610 results about "Hierarchical clustering" patented technology

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The invention discloses a safety control method based on a knowledge graph, and belongs to the field of safety control, and the method comprises the following steps: crawling various types of data according to keywords through a crawler program to construct a fireproof database; carrying out semantic analysis on data in the fireproof database by using a natural language processing technology, and carrying out data processing on the fireproof database after semantic analysis through word vector modeling and entity relationship extraction; constructing an internal large-scale language model by using the processed fireproof database, and performing hierarchical clustering on the internal large-scale language model by adopting a Leiden technology; establishing a multi-dimensional mapping model based on historic building spatial features, cultural relic features and disaster-inducing factors in a hierarchical clustering result, and constructing a multi-modal dynamic association fireproof knowledge graph by using a knowledge graph technology based on the multi-dimensional mapping model; and dynamically selecting a search mode based on a user query keyword, and generating a security control result according to the query keyword based on the search mode.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention discloses a personalized care method, device and equipment based on an AI technology and a storage medium, and solves the technical problem that an artificial intelligence dialogue system lacks dynamic memory, multi-dimensional user portrait updating and personalized care strategies in the prior art. The method comprises the steps of collecting data and preprocessing the data; the method comprises the following steps: generating a dynamic user feature vector by constructing an LSTM-GRU hybrid neural network structure; dynamic evolution of a user portrait is realized through incremental learning and memory attenuation, and a user multi-dimensional portrait is constructed; constructing a memory storage hierarchical architecture; extracting and optimizing keywords based on a medium-term memory storage system, and optimizing and constructing an interest graph by utilizing a hierarchical clustering method according to a keyword weight enhancement formula; based on a memory storage hierarchical architecture, establishing a causal relationship and a sequential relationship between entity nodes and emotion nodes to construct a memory association knowledge graph; and establishing a trigger mechanism, performing emotion memory composite analysis based on the memory association knowledge graph, and generating a care strategy. The method can be widely applied to the artificial intelligence technology field.

The invention relates to the technical field of knowledge extraction, in particular to a knowledge graph construction method and system based on a large model, and the method comprises the following steps: obtaining a current input statement of a user through a dialogue state tracker, inputting the statement into a BERT intention classification model for domain label analysis, behavior type recognition and emotional tendency detection, and outputting a three-dimensional classification vector; and extracting entity lexical items and relation predicates based on an LSTM sequence tagging device, and generating an original semantic structural body. According to the method, intention classification, behavior recognition and emotion detection are fused through three-dimensional semantic analysis, semantic comprehension granularity is improved, dynamic entity disambiguation is combined with a Manhattan distance threshold value and dialogue history tracking, semantic boundaries are defined to reduce anaphora ambiguity, and cross-modal alignment is enhanced through relation predicate hierarchical clustering and knowledge base dynamic matching; generative reply and semantic coherence reordering collaboratively keep topic continuation, and structured analysis and unstructured generation closed loop optimize semantic output and interaction fluency.

The embodiment of the invention provides a data processing method and system for an enterprise digital transformation platform, and belongs to the field of data processing. The method comprises the steps that structured field information from all heterogeneous data sources is acquired, and preprocessing operation is executed on the structured field information; constructing the processed structured field information into an embedded input sequence, splicing the embedded input sequence into a natural language fragment according to a preset template, and inputting the natural language fragment into a fine-tuned semantic coding model to obtain a corresponding semantic embedded vector; identifying similar field groups by adopting a clustering algorithm based on density or a hierarchical structure, and classifying each group of structured field information into a semantic cluster; and generating a corresponding standard field identifier for each semantic clustering cluster, and storing the generated standard field identifier in a standard field index database of the platform after digital transformation. According to the scheme, the field unified management and cross-system data alignment capability of the enterprise digital platform is remarkably enhanced.

The invention discloses an altered mineral analysis-based prospecting target prediction method and system, and relates to the technical field of prospecting target prediction. An altered mineral analysis-based prospecting target prediction system comprises a data acquisition module, a clustering analysis module, an alteration combination discrimination module, a spatial modeling module, a space-time coupling module and a metallogenic evaluation module. According to the method, altered minerals and symbiotic combinations of the altered minerals are subjected to layered clustering treatment by introducing mineral thermodynamic phase diagram constraints, multi-stage superposed alteration information in a complex structure area is effectively analyzed, and a mineral symbiotic combination structure model with cause difference expression ability is established; by constructing cause period labels and forming a time sequence decoupling model, systematic distinguishing of alteration bodies formed under the mineralization effect of different times is achieved, and a time sequence basis is provided for identification of the multi-stage mineralization process.

The invention relates to the field of 3D rendering, in particular to an intelligent scenic area three-dimensional image rendering method, which introduces a differentiable discrete decision into 3D fusion, supports end-to-end learning of a k value, performs discrete-continuous optimization based on an activation function, predicts an optimal k value of each voxel, introduces feature adaptive fusion based on dynamic neighborhood bidirectional retrieval, and realizes the 3D image rendering of the scenic area. The alignment of the color image and the point cloud is enhanced, the false detection rate of a small target is reduced, the detail reconstruction capability of a large target is improved, and the comprehensive rendering capability of a scenic spot is improved; according to the method, a lightweight grid is adopted to express a scenic spot subject, residual Gaussian is introduced to supplement high-frequency detail features, the number of Gaussian is reduced, rendering efficiency and capability are improved, textures are generated based on initial rendering back projection, fuzzy view angle dependence is avoided, hierarchical clustering and contour extraction from bottom to top are adopted on the basis, and the method is more efficient and efficient. The point cloud vertical structure change is dynamically detected, the point cloud is complemented, the accurate contour is extracted, the number of grid vertexes is reduced, and the rendering integrity is improved.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention discloses a high-dimensional time series data cluster structure prediction method based on dynamic hierarchical clustering and LSTM fusion. The method comprises the following steps: receiving a high-dimensional heterogeneous time series data set; performing clustering processing on the high-dimensional heterogeneous time series data set to obtain a cluster structure and a centroid matrix at the current moment; based on the cluster structure and the centroid matrix, time sequence features are extracted from three dimensions of a centroid track, a topological structure and scale dynamic, and a multi-channel time sequence feature tensor is generated through coding; and inputting the multi-channel feature tensor into the LSTM, and outputting a prediction result of the centroid offset and the topology change probability at the next moment by applying an attention gating mechanism. According to the method, the system and the equipment provided by the invention, efficient clustering and accurate prediction of high-dimensional heterogeneous time series data are realized through deep fusion of dynamic hierarchical clustering and LSTM, and the timeliness, the accuracy and the calculation efficiency of time series data processing are remarkably improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention provides a data processing method for seabed-based monitoring equipment, which belongs to the technical field of electric digital data processing, and comprises the following steps: normalizing acquired data, removing abnormal values, extracting key features by using a singular value decomposition method, and segmenting by using a self-adaptive sliding time domain window to form time sequence data blocks. According to the method, a CUDA parallel processing flow is creatively used to construct a disorder matrix and a drift matrix, and a disorder mode feature vector, a drift principal component and an abnormal feature matrix are fused through a tensor decomposition technology to construct a seabed environment state tensor model. Meanwhile, a drift rule in a long-time sequence is learned by utilizing a deep sea environment perception attention network model, real-time monitoring data is corrected and compensated, and finally a seabed state evaluation standard is established by adopting a hierarchical clustering algorithm, so that the key technical problem that environment change and equipment drift in seabed monitoring data are difficult to distinguish is effectively solved.

The invention relates to the technical field of health management of old people, and discloses a method and a system for predicting multiple diseases of old people. The method comprises the following steps: acquiring multi-modal health monitoring data of a target old person in a preset time period, wherein the multi-modal health monitoring data comprises physiological index time sequence data, a medication record sequence and a daily activity ability evaluation result; and performing cross-modal correlation analysis on the multi-modal health monitoring data to generate a disease interaction characteristic matrix containing metabolic disease correlation degree, circulatory system disease coordination index and neurodegenerative disease progress rate. And performing hierarchical clustering processing on the feature matrix by adopting a dynamic weight distribution algorithm, and outputting potential common disease combinations of the target old people and a priority score of each common disease combination. And generating a personalized intervention strategy set including a drug interaction avoidance scheme, a rehabilitation training intensity adjustment scheme and a nutrition intake ratio scheme according to the priority score.

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention provides an unknown encrypted traffic identification method and system based on small sample incremental learning, and a storage medium, and the method comprises the steps: 1, carrying out the fine-grained classification of known traffic: extracting the features of each level of original encrypted traffic, building a respective variational auto-encoder for each type of known traffic, generating a potential representation, and carrying out the fine-grained classification of the known traffic; inputting into a classifier to classify known attacks in a fine-grained manner; step 2, specific label distribution of unknown traffic: judging whether the sample is a drift sample or an unknown sample by adopting a scoring function, and performing hierarchical clustering on the samples according to each hierarchical feature to realize label distribution of the unknown traffic; and step 3, dynamically updating the classification model: training a new classifier by adopting a new sample, connecting other classifiers to form a classification graph, and updating nodes of the classifiers by adopting a graph attention network to realize small sample incremental learning. The method has the beneficial effects that low-sample incremental modeling of a new class is effectively supported, and the fine-grained recognition capability and the model generalization adaptability are remarkably improved.

Disclosed in the present invention is an unmanned aerial vehicle flight trajectory anomaly tracing method based on a fusion clustering algorithm. A hierarchical clustering algorithm is applied to classify and organize complex and sensitive spatio-temporal data features to identify a potential anomaly point; then, weights of various data features obtained by using a Lasso regression algorithm are used to calculate the sum of products of the potential anomaly point and the weights of the various data features; a system detects an anomaly point in a flight trajectory of an unmanned aerial vehicle to ensure flight safety and stability; the anomaly point is confirmed; and the system instantly calls an improved KD tree algorithm to quickly notify nearest on-duty personnel to go to the site, thereby comprehensively inspecting and thoroughly verifying factors interfering with the normal flight of the unmanned aerial vehicle, quickly and accurately processing anomaly conditions of the flight of the unmanned aerial vehicle, ensuring the flight safety and data integrity of the unmanned aerial vehicle, and providing reliable guarantee and support for a flight system of the unmanned aerial vehicle.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention discloses a code repository question and answer method based on a hierarchical code knowledge graph, which comprises the following steps: collecting all files of a target code repository, and preprocessing to obtain code files and text files; generating an abstract syntax tree from the code file, and extracting related entities; carrying out block segmentation on the text file, and extracting to obtain text blocks; constructing a code knowledge graph representing a code structure based on the extracted entities and text blocks; generating a hierarchical code knowledge graph by adopting a community detection algorithm and a hierarchical clustering method, and generating a functional abstract for each community in combination with a large language model; a Monte Carlo tree search-based code warehouse question and answer agent is used for performing question and answer path optimization, an optimal query track is dynamically planned through the steps of node selection, expansion, simulation, reward evaluation and the like, and a path with the highest accumulated reward is selected to generate a final question and answer result. The code knowledge modeling quality and the reasoning accuracy and efficiency of the question answering system can be effectively improved.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention relates to the technical field of image processing, and provides an image deduplication method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting the image features of each image in a to-be-deduplicated image set; based on the image features of the images, clustering the image set to obtain a plurality of initial clusters; performing hierarchical clustering adjustment on each initial cluster based on a clustering quality evaluation index to obtain a de-weighting result corresponding to the image set; the clustering quality evaluation index is used for reflecting the cluster quality of each initial clustering cluster, the defects of low efficiency, high calculation complexity and poor robustness of a traditional deduplication mode are overcome, initial clustering is carried out firstly, then the cluster quality is evaluated, hierarchical clustering adjustment is automatically triggered, the buckle process of the cluster structure is dynamically optimized, and the clustering quality of the cluster structure is improved. According to the method, the problems of over-fitting and under-fitting are well solved, unbalance of a cluster structure is avoided, manual intervention is reduced, high-robustness and high-efficiency image de-duplication is realized, and the redundancy problem of massive image data can be effectively solved.

The invention discloses a layered densification Gaussian sputtering method based on visibility, and relates to the technical field of artificial intelligence and computer vision. According to the scheme, an initial three-dimensional Gaussian primitive set is generated based on sparse multi-view observation data, and initial scene representation is established by extracting spatial distribution parameters, morphological parameters and radiation parameters; performing fusion analysis on the Gaussian primitives based on the multi-dimensional observability parameter set to obtain comprehensive observability index data; executing hierarchical clustering according to the index data, and constructing a multi-layer Gaussian structure of a significant layer, a transition layer and a background layer; performing density enhancement, geometric continuity constraint and parameter update processing on different levels of Gaussian structures, and generating a rendered image of a target view angle under a volume light traveling and transparency hybrid mechanism; according to the method, continuous reconstruction of a scene structure and accurate expression of radiation characteristics can be realized under the sparse view condition, and the geometric fidelity and rendering consistency of new view angle synthesis are improved.

The invention discloses a hardware simulation platform environment anomaly detection method based on dynamic hierarchical clustering. The method comprises the following steps: S1, forming a standardized feature vector data stream; s2, constructing a multi-dimensional feature vector set reflecting the environment state of the hardware simulation platform according to the standardized feature vector data stream; s3, generating an initial clustering center and an initial hierarchical division structure; s4, updating the hierarchical structure, the clustering center and the classification standard of the initial hierarchical clustering model in real time; s5, on the basis of the hierarchical clustering model updated in real time, calculating the deviation degree between each feature vector and the clustering center to which the feature vector belongs, and carrying out anomaly judgment on the feature vectors of which the deviation degree exceeds a preset threshold value, so as to realize the recognition of the abnormal environment data of the hardware simulation platform; and S6, generating an abnormal alarm signal for the identified abnormal data. According to the invention, slight anomalies and serious anomalies can be effectively distinguished, and the anomaly detection precision is improved.

The invention discloses a federated learning method for realizing client selection based on data feature clustering, which comprises the following steps of: firstly, performing singular value decomposition on local data by a client, and combining left singular vectors corresponding to first k singular values as local data features; secondly, performing hierarchical clustering on clients by taking included angles between local data features as similarity measurement standards; and then, constructing a client contribution quantification model, comprehensively considering data quality, sample capacity and selected times, refining contributions of the clients, and selecting h clients with the highest current contribution degree to participate in global training. And finally, during each global training, the server selects h clients from each group to carry out current global training, and the server carries out weighted aggregation to obtain a new global model. According to the method, the data isomerism is effectively relieved, the global model prediction precision is improved, and the problem of low global model precision caused by the data isomerism in a federated learning scene is solved.

The invention discloses a single-molecule conductance signal semantic segmentation method based on multi-domain feature fusion, and relates to the field of single-molecule electric transport data analysis. The core of the method is a deep learning framework, and the deep learning framework comprises a time domain-frequency domain double-flow encoder, an attention mechanism module, a projection layer, a hierarchical clustering module and a segmentation head. The method specifically comprises the following steps of: performing data enhancement on time domain and frequency domain information of an input sample, generating views which are related to semantics and have different forms, and inputting the views into corresponding encoders; aligning two-modal coding features by adopting an attention mechanism; inputting the projected original time domain features into a hierarchical clustering module to generate a high-quality pseudo-tag, and taking the high-quality pseudo-tag as a supervision signal training segmentation head; and performing joint optimization by taking the weighted sum of the comparison loss and the segmentation loss as a total training target. The evaluation indexes comprise the accuracy rate, the F1 score and the MIoU. The method is applied to single-molecule electrical transport signal analysis, and provides effective data method support for single-molecule electronics basic research and application research.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

The invention discloses a highway tunnel surrounding rock deformation prediction method, and relates to the technical field of geotechnical engineering monitoring, and the method comprises the steps: collecting existing data, determining a monitoring arrangement scheme, and collecting on-site monitoring data through a monitoring data real-time feedback technology; processing multiple types of monitoring data and screening proper input features; clustering analysis is carried out on the monitoring data of the multiple sections, and classification of the excavated sections is completed; extracting the shape center of each section classification, and comparing the shape center with early-stage monitoring data of a predicted section to realize classification of the predicted section; and establishing a prediction model according to the classification of the prediction sections, and performing long-term prediction on the tunnel surrounding rock deformation response. According to the method, fine processing of the monitoring data is realized through feature screening and hierarchical clustering, and classification and classification of the known section and the predicted section are realized by fully utilizing multiple types of monitoring data, so that the long-term response of tunnel section surrounding rock deformation is predicted more accurately.

The invention discloses a double-platform point cloud-oriented bidirectional hierarchical clustering single tree segmentation method, which belongs to the technical field of forestry monitoring, and comprises the following steps of: firstly, preprocessing and filtering an original point cloud, and then vertically layering along a Z axis according to a set slice thickness; in each layer, a clustering radius is adaptively determined by using a local point density estimation result, point cloud clustering is carried out by using a region growing method based on a search radius, and an initial single wood structure unit is extracted; and then, space connection operation is executed between the upper layer and the lower layer, points with continuous structures are assigned to the same target based on triple constraints of adjacent distance, lateral offset and height continuity, and finally accurate segmentation of the complete single-tree point cloud is realized. The BLS method supports a top-down segmentation strategy and a bottom-up segmentation strategy, automatic adaptation can be carried out according to platform types, trunk point clouds are preferentially segmented in TLS data, and a canopy structure is preferentially constructed in ULS data.

A privacy protection heterogeneous federal learning method with Byzantine robustness includes training a local model and calculating a sketch, removing an abnormal local model, selecting a client with quick response capability, encrypting local update and submitting ciphertext, executing weighted aggregation and distributing an updated global model. The method has the beneficial effects that a high-dimensional local model is converted into a low-dimensional sketch by adopting locality sensitive hashing, and the quality of the local model is effectively evaluated on the premise of protecting data privacy. Then, the Byzantine clients are identified and removed based on the hierarchical clustering technology, aggregation weights are distributed to the remaining clients according to the local model quality, and the global model convergence speed is increased; in addition, by selecting the clients which are high in response speed and have representative data sets to participate in model training, the problem of outdated persons caused by system isomerism is solved under the condition that the global model accuracy is not affected.

The invention relates to the technical field of video behavior analysis, in particular to a post-examination illegal behavior detection method based on cross-mirror tracking and identity authentication, and the method comprises the steps: collecting monitoring videos of all cameras in an examination scene; carrying out identity authentication by adopting Reid identity matching, and forming and maintaining a personnel identity mapping relation; determining a target bounding box, dynamically evaluating and enhancing the quality score of the target bounding box, constructing an optimized trajectory feature, and tracking a trajectory through hierarchical clustering to obtain a corresponding identity ID; a historical feature library is established and dynamically updated, identity conflict detection is executed, and an identity ID is bound or reset; inputting a GAN generator to synthesize an enhanced spatiotemporal feature sequence, inputting the enhanced spatiotemporal feature sequence into a Bi-LSTM discriminator in combination with an image block sequence, executing adversarial discrimination and action classification, and identifying illegal behaviors; when the illegal behavior is identified, real-name system alarm information including an identity ID and an illegal behavior type is generated; therefore, the automatic discovery and real-name traceability of illegal behaviors after examination can be improved.

The invention discloses a topology identification method based on intelligent measurement data of a low-voltage transformer area. According to the technical scheme, the topology identification method comprises the steps that 1, a master station issues a timing command to an intelligent measurement terminal and an electric meter; step 2, the master station issues a transformer area electric meter file to the intelligent measurement terminal, configures an acquisition task and issues the acquisition task to the intelligent measurement terminal; step 3, establishing an electric quantity sum linear regression model based on a Lasso algorithm by using daily frozen electric quantity data, and converting the identification problem of the transformer area user-transformer relation into regression coefficient solving; step 4, on the basis of user change relation identification, performing dimension reduction on historical voltage data by using a t-SNE algorithm to improve the identification efficiency of the model; and step 5, clustering the voltage data after dimension reduction by using agglomerated hierarchical clustering, identifying a user phase and a meter box to which the user belongs, and completing identification of a topological structure. According to the method, the power grid topology information of the transformer area can be automatically identified, the efficiency and the accuracy of topology identification are improved, and the method has far-reaching significance for construction of an intelligent power grid.