4126 results about "Cluster result" patented technology

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention relates to the technical field of data processing, and discloses a time sequence feature analysis method and system based on multi-dimensional data. The method comprises the following steps: performing spatial-temporal feature extraction and dimension reduction processing on a multi-dimensional time sequence precipitation data set to obtain a precipitation spatial-temporal feature vector set; performing multi-scale spatio-temporal pattern recognition and clustering analysis on the precipitation spatio-temporal feature vector set to obtain a precipitation spatio-temporal pattern library, a clustering result and an abnormal precipitation pattern list; constructing a deep learning rainfall prediction model to obtain an end-to-end global atmospheric reanalysis rainfall fusion prediction model; performing dynamic updating and online learning on the global atmospheric reanalysis rainfall fusion prediction model to obtain a target rainfall space-time prediction model; and performing multi-dimensional space-time analysis on the target rainfall space-time prediction model to obtain a global atmosphere reanalysis rainfall fusion data analysis report and a weather processing assembly line scheduling strategy. According to the invention, the accuracy of time sequence feature analysis based on multi-dimensional data is improved.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The present invention relates to a data interaction method, device, equipment and storage medium of a multi-layer stacked memory, comprising the following steps: based on the access frequency clustering result, the target transmission data is mapped to a storage location to obtain a multi-layer storage allocation strategy table; based on the multi-layer storage allocation strategy table, the target transmission data is interactively planned to obtain an optimal interactive path plan; based on the optimal interactive path plan, the target transmission data is hierarchically migrated and scheduled to obtain a data migration execution queue; and the data migration execution queue is transmitted in parallel through an asynchronous pipeline transmission mechanism, which solves the technical problem that the existing data transmission strategies often lack in-depth analysis of the historical access characteristics of the storage hierarchy, resulting in unreasonable data distribution, and frequent cross-level data migration increases system delay and energy consumption.

The invention provides a cold and hot data object life cycle feature extraction method based on deep learning, and the method comprises the steps: carrying out the clustering of feature vectors through employing a clustering algorithm, dividing the data into different viscosity level data clusters according to a clustering result, and enabling the data in each viscosity level data cluster to have similar access modes and life cycle features; for each viscosity level data cluster, analyzing the access time distribution and life cycle stage of the data cluster, and judging the heat type to which the data cluster belongs, including continuous heat, periodic heat, temporary heat and cold; according to the current popularity type and the historical access mode of the viscosity level data cluster, predicting the popularity change trend of the viscosity level data cluster in a future period of time, and obtaining a popularity change curve of each cluster; by monitoring the access condition and the heat change of the data in real time, the migration and replication modes of the data between different storage layers are dynamically adjusted, and when the actual access mode of the data is deviated from the predicted mode, data re-clustering and storage optimization are triggered.

The invention provides a methane emission remote sensing intelligent detection method and system based on sparse spectrum reconstruction. The method comprises the following steps: performing first-order Taylor expansion on a Beer-Lambert law; introducing a spatial continuity constraint and a physical diffusion constraint term based on a wind field, and deducing an optimization objective function for inversion of atmospheric methane increment; simulating remote sensing observation hyperspectral radiation brightness values under different methane increment concentrations, and calculating to obtain a unit absorption characteristic spectrum; carrying out dimension reduction processing and clustering operation on the remote sensing hyperspectral image; performing matrix decomposition and sparse reconstruction on the spectrum value according to a clustering result; the hyperspectral image obtained after matrix sparse decomposition reconstruction is equivalent to a background spectral image, and atmospheric methane increment is obtained through calculation; generating an atmospheric methane plume sample based on a large eddy simulation program; constructing a training sample set of methane emission rate machine learning estimation; constructing a machine learning model for estimating the atmospheric methane emission rate; and based on an atmospheric methane increment inversion result, completing methane point source emission rate estimation of a real scene.

The invention discloses an intelligent customer service method for adaptively adjusting an interaction strategy by using reinforcement learning, and the method comprises the following steps: S1, obtaining a natural language text inputted by a user, and generating a multi-dimensional state vector; s2, grouping the users by using a real-time dynamic clustering algorithm, and updating a clustering result; s3, generating an action space in a specific state, and enriching the action space in combination with a rule-based strategy and a learning-based strategy; s4, aggregating neighbor node features through a graph convolutional network, and extracting local features; s5, weighting adjacent node features by using a graph attention network, and dynamically allocating attention weights; s6, carrying out interaction strategy optimization by adopting a spiral ladder type deep exploration network, and optimizing action selection; and S7, combining online learning with an experience playback mechanism, continuously updating an interaction strategy, and making a quick response to dynamically changing user demands. According to the method, reinforcement learning, the graph neural network and the sentiment analysis technology are combined, and intelligent customer service interaction strategy self-adaptive adjustment and optimization are achieved.

The invention relates to a shipping risk assessment method, which comprises the following steps of: S1, establishing a semantic network of entities related to shipping and relationships among the entities, and determining a shipping basic information knowledge graph; s2, acquiring multi-source data related to shipping risks, and extracting risk entities and risk information from the multi-source data; s3, expanding a shipping basic information knowledge graph according to the risk entity and the risk information, constructing a risk association knowledge graph, and obtaining relationships between different risk factors and ship risks; s4, the risk factors are clustered, a risk identification model is constructed according to a clustering result to carry out quantitative evaluation on risks, a shipping risk level is obtained according to a shipping risk inference rule, if the shipping risk level is a low risk or a high risk, direct output is carried out, and otherwise, S5 is carried out; and S5, based on a quantitative evaluation result, carrying out weighted average calculation on the multi-factor risk to obtain a risk index, and determining a shipping risk level of the ship according to the risk index. Compared with the prior art, the method has the advantages of being high in precision and reliability.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for constructing training data. The method includes determining multiple clusters by clustering prompts in a training dataset; and determining, based on multiple cohesion levels of the multiple clusters, multiple sampling probabilities corresponding to the multiple clusters, where the cohesion levels indicate intra-cluster distances in the clusters. The method further includes determining, according to the multiple sampling probabilities, a target cluster for sampling. The method further includes constructing target training data by sampling target prompts from the target cluster. According to embodiments of the present disclosure, when fine-tuning a language model, prompts can be screened according to a clustering result of the prompts, so as to make the determined prompts more valuable for annotation, thereby ensuring output results of the language model obtained by training to be comprehensive and diverse.

The invention discloses an abnormal behavior detection method and system based on a multi-source log and electronic equipment, and the method comprises the steps: collecting log data, carrying out alignment processing and data preprocessing, and generating normalized log data; carrying out attribute screening on the standardized log data set, and packaging the screened logs into a JSON format; extracting features of the log data, converting the features into data points, clustering the data points, and retaining a log cluster according to a clustering result; mining a hidden association mode between log attribute items in the log cluster by using an improved Apriori algorithm, generating an association rule, and constructing a knowledge graph based on the association rule; and constructing a time sequence of the log data by using a self-adaptive time window, and inputting the time sequence and the tag sequence of the log data into the LSTM neural network by combining context information in the knowledge graph and a tag sequence generated by an association rule to obtain an abnormal behavior detection result. According to the method, the log data format is unified, correlation analysis is enhanced, and the accuracy of an anomaly detection result is obviously improved.

The invention is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention provides a self-organizing closed-loop feedback driven iterative optimization power exception processing method and system, and relates to the technical field of power exception processing. Comprising the steps of inputting modal data into a hybrid expert model, and obtaining comprehensive features of the multi-modal data through a weighted fusion method; based on the comprehensive characteristics, detecting the abnormality of the power system; adopting a reinforcement learning method to generate a plurality of processing suggestions for the power system abnormity; obtaining feedback information of a user on various processing suggestions, inputting the feedback information into the self-organizing mapping model for clustering, and dynamically adjusting the weight of each modal data in the hybrid expert model based on a clustering result; the feedback information serves as a reward for generating multiple processing suggestions in reinforcement learning, and generation of the multiple processing suggestions in the next round is optimized; and iteration is carried out until an optimal processing suggestion is obtained. According to the method, the accuracy, the real-time performance and the individuation degree of anomaly detection and processing can be effectively improved.

The invention relates to the technical field of data analysis, and discloses a data analysis method based on a chain operation benefit supervision system, and the method comprises the steps: carrying out the collection and preprocessing of the multi-dimensional data of each store of a chain operation enterprise, and obtaining the multi-dimensional time series data; performing multi-dimensional similarity calculation and fuzzy clustering to obtain a store clustering result and a fuzzy membership degree; constructing a decision tree model and performing store operation state evaluation to obtain a store operation state evaluation result; time sequence feature extraction is carried out, a semantic segmentation network is constructed, and a store business prediction model is obtained; performing deep prediction on the multi-store business data to obtain a store business prediction result; and based on the store operation state evaluation result, the store business prediction result and the fuzzy membership degree, comprehensive anomaly prediction and resource optimization configuration are carried out to obtain an anomaly prediction report and an optimization scheme, so that the accuracy of operation state evaluation is improved.

The invention relates to the technical field of data processing, in particular to a high-precision numerical control machine tool operation state abnormity monitoring method and system, and the method comprises the steps: determining a plurality of initial clustering clusters when a pre-obtained target sequence is clustered through an ISODATA algorithm, and carrying out the clustering of a plurality of initial clustering clusters during the subsequent iteration clustering; taking the reliability of each sampling point in each initial clustering cluster as a weight, carrying out weighted summation on the positions of all the sampling points in the corresponding initial clustering cluster to obtain a plurality of weighted cluster centers during current clustering until a clustering result converges to obtain a plurality of clustering clusters, and carrying out clustering on the plurality of clustering clusters; and according to a comparison result of the normalized value of the distance between each sampling point in each clustering cluster and the clustering center of the corresponding clustering cluster and a preset threshold value, monitoring the abnormal operation state of the numerical control machine tool. The accuracy of monitoring the abnormal operation state of the numerical control machine tool can be improved.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention discloses a dynamic and static target continuous positioning and tracking method and system based on millimeter waves. The method comprises the steps that dynamic point cloud data and static point cloud data reflected by a target are acquired through a millimeter wave radar sensor, the dynamic point cloud data reflect target motion state information, and the static point cloud data reflect vital sign information of the target in a static state; fusing the dynamic point cloud data and the static point cloud data to obtain fused point cloud data, and clustering the fused point cloud data by using a density-based clustering algorithm by taking a reflection point for distinguishing a target from an environment as a target to obtain a clustering result; and on the basis of the clustering result, executing continuous positioning and tracking on the plurality of targets to obtain a tracking result. The method can effectively extract the dynamic and static information of the target, achieves the continuous positioning and precise tracking of multiple targets, and is higher in practicality and application value.

Embodiments of the present description provide a root cause location method, system and device. The root cause locating method comprises: acquiring historical link data corresponding to a microservice, and determining a target anomaly location model corresponding to the historical link data; determining link data sets to be processed corresponding to the microservice, clustering said link data sets, and determining cluster center link data on the basis of a clustering result; inputting the cluster center link data into the target anomaly location model to obtain abnormal link node information corresponding to the cluster center link data; and on the basis of the abnormal link node information, determining a target abnormal link corresponding to the microservice, and determining an abnormal link node in the target abnormal link. Model fine adjustment is carried out on the basis of an initial anomaly location model, so that the initial anomaly location model has wide applicability and high generalization ability. Cluster center link data is selected for root cause location, so that the root cause location speed of the microservice is increased.

The invention relates to the technical field of medical information processing, in particular to an information sharing method of a pediatric clinical nursing system. And obtaining complete medical record sample data including age, symptom results, treatment cycles and drug doses. Comprehensively analyzing the similarity between the age and the disease result, determining a difference characteristic value, and carrying out initial clustering to form a cluster; disease distribution and influence characteristics are analyzed from disease results in view of differences of disease response and treatment requirements of child patients of different age groups. Selecting a to-be-analyzed disease, determining a prominence index by combining the distribution density and the treatment cycle of the to-be-analyzed disease in the cluster, and reflecting distribution characteristics; meanwhile, the occurrence frequency, the difference characteristic value, the treatment cycle and the medication dosage are analyzed, and the influence degree index is judged. Clinical indexes are determined by combining the influence degree and the outburst index, and individual features of the child patient are accurately represented. Clustering analysis is carried out based on clinical indexes, a more accurate clustering result is obtained, and the information sharing accuracy is improved.

An air conditioner load monitoring and anomaly detection method and system. The method comprises: acquiring user power consumption data and meteorological data, the user power consumption data and the meteorological data comprising the total power of an electricity meter, the power of an air conditioner, and the outdoor temperature, preprocessing the data, and computing electrical characteristics of the user power consumption data; on the basis of the electrical characteristics, learning by using a two-tower neural network so as to obtain operating rules of the air conditioner, and extracting an air conditioner operation curve; on the basis of the air conditioner operation curve, computing electrical characteristics and energy consumption characteristics of the air conditioner, and performing cluster analysis by using a K-means clustering algorithm; and, on the basis of clustering results, comparing the clustering results with clustering results of collected electrical data of the air conditioner under normal and abnormal operating conditions to determine whether the air conditioner is in an abnormal state, so as to implement detection of anomalies in the operation of the air conditioner, thereby improving the precision of load decomposition of the air conditioner, reducing application costs, providing a decision-making reference for equipment maintenance personnel, and prolonging the service life of the equipment.

The invention discloses an Internet of Things card anomaly detection method and system based on an improved K-means algorithm. The method comprises the following steps: S1, collecting and preprocessing feature data of an Internet of Things card; s2, performing clustering analysis on the preprocessed feature data by using an improved K-means algorithm; s3, calculating a distance threshold value of abnormal judgment according to a clustering result; and S4, judging whether the to-be-detected Internet of Things card is abnormal or not according to the threshold value. The system comprises a data preprocessing module, a clustering analysis module and an anomaly detection module. The invention aims to provide an efficient and intelligent anomaly detection method for the use behavior of the Internet of Things card through an improved K-means algorithm, and by automatically identifying the abnormal use behavior of the Internet of Things card in different application scenes, the accuracy of management and safety monitoring is improved, and malicious abuse and illegal behaviors are reduced.

The invention discloses a thermal power plant immersive equipment state monitoring method and system based on AI, and relates to the technical field of equipment monitoring, and the method comprises the steps: building a digital twinborn model of a thermal power plant after a monitoring sensor is deployed on equipment of the thermal power plant; executing attraction clustering according to an equipment topological relation in the digital twin model, and establishing a multi-level clustering result; reading a monitoring data set of the monitoring sensor; activating an independent anomaly recognition network, and establishing an independent anomaly recognition result; establishing equipment criticality according to the independent anomaly recognition result; establishing equipment abnormity joint early warning; and carrying out immersive visual presentation. The technical problems that in the prior art, the operation state of the thermal power plant equipment is not comprehensively and accurately monitored, the equipment management efficiency is low, and the reliability is difficult to guarantee are solved, and the technical effects that the operation state of the thermal power plant equipment is comprehensively and accurately monitored, and the efficiency and the reliability of equipment management are effectively improved are achieved.

The invention discloses a multi-literature intelligent classification and automatic review generation method based on a large language model, which is characterized by comprising the following steps of: retrieving literatures or uploading local literatures, and extracting text contents; identifying keywords by utilizing an algorithm or a large language model; converting the keyword into a vector form by using a vectorization model; summarizing the research direction of the clustering result by using a large language model; comprehensively summarizing the single literature and literatures in different research directions by using a large language model; and executing the steps of reviewing, quotation insertion and the like through a large language model and a vectorization semantic matching technology. Compared with the prior art, the method has the advantages that the efficiency and quality of document classification and review generation are remarkably improved, technologies such as text vectorization, topic clustering and large language model generation are well fused, efficient and accurate multi-document review automatic generation is achieved, the method is suitable for interdisciplinary academic document analysis and information integration application, and the method is suitable for popularization and application. Wide application potential is shown.

The invention discloses a smart city planning design method, and relates to the field of city planning, and the method comprises the steps: collecting monitoring data; determining two targets of land utilization rate maximization and traffic jam minimization according to the preprocessed monitoring data; an epsilon-constraint method is adopted to convert two targets of land utilization rate maximization and traffic jam minimization into a single-target optimization problem; solving a single-target optimization problem by adopting a swarm intelligence algorithm MOEAD to obtain a Pareto non-inferior solution set; performing clustering analysis on the Pareto non-inferior solution set by adopting a K-means clustering algorithm, and dividing the Pareto non-inferior solution set into K clusters according to a clustering result; and according to the preprocessed monitoring data, constructing a three-dimensional city information model by using a smart city CIM technology, mapping each solution in the representative solution set to a corresponding planning parameter in the three-dimensional city information model, and generating a corresponding city planning design scheme. The contradiction between the land utilization rate and the traffic efficiency in urban planning is balanced.

The present application provides a data clustering method and apparatus, a system and a computer-readable storage medium. The method comprises: acquiring a node feature vector of each node and a node relationship network, the node feature vectors indicating attribute features of entity objects, and the node relationship network indicating a real interaction relationship between the entity objects; and in a hyperbolic space, generating node representations according to the node relationship network and the node feature vectors, and outputting a clustering result on the basis of the node representations. Using the method can fully capture contained low-order and high-order topological structure information in the hyperbolic space, thus achieving a better clustering effect.

The invention relates to the technical field of data analysis, in particular to a diabetes clinical test data intelligent clustering analysis system and method based on federal learning. Comprising a data acquisition and preprocessing unit; a federal privacy protection unit; the dynamic clustering modeling unit is used for constructing a clustering model of self-adaptive diabetes data features, and realizing joint clustering analysis of multi-source heterogeneous data by adopting a hierarchical federal architecture and a dynamic parameter aggregation algorithm and combining a diabetes course time decay factor and a clinical feature weight adjustment strategy; a double-track verification optimization unit; and an intelligent decision support unit. According to the method, the incidence matrix of the diabetes disease course time decay factor and the clinical characteristics is introduced, the dynamic weight vector is constructed and applied to clustering distance calculation, so that the model can adapt to dynamic changes of the clinical characteristics in different disease course stages, the adaptability to multi-center heterogeneous data is improved, and the stability of a clustering result is enhanced.

The invention discloses a ternary internet-of-things battery management system, particularly relates to the technical field of lithium battery management, realizes lithium battery thermal safety closed-loop management through multi-sensor fusion, and comprises the following steps: deploying a digital sensor array to obtain time-space associated data; constructing a spatio-temporal feature vector including fusion of a local temperature gradient, a neighborhood temperature gradient and a stress coupling ratio; a density clustering algorithm is adopted to dynamically divide spontaneous heating, radiant heat and stress coupling clusters, and cluster boundaries are updated in real time; calculating a dual thermal coupling coefficient based on a clustering result, and eliminating the interference of temperature reading through a dual temperature compensation formula; a three-level alarm mechanism of a radiant heat proportion, a stress coupling degree and a compensation error is established, a side gateway is linked to realize parameter adaptive optimization, and the thermal runaway early warning precision and the structural failure detection rate are improved; strain monitoring resource optimization is realized through physically-driven key point screening and dynamic graph neural network prediction.

The invention discloses a semantic understanding-based technical literature multi-dimensional analysis method, which comprises the following steps of: performing text vectorization processing on text data by utilizing a Sension-BERT model to generate dense vector representation of a text; vector dimensionality reduction is carried out by adopting UMAP, and redundant features are removed; performing unsupervised clustering analysis by using HDBSCAN to generate a clustering result; extracting a subject term from a clustering result by adopting a TF-ICF method; and carrying out multi-dimensional analysis on the clustering result. Compared with the prior art, a comprehensive and efficient patent analysis tool is provided through an advanced semantic understanding patent clustering method, so that deeper interpretation and analysis of technical literatures are realized while the cost benefit is ensured.

The invention provides a keyword-based file information identification method and system, and the method comprises the steps: obtaining existing files, building a file database based on the existing files, carrying out the clustering of the existing files in the file database according to the obtained preset industry term data, and obtaining a first clustering result of keywords; the method comprises the following steps: acquiring a correlation score of an existing file and a query keyword based on a BM25 model, performing optimization clustering on a first clustering result of the keyword based on the correlation score, acquiring a second clustering result of the keyword, constructing a keyword semantic association magnetic field according to the second clustering result of the keyword in combination with an electromagnetic field simulation mode, and acquiring historical query data of a user. A personalized dynamic weight is established based on historical query data of the user, a third clustering result of the keywords is obtained according to the personalized dynamic weight and the keyword semantic association magnetic field, and a corresponding existing file is recognized and matched in the file database for the user based on the third clustering result of the keywords. Through the method, the accuracy of querying the matched document file by the user can be improved.

The invention discloses a power transmission line abnormity early warning and fault positioning system, which belongs to the technical field of line detection, and specifically comprises the following steps: dividing early warning equipment in a line into sub-regions according to distribution through a region delimiting module; when a fault occurs, the region positioning module quickly marks the related sub-regions and equipment as undetermined states; the data acquisition module is used for collecting data of the to-be-determined equipment in a set time window before a fault, and preprocessing the data to judge whether the fault is caused by an electrical problem or not; if yes, collecting historical power consumption data of the to-be-determined area; the fault positioning module normalizes historical data, constructs a time sequence model, extracts a current value change sequence, divides the current value change sequence into a plurality of subsequences and then performs clustering analysis; judging the fault reason according to the offset condition of the category cluster where the undetermined current value change sequence is located in the clustering result; if the offset exceeds a preset threshold value, considering that the electric equipment is abnormal; otherwise, the aging of the line insulating material is caused.