117 results about "Hierarchical cluster algorithm" patented technology

The embodiment of the invention provides a fraud phone real-time recognition method and device based on AI semantic understanding, and the method and device achieve the precise understanding of the dialogue content through the innovative construction of a voice analysis mechanism, the grammatical feature extraction and the semantic role marking. And designing a scene discrimination model based on verbal skill recognition, and establishing a fraud verbal skill recognition strategy for intelligent classification in combination with semantic pattern matching and hierarchical clustering algorithms. A residual fusion assessment mechanism is introduced, and accurate assessment and timely prevention and control of call risks are realized through historical case feature fusion and risk scoring. According to the method, the defects of the traditional technology in the aspects of speech understanding, verbal skill recognition, risk assessment and the like are effectively overcome, and the accuracy and reliability of fraud phone recognition are remarkably improved.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The embodiment of the invention provides a point cloud denoising method and device, a medium and a product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring target point cloud data subjected to abnormal point elimination; according to the target point cloud data, constructing a density-based hierarchical clustering algorithm and a de-noising network of an operation selection strategy, and generating a de-noising model based on the de-noising network; and inputting the to-be-denoised point cloud data into the denoising model, and outputting the denoised point cloud data. According to the scheme, isolated noise possibly misleading path decision is filtered in advance, and interference is cleared for follow-up path selection; a denoising network based on a density hierarchical clustering algorithm and an operation selection strategy is constructed, a point cloud structure is accurately divided, representative elite points are screened in combination with the operation selection strategy, under extreme conditions, the elite points are preferentially used as core extension paths, noise point dominant decision making is avoided, and the accuracy of the system is improved. The problem that in the prior art, a single path is poor in adaptability in an extreme scene is solved, and the denoising accuracy is improved.

The invention relates to the field of offshore wind power new energy, and discloses an automatic identification method, device and equipment for modal parameters of an offshore wind generating set. The method comprises the steps that orthogonal acceleration data, collected on site, of the offshore wind generating set at different heights are processed through a random subspace recognition covariance method based on Monte Carlo simulation, and a series of candidate modes are generated; designing and implementing an iterative elimination mechanism, and automatically eliminating unstable or incredible candidate modals based on modality stability and statistical consistency criteria; a machine learning technology is introduced, vibration mode false features are identified through an XGBoost model, and false modals caused by sensor noise, structure nonlinearity or measurement errors are further eliminated; and performing clustering analysis on the remaining effective modal candidates based on a hierarchical clustering algorithm, extracting a structural modal, and completing a final modal recognition result. The method can effectively improve the accuracy and reliability of modal analysis of the offshore wind generating set.

The invention discloses a multi-disturbance scene-oriented wind turbine generator coupling modeling and clustering analysis method, and relates to the technical field of wind turbine generator running state monitoring. According to the method, running data of a unit in mechanical, electrical and wind regime physical domains are synchronously acquired through multi-source data, and a cross-domain coupling correlation tensor is constructed based on a phase-space reconstruction theory; extracting a coupling feature representing a multi-physical domain dynamic interaction relationship; forming a fusion scene feature vector by combining the shape feature and the topology invariant of the disturbance signal; based on a self-adaptive hierarchical clustering algorithm of a shape sensing distance, accurate classification of multiple disturbance scenes and typical scene center identification are realized; carrying out clustering result verification and scene identification; a self-adaptive response strategy is generated based on typical scene features, and intelligent optimization and safety early warning of the unit operation state are achieved; and the state sensing and self-adaptive regulation and control capabilities of the wind turbine generator in a multi-disturbance scene are effectively improved.

The invention is suitable for the technical field of education, and provides a content recommendation method and system based on children cognition, and the method comprises the steps: obtaining historical learning data, carrying out the construction of a knowledge graph based on the historical learning data, and generating a knowledge structure graph; performing vector mapping processing on the plurality of knowledge nodes and the edges connecting the plurality of knowledge nodes by using a graph embedding algorithm to generate a knowledge vector set; according to the knowledge vector set, calculating a semantic association degree among the plurality of knowledge nodes, and if the semantic association degree is greater than a preset threshold value, extracting a key knowledge node from the plurality of knowledge nodes by using a hierarchical clustering algorithm; determining a learning sequence corresponding to the key knowledge nodes according to a preset knowledge learning strategy, and generating a knowledge progressive sequence; generating a target knowledge node sequence based on the knowledge progressive sequence and the historical learning data by using a preset reinforcement learning algorithm; and the target recommendation content is generated according to the target knowledge node sequence and recommended to the user, so that more appropriate learning content can be recommended.

The invention belongs to the technical field of pilot ability assessment, and particularly discloses a pilot ability assessment method based on dynamic time warping and hierarchical clustering, which comprises the following steps of: acquiring eye movement data of a tested pilot in a flight simulation task process, preprocessing the eye movement data, extracting behavior indexes of each stage of a flight task, and calculating a pilot ability assessment result; generating a fixation area number sequence according to the fixation point position; in the selection target evaluation stage, a dynamic time warping algorithm is used for carrying out nonlinear alignment on the gaze sequences of all the pilots, and an eye movement difference degree matrix between the pilots is generated; based on the difference degree matrix, adopting a hierarchical clustering algorithm to group the pilots; and outputting an ability evaluation result of the pilot according to the distribution of the pilot in the difference degree matrix and the deviation information of the pilot and the teacher watching sequence. According to the method, structured comparison and capability grade evaluation of complex cognitive behaviors can be realized, and an evaluation result has relatively high objectivity and interpretability.

The invention relates to the technical field of mining area risk assessment, and discloses a mining area operation risk level assessment system. A geological data acquisition module of the system identifies a potential rock stratum instability risk area through multi-dimensional sensing monitoring and a hierarchical clustering algorithm; the mining area image analysis module is used for marking a thermal anomaly feature region by means of thermal infrared image acquisition and feature separation and mode recognition; the risk area synthesis module integrates the two areas to generate a comprehensive risk monitoring area; the structural displacement evaluation module analyzes rock stratum displacement vector change to judge the abnormal fluctuation degree; the ground pressure interaction evaluation module analyzes the interaction between the rock stratum stress and the underground water pressure and evaluates a geological stress field coupling imbalance index; and the risk level judgment module determines the risk level classification of the comprehensive risk monitoring area according to the evaluation result, so that the mining area operation risk can be comprehensively and accurately evaluated, and the operation safety is guaranteed. According to the system, the accuracy of rock mass stability judgment is improved, and the possible rock stratum instability hidden danger can be found earlier.

The invention discloses an industrial and commercial time-of-use electricity price dynamic adjustment model considering an electric power spot market. The method comprises the steps of obtaining a historical load curve of a preset category; dividing the historical load curve into a first time period group based on a first preset quarter and a second time period group based on a second preset quarter based on a hierarchical clustering algorithm and by combining a load weighted Euclidean distance and a Ward connection method according to the historical load curve; obtaining a clustered load curve according to the first time period group and the second time period group; determining load characteristics according to the clustered load curve; adjusting a load elasticity coefficient according to the load characteristics and historical load data; constructing an optimized load model according to the load elastic coefficient; inputting the optimized load model into a single-target artificial hummingbird algorithm, and determining the electricity price of each time period; and determining a final electricity price scheme according to the electricity price of each time period. The invention solves the problem of insufficient seasonal load adaptability of the current power system.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention discloses an event ontology mode construction method and device under the assistance of a large model, and belongs to the technical field of ontology construction and evolution.The event ontology mode construction method comprises the steps that a large language model is used for precisely screening corpus metadata and automatically extracting event elements; the method comprises the following steps: converting an event quintuple into semantic, time sequence and relation vectors through a deep semantic representation model, dynamically fusing time sequence and relation characteristics by taking the semantic vector as a dominant, and iteratively merging the dynamic event vectors by adopting a time sequence-relation enhanced hierarchical clustering algorithm to construct a clustering tree with a father-child hierarchical relation; generating general Chinese class names for non-leaf nodes through a large language model, and forming an event ontology skeleton which is easy to understand; and finally, the ontology skeleton is mapped into OWL ontology classes, individuals and object attributes conforming to the RDF / OWL specification, a standardized ontology file is generated, automatic construction and output of the event ontology mode are achieved, and the automation degree and normalization of event ontology construction are improved.

The invention discloses a sales demand analysis method and system based on AI and portraits. The method comprises the steps of collecting multi-source heterogeneous data such as basic information, transaction records and online behaviors of customers; according to the method, a self-developed large model of the Chinese human life research and development center is adopted, implicit requirements in a customer behavior sequence are deeply mined through an attention mechanism, a hierarchical clustering algorithm is used for generating a tree-shaped clustering graph based on a Ward connection method, and high-value features with variances larger than 0.8 and absolute values of correlation coefficients larger than 0.5 are screened out in combination with a rule engine; it is ensured that the extracted features have statistical significance and are directly associated with business targets, invalid feature redundancy is avoided, the efficiency and accuracy of subsequent portrait construction are improved, product recommendation priorities are determined through a decision tree algorithm, and a Q-learning framework is combined to optimize communication verbal skills and cross sales strategies, so that the sales strategies meet the current demands of customers, and the customer experience is improved. Potential requirements are guided, and the conversion rate and the customer satisfaction degree are both improved.

The present application relates to the technical field of mine risk assessment, and discloses a mine operation risk grade assessment system. The geological data acquisition module of the system identifies potential rock layer instability risk areas through multidimensional sensing monitoring and hierarchical clustering algorithm; the mine image analysis module labels thermal anomaly feature areas with the help of thermal infrared image acquisition and feature separation and pattern recognition; the risk area synthesis module integrates the two areas to generate a comprehensive risk monitoring area; the structure displacement evaluation module analyzes rock layer displacement vector changes to determine abnormal fluctuation degree; the ground pressure interaction evaluation module analyzes rock layer stress and groundwater pressure interaction to evaluate geological stress field coupling imbalance index; and the risk grade determination module determines the risk grade classification of the comprehensive risk monitoring area according to the above evaluation results, which can comprehensively and accurately evaluate mine operation risk and ensure operation safety. The system improves the accuracy of rock mass stability judgment and can detect potential rock layer instability hazards earlier.

The application relates to a kind of millimeter wave radar multi-vehicle tracking methods based on traffic scene, comprising: the original echo data of millimeter wave radar is preprocessed, and a plurality of frames of point cloud data are obtained;Each frame of point cloud data is clustered using a hierarchical clustering algorithm based on target information;Rectangular association wave door is set in combination with standard lane width and safe following distance, and the rectangular association wave door is corrected according to the historical information obtained by target tracking;All target centroid points in the corrected rectangular association wave door are fused according to probability;The existing track associated with the equivalent centroid point and the existing track not associated with the equivalent centroid point are updated, and a plurality of updated tracks are obtained;For the target centroid point not assigned to the existing track, the logical method is used for track initiation, and a plurality of stable starting tracks are obtained;The target tracking track is obtained by managing the track. The method combines the characteristics of radar measurement distribution and motion mode of vehicle target, and realizes the robust tracking of multi-vehicle target.

The present invention relates to a measurement method for high-density three-dimensional composite urban form characteristics, and a computing apparatus and a storage medium. The measurement method comprises: delineating a three-dimensional identification boundary, and acquiring raw map data; performing screening processing on the raw map data, and performing three-dimensional modeling within the identification boundary; calculating three-dimensional characteristic measurement indices; selecting three-dimensional base coverage rate and three-dimensional base plot ratio to construct a coordinate system, and classifying sample indices into hierarchies according to distribution characteristics; using a hierarchical clustering algorithm to constantly perform iteration until all data is combined into one category; and storing the data, building a three-dimensional urban database, and keeping the data updated. The present invention is aimed at the form characteristics of three-dimensional composite urban development during the renewal of high-density cities, and solves the problems in the prior art of it not being possible to precisely express three-dimensional urban forms and it also not being possible to perform effective quantitative analysis on the three-dimensional urban forms during urban design.

The application discloses a patent theme graph generation method and system based on deep semantic hierarchical clustering, comprising: obtaining a patent literature set PT to be generated into a patent theme graph; using a patent deep semantic representation model to perform semantic coding on each patent literature in the patent literature set PT, and obtaining a semantic representation vector matrix V={v1,...,v N}; inputting the semantic representation vector matrix V into a hierarchical clustering algorithm to obtain a hierarchical clustering tree structure corresponding to the patent literature set PT; generating a corresponding theme description for each non-leaf node on the hierarchical clustering tree structure; and combining the generated theme description and the hierarchical clustering tree structure into a patent theme graph with an upper and lower hierarchical structure. The patent theme graph generation method and system can help users quickly mine upper and lower hierarchical relationships of patent literatures at a theme level, meet the analysis needs of users for large-scale patent information, and improve the efficiency of patent analysis of users.

The application provides a prostate cancer biochemical recurrence prognosis risk prediction model based on fatty acid metabolism and cancer cell stemness genes and a construction method thereof, wherein the construction method comprises the following steps: S1: data collection: obtaining prostate cancer sample transcriptome data with biochemical recurrence information from a database, and dividing the data into a model training set and a model test set; S2: stemness score analysis; S3: fatty acid metabolism score analysis; S4: based on the analysis results of S2 and S3, identifying a co-expression gene module related to fatty acid metabolism and stemness characteristics in prostate cancer by a co-expression similarity algorithm and a hierarchical clustering algorithm, and obtaining a fatty acid metabolism and stemness-related gene set; S5: constructing a prostate cancer BCR prognosis risk prediction model; S6: constructing a nomogram model; S7: extracting RNA of a to-be-tested sample, constructing a cDNA library, quantifying the expression of the above genes, and calculating the prognosis risk level of prostate cancer through the expression level.

The invention discloses a KRAS protein conformation change simulation method and system based on multi-sample analysis, and belongs to the technical field of multi-sample analysis. The method comprises the following steps: collecting multi-sample molecular dynamics simulation data, calculating energy difference between conformations, and constructing an energy difference mapping matrix; then, based on a joint criterion of an energy difference mean value and an energy gradient change rate, a hierarchical clustering algorithm is adopted to divide a conformation energy space into a main steady-state region, a metastable-state region and a transition-state region, and a conformation hierarchical tree structure is formed; and extracting a key channel and a stable sub-state of conformation transformation according to the relationship between the energy fluctuation characteristics and transformation paths of various intervals, thereby realizing quantitative description and energy path identification of the KRAS protein conformation change. According to the method, dynamic conformation relations under different energy levels are effectively distinguished, a new calculation and analysis means is provided for conformation regulation and control and drug binding site recognition of a complex biomolecular system, and the method has the advantages of being high in accuracy, high in universality and good in expandability.

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.

This invention discloses an intelligent analysis method and system for underground structures based on multi-source geological data fusion, relating to the fields of engineering geology and geotechnical engineering. The method includes: S1, collecting raw geological data and applying a hierarchical clustering algorithm to group scale-scale fractures and interlayer slippage to obtain a preliminary stiffness distribution map of scale-scale rock mass units; S2, based on the preliminary stiffness distribution map of the scale-scale rock mass units, using finite element analysis to simulate the stress transfer path between units and determine the deformation mode and failure path of the scale-scale geological blocks. This intelligent analysis method and system for underground structures based on multi-source geological data fusion can accurately identify stress concentration, uneven settlement, and potential sliding surface locations in key areas such as weak interlayers in dam foundations, reducing judgment bias caused by empirical simplification. It provides more objective, stable, and repeatable analysis results for stability evaluation and risk prediction of underground engineering, and has good engineering applicability and promotional value.

The application provides an intelligent insurance evaluation monitoring and early warning system based on a whole process of logistics transportation, and relates to the technical field of logistics monitoring. The system comprises the following steps: collecting transportation data in the process of logistics transportation; preprocessing the transportation data; using a condensed hierarchical clustering algorithm to perform unsupervised clustering on the preprocessed logistics data, dynamically dividing the transportation risk types, and obtaining risk cluster labels; constructing an intelligent prediction model combining a lightGBM model and a neural network model to obtain an output risk probability distribution threshold value at a future t time; and judging whether real-time detection data exceeds the preset risk probability distribution threshold value, and if so, issuing an alarm and reminding. The intelligent prediction model combining the lightGBM model and the neural network model is used to predict the risk probability distribution threshold value at the future t time, so that the time and computing resources required for model training are reduced, and the efficiency of the whole logistics risk prediction work is improved.

The invention relates to the field of privacy protection, in particular to a geographic indistinguishability-based vehicle track protection method and device, and the method comprises the steps: obtaining vehicle track data, generating a hierarchical clustering tree through a hierarchical clustering algorithm, merging vehicle track data clusters in the hierarchical clustering tree through a merging function, and obtaining a final vehicle track data cluster. Generating a privacy protection level of each final vehicle trajectory data cluster; extracting trajectory data of each time step in the vehicle trajectory data, and performing privacy budget distribution on the trajectory data of each time step according to the final vehicle trajectory data cluster and the privacy protection level of each final vehicle trajectory data cluster to obtain scrambled trajectory data of each time step; and acquiring the trajectory speed, acceleration and coordinate information of the scrambled trajectory data of each time step, and performing dynamic Kalman filtering smoothing to complete privacy protection of the vehicle trajectory data. According to the invention, the vehicle track protection intensity based on geographical indistinguishability can be improved.

The application discloses a fabric classification method and device based on color characteristics, and a storage medium. It relates to the field of data processing. The method comprises the following steps: printing a color standard on each fabric to be classified; using a spectrophotometric colorimeter to collect the CIE Lab color values of each color sample of the color standard on each fabric to be classified; constructing a curve corresponding to the ink of each color on each fabric to be classified; calculating the curve difference between the curves corresponding to the ink of each color between any two fabrics to be classified; obtaining the fabric difference value between any two fabrics to be classified; constructing a symmetric difference matrix; using a hierarchical clustering algorithm, based on the symmetric difference matrix, classifying the fabrics to be classified, and obtaining a classification result. Through the application, the problem of low efficiency of color management of fabrics due to the inability to classify fabrics based on the color characteristics of the reaction between fabrics and ink relying on artificial experience in related technologies is solved.

The invention relates to the technical field of 5G video customer service, and discloses an intelligent video customer service access model establishment method based on 5G. The method comprises the following steps: collecting real-time video stream data transmitted by a user terminal through a 5G network, extracting multi-dimensional session features such as facial expression change, voice intonation, gesture action and the like, and constructing a dynamic intention weight matrix in combination with a historical session record intention classification result; performing noise reduction on the video stream by using an adaptive filtering algorithm, extracting a key action frame and calculating a time sequence correlation degree; the dynamic intention weight matrix and the key action frame time sequence correlation degree are fused, and an enhanced user intention vector is generated; based on the matching degree of the vector and historical session record intentions, dividing user intention categories through a hierarchical clustering algorithm, and establishing a mapping relationship between the user intention categories and a customer service strategy; and finally, generating an access instruction according to the mapping relation, dynamically allocating optimal customer service resources, and adapting to 5G network characteristics.

The invention discloses an intelligent algae monitoring method and system based on a floating bed process. The method comprises the following steps: constructing a floating bed area map model; collecting multi-dimensional algae and environmental parameters before and after floating bed regulation; performing linear regression prediction on the serialized parameters to form new parameter data, and vectorizing the parameters to obtain algae and environment feature vectors; introducing a hierarchical clustering algorithm to evaluate regional feature similarity, and analyzing an algae reproduction and migration direction and an environment matching region; and comparing and analyzing the algae migration trend and the environment change before and after regulation, evaluating the floating bed regulation effectiveness and optimizing the floating bed arrangement scheme. According to the method, intelligent algae monitoring and water ecology multi-point trend prediction are achieved, the algae migration law is accurately recognized, the effectiveness of floating bed setting is scientifically evaluated, water body deterioration is effectively relieved, the water ecology regulation and control efficiency is improved, and early warning hysteresis is reduced.

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 invention relates to the technical field of water quality information monitoring, in particular to a water quality monitoring method, system and device for environmental protection. According to the method, an aggregation hierarchical clustering algorithm is utilized, monitoring nodes are clustered based on pollution degree differences among the monitoring nodes, and after a tree diagram is obtained, the tree diagram is segmented to obtain a combination of clustering clusters segmented each time. For each time of segmentation, representative nodes are screened out based on the pollution degree difference between the nodes, the feature significance of the representative nodes obtained through current segmentation is evaluated in combination with distribution of the representative nodes in the drainage pipe network and the influence degree of key areas, the reasonable degree is obtained, the optimal segmentation process is screened out, and key monitoring nodes are determined. Based on the hierarchical clustering algorithm, the key monitoring node position with remarkable characteristics can be determined based on the dynamic characteristics of the water area of the current drainage pipe network, and effective pollution information in the current drainage pipe network can be extracted based on the key monitoring node position.

The application provides a defense strategy determination method based on industrial control network flow data, comprising the following steps: firstly, obtaining at least one archived industrial control flow data of a target industrial control network node; obtaining flow service scenario vectors of a plurality of archived industrial control flow data based on a K-means clustering algorithm; for each of the archived industrial control flow data, determining a first risk coefficient of the archived industrial control flow data from a strategy table for storing a first risk defense strategy; then, performing a hierarchical clustering algorithm on the plurality of archived industrial control flow data to obtain a first risk classification vector; constructing a risk defense mapping library of the first risk defense strategy according to the first risk classification vector of each of the archived industrial control flow data and the service scenario to which each of the archived industrial control flow data belongs in the flow service scenario vector; and constructing a network attack defense mechanism of the target industrial control network node based on the risk defense mapping library. The above method can improve the security of the entire industrial control 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 application provides a wireless network interference coordination and resource scheduling method based on a hierarchical clustering algorithm, and relates to the technical field of wireless communication, which comprises the following steps: obtaining a set of user equipment with unmet demands; according to the hierarchical clustering algorithm, classifying the equipment with mutual interference less than a threshold value into the same class and the equipment with mutual interference greater than the threshold value into different classes to obtain an optimal non-overlapping clustering result; adjusting and optimizing the optimal non-overlapping clustering result to cause overlapping between different classes and obtain an optimal clustering result; and according to the optimal clustering result, allocating a wireless resource unit to each class, stopping the allocation when the demands of all the equipment are met or the resources are exhausted, and obtaining a final resource allocation result. The application adopting the above scheme can effectively reduce the consumption of wireless resources while meeting the QoS of all the equipment during wireless resource scheduling.