bidirectional nearest neighbor search, calculating similarity of mapping points, and performing data screening through the similarity to obtain an optimized data set; and constructing a joint loss function of the optimized data set according to a conversion relation among the calibration plate, the laser and the camera coordinate system, and solving through a least square method to obtain an external parameter matrix. According to the method, through intensity correction, image distortion compensation, cross-modal feature manifold mapping, bidirectional nearest neighbor matching and multi-constraint joint optimization, high-precision external parameter calibration is realized, and the robustness and real-time performance of calibration in a dynamic scene are improved.

A control circuit accesses a digitized image of a particular item and then processes that digitized image to thereby assign various aspects of the digitized image to various dimensions in N-dimensional Euclidean space to thereby provide an N-dimensional representation. The control circuit then accesses a database containing a plurality of various clustered groups of N-dimensional representations. At least some of the clustered groups have a corresponding identifying label. Conducting a nearest neighbor search serves to identify a clustered group to which the particular item most likely belongs to thereby provide an identified cluster group. When the identified cluster group does not have a corresponding identifying label, the control circuit can then further determine whether a predetermined condition has also been met. When true, the control circuit alerts a user via a user interface that the identified cluster group should be assigned an identifying label.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) / map tightly integrated navigation positioning method and system based on road marking matching, and belongs to the technical field of navigation positioning, and the method comprises the following steps: acquiring priori road map data, foresight road image data, a GNSS original observation value and an INS original observation value; a semantic segmentation network is adopted to extract road marking semantic features in the foresight road image data, and a road marking feature instance is obtained; performing nearest neighbor search on the road marking feature instance and map elements in the prior road map data, and obtaining corresponding map matching observation based on a nearest neighbor search result; and coupling the GNSS original observation value, the INS original observation value and map matching observation to obtain a navigation positioning result. According to the method, a tight combination model based on GNSS / INS and map matching original observation values is provided, the positioning performance of a navigation system in a complex city scene is improved, and omni-directional pose constraint is provided for a vehicle.

The invention relates to a K-nearest neighbor search method based on cluster structure optimization and a triangular inequality strategy, which comprises the following steps: preliminarily dividing a to-be-searched sample set into M clusters by adopting a k-means + + clustering algorithm, and obtaining cluster structure information of the sample set; calculating the representative distance of each sample in each cluster, constructing an unqualified cluster judgment rule based on the representative distance, and judging all clusters to obtain unqualified clusters; based on a k-means + + clustering algorithm and an unqualified cluster judgment rule, decomposing each unqualified cluster into a plurality of qualified sub-clusters, updating M and cluster structure information, and obtaining a sample set after structure updating; and based on the training sample set after structure updating, performing K-nearest neighbor search on a to-be-queried sample by adopting a triangular inequality check strategy. The method is suitable for rapid neighbor search under the condition of a large-scale high-dimensional data set, and can meet the equipment fault diagnosis requirement under a complex industrial scene.