51 results about "Medoid" patented technology

The invention belongs to the technical field of power system operation control, and discloses a virtual power plant aggregation operation optimization method considering flexible and adjustable resource hierarchical and partitioned regulation, which is characterized in that dynamic clustering and clustering are performed on a distributed power supply based on an improved K-medoids algorithm, and response characteristic analysis is performed on an electric vehicle, an energy storage load and a temperature control load; by establishing a self-adaptive layering and partitioning mechanism, dynamic layer region boundary adjustment is realized. An optimization model with the lowest cost as the target is constructed, the optimal resource scheduling scheme is solved by comprehensively considering various cost factors and operation constraint conditions, and efficient configuration of resources and effective control of the cost are achieved; and the adjustable load and the energy storage resource are guided to participate in system regulation and control, so that the peak-valley difference of the system is reduced with relatively low regulation and control cost. According to the method, the resource scheduling efficiency is effectively improved, high-quality peak regulation auxiliary service is provided for the power grid, and efficient utilization of distributed energy is assisted.

The invention discloses a transformer fault intelligent diagnosis system and method based on a K-Medoid and SMOTE optimization support vector machine, and the system comprises a data collection module which is used for collecting dissolved gas and operation parameters in transformer oil in real time; the data preprocessing module is connected with the data acquisition module and is used for completing data standardization and abnormal value detection; the sample balancing module is connected with the data preprocessing module and used for reducing majority class redundancy through K-Medoid clustering and expanding minority classes through an SMOTE algorithm; the intelligent classification module is connected with the sample balance module and adopts a weighted SVM classifier to train and predict; and the result output and visualization module is connected with the intelligent classification module and is used for displaying the classification result, the confidence coefficient and the key performance indexes. According to the method, the problem of classification performance degradation caused by sample imbalance in the prior art is solved, and the recognition capability of the SVM model on minority class faults and the overall diagnosis precision are improved.

The invention discloses a rural energy system multi-energy load scene construction method, and relates to the field of comprehensive energy, and the method comprises the steps: obtaining a historical multi-energy load time sequence of a rural energy system; according to the historical multi-energy load time sequence, a scene generation model is adopted to generate a multi-energy load scene set in a future time period; the scene generation model is constructed based on a Transform model and a regularization relative loss generative adversarial network, and a cosine annealing algorithm and a hot restart mechanism are adopted to dynamically adjust the learning rate when the scene generation model is trained; and reducing scenes in the multi-energy load scene set by adopting a dynamic time warping distance method and a K-Medoids clustering method to obtain a typical multi-energy load representative scene set. According to the method, on the basis that the generated scene keeps a certain diversity, the historical data evolution law and the coupling characteristic between the source and the load are restored to the maximum extent, and extraction and reduction of the representative scene are achieved.

A wind storage joint optimization regulation and control method considering conditional value-at-risk comprises the following steps: acquiring prediction data of wind power plant output and day-ahead electric energy market clearing price in each transaction period, and generating a large number of uncertain scenes through a Monte Carlo simulation method; then scene reduction is carried out through a K-medoids clustering algorithm to generate a reasonable number of typical day-ahead spot electricity price and new energy output scenes, the two typical scenes are permutated and combined to obtain final scene data, and the final scene data is input into a pre-constructed wind storage joint participation day-ahead electricity market output regulation and control optimization model; taking the day-ahead expected net income maximization as the target, introducing CVaR to measure the income risk, setting a corresponding target function and constraint conditions, and solving to obtain an output regulation and control plan and a bidding decision of the wind power plant and the novel energy storage in each simulation transaction scene. According to the method, the complementary advantages of novel energy storage and new energy are utilized, the new energy output fluctuation and deviation punishment risks caused by prediction deviation are reduced, the market competitiveness of clean energy can be improved, and new energy consumption is promoted.

The invention relates to the technical field of big data management and database optimization, in particular to an index tuning method and system for medical multi-modal data mixed query, and the method comprises the following steps: accessing structured and unstructured medical data into a data lake; performing expansion processing on a nested structure in the structured data according to field types, extracting semantic feature vectors from unstructured data, packaging the unstructured data into a standardized JSON format, performing data fusion, and loading the fused data to a PostgreSQL database; analyzing a mixed query SQL statement submitted by a user, and constructing high-dimensional mixed feature representation; carrying out clustering compression on the query request set by adopting a K-Medoids clustering algorithm to generate a representative query set; performing virtual index simulation evaluation on the structure field by utilizing HypoPG, creating a real index for the vector field in combination with a pgvector plug-in, and collecting a query performance index; and based on IBST multiplexing historical evaluation data, in combination with a structure change prediction model based on a Transform architecture, outputting an optimal index combination. According to the method, the query performance and the index recommendation efficiency are remarkably improved.

The invention relates to a hydrogen energy dynamic scheduling method and system considering source load uncertainty in an industrial park, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining wind-solar power generation and hydrogen load historical data, and constructing a data set; obtaining an intrinsic mode function component through variational mode decomposition; constructing a modal feature matrix based on the components, and performing K-medoids clustering by adopting a dynamic time warping distance to generate a typical scene and probability; establishing a day-ahead scheduling optimization model, and making a hydrogen production and energy storage plan by taking total cost minimization as a target; and in actual operation, a real-time scheduling instruction is generated by minimizing the plan deviation and the equipment variation through the intra-day rolling optimization model. Through multi-scale optimization and scene generation, the scheduling precision, economical efficiency and real-time adaptability are improved, and the method is suitable for an electric-hydrogen coupling system in an industrial park.

The application discloses a differential privacy data desensitization method based on time sequence random mapping and related devices, comprising: obtaining various target time sequence data that needs to be desensitized in a power transaction center, and clustering the target time sequence data by using a k-medoids clustering algorithm based on dynamic time warping distance measurement to obtain time sequence data sets of different clusters; based on the maximum compression ratio of the target time sequence data, the window value size corresponding to different target time sequence data in the same cluster is calculated, and the average compression ratio of all target time sequence data is determined, and the average compression ratio is taken as the target window size set during desensitization; based on the pre-determined security protection requirement level of the target time sequence data, the privacy budget required by differential privacy is determined, and the target time sequence data is processed according to the target window value size and the differential privacy desensitization mode of time sequence random mapping to obtain power market subject data after time sequence desensitization.

The application discloses a transformer winding looseness fault diagnosis method based on an improved MPE and K-medoids algorithm, which is used for forming a transformer winding looseness MPE value criterion and realizing winding looseness fault diagnosis. The method steps are as follows: 1, measuring points are arranged on a transformer box body, vibration signals of each measuring point are acquired, and a vibration amplitude maximum measuring point D is selected as an optimal measuring point; 2, vibration signals of the transformer winding in different states are measured; 3, a particle swarm optimization algorithm is used to optimize parameters in a traditional MPE algorithm, so that the overall change of the MPE value is stable; 4, the MPE value of the measuring point D is calculated by using the optimized MPE algorithm; 5, the MPE values under two adjacent scale factors are selected as horizontal and vertical coordinates of a clustering coordinate system; 6, the K-medoids algorithm is used in the coordinate system to realize accurate classification of the transformer winding fault type; and 7, the MPE value criterion is summarized and formed, and a database is established. The method reduces the cross aliasing phenomenon of the traditional MPE algorithm, and realizes accurate judgment of the transformer fault type.

The application discloses a kind of based on trust value improvement K-medoids's blockchain trusted sensor anomaly detection method, comprising the following steps: S1, trust value initialization, and the trust value of the BTS node participating in this consensus is updated by node trust value update formula;S2, n BTS node is clustered into k groups, and the optimal BTS node is determined as center point;S3, partition for the rest BTS node;S4, when there is client to provide new transaction request, BTS layer according to partition voting result, the same partition vote different from other BTS node is regarded as abnormal target set A, the node set of credit value below 60 points is recorded as B, the node in the intersection of A intersection B is judged as abnormal node, the beneficial effects of the application are: improved K-medoids clustering algorithm, more in line with the demand of BTS practical application, can more accurately and efficiently exclude BTS abnormal node;While clustering result has strong stability, abnormal BTS node under different environments can maintain higher detection rate.

The invention belongs to the field of computer software, and provides an unmanned aerial vehicle intelligent body task execution and optimal trajectory generation method based on deep reinforcement learning. The unmanned aerial vehicle intelligent body task execution model constructed through the TD3 algorithm can realize more accurate unmanned aerial vehicle action value evaluation according to the unmanned aerial vehicle observation value. During iterative training of an unmanned aerial vehicle agent task execution model, task execution actions of an unmanned aerial vehicle are constrained by using an observation space and an action space, and it can be ensured that each decision in task execution of the unmanned aerial vehicle is beneficial for the unmanned aerial vehicle to complete a task execution target; the unmanned aerial vehicle agent task execution model is trained through the unmanned aerial vehicle agent task execution function, and trajectory prediction of task execution of the unmanned aerial vehicle is improved. Clustering analysis is carried out on trajectory information of all execution tasks of the unmanned aerial vehicle intelligent body through a K-Medoids center clustering algorithm, the limitation of single reasoning decision can be effectively avoided, and the task completion reliability is improved.

The invention relates to the technologies of the Internet of Vehicles, block chains, consensus algorithms and the like, and discloses a dynamic clustering reputation collaborative hierarchical consensus architecture for the Internet of Vehicles. In the method, an architecture of'cluster grouping optimization topology + reputation dynamic management and control + hierarchical consensus efficiency improvement 'is constructed: grouping is carried out according to a communication distance between a vehicle and a clustering center, equipment computing power and a reputation value by using an enhanced K-Medoids algorithm, and a low-delay topology foundation is laid; based on an improved EigenTrust algorithm, node reputation is evaluated in combination with data authenticity, consensus participation degree, resource contribution degree and historical impact factors, and node permission is dynamically controlled; layered efficiency improvement is performed according to the number of vehicle nodes, a multi-layer architecture is established, local efficient processing is adapted, global consensus is realized, malicious interference and communication consumption are reduced, and the security of a consensus network is improved.

This invention discloses a method for evaluating the quality of marine controlled-source electromagnetic data, belonging to the field of data quality evaluation technology. The method includes: projecting the acquired marine controlled-source electromagnetic data onto a two-dimensional plane, whereby the real and imaginary parts of the data signal are projected onto the horizontal and vertical coordinates to obtain the data distribution; performing cluster statistics on the data distribution using the K-medoids clustering method to obtain cluster centers; and using the cohesion (CP) calculation formula to perform aggregation degree analysis on the data distributed according to the cluster centers to obtain the data quality assessment result. This invention can directly quantify and evaluate data based on the cohesion of the electromagnetic data distribution characteristics, without defining empirical functions, requiring no specific data volume, and is unaffected by fly-spot data, thus improving the applicability and accuracy of the evaluation method.

The application discloses a rural energy system multi-energy load scene construction method, relates to the field of comprehensive energy, and comprises the following steps: acquiring a historical multi-energy load time sequence of a rural energy system; generating a multi-energy load scene set of a future time period by using a scene generation model according to the historical multi-energy load time sequence; the scene generation model is constructed based on a Transformer model and a regularized relative loss generation adversarial network, and the scene generation model dynamically adjusts a learning rate by using a cosine annealing algorithm and a hot restart mechanism during training; and the scenes in the multi-energy load scene set are reduced by using a dynamic time warping distance method and a K-Medoids clustering method, so that a typical multi-energy load representative scene set is obtained. According to the application, the generated scenes can restore the coupling characteristics between the historical data evolution law and the source load to the maximum extent on the basis of maintaining a certain diversity, and the representative scenes can be extracted and reduced.

The invention relates to a topological clustering method for a power grid operation scene fusing a graph editing distance and K-medoids, and the method comprises the steps: carrying out the construction and preprocessing of a power grid topological graph, abstracting a power system into a graph structure, carrying out the unified coding of nodes and edges, and guaranteeing the consistency of the graph structure; counting dissimilarity of the topological graph by adopting a heuristic algorithm based on A *; k topologies are randomly selected from the topologies to serve as initial clustering centers, each non-central topology is distributed to a cluster where a central point closest to the non-central topology is located, for each cluster, the central point is tried to be replaced, the topology with the minimum GED sum from all points in the cluster to the central point is selected to serve as a new central point, and the distribution and updating steps are repeated; the central point does not change any more or reaches the maximum number of iterations; k clustering clusters are output, topologies far away from all the clusters are screened out and judged to be of an abnormal or non-standard structure, each cluster represents the center topology of the cluster, and each cluster center corresponds to a typical power grid topology mode.

The application discloses a kind of combination clustering scene reduction method and system based on potential decision result, the uncertainty factor of each node in power system is expressed as multidimensional curve input domain in the form of time sequence curve;Based on multidimensional curve input domain, the power supply planning problem solving model under different time scale scene is constructed, considering operation checking, multidimensional curve input domain is converted into potential decision result domain for clustering analysis;SOM neural network clustering is carried out to potential decision result, and preliminary clustering result is obtained;SOM and k-medoids combination clustering method is used to finally classify preliminary clustering result, and finally the typical scene used in operation checking is obtained.The application is used to cluster the most representative scene set in power supply planning typical scene operation checking, to reduce the complexity of solving model to the greatest extent under the condition of guaranteeing the reliability of checking result, improve calculation efficiency.

The application discloses a response potential evaluation method of adjustable resource cluster based on an AE-LSTM algorithm, and comprises the following steps: 1, collecting original power data and excitation intensity data; 2, decomposing the power consumption power sequence of the adjustable resource individual by using a discrete wavelet transform; 3, classifying the decomposed sequence of the adjustable resource cluster by using K-medoids clustering based on a DTW algorithm; 4, extracting the characteristic value of the center point data of each sequence of the clustered adjustable resource cluster by using the AE-LSTM algorithm, obtaining the parameters of the potential evaluation probability distribution, and calculating the response potential probability distribution of the adjustable resource individual. The application can effectively improve the characteristic value extraction efficiency of the AE-LSTM neural network, solve the problem that individual differences are neglected in adjustable resource potential evaluation, and thus can realize effective evaluation on the scheduling potential of the adjustable object in the adjustable cluster.

The application relates to the technical field of motion detection, in particular to a real-time motion mode switching identification system and method based on a single inertial sensor, which comprises a motion mode conversion data stream automatic acquisition module, a space-time gait parameter feature processing module and an improved K-medoids clustering algorithm model which are sequentially connected; the motion mode conversion data stream automatic acquisition module comprises an inertial motion sensing unit and a probability distribution modeling unit; the space-time gait parameter feature processing module comprises a feature extraction unit, a sorting unit and a cross-validation unit; and the improved K-medoids clustering algorithm model is used for classifying and training and predicting different switching motion states according to selected gait features, so as to realize accurate identification of a complex human motion switching process. The real-time motion mode switching identification system based on a single inertial sensor provided by the application mainly solves the problems of low accuracy and large complexity when a wearable device switches motion modes.

The invention relates to the technical field of power systems, in particular to a wind power output analysis method and device based on integrated clustering, equipment and a medium. According to the method, a K-medoids algorithm is adopted to perform preliminary clustering to obtain a clustering center matrix, and on this basis, a hierarchical clustering algorithm is adopted to perform secondary clustering to obtain a clustering center. And finally, dividing the original data according to the final clustering center, and using the center point as typical output for subsequent analysis. On the basis that original data information is reserved as far as possible, the high efficiency of the K-medoids algorithm and the accuracy of hierarchical clustering are exerted, the clustering speed is increased, and the clustering effect is improved.

The present invention discloses a zero-sample object instance segmentation system and method in a robot environment, which belongs to the field of robot visual perception technology. First, the depth image is preprocessed with Viridis color mapping, and then the SAM (Segment Anything Model) model is used to generate initial object mask candidates; at the same time, the pre-trained ViT (Vision Transformer) is used as the feature description model of the scene to process the image, extract the attention map of the last layer, and construct a feature weighting mechanism based on the information entropy of the attention map; then the similarity matrix of the image block and the background block is calculated to remove the non-object mask, and the K-Medoids clustering algorithm is used to obtain representative sampling points for each remaining object mask candidate. Finally, these points are input into the SAM model as prompt information to obtain accurate object instance segmentation results. The present invention makes full use of the zero-sample generalization ability of the visual basic model and can achieve accurate segmentation of unseen objects without additional training, with good versatility and practical value.

The invention discloses an active power distribution network operation risk assessment method and system considering element uncertainty, and the method comprises the steps: inputting basic data, and inputting the basic data into a pre-constructed power distribution network wind-solar output and load demand uncertainty model; constructing a multi-level active power distribution network risk assessment index system; the method comprises the following steps: taking wind power, photovoltaic and load output data as input, generating an operation condition scene by adopting LHS sampling of global sensitivity analysis, carrying out scene reduction by utilizing a K-medoids clustering algorithm to obtain an appropriate scene number and a probability of each scene, calculating a power flow in an s-th operation scene, and obtaining an output node voltage matrix and a line active and reactive power matrix; and after all scenes are traversed, the operation risk is calculated according to a pre-constructed risk index system. The method has the advantages that accurate evaluation and weak link positioning of the operation risk of the power distribution network are achieved, and the problem that a traditional risk evaluation method is insufficient in precision in the complex active power distribution network is effectively solved.

The application provides a power grid line impedance prediction method and system, obtains impedance historical data of a power grid line; performs time series decomposition on the obtained impedance historical data to obtain a plurality of trend signal data; performs clustering on the obtained trend signal data by using a derivative dynamic time warping algorithm distance optimized k-medoids clustering algorithm to obtain a plurality of classifications; and obtains impedance prediction data of a future set time period of the current power grid line by using a pre-trained prediction model on the trend signal data of the same classification; the application divides a plurality of long-time line impedances into a plurality of sequences for clustering analysis after noise reduction, can effectively reduce curve noise and extract periodicity of the curve, and improves the prediction accuracy by using a classification prediction method.

The application discloses a traditional residence environment quality evaluation method based on semantic information, first, through a mobile terminal carrying a collector, environment data of traditional residence buildings are collected, and the data are transmitted to a cloud server to pre-process original data; then, semantic information of each sampling point is calculated in combination with multi-source information, then, based on the semantic information, K-medoids clustering is carried out to obtain pollution source distribution and pollution propagation trend conditions in the whole traditional residence environment, regional division is carried out, and visual output is carried out; finally, according to the self-adaptive adjustment of environment parameter weights of each divided region, a fuzzy comprehensive evaluation method is used to evaluate the collected traditional residence environment. In the traditional residence environment quality evaluation process, the application can collect environment data with high quality, low cost and high accuracy, and can reasonably evaluate the traditional residence environment quality, so as to provide decision makers with optimization and processing.

The application discloses a flood classification method and system based on dynamic time warping and multi-cluster coupling. The application firstly collects and processes flood flow time series data to obtain a data set of standardized flood composition; then adopts a dynamic time warping method to calculate the similarity between each two of K fields of flood, to form a similarity matrix; and finally carries out flood classification based on DTW coupling hierarchical clustering and K-medoids multi-clustering. The system of the application comprises a data acquisition module, a data preprocessing module, a comparison module and a classification output module. The application optimizes the hierarchical clustering again, combines the multi-clustering method, improves the rationality of flood classification, and helps to improve the accuracy of flood classification. The application can be applied to the fields of flood warning and flood control scheduling, and has important practical value and social significance.

The invention provides an axle-spoke type public transit network design optimization method and system considering automatic driving formation, and belongs to the technical field of traffic route optimization design, and the method comprises the steps: carrying out the recognition of junction stations, obtaining the data of bus stations and the data of historical demands of passengers, carrying out the clustering of the demands of passengers through employing a K-Medoids clustering algorithm, and obtaining the optimal position of the junction stations; and establishing a double-layer optimization model of the axle-spoke type public transit network, and solving the double-layer optimization model of the axle-spoke type public transit network based on a genetic algorithm to obtain an optimal axle-spoke type public transit network scheme. According to the invention, whole-network collaborative optimization from hub site selection to line planning is realized; the network design is carried out by considering the characteristics of the automatic driving bus, and the operation cost and the passenger cost are balanced through modeling; the operation efficiency and economic benefits of the public transportation system are improved, and the designed genetic algorithm solving framework can be suitable for optimization calculation of a large-scale actual road network.

A new energy output modeling method based on data driving, comprising the following steps: acquiring output data of different new energy units in a new energy station; using Lagrange multi-item interpolation method to interpolate and fill in the missing output data; establishing a mathematical model of wind turbine power-wind speed and a mathematical model of photovoltaic unit power-illumination intensity; fitting the parameters of the mathematical model, using the fitted mathematical model and the Laplace criterion to screen out abnormal power data, and using the fitted mathematical model to fill in the abnormal power data; using K-medoids clustering method to classify new energy units; using principal component analysis method to reduce the wind speed / illumination intensity of different categories of new energy units to one dimension as the characteristic data of the new energy station; and establishing a mathematical model between the total power of the new energy station and the wind speed / illumination intensity. The design not only makes the model simple and the calculation small, but also makes the model high in accuracy.

The application discloses a congestion pre-control range division method based on local node importance, and comprises the following steps: firstly, determining a congestion early warning starting threshold value through congestion sample analysis; secondly, determining a congestion pre-control node set based on an adjacency matrix for a road section reaching the early warning threshold value; then, defining a local node importance evaluation index capable of representing the congestion road section in combination with a correlation coefficient of a traffic state associated road section, and performing node importance evaluation by using an analytic hierarchy process; and finally, dividing the local node importance level by using a K-Means++ Medoids clustering algorithm, and determining a graded pre-control range. The application has higher application value by identifying potential congestion points, finding a graded congestion pre-control range, improving road network operation efficiency, and reducing congestion control cost.

The invention provides a dynamic aggregation control method and device for a micro-grid group to participate in primary frequency modulation, and relates to the technical field of power grid frequency modulation control. The method comprises the following steps: establishing a frequency control target of a micro-grid group based on a K-medoids aggregation frequency control model, and establishing a minimum aggregation unit model; on the basis of the aggregation condition, carrying out on-line solving on the direct current gain of each device, and obtaining transmission characteristics required by each minimum aggregation unit according to a frequency control target; and constructing a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit, and solving control parameters by using each controller model so as to realize dynamic aggregation control of the micro-grid group participating in primary frequency modulation. According to the dynamic aggregation control method and device for the micro-grid group to participate in the primary frequency modulation, the dynamic response capability of the primary frequency modulation of the micro-grid group can be improved.

The application discloses a protection channel automatic switching protection method and system based on machine learning, relates to the technical field of relay protection, and extracts a channel original data set from an operation record interface of a main protection channel and a backup protection channel, executes data preprocessing and time window division processing, and forms basic state data; constructs a main channel time sequence sample, identifies main channel state migration types, extracts stable reference segments with unmarked and zero abnormal event and alarm state, judges the off-stable state of the main channel and the backup channel and the double-channel abnormal disturbance relationship; a logic regression model is constructed to calculate the switching confirmation probability corresponding to each time index, determine the heterogeneous switching confirmation window, and then screen the target confirmation window through k-medoids clustering, and output an automatic switching instruction according to the switching start time index of the target confirmation window. The method is suitable for protection channel operation state recognition, switching time judgment and switching suppression analysis scenes.

The invention provides a sub-codebook composite codebook generation method and system based on regional similarity clustering, and the method comprises the steps: obtaining a matrix training set based on a channel covariance matrix of a large-scale MIMO system; extracting an upper triangular element of each channel covariance matrix, separating a real part and an imaginary part, and constructing a high-dimensional covariance feature training set; pCA dimension reduction processing is carried out, and a K-Medoids clustering algorithm is combined with contour coefficient optimization and an optimal number of covariance mode clusters are identified; based on the sample density weight, distributing the total code word number to each cluster in the covariance mode cluster according to a proportion, and taking the total code word number as the exclusive code word number of each cluster; obtaining an exclusive sub-codebook corresponding to each cluster; and integrating the exclusive sub-codebooks of all clusters, and constructing a final composite codebook. By extracting the triangular elements on the covariance matrix and separating the real and imaginary parts, the channel covariance mode features are accurately captured, and the adaptive capacity of the codebook to complex propagation scenes with multipath reflection, shielding and the like is effectively improved.

This invention discloses a method for detecting surface defects in aircraft electrical equipment based on a standard sample library. By establishing a structural functional region segmentation model, differentiated weights, sampling rules, and judgment thresholds are configured for regions with different risk levels, achieving accurate detection with high sensitivity in high-risk areas and low false detection rate in normal areas, thus meeting the safety requirements of aviation products. Compared with the basic PatchCore model, the method utilizes HRNet combined with CBAM to maintain high-resolution features while taking into account both local and global features, enabling better detection of minor defects. The method employs weighted K-medoids clustering to construct a memory library, which compresses the memory library size, improves detection efficiency, and avoids uneven feature distribution, thereby ensuring detection accuracy. Based on the anomaly heatmap obtained from PatchCore, the SAM model is used for defect segmentation, which can quantify the size of defects, determine their morphological characteristics, and assess their severity.