268 results about "Neighbor algorithm" patented technology

The invention relates to the technical field of elevator fault prediction, in particular to an elevator fault prediction method and system. The method comprises the following steps: collecting an operation vibration signal of an elevator brake through a vibration sensor, performing frequency domain conversion, performing vibration frequency disorder structure analysis, analyzing abnormal vibration frequency intensity, analyzing elevator braking force increment loss, and obtaining time sequence increment data; thirdly, incremental gradient nonlinear induction is carried out on the time sequence incremental data, and an elevator fault prediction model is constructed based on a K-nearest neighbor algorithm; the elevator fault prediction technology is optimized, so that the elevator fault prediction technology is more accurate.

The invention discloses a dynamic self-adaptive recommendation strategy optimization method for a business handling failure scene, and relates to the technical field of business handling recommendation and intelligent decision making, and the method comprises the steps: firstly collecting various types of data of a whole business handling process, and guaranteeing the integrity and real-time performance at a frequency of 100 milliseconds per time; a decision tree and Bayesian network fusion algorithm is used for attribution, and direct and indirect reasons are clarified; integrating data to construct a user portrait, and mining potential and subsequent demands; generating a recommendation scheme set based on attribution and portraits, and adjusting priorities and forms in combination with scene features; feedback data is introduced, a strategy weight is optimized by using a gradient descent algorithm, and the scheme is updated regularly; a multi-dimensional index weighted evaluation effect is set, and emergency optimization is carried out if the evaluation result does not reach the standard; and establishing a distributed strategy library, and reusing the optimal strategy of the similar scene by using a K-nearest neighbor algorithm. According to the method, failure reason accurate positioning and personalized recommendation are realized, the recommendation effect is continuously optimized along with data accumulation, and the method is adaptive to multiple service types and user groups.

The invention discloses a large model reasoning enhancement method based on knowledge graph sub-graph matching. The method comprises the following steps: firstly, establishing an index based on attribute information of nodes and edges in a multi-hop knowledge graph; and then, according to the established index, performing retrieval based on a nearest neighbor algorithm. And then, according to a retrieval result, constructing a minimum correlation subgraph, and performing reasoning enhancement. And finally, based on the minimum correlation subgraph, extracting and processing information, and generating a natural language answer understood by the user. According to the method, the multi-hop reasoning problem under a complex graph structure is effectively solved through the multi-stage optimization process, and the response quality of user query and the practicability of the system are improved.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention relates to a fault diagnosis method and device based on a graph attention convolution auto-encoder and a medium, and the method comprises the steps: S1, obtaining an original fault data set, and carrying out the standardization operation of the fault data set obtained after slicing; s2, calculating the distance between variable feature vectors in the standardized fault data set, and constructing an adjacency matrix by adopting a K adjacency algorithm; s3, inputting the adjacent matrix and the standardized fault data set into a graph attention convolution auto-encoder to extract spatial correlation features and fault information features; s4, inputting the fault information features into a double-layer full-connection layer, and outputting a classification prediction category; s5, calculating a loss function of the graph attention auto-encoder, and training the model through back propagation of the loss function; and S6, performing fault diagnosis on a fault data set to be diagnosed by using the trained graph attention convolution auto-encoder model, and outputting a classification prediction category. Compared with the prior art, the method has the advantages of high interpretability and high accuracy.

The invention discloses a fuzzy-control-based autonomous obstacle avoidance system for an underwater robot, and relates to the technical field of fuzzy control, which comprises the steps of realizing accurate perception and boundary correction of obstacles by analyzing sonar data, optical images and inertial navigation information, extracting a target position in combination with echo signal features, enhancing the accuracy of environmental data, and obtaining an autonomous obstacle avoidance result. A fuzzy K-nearest neighbor algorithm is adopted, target clustering, moving target classification and environmental impact factor calculation are carried out based on dynamic information of obstacles, so that obstacle avoidance path planning can adapt to different topographic conditions and obstacle distribution, the applicability of path calculation is improved, a Dikstra algorithm is adopted to demarcate a traffic area, and the obstacle avoidance path planning efficiency is improved. And combining path interference calculation and risk assessment, optimizing a path weight matrix, and ensuring that an obstacle avoidance path realizes an optimal decision at a balance point of safety and trafficability. The path optimization link is based on energy consumption calculation and propulsion power optimization, the navigation stability is improved, and the burden of a propulsion system is reduced.

The invention discloses a tunnel stratum recognition method based on shield tunneling machine tunneling parameter machine learning, and relates to the technical field of monitoring analysis, and the method comprises the steps: data collection: constructing a project data set, collecting rock core samples for drilling and coring from the project data set, and determining a learning sample set based on the rock core samples; data feature mining, which is used for obtaining tunneling parameters corresponding to various types of stratums from the learning sample set, mining a statistical feature set responding to stratum changes based on the tunneling parameters corresponding to various types of stratums, and forming a first feature parameter matrix; data feature screening, which is used for carrying out sensitivity analysis on the first feature parameter matrix so as to screen out a second feature parameter matrix; the model construction is used for constructing a stratum recognition model by adopting a K nearest neighbor algorithm based on the learning sample set and the second characteristic parameter matrix; and stratum identification, which is used for identifying the target stratum according to the stratum identification model. The method has the effect of improving the stratum recognition efficiency.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly discloses an automatic tooth segmentation method and system for oral cavity scanning point clouds, and the method comprises the following steps: collecting and preprocessing dentition three-dimensional point clouds, extracting point cloud features containing coordinates and normal directions, and constructing a neighborhood structure; setting a first-stage multi-branch network, and performing tooth and gingiva coarse segmentation and instance initial clustering on the point cloud; setting a second-stage semantic refinement network, and carrying out local cutting on the original point cloud; setting a boundary detection and boundary offset network to enhance a tooth contact area, and refining a boundary instance by clustering to obtain a boundary enhanced instance tag; and performing label fusion, and propagating the fused label to the original high-resolution point cloud through a nearest neighbor algorithm to obtain a final semantic label and a final instance label. By adopting the technical scheme, high-precision instance-level segmentation of the complete dentition point cloud is realized through semantic segmentation, boundary detection and clustering cooperative work based on a biased field.

The present disclosure provides a cross-file question and answer knowledge extraction method and system, and an electronic device. The method includes: obtaining a user question; converting the user question into a user question embedding vector by using an embedding function; determining the user question embedding vector and a first similarity vector of a root node of a file embedding vector tree of each professional knowledge file; determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes; determining a candidate node set by using the K-nearest neighbor algorithm based on all the similar vector trees; determining an optimally-matched node set based on the candidate node set; and determining, based on the optimally-matched node set, file knowledge content corresponding to the user question. The present disclosure improves accuracy of extracting the cross-file question and answer knowledge.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

The present invention discloses a method for completing missing power data, which includes obtaining original power data and dividing it into a complete data set and a missing data set; using the dynamic time warping algorithm to determine the dynamic time warping distance, and using the K-nearest neighbor algorithm to construct a nearest neighbor data matrix according to the dynamic time warping distance of the power sequence; optimizing the weight distribution of the nearest neighbor data matrix to obtain a first completion value; calculating the attribute correlation influence coefficient according to the nearest neighbor data matrix as the second completion value; determining the completion value of the power sequence in the missing data set according to the first completion value and the second completion value; moving the completed power sequence out of the missing data set and adding it to the complete data set. The present invention can accurately complete the missing data in the power data. The effective repair of the missing values through the completion method can truly reflect the real electricity consumption situation of users, providing complete and effective basic power data for the analysis of user-related electricity consumption behavior research.

The invention discloses a mining area exploration system based on artificial intelligence. The mining area exploration system comprises a multi-source data acquisition module, a data optimization module, a mining area candidate area potential identification module and a mining area intelligent exploration module. The invention relates to the technical field of artificial intelligence data analysis and computer vision, in particular to a mining area exploration system based on artificial intelligence, according to the scheme, a mining area candidate area potential identification module and a mining area intelligent exploration module are innovatively combined, and the accuracy of mining area identification and the pertinence of exploration are improved; a weighted graph structure is constructed by adopting a K nearest neighbor algorithm, and an improved graph convolutional neural network with a parallel updating mechanism is introduced, so that high-precision automatic potential classification of a large-range candidate mining area is realized; and a composite chaotic mapping initialization method and a sine index inertia weight improved particle swarm optimization algorithm are introduced, so that the accuracy and stability of yield prediction are improved, and high-precision yield prediction of each sub-region of the mining area is realized.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

The invention discloses a method and system of an Internet of Things management platform for big data, and relates to the technical field of Internet of Things, comprising the steps of establishing a signal difference model through historical signal data to generate radio frequency fingerprint features of physical layer signal data, and obtaining acceleration features of application layer flow data through calculation; the method comprises the following steps: acquiring two features, fusing the two features to obtain a cross-protocol stack fusion feature vector, calculating a distance between physical devices by using a K nearest neighbor algorithm to obtain a device contact table, sorting the cross-protocol stack fusion feature vector into a three-dimensional time sequence table, and adjusting the device contact table through a space-time diagram convolutional network to obtain a space-time feature table. According to the invention, deep integration of physical layer identity characteristics and application layer behavior trends is realized. And comprehensive feature representation with higher expressive power is provided for an Internet of Things management platform. The limitation of traditional single-layer feature extraction is broken through, and the accuracy of anomaly detection is remarkably improved.

The invention provides a mechanical equipment structure key pressure-bearing component real-time simulation method based on digital twinning, which takes a cubic press hinge beam as an example, and combines finite element analysis, Latin hypercube sampling, a K nearest neighbor algorithm, Gaussian interpolation and an RBF (Radial Basis Function) proxy model to realize stress-strain rapid prediction and three-dimensional visualization. According to the method, an efficient prediction model is established through structure database construction, dimension reduction processing, neighbor search and interpolation calculation, a simulation result is presented in real time by utilizing Python and Unity interaction, the design efficiency and accuracy are improved, and the method is suitable for structure optimization analysis under complex working conditions.

The invention discloses a talent evaluation and post matching system and method, and relates to the technical field of information, and the system comprises a personality evaluation module, a post personality demand modeling module, a matching calculation module, a feedback optimization module and a data storage module. The personality evaluation module is used for acquiring a nine-dimensional personality feature vector of an employee based on a nine-type personality theory; the post personality demand modeling module is used for constructing and storing a nine-type personality weight vector model of a post; the matching calculation module calculates matching degree scores between the employees and the posts and generates a recommendation sequence based on a rule algorithm and a K-nearest neighbor algorithm; a feedback optimization module collects performance feedback data after employees enter the job, and automatically updates a matching model to improve prediction accuracy; and the data storage module is used for centrally managing evaluation data, post models and feedback records. According to the method, personalized matching and dynamic optimization between the employees and the posts are realized, and the organization configuration efficiency and the man-post adaptation quality can be improved.

Devices and techniques are generally described for natural language processing interfaces. In various examples, first natural language data may be received from an input device. First embedding data representing the first natural language data may be generated. A nearest neighbor algorithm may determine first data representing similarity between the first embedding data and second embedding data, the second embedding data representing second natural language data associated with a first skill. The nearest neighbor algorithm may determine second data representing similarity between the first embedding data and third embedding data, the third embedding data representing third natural language data associated with a second skill. First output data that indicates that the first skill and the second skill are candidates for processing the first natural language data may be generated.

The invention discloses a GNSS (Global Navigation Satellite System) monitoring data abnormal value detection method based on CEEMD (Compensation Empirical Mode Decomposition) and a local abnormal factor. The method comprises the following steps: carrying out preprocessing, gross error removal, interpolation processing and mirror image expansion on a three-dimensional direction coordinate time sequence of a GNSS monitoring terminal; cEEMD decomposition is carried out on the extended time sequence; determining a boundary layer of the noiseless signal component and the noise signal component according to a cross validation model; reconstructing a time sequence according to the boundary layer; determining an abnormal point according to a K-nearest neighbor algorithm and a local abnormal factor algorithm; abnormal points in the residual term are removed, interpolation processing is carried out, and an accurate residual term is obtained; and reconstructing a GNSS coordinate time sequence according to the precise residual term and the trend term. The method is especially suitable for processing a data set with high complexity and great local density difference, the accuracy of a processing result is improved so as to solve the problem of accurate detection and elimination of the gross error of the GNSS monitoring data, and the invention further discloses a device for realizing the method, electronic equipment and a computer readable storage medium.

This application provides a target recognition method and apparatus, a device, and a storage medium. The method includes: performing, by using a preset visual sensor, event signal collection on a target object to obtain a target event signal; performing a slice division operation on the target event signal based on a preset time interval to obtain a signal sample; and performing a graph construction operation on the signal sample based on an event timestamp and a target nearest neighbor algorithm to obtain a distance relationship graph of the signal sample, and performing density clustering on the distance relationship graph to recognize the target object. In this way, graph construction is performed based on characteristics of event data, and a density clustering algorithm is used to perform target recognition and detection on a constructed graph, so that effect of dynamic recognition is achieved through iterative calculation on different time slices.

The invention relates to the technical field of aero-engine rotating blade health monitoring, and provides an aero-engine rotating blade timing undersampling vibration signal reconstruction method and system, and the method comprises the steps: constructing a reconstruction model and a recovery matrix; setting an initial residual error and a support set of the recovery matrix; calculating the inner product of the column vector of the recovery matrix and the initial residual error; the column vectors with the absolute values of the inner product values exceeding a threshold value are selected as matching vectors to be combined into a first set; projecting the first set to a parameter space; clustering is carried out by adopting a K-nearest neighbor algorithm, and column vectors with maximum inner products in each class are reserved to form a second set; adding the second set into a support set, and solving a sparse coefficient by adopting a least square method by taking the minimum initial residual error as a target; a new residual error is calculated, iteration is terminated until the support set is not updated any more, and a final vibration signal sparse coefficient is obtained; and calculating to obtain a blade end vibration signal of the rotating blade of the reconstructed aero-engine. According to the invention, the non-contact online real-time health monitoring effect of the blade can be improved.

The invention discloses a crop three-dimensional point cloud branch and leaf separation method of a semantic prototype driven graph attention network, and relates to the technical field of plant high-throughput phenotypic analysis and three-dimensional computer vision, local geometric features are extracted by adopting random downsampling and an attention pooling mechanism, the point cloud scale is reduced by randomly generating a pooling index, and the point cloud branch and leaf separation efficiency is improved. And performing weighted aggregation on neighborhood features by using geometric topological coding, constructing a dynamic topological structure based on a K-nearest neighbor algorithm, aggregating node features in a multi-level manner through a graph attention network, establishing dual constraints of geometric difference perception and feature association, and realizing deep integration of local and global context information. The method comprises the following steps of: performing up-sampling on low-resolution features step by step by using K-neighbor interpolation based on attention weighting to reconstruct high-resolution features, performing weighted fusion on the high-resolution features corresponding to a coding stage through jump connection, improving a distribution structure of a category feature space through an inter-class separability discrimination optimization function, and ensuring the stability of feature distribution.

The invention relates to a gearbox fault diagnosis model construction method based on metric guide graph comparative learning, and belongs to the field of gearbox fault diagnosis model construction. The method comprises the following four core stages: firstly, carrying out frequency domain conversion and normalization on vibration signals of the gearbox to generate a node characteristic matrix; secondly, a cosine distance and an Euclidean distance are fused to construct a mixed distance matrix, and a fault diagnosis graph is generated based on a K-nearest neighbor algorithm; then unsupervised graph comparison pre-training is realized through graph data enhancement and a dynamic graph attention network (DGAT); and finally, weak supervision fine tuning is carried out by using a small number of marked samples to complete construction of the gearbox fault diagnosis model. According to the method, the construction of the high-precision gearbox fault diagnosis model can be realized in a scene with extremely few marked samples (1-10 samples per class), and the method is suitable for planetary gearbox health monitoring in the fields of wind turbines, helicopters, hybrid electric vehicles and the like.

The present invention relates to a monochromatic cloth defect detection method based on weakly supervised learning. By collecting the original cloth images and performing preprocessing, a normal image feature library is established; based on the normal image feature library, the abnormal threshold TH is set, the features of the processed image to be detected are extracted, and the K nearest neighbor algorithm is used to retrieve the K flawless image with the closest similarity to the image to be detected in the normal image dataset, and the K abnormal score between the TH flawless image and the image to be detected is calculated; threshold segmentation is performed using the abnormal threshold to obtain the segmentation mask image of the abnormal area corresponding to the cloth defect. The detection of the present invention is more targeted, has a higher recall rate for cloth image defects, does not require a large amount of time for dataset production and model training, and improves the detection sensitivity for small defect targets while ensuring the real-time performance of detection.

The invention discloses an organizational structure division method for spatial transcriptome data, which comprises the following steps of: firstly, performing coordinate calibration, hypervariable gene screening and gene expression standardization preprocessing on original spatial transcriptome data, and constructing a standardized data set containing a spatial adjacency relation and a standardized gene expression profile; an initial hyperedge is generated based on a k-nearest neighbor algorithm, cross-domain noise is dynamically eliminated in combination with a hyperedge decomposition algorithm guided by gene expression, and a hypergraph structure with double constraints of spatial proximity and gene expression homogeneity is formed; designing an auto-encoder architecture comprising a hypergraph attention layer, compressing high-dimensional data to a low-dimensional potential space, reconstructing a loss optimization architecture by using gene expression, and generating low-dimensional representation with topology retentivity and function consistency; and finally, organizational structure division is carried out on the low-dimensional representation clustering based on a Gaussian mixture model. According to the method, the spatial continuity of organization structure division is remarkably improved, and a semantic gap of cross-resolution data is effectively bridged.

The invention belongs to the technical field of intelligent traffic, and discloses a traffic prediction method based on multi-layer K-nearest neighbor, a storage medium and computing equipment, and the method comprises the steps: S1, collecting traffic flow data; s2, calculating a correlation coefficient of time sequence data between sampling points, selecting # imgabs0 # neighbors by adopting a K nearest neighbor algorithm, and constructing current and historical state vectors; s3, selecting # imgabs 1 # neighbors by adopting a K nearest neighbor algorithm according to the Euclidean distance; s4, calculating a first-order difference value of the traffic flow data of the sampling points, and constructing a current state vector and an amplitude change trend vector of Euclidean distance neighbor data; adopting a K nearest neighbor algorithm to select # imgabs2 neighbors; and S5, predicting the traffic flow by using a support vector regression algorithm. According to the method, the high-correlation state vector is constructed, and dual screening of Euclidean distance and amplitude variation trend is performed on the state vector, so that the characteristics of strong nonlinearity and randomness of urban traffic data can be effectively handled, and the accuracy of a prediction algorithm is improved.

The invention relates to the technical field of computers, and particularly discloses a security product self-evaluation report intelligent auditing method based on artificial intelligence assistance, and the method comprises the steps: analyzing a self-evaluation report to generate structured detection item data; carrying out multi-modal feature extraction and fusion on the text and image proof materials; semantic conflicts, configuration compliance, evidence credibility, image-text consistency and historical risk matching degree are analyzed in parallel through an artificial neural network, a support vector machine, a random forest, logistic regression and a weighted neighbor algorithm; according to a preset weight, dynamically fusing results of all dimensions to determine a compliance probability; similar historical cases are retrieved in combination with a security product compliance analysis knowledge graph, and auditing instructions and improvement suggestions with violation positioning bases are generated; and finally, outputting a structured auditing report and supporting continuous optimization of a manual reexamination feedback driving model. According to the method, high-precision, full-dimension and automatic security product self-evaluation report auditing can be realized, and the auditing efficiency, objectivity and large-scale processing capability are remarkably improved.

The invention provides a cross-domain rolling bearing intelligent fault diagnosis method based on domain adaptation. The method comprises the following steps: (1) carrying out fast Fourier transform on an original time sequence signal, extracting frequency domain information, normalizing the frequency domain information, and reshaping the frequency domain information into two-dimensional grayscale image data; (2) constructing an SCG-TNet network model, including initial dimensionality reduction convolution, lightweight reverse residual convolution blocks based on channel shuffling and two-layer feature enhancement convolution, and realizing global feature extraction; a graph data structure and a graph convolution module of the high-dimensional feature graph are constructed based on a K-nearest neighbor algorithm, and global feature fusion is realized; (3) designing a CGWloss function, reducing the data distribution difference between a source domain and a target domain, and realizing cross-domain feature alignment; (4) balancing classification loss and cross-domain alignment loss by adopting a dynamic loss weight adjustment strategy, and optimizing a training process; and (5) using a back propagation mechanism and an Adam optimizer to minimize a loss function value, and training the model. The method is good in performance under different working conditions and can effectively adapt to complex working conditions.

The invention discloses a data-driven pressure-bearing equipment damage identification and diagnosis method and device, equipment and a medium, and relates to the field of equipment damage identification, and the method comprises the steps: according to the basic data of target pressure-bearing equipment and a pressure-bearing equipment damage prediction model with the same device type as the target pressure-bearing equipment, carrying out the data-driven pressure-bearing equipment damage identification and diagnosis on the target pressure-bearing equipment; identifying a damage mode and a damage rate of the target pressure-bearing equipment; the method for determining the damage prediction model of the pressure-bearing equipment comprises the following steps: acquiring a sample data set; the sample data set comprises basic data of pressure-bearing equipment of different device types and corresponding label data; performing sample data balance on the sample data set by adopting a synthetic minority class oversampling technology and an editing nearest neighbor algorithm to obtain an expanded data set; and on the basis of the expanded data set, performing optimization training on hyper-parameters in the machine learning model by adopting a genetic algorithm to obtain pressure-bearing equipment damage prediction models of different device types, and the method can quickly and accurately judge the damage mode and predict the damage rate.