191 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 motion state detection method and device based on a multi-modal sensor, and the method comprises the following steps: carrying out the inertial parameter extraction of an original acceleration signal in the motion process of a human body through a three-axis acceleration sensor, and obtaining a motion acceleration feature vector; the original pulse wave signals in the movement process are monitored in real time, and a cardiovascular physiological feature sequence is obtained; performing time-frequency domain conjoint analysis on the time sequence myoelectricity feature data set, the motion acceleration feature vector and the cardiovascular physiological feature sequence by adopting an adaptive wavelet transform technology to obtain a multi-dimensional feature fusion matrix; and performing dynamic segmentation on the multi-dimensional feature fusion matrix through a hierarchical clustering algorithm to obtain motion state feature subspaces, thereby solving the problem that a traditional motion monitoring method mainly depends on a single type of sensor, although the motion condition of a human body can be reflected to a certain degree, the motion state feature subspaces cannot be monitored. However, the technical problem of lack of comprehensive understanding of complex motion states is solved.

The invention provides an online multi-target multi-camera vehicle tracking method based on deep learning. The method mainly comprises the following steps: capturing vehicle video images from a plurality of cameras; identifying and positioning a vehicle instance by using a YOLOv11 algorithm; 2048-dimensional appearance features of the vehicle are extracted through a ResNet101IBN convolutional neural network; applying a single-camera multi-target tracking algorithm to generate a target track under each camera; and through a hierarchical clustering algorithm, in combination with feature cosine distance and Dunn index optimization, clustering is performed on a target trajectory, and cross-camera target matching is completed. The method iteratively executes the steps, and meets the multi-target vehicle tracking requirement in a continuous video stream. By integrating a lightweight vehicle detector, efficient appearance feature extraction, accurate single-camera tracking and an advanced cross-camera association strategy, the real-time performance, accuracy and robustness of vehicle tracking are remarkably enhanced, and the method is suitable for complex multi-camera monitoring environments such as urban monitoring, traffic management and automatic driving assistance systems.

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 invention discloses a cyberspace advanced long-term threat (APT) trapping method based on a graph neural network, a storage medium and a cyberspace advanced long-term threat trapping device, and relates to the technical field of computers, and the method comprises the steps: carrying out the dimension reduction of original encrypted traffic data based on a hierarchical clustering algorithm, retaining an abnormal traffic cluster, and removing normal traffic redundant data; designing an E-ResGAT model to perform encrypted semantic modeling, and introducing a residual connection technology to retain original traffic features; and a dynamic trapping-protocol inference closed-loop system is further constructed, and a honeypot environment is utilized to simulate attack sample interaction behaviors and capture traffic. According to the scheme, efficiency balance of encrypted traffic data reduction and semantic reasoning is realized through collaborative optimization of hierarchical clustering and a graph neural network, the capture capability of the model on a hidden attack mode is enhanced in combination with a residual attention mechanism, and a multi-dimensional analysis basis is provided for high-security scenes such as a military private network; and an innovative solution is provided for improving the security defense capability of a military information system.

The invention discloses a multi-scale spatial-temporal feature extraction and fusion weather prediction method and system, and the method comprises the steps: constructing a multi-scale spatial structure through employing a hierarchical clustering algorithm, extracting a trend component according to node data, carrying out the detrending of the node data, obtaining a season component, and obtaining a final time feature; calculating delay values of sub-nodes in the multi-scale space structure relative to father nodes, sorting the delay values to obtain a sequence index, and further obtaining space features; performing feature fusion on the time features and the space features to obtain space-time fusion features; and fusing the space-time fusion features of different scales to obtain a multi-scale feature fusion result, and performing linear mapping on the multi-scale feature fusion result to obtain a weather prediction result. According to the method, the calculation cost and the prediction error are reduced, the irregularly distributed meteorological station data can be flexibly processed, and the processing capability of the complex station layout data is improved.

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 technical field of digital education, in particular to a digital teaching material intelligent generation system based on digital education, which comprises an education resource acquisition module, a knowledge graph construction module, a teaching material content generation module, a knowledge tracking module and a teaching material presentation module, according to the method, a graph embedding method based on a hyperbolic space graph convolutional network and a hierarchical clustering algorithm with improved robustness are introduced, low-dimensional embedding and multilayer structure optimization are performed on an initial knowledge graph, and an educational knowledge graph with a semantic hierarchical structure is constructed; a graph structured learning behavior modeling method is introduced, a student-knowledge point heterogeneous graph structure is constructed, a heterogeneous graph convolutional network is adopted to model interaction behaviors of students and knowledge points, a knowledge state graph of each student is obtained, and the accuracy of individual knowledge mastery degree modeling is improved.

The invention relates to the technical field of honing positioning, in particular to an automobile part positioning method and system for numerical control machine tool machining, and the method comprises the steps that a grey-scale map of an engine cylinder body fixed to a station is obtained, and the engine cylinder body is provided with a standard positioning hole; constructing a feature point for each pixel point in the grey-scale map, wherein the feature point represents the position and grey-scale value information of the corresponding pixel point; and classifying the feature points by using a condensation type hierarchical clustering algorithm to obtain a plurality of optimal clusters, calculating the possibility that each optimal cluster is a positioning hole region, taking the region corresponding to the optimal cluster with the possibility located in a preset threshold interval as the region of the positioning hole, and obtaining the position coordinates of the positioning hole. And the positioning center of the engine cylinder body cylinder hole is further obtained. According to the method, the phenomenon of wrong recognition during clustering combination is avoided, and the positioning hole can be accurately recognized and distinguished from other hole areas.

The invention relates to the technical field of microbe traceability analysis, in particular to a microbe traceability analysis method based on an infrared absorption spectrum and a database, and the method comprises the following steps: acquiring absorbance spectral data: acquiring background spectral data for multiple times, performing background correction, acquiring spectral data of a sample containing a strain, and finally calculating the absorbance spectral data; preprocessing the absorbance spectrum data: smoothing the absorbance spectrum data by adopting an SG smoothing algorithm, and calculating a first-order derivative or a second-order derivative of the smoothed absorbance spectrum data to obtain an absorbance spectrum data set; spectral range selection: presetting different spectral ranges, and selecting different spectral ranges for analysis according to requirements; analyzing the similarity of the strains: analyzing the similarity of the absorbance spectral data set of the strains by adopting an improved hierarchical clustering algorithm; bacterial strain traceability analysis: determining a classification threshold by adopting a threshold determination algorithm, and judging microorganism traceability information; the traceability analysis precision is high, and the reliability, the stability and the practicability are high.

The invention belongs to the technical field of testability design of integrated circuits, and discloses a 3D IC MBIST test method based on an intelligent algorithm, and the method comprises the steps: carrying out the isomorphic grouping through analyzing the space coordinates, hierarchical affiliation and multi-dimensional attributes of a memory; establishing a three-dimensional layout relationship based on the improved interlayer distance model; the hierarchical clustering algorithm is adopted to merge memory clusters according to spatial proximity, and a grouping architecture with the minimum controller number is constructed; a dynamic test scheduling model is established in combination with a multi-target simulated annealing algorithm, and test time is optimized under power consumption constraint through a temperature attenuation mechanism. According to the method, three-dimensional collaborative optimization is carried out on spatial layout, time sequence constraint and power consumption budget, the contradiction between area overhead and test efficiency of a traditional memory built-in self-test scheme in a 3D IC is solved, and lamination test challenges under the condition that the test temperature is limited are effectively handled; and the test economy and reliability of the heterogeneous integrated chip are obviously improved.

The invention discloses a short-term power prediction method and system based on a wind power plant cluster. The method comprises the following steps: constructing a similarity distance matrix of a key meteorological element time-varying rule between different stations; calculating a Copula entropy between the historical power samples of each station, and constructing a spatial similarity matrix of station power according to each Copula entropy; performing normalization processing on the similarity distance matrix and the spatial similarity matrix, and summing the similarity distance matrix and the spatial similarity matrix after normalization processing to obtain a space-time similarity distance matrix; clustering the similarity distance matrix by adopting a hierarchical clustering algorithm, determining a partitioning scheme in combination with the geographic distribution of the new energy stations, and dividing each wind power plant in the target region into each region; and outputting a total power prediction result of the obtained region according to the LSTM time sequence prediction model. The overall precision and robustness of power prediction are improved, and a more reliable scheduling basis is provided for large-scale access of a new energy station to a power grid.

The invention discloses a sepsis typing division method and a typing prediction model construction and application method and device, and relates to the field of machine learning, and the method comprises the steps: obtaining central venous oxygen saturation and peripheral perfusion indexes of a plurality of patients in a preset time period; calculating and acquiring a dynamic blood flow oxygen flow index track of each patient in the preset time period according to the central venous oxygen saturation and the peripheral perfusion index of the patient in the preset time period; evaluating trajectory similarity by using a dynamic time warping algorithm, and grouping trajectories by using a hierarchical clustering algorithm to obtain a plurality of sepsis types; and training the machine learning model by using each sepsis type and the corresponding clinical features of the patient to obtain a sepsis type prediction model. According to the application, sepsis typing division is carried out based on the longitudinal data of the central venous oxygen saturation and the peripheral perfusion index, a prediction model is established for each typing based on clinical features, and finally division and accurate prediction of sepsis crowds are realized.

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 discloses a taxi route recommendation method based on a FastDTW time sequence clustering algorithm. The method comprises the following steps: firstly, acquiring original GPS track data of a taxi from a public data platform, and preprocessing the data; thirdly, calculating the similarity between the trajectories by using a FastDTW algorithm, and constructing a distance matrix between every two trajectories; then, the distance matrix is applied to a hierarchical clustering algorithm, a high-frequency destination hot spot area is mined, and a high-probability passenger-carrying destination is obtained; and finally, in combination with factors such as the driving distance, the driving time and the driving cost, calling an Amod map # imgabs0 # to obtain detailed route information, and calculating and recommending an optimal route through comprehensive scores. According to the invention, based on hot destination prediction and multi-target route optimization of data mining, the optimal passenger searching route is recommended for the unloaded taxi, the unloaded rate of the taxi is reduced, the passenger loading probability of the unloaded taxi is improved, and time and resource consumption are reduced, so that the vehicle scheduling efficiency and the driver operation efficiency are 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 method and a system for processing a small-amount unhealthy asset litigation case, and relates to the field of knowledge maps, and the method comprises the steps: obtaining the data of the small-amount unhealthy asset litigation case; preprocessing the case data to obtain a structured case attribute table; a dynamic knowledge graph is constructed, the dynamic knowledge graph is interconnected with an external legal knowledge base, and knowledge reasoning is introduced for dynamic expansion of knowledge; designing a similarity calculation function including legal relationship constraints and case feature ontologies; embedding the similarity calculation function into a hierarchical clustering algorithm, and performing case clustering and grouping by using the embedded hierarchical clustering algorithm; and according to a grouping result, generating a case filing material by adopting knowledge reasoning. Aiming at the problem that law compliance and case similarity are difficult to consider in existing small-amount unhealthy asset litigation case grouping, an external law knowledge base is introduced to update a domain knowledge graph, and a law relationship is set as a constraint, so that the law interpretability of case grouping is improved.

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.

An aurora form unsupervised classification method based on an all-sky multi-expert model comprises the steps that 1) the all-sky multi-expert model refines aurora form characteristics from different angles by adopting a mode of cooperative work of a plurality of expert networks in order to solve the problem of complex content in an all-sky view, and the model characterization capability is enhanced; 2) for the problem of aurora local structure diversity, dynamic convolution and dynamic snakelike convolution are introduced into an all-sky multi-expert model to construct an attention module, so that accurate representation of the aurora local structure is realized; 3) adopting a rapid hierarchical clustering algorithm, and obtaining a complete hierarchical structure representing different granularity levels of the aurora form by constructing an adjacent matrix; and 4) introducing an abnormal value screening strategy into the hierarchical clustering algorithm to improve the reliability of a clustering result. The method provided by the invention can efficiently capture the morphological characteristics of the all-sky aurora image, and explores a natural classification mode of the aurora morphology under an unsupervised condition.

The present invention belongs to the technical field of integrated circuit testability design, and discloses a 3D IC MBIST test method based on an intelligent algorithm. It performs isomorphic grouping by parsing the spatial coordinates, hierarchical attribution, and multi-dimensional attributes of memories; establishes a three-dimensional layout relationship based on an improved interlayer distance model; uses a hierarchical clustering algorithm to merge memory clusters according to spatial proximity, and constructs a grouping architecture with a minimized number of controllers; combines a multi-objective simulated annealing algorithm to establish a dynamic test scheduling model, and optimizes the test time under power consumption constraints through a temperature attenuation mechanism. The method of the present invention performs three-dimensional collaborative optimization of spatial layout, timing constraints, and power consumption budget, solves the contradiction between area overhead and test efficiency faced by traditional memory built-in self-test schemes in 3D ICs, effectively copes with the stacked layer test challenges under limited test temperature conditions, and significantly improves the test economy and reliability of heterogeneous integrated chips.

The invention discloses a short text unbalanced classification method based on semi-supervised clustering and dynamic resampling, which comprises the following steps of: S1, performing cleaning and feature extraction on original short text data, removing features of which the missing rate exceeds 20%, performing vectorization expression on residual texts, and constructing an initial feature space; s2, under a'compact cluster 'assumption, based on the guidance of marked data, performing iterative segmentation on majority class and minority class samples by adopting a semi-supervised hierarchical clustering algorithm, generating a plurality of discontinuous clusters, and revealing inter-class and intra-class unbalanced distribution characteristics; s3, based on a clustering result, performing dynamic under-sampling on majority of samples; s4, based on a clustering result, carrying out dynamic oversampling on minority class unmarked data; according to the method for introducing semi-supervised clustering into short text unbalanced classification for mixed sampling, the marked data and the unmarked data are clustered, the basic distribution characteristics of the short text data are effectively captured, and follow-up sampling is facilitated.

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 invention relates to a multi-parameter transcranial magnetic stimulation quantitative analysis method, which comprises the following steps of: acquiring neuron activity index data, and performing standardization processing on the neuron activity index data to generate a first data set F1; identifying the acquired data by using a DBSCAN clustering algorithm and removing noise points to obtain a second data set F2 only containing core points and boundary points; and S3, calculating a distance metric and a link distance based on the second data set F2 obtained in the step S2, applying a hierarchical clustering algorithm to obtain a clustering tree diagram, and determining a classification threshold and performing classification according to the form of the clustering tree diagram and the requirements of a five-level quantitative classification system. According to the quantitative analysis method, the parameters such as the stimulation intensity, the frequency and the pulse mode are quantitatively graded, so that a clinician can more accurately select a proper treatment scheme according to the specific conditions such as the age, the gender and the disease state of a patient.