252 results about "Hierarchical cluster algorithm" patented technology

The invention provides a domain data management method and system based on an artificial intelligence platform, and the method comprises the steps: obtaining financial transaction data through a data warehouse, carrying out the preprocessing, and extracting transaction metadata and semantic association thereof through a natural language technology; and classifying the metadata by adopting a hierarchical clustering algorithm, and constructing a transaction data consanguinity map. And extracting time sequence and spatial features from the atlas, calculating a node interaction index by using a pre-configured data interaction prediction model, and marking a high-heat node. And extracting high-dimensional transaction association features based on the graph neural network model, and identifying abnormal transaction data. And marking an abnormal node in the blood relationship map, and tracking and positioning an origin node of abnormal data. According to the method, multiple advanced technologies are comprehensively utilized, and rapid and accurate risk identification can be realized in the face of massive financial transaction data.

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 the technical field of image processing, in particular to an intelligent detection method and system for shaft forgings. The method comprises the following steps: acquiring gray values, positions and gradient directions of all pixel points in a surface image of the shaft forging; clustering all pixel points in the surface image by using a condensed hierarchical clustering algorithm to obtain a plurality of regions; calculating the suspicious degree of each region; determining a scratch burr area in the surface image of the shaft forging to realize intelligent detection of the shaft forging; in each round of iteration process of the condensed hierarchical clustering algorithm, for two to-be-combined clusters in each round of iteration process, calculating the separation degree of the two clusters; calculating the feature similarity of the two clustering clusters; determining the number of clustering clusters of the current iteration; a plurality of regions of the surface image of the shaft forging are determined. By dynamically adjusting the number of clusters, the problem of wrong merging or premature iteration stopping of a traditional condensed hierarchical clustering algorithm under the fixed number of clusters is avoided.

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 discloses a fan operation state anomaly detection method. The method comprises the following steps: S1, generating a fan operation voiceprint signal data set; s2, preprocessing the fan operation voiceprint signal data set; s3, constructing a feature data set; s4, performing preliminary classification on the fan operation voiceprint signals by using a hierarchical clustering algorithm based on the feature data set; s5, performing secondary subdivision in each fan operation state cluster by further using a hierarchical clustering algorithm; s6, establishing a fan normal operation voiceprint signal feature model based on the fan voiceprint signal feature data of the normal state sub-cluster, and establishing a fan potential abnormal state voiceprint signal feature model based on the voiceprint signal feature data of the potential abnormal state sub-cluster; s7, judging the running state of the fan by the system; and S8, when the running state of the fan is detected to be abnormal, the system sends out an alarm signal. The method has remarkable beneficial effects in the aspects of fan operation state monitoring and anomaly detection.

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-reactive power resource space-time coordination control method and system under high-proportion optical storage access, and the method comprises the steps: building a full-dimensional sensitivity matrix on the basis of a reactive power-voltage sensitivity matrix according to the coupling of a reactive power source control node and other load nodes; a g-dimensional reactive power source control space is constructed, and the Euclidean distance is adopted to represent the distance between the nodes; other load nodes refer to nodes except the reactive power source control node; performing reactive voltage control partitioning by using an improved hierarchical clustering algorithm to ensure reactive reserve margin charging of each partition, and selecting a dominant node based on a comprehensive sensitivity improvement index; on the basis of the second step, according to the effectiveness and economy of voltage regulation, cooperative control over different types of reactive power resources is achieved on the basis of the multi-reactive-power-source cooperative control principle of voltage-power sensitivity and voltage-cost sensitivity.

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 invention provides a question bank generation method and device, a storage medium and electronic equipment. The method comprises the following steps: collecting a first feature data set containing influence factors of wrong question making of students by using an intelligent education terminal; a hierarchical clustering algorithm is utilized to screen out the characteristics of wrong questions made by the students due to objective factors from the first characteristic data set; utilizing a preset wrong question correlation evaluation model to evaluate correlation scores among the characteristics of the wrong questions made by the students caused by the objective factors, sorting the characteristics of the wrong questions made by the students caused by the objective factors from large to small according to the correlation scores, and determining knowledge points corresponding to the characteristic with the maximum correlation score as a first target knowledge point cluster; based on the basic knowledge point cluster and the first target knowledge point cluster, question banks corresponding to the questions of different difficulty levels are generated and mapped on a learning map, the learning map comprises one or more key learning grids, and the key learning grids are grids corresponding to the first target knowledge point cluster.

The invention relates to the technical field of information, and provides a power grid equipment information visualization method and system, equipment and a medium, and the method comprises the steps: carrying out the region division of main network equipment through employing a hierarchical clustering algorithm according to the attribute of the main network equipment; when it is monitored that the line loss rate of a certain line exceeds a preset line loss rate threshold value, automatically obtaining region division information of equipment to which the corresponding line belongs, and extracting historical operation data of the equipment from a cloud database of the equipment in the corresponding region; an Apriori association rule mining algorithm is adopted to analyze the relevance between the historical operation data of the equipment and the line loss rate abnormity, and an equipment defect and line loss rate relevance analysis result is obtained; and acquiring real-time monitoring data of equipment corresponding to the line with the abnormal line loss rate, and verifying and correcting an equipment defect reason judgment result by comparing and analyzing the real-time monitoring data with historical operation data of the equipment and adopting an isolation forest anomaly detection algorithm. According to the invention, rapid positioning of line loss rate abnormity and accurate prediction and visual management of equipment defects are realized.

The invention relates to an optimized regulation and control method based on a model considering an uncertain source load operation scene, and the method comprises the steps: solving a predicted value of conventional load power through a load power prediction method, such as a neural network method, and solving predicted values of wind power output and photovoltaic output through a distributed power output prediction method, such as a data statistics prediction method; a prediction error distribution function of wind power output and load power is combined, a source load operation scene generation model considering prediction errors is established, a large number of original source load operation scenes are generated, an improved hierarchical K-means clustering algorithm is used for extracting the original source load operation scenes, and a source load uncertainty model based on the multi-scene technology is established; compared with the prior art, the method has the advantages that the calculation complexity is simplified while the uncertainty influence caused by prediction errors is fully considered, the dependence of a traditional clustering algorithm on the number of target scenes is avoided, and the strong uncertainty of the source side and the load side of the power distribution network is accurately described.

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 chemical plant area intelligent evacuation, and discloses an emergency evacuation place intelligent management system and method, and the method comprises the steps: constructing a chemical risk semantic unified representation space through a risk semantic mapping algorithm, and generating a mapping relation between a risk state and semantic expression; applying a feature portrait algorithm to generate an employee feature representation model; on the basis of the chemical risk semantic personalized adaptation model, personalized semantic guidance for employees with different characteristics is generated; generating a multilevel chemical risk semantic model by using a regional hierarchical clustering algorithm; processing real-time position and risk change data through a collective intelligent optimization algorithm, and calculating a dynamic evacuation path planning scheme; differentiated safety management based on employee characteristics, hazard source distribution and semantic understanding is realized, the problem of low efficiency caused by one-step instructions in traditional evacuation management is solved, and the emergency evacuation efficiency and safety of the chemical plant area are improved.

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 discloses a low-voltage transformer area topology modeling method and system based on hierarchical clustering, and the method comprises the steps: building a sensitivity relation between voltage and current, calculating a sensitivity coefficient of voltage to current, and calculating a sensitivity matrix according to the resistance value and reactance value of each line in transformer area topology; subtraction is carried out on the electrical quantity under the continuous time section, a sensitivity relational expression is converted, and an associated impedance matrix is obtained and comprises an associated resistance matrix and an associated reactance matrix; based on an improved hierarchical clustering algorithm, inputting an associated resistance matrix or an associated reactance matrix to generate a tree diagram; and identifying hidden nodes according to the tree diagram, determining a generation sequence of the hidden nodes and a subordinate connection relationship between the nodes, and constructing a transformer area topology. By implementing the method, not only can the correlated impedance between the end nodes be accurately calculated, but also all hidden nodes in the network can be identified based on the correlated impedance matrix and the network topology can be correctly deduced, high accuracy and robustness are shown, and the cost is low.

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 provides a technical avoidance design method based on a large language model, which comprises the following steps of: performing semantic analysis on an existing technical text by adopting a natural language processing method, and performing multi-dimensional and hierarchical division on technical data by utilizing a hierarchical clustering algorithm in combination with a decentration characteristic of a block chain to obtain technical data; converting the technical data into ordered knowledge modules with different functions, and constructing a knowledge base by the plurality of knowledge modules; according to the method, updating of the technology is continuously tracked, a knowledge base is dynamically managed and optimized, and optimization strategies of local coordination and global distribution are continuously enriched through modes of regularly updating patent data, re-evaluating a technical scheme and adjusting and optimizing algorithm parameters.

The invention relates to an Internet of Vehicles threat analysis method and system based on a knowledge graph, and a medium. The method comprises the following steps: U1, collecting data information of an Internet of Vehicles entity object and data information of Internet of Vehicles threat intelligence; and U2, based on the data information of the Internet of Vehicles threat intelligence, extracting the feature information of the Internet of Vehicles threat intelligence by adopting a hierarchical clustering algorithm based on expected cross entropy to obtain data information of a feature matrix of the Internet of Vehicles threat intelligence, and representing the relevance between the entity object and the feature matrix of the Internet of Vehicles threat intelligence by adopting a cross validation algorithm based on a Spearman rank correlation coefficient to obtain data information of the relevance between the Internet of Vehicles entity object and the threat intelligence. According to the method, the problem that the threats of all dimensions of the Internet of Vehicles cannot be comprehensively covered can be solved, the threat information of the Internet of Vehicles can be visually displayed from all angles of the threat knowledge graph of the Internet of Vehicles, and automobile safety analysts can conveniently and flexibly analyze the safety situation of the Internet of Vehicles.

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.