33 results about "Agglomerative hierarchical clustering" patented technology

The invention relates to the technical field of information, in particular to a sleep health dynamic assessment method and system based on user feedback driving and a medium. The method comprises the following steps: constructing an individual knowledge graph reflecting an individual health causal relationship by adopting a time sequence causal analysis method; constructing a group knowledge graph reflecting a group health causal relationship in combination with an agglomerated hierarchical clustering method and a hierarchical federated learning method; based on user feedback information and health factor data which are acquired in real time, triggering individual knowledge graph updating, and triggering group knowledge graph delay increment reconstruction; and dynamically fusing the weight of the updated double atlases based on the accumulated quantity of the user feedback information to obtain the comprehensive weight of the user health factor, and carrying out weighted calculation on the newly obtained health factor data to obtain a sleep health assessment score. Therefore, real-time feedback data is deeply utilized, dynamic, accurate and explainable sleep health assessment is realized, and the technical prejudice that groups and individuals are irreconcilable is broken through.

The invention provides a key event discrimination method and system based on a hierarchical processing architecture, and the method comprises the steps: obtaining reported events from different channels, carrying out the data preprocessing of the reported events, obtaining processed event texts, and classifying the event texts; clustering the classified texts by adopting a semantic regularization agglomeration hierarchical clustering method; and according to a clustering result, utilizing an in-cluster entity keyword extraction and comparison mechanism to discriminate repeated events in the text to obtain a key event discrimination result. According to the key event identification method provided by the invention, a classification-clustering-discrimination hierarchical processing architecture is constructed, the technical problem existing in one-event multi-report screening is effectively solved, and finally, the screened repeated report events are taken as key events needing to be marked. Therefore, related managers can preferentially dispose the key events or give full attention to the key events.

The invention relates to a transformer area distributed photovoltaic power ultra-short-term prediction method and system, and belongs to the technical field of distributed photovoltaic power prediction. On the basis of an agglomerate hierarchical clustering algorithm, transformer area photovoltaic in a region is clustered into a sub-region set with homogenized output characteristics, decoupling representation of spatial-temporal characteristics in the sub-regions is realized in combination with a GraphSAGE graph neural network and a Transform encoder model, and finally, a photovoltaic power prediction result of each transformer area in the sub-regions is synchronously output on the basis of the composite spatial-temporal characteristics. Furthermore, a proper migration modeling strategy is constructed based on shared features among the sub-regions, rapid lightweight modeling of each sub-region prediction model is realized, and finally, a region total power prediction result is obtained through spatial aggregation of sub-region prediction power.

The invention provides a TCN-LSTM-AM-based hydropower output prediction method, and the method comprises the steps: constructing a TCN-LSTM-AM network which employs a causal convolution and cavity convolution structure, extracts the local fine features and long-distance dependence relation of meteorological-water level-output data in a time sequence, and outputs a multi-scale feature sequence; the LSTM processes the feature sequence output by the TCN through a gating mechanism of a forgetting gate, an input gate and an output gate, adaptively retains historical key information, and outputs a hidden state sequence containing time sequence dynamics; the AM dynamically allocates attention weights to a hidden state sequence output by the LSTM, focuses on key time step features of prediction, and generates a final prediction value; classifying meteorological data based on Euclidean distance by adopting an agglomerated hierarchical clustering algorithm, dividing similar hydroelectric output mode clusters under different climate conditions, and training a TCN-LSTM-AM network for each cluster to enable the model to adapt to output laws under different climate conditions; and the trained TCN-LSTM-AM network is used to generate a next-day hydropower output prediction value.

The application discloses a kind of methods for automatically analyzing and evaluating report, it is related to computer software and information processing technical field, including: based on graph neural network and visual-textual dual modal feature extractor, realize the adaptive analysis of complex non-standard structure report version, and the document is parsed into two-dimensional data table containing different classification dimensions;Based on BERT vectorization, etc. Adaptive clustering identifies the cluster structure of uneven semantic distribution, adopts the agglomerative hierarchical clustering to construct tree-shaped multi-granularity semantic hierarchy, realizes semantic aggregation;The application realizes the text content of non-standard form, the identification and processing of complex semantics;Overcome the defects of low efficiency, easy to make mistakes, subjective influence and only read specific format or specific location of text content, lack of flexibility and unable to handle complex semantics in prior art manual input.

The invention relates to an urban inland inundation modeling method considering spatio-temporal heterogeneity. The urban inland inundation modeling method comprises the following steps: constructing a rainfall time sequence and a multi-scale spatio-temporal grid with spatio-temporal heterogeneity; data preprocessing: dividing a research area into initial response units based on topographic features, land utilization types and drainage system distribution; measuring the waterlogging response process similarity among the response units by adopting a dynamic time warping method, and performing time sequence clustering on a division result according to the waterlogging response process similarity; further clustering and determining an optimal partition number by adopting an agglomerated hierarchical clustering method, and introducing a spatial adjacency constraint to ensure spatial connectivity; different hydrological parameters are given to different response units; and utilizing the multi-scale rainfall space-time grid data and the response unit division result to drive modeling of an urban inland inundation earth surface-pipe network coupling model. According to the method, the precision of urban waterlogging modeling is improved by constructing the multi-scale rainfall grid and dividing the urban waterlogging response units, and the modeling and simulation method of an existing urban waterlogging model is expanded.

The present invention belongs to the technical field of aviation airborne system safety analysis, and specifically relates to a method and system for generating FMES data for airborne system safety analysis. The method comprises: S1: acquiring FMEA data of the airborne system, performing data processing, and extracting data features using the BERT model; S2: using sinusoidal similarity to measure the FEMA structured data feature vectors and calculate the similarity of the FEMA structured data features; S3: obtaining the FEMA structured data feature similarity matrix and performing a first agglomerative hierarchical clustering analysis; S4: performing a second clustering analysis on the tree-like clusters of the FEMA structured data features based on fault tree analysis (FTA); and S5: generating structured Failure Mode and Effect Summary (FMES) data based on the second clustering results. The present invention proposes a complete framework for automatically generating FMES data, and improves the quality of the automatic generation through secondary clustering.

The invention discloses a differentiated regulation and control method for irregularity of a large-span bridge track of a high-speed railway. The method comprises the following steps: integrating track irregularity historical detection data at different environment temperatures into multi-stage track irregularity historical detection data with environment temperature labels; establishing a multivariate empirical wavelet transform method, and obtaining a plurality of intrinsic mode components; defining the intrinsic mode component as an intrinsic mode, extracting multi-dimensional physical feature vectors of all the intrinsic modes to construct feature vectors, and performing unsupervised classification on all the intrinsic modes by using an agglomerated hierarchical clustering algorithm; component category division is carried out according to a classification result, and different differentiation regulation and control strategies are made according to different component categories; and when the orbit adjustment amount required by the classification exceeds the limit, establishing an optimization model containing the adjustment amount physical constraint and the smoothness expectation target, and solving the optimal orbit adjustment amount considering the current state and the future temperature adaptability.

The present application relates to a kind of table area distributed photovoltaic power ultra-short term prediction method and system, belong to distributed photovoltaic power prediction technical field.Based on the condensed hierarchical clustering algorithm, the table area photovoltaic in region is clustered into the homogeneous sub-region set of output characteristics, combined with GraphSAGE graph neural network and Transformer encoder model, the decoupling representation of space-time characteristics in sub-region is realized, based on composite space-time characteristics, finally synchronously output the photovoltaic power prediction result of each table area in sub-region, further based on the shared feature between sub-region, construct suitable migration modeling strategy, realize the fast lightweight modeling of each sub-region prediction model, finally, the spatial aggregation of sub-region prediction power obtains regional total power prediction result.

The invention provides an intelligent insurance evaluation monitoring and early warning system based on a logistics transportation whole process, and relates to the technical field of logistics monitoring. Comprising the steps of collecting transportation data in a logistics transportation process; preprocessing the transportation data; performing unsupervised clustering on the preprocessed logistics data by using an agglomerated hierarchical clustering algorithm, and dynamically dividing transportation risk types to obtain risk cluster labels; constructing an intelligent prediction model fusing a light GBM model and a neural network model, and obtaining and outputting a risk probability distribution threshold within a future t moment; and judging whether the real-time detection data exceeds a preset risk probability distribution threshold, and if so, giving an alarm and reminding. According to the invention, through the intelligent prediction model fusing the light GBM model and the neural network model, the risk probability distribution threshold in the future t moment is predicted, the time and computing resources required by model training are reduced, and the efficiency of the whole logistics risk prediction work is improved.

The application provides a key event identification method and system based on a hierarchical processing architecture. The method according to the application comprises: acquiring reported events from different channels, performing data preprocessing on the reported events to obtain processed event texts, and classifying the event texts; adopting a semantic regularization condensed hierarchical clustering method to cluster the classified texts; identifying repeated events in the texts according to the clustering results by using a cluster-in-entity keyword extraction and comparison mechanism to obtain key event identification results. That is, the key event identification method proposed in the application constructs a hierarchical processing architecture of "classification-clustering-identification", effectively solves the technical problems existing in the screening of one event reported by multiple persons, and finally screens out repeated reported events as key events that need to be marked, so that relevant management personnel can give priority to the disposal or sufficient attention to these key events.

The application discloses a shortest path divide-and-conquer search method based on a condensation level, improves the operation efficiency of an algorithm by introducing network layering, limiting a search level, divide-and-conquer search and the like, wherein the network layering is adopted to divide network nodes according to a condensation level clustering method, a plurality of level subgraphs are obtained, and the complexity of the search is reduced; the search range is limited within a certain level by limiting the search level, and invalid calculation in the search process is avoided; meanwhile, in order to further improve the search efficiency, the divide-and-conquer search is adopted to divide the whole search process into a plurality of subtasks and perform simultaneously, and the whole search method can accurately and efficiently perform the shortest path search in an edge computing network.

The application discloses a kind of dynamic blockchain sharding method and system based on composite clustering algorithm, the method includes: based on blockchain sharding system, determine transaction frequency matrix and sharding allocation matrix, construct the target optimization transaction model of blockchain sharding system;Based on the target optimization transaction model of blockchain sharding system, preliminary global division processing is carried out through agglomerative hierarchical clustering algorithm;The preliminary blockchain sharding result is divided and processed by DBSCAN clustering algorithm, and the blockchain sharding result after secondary division is obtained;Based on the blockchain sharding result after secondary division, carry out noise account division processing, obtain the final blockchain sharding result.The application can preferentially aggregate high-frequency associated accounts and classify low-frequency accounts, minimizing the proportion of cross-shard transactions and achieving load balancing.The application is a kind of dynamic blockchain sharding method and system based on composite clustering algorithm, which can be widely applied in the field of blockchain transaction technology.

The invention relates to the technical field of electrical parameter measurement and industrial equipment fault diagnosis, in particular to a UV curing machine fault detection method based on data analysis, and the method comprises the steps: continuously collecting voltage data points of a UV curing machine, and calculating the voltage data points of the UV curing machine according to the difference between the voltage data points and the mean value of surrounding data segments; and determining a suspected noise factor according to the difference between the voltage data point and the adjacent voltage data point, and calculating the noise probability in combination with the suspected noise factor and the fluctuation possibility. In recursive aggregation of the agglomerate hierarchical clustering algorithm, every two clusters are used as a group, score values are calculated according to noise probabilities and distances of data points in the group, a group of data points on the basis of single-link aggregation is determined according to the score values so as to optimize the agglomerate hierarchical clustering algorithm, and anomaly detection is performed on voltage data points through the optimized algorithm. And determining whether the UV curing machine generates a voltage fault or not. According to the method, the accuracy of fault detection of the UV curing machine is improved.

The application discloses a temperature representative value determination method and system, and relates to the field of statistics, which comprises the following steps: using a cubic interpolation method to fit and replace abnormal temperature data in a year, and using Gaussian filtering to perform smoothing processing; calculating a temperature difference and using an adaptive dynamic time warping (Adaptive DTW) and a mean Euclidean distance to obtain a difference distance matrix of temperature change characteristics of different months; using a weight parameter to combine the Adaptive DTW and the Euclidean distance, calculating a comprehensive distance matrix, performing a condensed hierarchical cluster analysis on the temperature change characteristics, and obtaining a plurality of temperature change modes; determining a typical summer temperature month and a typical winter temperature month according to the plurality of temperature change modes, and obtaining a daily extreme temperature difference therefrom; using a generalized extreme value distribution model to obtain a probability distribution of the daily extreme temperature difference in different return periods; and obtaining temperature action representative values in different return periods according to the probability distribution result. The application can accurately obtain temperature action representative values.

The application discloses a label flipping attack method for a machine learning model and a defense method thereof. In the label flipping attack method, firstly, training data to be attacked is selected by performing condensed hierarchical clustering on the training data, label flipping attack is performed on the selected training data, and then a classification model is trained by using a obtained contaminated data set to implement the attack; in view of the above attack, a small pure set and the contaminated training data are used, the TrAdaBoost method is used to update the weight of the contaminated data, the attacked data is identified according to the updated weight, the data without a label or with a wrong label is re-labeled, a new training set including the contaminated training set and the small pure set is obtained, and the classification model is re-trained by using the new training set to improve the model performance; the label flipping attack can be quickly and accurately performed on the training data vulnerable to contamination, and the suspicious contaminated data can be found, and the label flipping attack can be prevented by disinfecting the contaminated data.

The application relates to a time-of-use electricity price dynamic adjustment mechanism-based proxy electricity purchasing user power consumption behavior simulation method and system, which comprises the following steps: constructing multi-dimensional power consumption characteristics, performing improved K-means user portrait recognition based on condensed hierarchical clustering initialization, integrating an enhanced adaptive random forest model and a bidirectional-LSTM hybrid prediction model (EABRF-BiLSTM), performing transfer learning and inverse normalization power simulation, establishing a mapping relationship between the time-of-use electricity price dynamic adjustment mechanism and the proxy electricity purchasing user power consumption behavior, and outputting time-of-use power consumption prediction results under different electricity price schemes; the application can solve the problems that a traditional load prediction method cannot explain user price response behavior, a traditional clustering method is insufficient in stability, a few-sample user is difficult to start, and a normalized model is difficult to serve actual power decision-making.

The invention discloses a patient multi-dimensional data similarity measurement and queue discovery method based on artificial intelligence, relates to the technical field of smart medical treatment, and solves the technical problems that an intermediate state that a patient possibly crosses multiple clusters is ignored, the queue division dimension is single, and dynamic adaptability is lacked. By extracting dynamic characteristics, such as vital signs and symptom scores, of the patient, which change along with time, the limitation of only depending on static characteristics is avoided. And patients with short-term fluctuation but long-term stability and continuous deterioration can be distinguished conveniently. And subtype typing of chronic diseases is more accurate, and a time sequence mode is a key typing basis. And the time sequence track vector and the static feature are used for similarity calculation after being spliced or subjected to dimension reduction. And the single feature noise influence is reduced. Disease generality and individual heterogeneity are considered. Aggregate hierarchical clustering is adopted, similarity among samples is calculated, similar clusters are gradually combined, a tree diagram is generated, and patient grouping logic and correlation and causality distinguishing are visually displayed through a clustering tree.

The invention relates to the technical field of production line layout, in particular to a production line layout optimization method based on multi-agent reinforcement learning, and the method comprises the steps: constructing a topological structure of a production line through employing a directed weighted network based on a complex network theory; carrying out community division on nodes in the topological structure based on a modularity optimized condensed hierarchical clustering algorithm to obtain a plurality of communities; and carrying out optimized layout on the equipment in each community based on a multi-agent layout algorithm of MAPPO to complete layout of all the equipment in each community, and then carrying out manual layout on all the communities to obtain an optimized production line layout. According to the invention, on the basis of production line equipment community classification of a complex network, modular recombination of equipment clusters is realized through community division, and the cross-community logistics complexity is reduced.

The high-speed railway large-span bridge track irregularity differential regulation method comprises the following steps: integrating track irregularity historical detection data under different environmental temperatures into multi-period track irregularity historical detection data with an environmental temperature label; a multivariate empirical wavelet transform method is established to obtain a plurality of intrinsic modal components; the intrinsic modal components are defined as an intrinsic mode, a multi-dimensional physical characteristic vector of all intrinsic modes is extracted to construct a characteristic vector, and an unsupervised classification is performed on all intrinsic modes by using a condensed hierarchical clustering algorithm; according to the classification result, a classified division is performed, and different differential regulation strategies are formulated for different classified categories; when the track adjustment amount required by the classified category exceeds the limit, an optimization model containing a physical constraint of the adjustment amount and a smoothness expectation target is established, and the optimal track adjustment amount considering the current state and future temperature adaptability is solved.

The application relates to an indoor visible light positioning method based on improved condensed hierarchical clustering, which comprises the following steps: dividing a grid in a positioning area and selecting a plurality of reference points; installing LED light sources capable of being used for optical communication on a roof and laying photoelectric sensors and wireless nodes for data acquisition and transmission indoors; measuring the average power of the LED light sources and establishing a fingerprint library; adopting condensed hierarchical clustering to combine all data points into one cluster; selecting a required cluster number to construct a new sub-database; performing secondary classification on the new cluster according to the power of the light sources, and dividing the new cluster into a high-power area and a low-power area; in the positioning stage, comparing the Euclidean distance between the light power of a mobile terminal and a new clustering center, and putting the mobile terminal into the cluster closest to the mobile terminal; putting the power of the mobile terminal into the corresponding power area, and adopting a KNN matching algorithm to traverse the fingerprints to lock the target coordinate position.

The present invention relates to the technical field of image data processing, and in particular to a method for detecting the electrophoretic processing surface of an automobile part based on machine vision. The method comprises the following steps: dividing an image of the electrophoretic processing surface of an automobile part into multiple sub-blocks, obtaining an initial clustering result of the image of the electrophoretic processing surface of the automobile part using an agglomerative hierarchical clustering method; obtaining the primary fiber properties of the sub-block based on the minimum circumscribed rectangle size of each edge in the sub-block and the number of edge pixels in the sub-block; calculating the fiber factor of the sub-block; and performing a secondary cropping of the initial clustering result based on the fiber factor of each sub-block in the initial clustering result to obtain an inspection result of the electrophoretic processing surface of the automobile part, thereby effectively improving the accuracy of the inspection result of the electrophoretic processing surface of the automobile part.

The invention provides a principal component agglomeration hierarchical clustering method suitable for flight takeoff and landing type identification, and belongs to the field of flight data processing, and the method comprises the steps: obtaining original flight takeoff and landing data, and carrying out the preprocessing of the data, and obtaining a flight parameter data set; calculating correlation coefficients between the flight parameters in the flight parameter data set to obtain a correlation coefficient matrix, screening the correlation coefficient matrix according to a correlation coefficient threshold to obtain required flight parameters, and forming a principal component analysis flight parameter data matrix by the required flight parameters and the discrimination parameters; calculating a correlation coefficient matrix of the principal component analysis flight parameter data matrix and performing eigendecomposition to obtain principal component eigenvalues and eigenvectors, sorting the principal component eigenvalues, obtaining principal component contribution rates according to the principal component eigenvalues, and determining principal components by accumulating the principal component contribution rates; and carrying out agglomeration hierarchical clustering on the principal components to obtain two-dimensional space distribution of flight takeoff and landing in any two principal components, and carrying out flight takeoff and landing type identification according to the two-dimensional space distribution and discrimination parameters.

The application discloses a kind of sand earthquake liquefaction discrimination method and system based on SSA-CNN-SVM model coupling, and the present application relates to the technical field of seismic safety evaluation, comprising the following steps: obtaining historical sand liquefaction sample data set, including key influence index and liquefaction state label, while collecting geographic location information, extract statistical characteristic parameters to calculate anti-liquefaction intensity index and vibration intensity index, and according to the weighted similarity measurement of geographic characteristic parameter, the sample area is divided into multiple geological regions by condensation hierarchical clustering algorithm;In each region, the Mahalanobis distance between sample regions is calculated, and a secondary clustering is carried out using a density-based clustering algorithm to identify similar liquefaction mechanism sample clusters, and a prediction model coupled with a deep learning model and an optimization algorithm is established for each cluster to assess liquefaction risk, significantly improving the accuracy and robustness of sand earthquake liquefaction discrimination.

The present invention discloses a method and system for filtering thermal process alarm data based on the AHC-GP hybrid model, which belongs to the field of data processing technology. Existing data processing solutions cannot effectively eliminate redundant alarms and cannot suppress alarm overflow in time, exacerbating the "alarm flooding" problem caused by abnormal propagation. The present invention provides a method for filtering thermal process alarm data based on the AHC-GP hybrid model, which can pre-process the data set, and use the nearest neighbor propagation algorithm to determine the optimal number of clusters, and then use the agglomerative hierarchical clustering algorithm to cluster the data set to distinguish different working conditions; secondly, the Gaussian process model is used to classify the data, and the posterior alarm probability estimate is combined to construct a data filtering model to achieve accurate filtering of thermal process alarm data. Furthermore, the present invention can accurately locate the key alarm data in the data set and eliminate redundant alarms; the missed detection rate and the false positive rate are both low, and it has good data filtering accuracy.

The present application relates to the field of information technology, in particular to a sleep health dynamic evaluation method and system based on user feedback driving and a medium. The method comprises: constructing an individual knowledge graph reflecting individual health causal relationship by using time series causal analysis method; constructing a group knowledge graph reflecting group health causal relationship by combining agglomerative hierarchical clustering method and hierarchical federated learning method; triggering individual knowledge graph update and triggering group knowledge graph delayed incremental reconstruction based on real-time acquired user feedback information and health factor data; dynamically fusing the weight of the updated double graph based on the cumulative number of user feedback information to obtain the comprehensive weight of the user health factor, and then performing weighted calculation on the newly acquired health factor data to obtain the sleep health evaluation score. Thus, the real-time feedback data is deeply utilized to realize dynamic, accurate and interpretable sleep health evaluation, and the technical prejudice that "group and individual are irreconcilable" is broken through.

The invention relates to the technical field of data processing, in particular to a food production data monitoring method and system. The method comprises the following steps: determining a target moment from a plurality of moments in a current food drying process, and determining a distortion degree value of the target moment; determining a true degree value of the target moment, and taking a ratio of the true degree value of the target moment to the distortion degree value as an evaluation value of the target moment; performing agglomeration hierarchical clustering on a plurality of temperature data points in the current food drying process to obtain a plurality of clusters, and for a cluster group formed by two clusters in the plurality of clusters, obtaining a merging distance of the cluster group according to an evaluation value of a moment corresponding to the temperature data points in the cluster group; and determining an abnormal detection result of the temperature in the current food drying process according to the merging distance of the plurality of cluster groups, so as to monitor the drying temperature of the food drying equipment according to the abnormal detection result. According to the technical scheme, temperature monitoring in the food drying process can be better achieved.

This invention discloses a method and system for identifying seismic liquefaction of sandy soil based on a coupled SSA-CNN-SVM model. This invention relates to the field of seismic safety assessment technology and includes the following steps: acquiring a historical sandy soil liquefaction sample dataset, including key influencing indicators and liquefaction state labels; simultaneously collecting geographical location information; extracting statistical feature parameters to calculate the liquefaction resistance index and seismic intensity index; performing weighted similarity measurement based on geographical feature parameters; dividing the sample area into multiple geological regions using a hierarchical clustering algorithm; within each region, calculating the Mahalanobis distance between sample areas; performing secondary clustering using a density-based clustering algorithm to identify clusters of samples with similar liquefaction mechanisms; and establishing a prediction model coupled with a deep learning model and optimization algorithm for each cluster to achieve liquefaction hazard assessment, significantly improving the accuracy and robustness of sandy soil seismic liquefaction identification.

The invention provides a mass balance rapid calculation method based on a hierarchical clustering algorithm, and belongs to the field of mathematical geology in earth science, and the method comprises the steps: carrying out the standardization processing of data through the calculation of content ratios R, arranging the ratios R of all elements according to a sequence from small to large, and constructing an ordered array [R]; performing systematic difference quantitative analysis on ratio elements in the ordered array R by using an agglomerated hierarchical clustering machine learning algorithm; and averaging the Rvalues corresponding to all the identified inactive elements, taking the obtained average value K as a balance constant, and carrying out quantitative calculation on the element migration quantity by taking the average value K as a reference. According to the method, various complex geological environments such as a complex water-rock reaction system, multi-element coupling migration and a multi-stage geological process can be effectively treated, and a solution with higher calculation precision and higher analysis efficiency is provided for quantitative evaluation of rock element migration in multiple fields such as mineralogy, petrology, environmental geoscience and resource exploration.

This application belongs to the technical field of spectral analysis and discloses a method, device, electronic device, and storage medium for identifying spectral transition times. The method includes: periodically acquiring real-time spectral data of the sample to be tested; employing an agglomerative hierarchical clustering method, combined with Mahalanobis distance and minimum variance method, merging the real-time spectral data into corresponding cluster tree structure data according to the time series; determining the transition point data that meets preset clustering distance abrupt change conditions and preset frequency domain feature jump conditions based on the merging distance in the cluster tree structure data using a sliding window algorithm and a Fourier transform algorithm; verifying the transition point data according to the isolated forest algorithm to determine the time point corresponding to the transition point data of non-abnormal data as the transition time point; the above method improves the identification efficiency of spectral transition times.