42 results about "Indicator matrix" patented technology

The invention discloses a business flow site selection optimization method based on multi-source data fusion, and relates to the technical field of big data processing and multi-source data fusion, and the method comprises the following steps: obtaining multi-source data of a to-be-detected region, constructing multi-source features, carrying out the cross-modal fusion of the multi-source features through an adaptive weight distribution mechanism, and generating a comprehensive feature representation; based on comprehensive feature representation, passenger flow trends and causal contributions of the candidate points in different scenes are output through a causal inference method; taking the passenger flow trend and the causal contribution as input, establishing a multi-objective optimization model, performing solving, and outputting a comprehensive score and a sorting result of the candidate points; constructing an explanatory index matrix according to the comprehensive score and the sorting result in combination with a causal contribution result, and outputting a site selection scheme through a visual platform; according to the method, the problems of single data source and incomplete candidate point description in the prior art are solved by fusing the spatial geographic data, the space-time traffic data and the consumption behavior data.

The embodiment of the invention provides a public opinion evaluation method based on a multi-dimensional knowledge graph and related equipment, and belongs to the technical field of public opinion evaluation. The method comprises the steps that multi-source heterogeneous public opinion data are obtained and preprocessed, a multi-dimensional public opinion knowledge graph is obtained, and the multi-dimensional public opinion knowledge graph comprises entity nodes and association relations of users, topics, events, spaces and figures; based on the multi-dimensional public opinion knowledge graph, determining a hierarchical index system comprising a target layer, a middle layer and an index layer to obtain multi-dimensional public opinion index data; performing matrix standardization processing on the multi-dimensional public opinion index data to obtain a multi-dimensional public opinion index matrix; and inputting the multi-dimensional public opinion index matrix into a pre-trained convolutional neural network model for feature extraction, quantitative analysis and weight distribution to obtain a public opinion dynamic evaluation result. According to the embodiment of the invention, objectivity, accuracy and dynamic response capability of public opinion risk assessment can be improved.

This invention belongs to the field of target detection technology, specifically relating to a method, system, and device for target area identification in complex electromagnetic environments. The method constructs a two-dimensional plane to form a planar coordinate system, using the center of the target area as the origin. The target area is discretized into a uniform grid, covering a square region centered at the origin. The inscribed circular region within the grid is considered the outer region, generating an outer region indicator matrix. This outer region indicator matrix is ​​then used to generate an arrival angle sector region indicator matrix, and further, an intersection region indicator matrix is ​​generated. Finally, the proportion of the arrival angle sector intersection region within the target area is calculated using the intersection region indicator matrix, and a judgment threshold is set. If the proportion is greater than or equal to the threshold, the target is determined to be within the target area; otherwise, the target is determined to be outside the target area. The system and device are used to implement this method. This invention significantly improves the accuracy, robustness, and computational efficiency of target area identification.

The invention provides a transformer operation and maintenance management method and system based on digital twinning, and relates to the technical field of power equipment operation and maintenance, and the method comprises the steps: carrying out the multi-source integration of transformer sampling data, and obtaining traceable state monitoring data through combining with a distributed block chain technology; adaptively decomposing the monitoring data according to frequency bands and extracting time-varying features, and introducing a cross-frequency domain feature fusion device based on mutual information maximization to analyze relevance of different frequency bands to obtain a state index matrix; constructing a state evolution diagram of the transformer parts, analyzing state propagation characteristics among the parts by adopting dynamic causal reasoning, extracting multi-scale time sequence characteristics, and performing layered interactive coupling to realize space-time conjoint analysis; and calculating the reliability index of each component based on probability distribution, tracing degradation time nodes and influence factors, and formulating a maintenance strategy. According to the invention, omnibearing monitoring and accurate prediction of the state of the transformer are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

An improved information entropy-based dynamic peak capacity analysis method and system for generator sets, characterized in that the method comprises the following steps: collecting performance indicator data of each generator set at each moment within a region, so as to form a performance indicator data matrix; normalizing the performance indicator data at a set moment and an offset value of the performance indicator data between adjacent moments, so as to respectively obtain a normalized performance indicator matrix and a normalized offset performance indicator matrix; calculating static and dynamic analysis peak capacities of each performance indicator of the generator set; and on the basis of the obtained peak capacities of the performance indicators and offset performance indicators, calculating an integrated peak capacity of each performance indicator of the generator set. By means of dynamically analyzing peak capacities at different moments, the present invention provides effective analysis rules for the power peak of generator sets, thereby determining the peak capacities of the generator sets at the current moment and different moments.

The invention discloses an economic development sharing degree evaluation method, device and equipment and a storage medium thereof, and relates to the technical field of power system optimization, and the method comprises the steps: collecting multi-source power data covering the power consumption behaviors and power supply service processes of urban and rural users, and carrying out the automatic arrangement processing of the multi-source power data, forming a standardized evaluation data set; generating an index matrix of evaluation indexes based on the standardized evaluation data set; based on the index matrix, determining the weight of each evaluation index by adopting a preset data driving mode; and based on the index matrix and the weight, determining an evaluation result of the economic development sharing degree of the power system. That is, a set of autonomous, dynamic, quantifiable and comparable evaluation mechanism is constructed in the power system, so that the economic development sharing effect of the economic development sharing degree in the power system is objectively depicted in multiple dimensions.

The invention discloses a new energy bearing capacity influence factor analysis method and system considering deep peak regulation, and belongs to the technical field of power system operation analysis and new energy consumption. The method comprises the following steps: for a power grid, generating deep peak regulation multi-gear evolution operation mode data, and carrying out static N-1 and transient N-1 security check on the evolution operation mode data; key operation indexes are extracted on the basis of the evolution operation mode data passing the security check, and an index matrix is constructed on the basis of the extracted key operation indexes; based on the index matrix, the influence weight of each index on the new energy bearing capacity is quantified, and the dominant factors influencing the new energy bearing capacity are identified. The method has important theoretical value and engineering guiding significance for improving the new energy consumption capability of the power grid and optimizing the deep peak regulation operation strategy.

The invention discloses a subjective and objective combination weighting evaluation method based on variance optimization, and the method comprises the steps: firstly evaluating the relative importance of each index, and obtaining the subjective weight of each index; constructing a sample-index matrix, calculating the information entropy value of each index, obtaining the weight of each index based on the information entropy, removing redundant indexes through the calculation of the index correlation degree and the correlation degree between the indexes, achieving the de-overlapping, calculating the absolute correlation degree of the indexes, and carrying out the normalization to obtain the objective weight of the indexes; setting subjective and objective weight coefficients, carrying out linear addition processing on subjective and objective weights, and on the premise that the sum of constraint condition coefficients is 1, enabling the variance to be as small as possible to obtain the optimal subjective and objective combined weight of each index; and judging subjective and objective weights of the indexes and the consistency of sorting arrays, and determining final weights of the indexes. On the basis of keeping the advantages of the original subjective and objective evaluation method, the advantage complementation of subjective weighting and objective weighting is realized, and the evaluation effect of the evaluation model is improved.

The application discloses a welding quality monitoring method of resistance spot welding data multi-neighbor feature analysis, which collects process data when the resistance spot welding is normal and implements standardization processing; obtains a regression coefficient matrix of the standardized data matrix; obtains a multi-neighbor relationship matrix through the regression coefficient matrix, further obtains a generalized eigenvalue problem and solves it; obtains a feature transformation matrix according to the solving result, further obtains a score matrix, a covariance matrix and a monitoring index matrix; obtains control upper limits of two monitoring indexes according to the monitoring index matrix; implements standardization processing on the latest process data; obtains a score vector and an error vector of the standardized data vector according to the feature transformation matrix, further obtains the two monitoring indexes; determines whether the welding quality is normal or not by judging whether the two monitoring indexes are within the control upper limits; and the method has the advantage that the reliability of the welding quality monitoring can be effectively improved.

The application discloses a traffic accident severity analysis method based on a hierarchical clustering evaluation model, and comprises the following steps: S1: selecting the number of light injuries, severe injuries and deaths in the accident as evaluation indexes, and then constructing an evaluation index matrix according to the following equation. The application aims to classify the severity of traffic accidents in a region every day, and analyze the time variation and influence factors of the severity, so that the corresponding comprehensive severity variation and influence factor analysis conclusion can be obtained, and the severity variation and influence factor analysis conclusion can be used as reference data for traffic management strategies under major public time.

The application relates to the technical field of vehicle network cloud service, in particular to a quality evaluation method and system for vehicle network cloud service; the method mainly comprises the following steps: constructing an index matrix based on unified measurement, defining index names, constructing an index model, extracting cloud service data, and analyzing and calculating output results according to the constructed index model; in the scheme, the measurement is unified, demanders can use the same measurement index to evaluate the quality of different cloud services, and the quality of different cloud services is evaluated from multiple dimensions, systems and business dimensions, so that the evaluation accuracy is improved; in the scheme, a universally applicable index matrix is constructed, the cloud service is measured according to a unified standard from multiple angles, cloud service log data is processed by using a data warehouse technology, the accuracy of index calculation is improved, and the index calculation process is simplified.

The invention relates to the field of data processing of disaster risk management and geographic information, in particular to a flood risk zoning method based on multi-source data entropy correction G1 empowerment. The method comprises the steps of obtaining multi-source data and performing metadata management to generate an original index set; performing quality control and aperture alignment to form a quality control index matrix; calculating entropy values based on the quality control data, and constructing an entropy difference set with difference measurement; generating an adjacent ratio mark set through stable item addition, approach threshold rollback, upper and lower limit truncation and monotone constraint; generating a weight vector by adopting hierarchical grouping and G1 inverted recursion; carrying out weighted summation to obtain a comprehensive risk scoring sequence; generating a five-level risk tag through K-means clustering; and finally, outputting a result data packet and a risk zoning map through GIS association visualization. According to the method, objectification, automation and traceability of weight distribution are realized through an entropy correction G1 weighting method, and the accuracy and practicability of flood risk zoning are remarkably improved.

The invention discloses a multi-dimensional fusion power system toughness index and evaluation method and device. The method comprises the following steps: collecting power grid data of a to-be-evaluated power grid and determining extreme event characteristics; obtaining a power grid function change curve through a power grid simulation or fault deduction method based on the power grid data and the extreme event characteristics according to pre-constructed power grid function measurement containing a power grid structure; calculating a power grid toughness index matrix according to the power grid function measurement and the power grid function change curve; and weighting the power grid toughness index matrix to obtain the power grid staged comprehensive toughness of the to-be-evaluated power grid.

This invention discloses a nonlinear control law evolution method based on a large language model and control effect evaluation index, including initializing the control law; converting the weights of the control law operators into sampling probabilities; sampling each control law operator according to the probability distribution of the control law operators in the large language model to generate a control law formula; inputting the set of control law formulas into an aircraft dynamics simulation model to obtain a tracking error sequence and determining the evaluation index matrix of the tracking error sequence; analyzing the relationship between the control law operators and the evaluation index and outputting the update gradient of each control law operator; updating the weights of each control law operator and generating multiple new control law operators, and initializing the weights of the new control law operators; ending the iteration when the number of iterations reaches the maximum limit and outputting the control structure and control parameters of the control law, otherwise resampling and iterating; this invention solves the problem that large language models cannot be embedded in the dynamics simulation environment and evaluate the effect of control laws.

The invention belongs to the technical field of power distribution line monitoring and analysis, and particularly discloses a power distribution line loss abnormity analysis and search method, device and equipment and a medium, and the method comprises the steps: obtaining the power grid operation data of a target power distribution line; determining a line section with suspected line loss abnormity according to the power grid operation data of the target distribution line; in the line section with the suspected line loss abnormity, constructing a characteristic index matrix of each node in the middle through multi-source data fusion; based on the feature index matrix of each intermediate node, calculating the weight of each feature index by adopting an improved TC-EWM; determining the detection priority of each intermediate node based on the weight of the feature index; and detecting based on the detection priority of each intermediate node, and determining a node with abnormal line loss according to a detection result. According to the invention, rapid positioning and efficient treatment of line loss abnormity can be realized, the precision and economical efficiency of line loss treatment are improved, and the operation safety and intelligent level of a power distribution system are improved.

The application discloses a kind of commercial traffic site selection optimization method based on multi-source data fusion, it is related to big data processing and multi-source data fusion technical field, including the following steps: obtaining the multi-source data of the region to be measured, constructs multi-source feature, and through adaptive weight distribution mechanism multi-source feature cross-modal fusion, generate comprehensive feature representation;Based on comprehensive feature representation, through causal inference method, output candidate point in different scene passenger flow trend and causal contribution;Passenger flow trend and causal contribution are input, a multi-objective optimization model is established, and is solved, and the comprehensive score and ranking result of candidate point are output;According to comprehensive score and ranking result, combine causal contribution result, construct interpretive index matrix, and output site selection scheme through visual platform;The application is fused spatial geographic data, space-time flow data and consumer behavior data, solve the problem that data source is single in prior art, candidate point is not comprehensive.

This invention discloses a method and system for intelligent evaluation of blasting effects, relating to the field of information processing technology. The method includes: acquiring the pre-blast step profile line and the corresponding post-blast pile outline of the target object from a server; extracting post-blast pile change feature values; normalizing the post-blast pile change feature values ​​to obtain standard score values; constructing a mapping space between the standard score values ​​and engineering indicators to obtain engineering indicator evaluation scores; weighting the engineering indicator evaluation scores to obtain a comprehensive score; and determining the blast pile morphology level based on the comprehensive score and a preset grading threshold. This invention improves the adaptability and domain transferability of intelligent blasting effect evaluation by extracting and normalizing blast pile change features; it enhances standardization by using a dimensional projection matrix to map low-dimensional morphological features to high-dimensional engineering indicators; and it improves adaptability by introducing a multi-indicator matrix evaluation method and a quality grading method, allowing for flexible configuration of evaluation preferences according to actual needs.

The invention belongs to the technical field of data analysis, and particularly relates to a method and system for enrollment data visualization and abnormity early warning, and the method comprises the steps: 1, reading enrollment data records stored in a database, forming enrollment index vectors according to a fixed sequence, and arranging the enrollment index vectors to form an enrollment index matrix, meanwhile, a space-time hierarchy model with a region layer, a college layer and a professional layer is formed; step 2, performing a space-time hierarchy graph matrix decomposition anomaly detection algorithm on the enrollment index matrix in combination with the space-time hierarchy model, and generating a final anomaly intensity value of each space-time unit node by using a total residual amount and a local residual average amount; and step 3, generating a time axis view and a spatial hierarchy view based on the spatial-temporal hierarchy model and the final abnormal intensity value, and outputting an abnormal early warning record containing corresponding spatial-temporal unit node information when the final abnormal intensity value meets an early warning condition. According to the method, the comprehensiveness of enrollment data analysis, the accuracy of anomaly detection and the timeliness of early warning response can be remarkably improved.

The invention provides an engineering data dimension reduction method and device based on improved exploratory factor analysis and a storage medium, and relates to the technical field of engineering data processing. The method comprises the following steps: importing multi-category original engineering data, preprocessing to obtain engineering indexes, and constructing an engineering index matrix; common factors are extracted from indexes in the matrix, and factor loads are calculated to obtain a factor load matrix; orthogonally rotating the matrix to obtain a rotated factor load matrix; and performing factor naming and definition on the factor load of the rotated matrix to obtain a comprehensive factor with engineering data meaning. Through the complete process of data preprocessing, common factor extraction, factor load matrix construction, orthogonal rotation and factor naming definition, multi-class high-dimensional original data in the engineering field is converted into a comprehensive factor with a clear engineering meaning, data dimension reduction simplification is achieved, the problem that a traditional dimension reduction method is poor in result engineering interpretation is solved, and the engineering efficiency is improved. And a visual and considerable analysis basis is provided for engineering decision making.

The invention provides an automatic driving simulation test method for scene automatic generation, and belongs to the technical field of automatic driving, and the method comprises the steps: loading a preset file to configure an executable initial scene set; collecting camera and LiDAR sensor data and vehicle state data on a CARLA simulation platform based on an unreal engine; after the data are preprocessed, a comfort index and a safety index are calculated; constructing a fitness function with hard constraint by using the index matrix, and realizing search by combining genetic algorithm global exploration and neighborhood disturbance local optimization; restarting the process from an unexplored area when continuous ten generations are not optimized and improved; and finally outputting a minimum uncomfortable triggering scene set, a high-risk safety violation scene set and a reproducible experiment package containing scene files, parameters and the like. According to the invention, 'safety-comfort 'two-dimensional evaluation is realized, the test cost is greatly reduced, the evaluation comprehensiveness and reliability are improved, and key test support is provided for landing of automatic driving.

The application provides a method for site selection of a phase modifier under synchronous stability constraint, comprising the following steps: constructing an index matrix of weighted active phase sensitivity of nodes based on active phase sensitivity of a power system and active power variation of nodes; selecting a maximum value element in the index of weighted active phase sensitivity of nodes, and determining a critical value of synchronous instability according to the size of elements in the column where the maximum value element is located in the index matrix; and determining the site selection range of the phase modifier under the synchronous stability constraint according to the critical value of synchronous instability in the node corresponding to the column where the maximum value element is located. The method overcomes the deficiency that the site selection of the phase modifier only considers small disturbance stability and overvoltage constraint in the current research results, and can provide a reference for site selection and capacity determination of the phase modifier in the sending end power grid under the constraint of synchronous stability.

This invention belongs to the field of computer data clustering technology and discloses a multi-view clustering method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-view data to be clustered, wherein the multi-view data is one of images, videos, audio, and text; determining the Laplacian matrix, depth decomposition matrix, and global consensus embedding matrix for each view based on the original data matrix of the multi-view data; performing hypergraph regularized deep non-negative matrix decomposition on each layer of the original data matrix through the depth decomposition matrix to obtain the decomposition result; performing high-level semantic alignment and consensus representation fusion on the decomposition result through the global consensus embedding matrix to obtain the final layer representation of the view; determining the clustering indicator matrix of the final layer representation according to the objective function; and processing the clustering indicator matrix using the k-means clustering algorithm to obtain the multi-view clustering result. Through the technical solution of this invention, the accuracy of multi-view clustering is achieved.

The invention relates to the technical field of sleep quality evaluation, discloses a sleep quality evaluation method and system, and aims at solving the problem that an existing sleep quality evaluation scheme is poor in accuracy. The scheme mainly comprises the steps that a plurality of sleep indexes of a to-be-evaluated user in multiple days are obtained, and a first index matrix is established; normalizing and weighting the first index matrix to obtain a second index matrix; determining a positive ideal solution and a negative ideal solution corresponding to the second index matrix, and determining daily sleep index groups in the second index matrix; respectively calculating a first similarity between each sleep index group and the positive ideal solution and a second similarity between each sleep index group and the negative ideal solution; and calculating a sleep quality score corresponding to each sleep index group according to the first similarity and the second similarity, and determining the sleep quality of the to-be-evaluated user according to the sleep quality scores. According to the invention, the comprehensiveness and accuracy of sleep quality evaluation are improved.

Embodiments of the present disclosure disclose a dynamic tracking detection method and device for the disabled. A specific embodiment of the method comprises: acquiring basic information, a set of training guide information and a sequence of work scene images for a target disabled person; performing data standardization on the basic information and the set of training guide information to obtain standardized basic information and a set of standardized training guide information; constructing an evaluation index matrix; in response to determining that the disability type meets a first preset type condition, performing target dynamic behavior detection on each work scene image in the sequence of work scene images by using a preset dynamic behavior detection model to generate a sequence of target behavior detection results; and performing ability assessment on the target disabled person according to the evaluation index matrix and the sequence of target behavior detection results to generate disability ability assessment information. The embodiment can improve the accuracy of the detection results.

This invention discloses a damage assessment method for UAV swarm networks based on the entropy weight method-TOPSIS. For UAV swarm networks and for damaged nodes, it obtains secondary indicator values ​​under four primary indicators: resilience, real-time performance, survivability, and communication recovery capability, constructing an indicator matrix corresponding to each primary indicator. Each primary indicator matrix is ​​standardized, and then weighted using entropy-related weight vectors to obtain an entropy-weighted standardized matrix for each primary indicator. The value score for each primary indicator is then calculated, and a comprehensive score for the damaged node is obtained by weighting the entropy-related weight vectors. The scores of each node's neighboring nodes are recursively accumulated, and this, combined with the comprehensive score of the currently damaged node, yields the final score for the damaged node. The final scores of all damaged nodes serve as the damage assessment result for the UAV swarm network.

The invention discloses a multi-layer heterogeneous network structure mining spectral clustering method based on adversarial learning. The method comprises the following steps: constructing a multi-layer heterogeneous network; acquiring a similarity matrix, a Laplacian matrix, a diagonal penalty term matrix and an indication matrix of each layer of network in the multilayer heterogeneous network, and acquiring spectrum information of each layer of network according to the Laplacian matrix and the diagonal penalty term matrix of each layer of network; performing weighted linear combination on the spectrum information of each layer of network to obtain an eigenvalue decomposition matrix; according to the eigenvalue decomposition matrix, obtaining an updated Laplacian matrix, an updated diagonal penalty term matrix, a clustering matrix and an updated indication matrix, and constructing a multilayer heterogeneous network model; the clustering matrix is iteratively optimized until convergence, an updated clustering matrix is obtained, and the multi-layer heterogeneous network model is updated; and obtaining a spectral clustering result of the multi-layer heterogeneous network according to the updated multi-layer heterogeneous network model. According to the method, the calculation efficiency and the expandability are remarkably improved.

The invention discloses a mining area ecological monitoring and evaluation method and system based on sky-ground multi-source data fusion, and relates to the technical field of ecological monitoring and evaluation, and the method comprises the steps: collecting satellite images of a mining area, unmanned plane data and ground sensor data, carrying out the clustering analysis, reflecting the data of a deviation part under a space-time sequence data scale, and carrying out the clustering analysis; carrying out bilinear interpolation and spatial scale fusion operation on the data reflecting the deviation part to obtain a multi-scale data set, fusing the relation among the multi-scale data through a cross attention mechanism to obtain ecological monitoring data, extracting a plurality of ecological indexes from the ecological monitoring data and constructing an index matrix, and obtaining a plurality of ecological indexes; and performing radial basis kernel function processing on the index matrix, and performing mining area ecological risk assessment. According to the method, through'sky-air-ground 'cooperative monitoring, clustering analysis and related fusion are carried out on time-space sequence data, semantic alignment and adaptive feature complementation of cross-scale data are realized, and a reliable and consistent data basis is provided for subsequent analysis.

The invention relates to the field of network model driving, and discloses an electromechanical system component identification system based on multi-protocol fusion and network model driving, which comprises the steps of constructing a multi-identification unit management framework through registration, indexing, loading and unloading of execution units, scheduling an identification unit set according to task types and working condition requirements, and identifying the components of the electromechanical system based on the multi-protocol fusion and the network model driving. Carrying out parameter instantiation and structure definition on the identification units, generating model parameters according to historical samples and feature mapping, detecting an input and output response relationship, calculating availability indexes through error analysis, response consistency and a time sequence stability algorithm, screening an identification unit set meeting precision requirements, carrying out multi-dimensional performance calculation, and carrying out multi-dimensional performance calculation. And a multi-working-condition simulation and performance analysis method is adopted to generate a performance index matrix, parameter instantiation and structure definition are performed on the identification units, model parameters are generated according to historical samples and feature mapping, and an updated executable identification unit set is output. The method has the advantage that the self-adaptability and the precision are improved.

The invention discloses a service dispute risk early warning method and system based on multi-dimensional data features, and relates to the field of risk prevention and control, and the method comprises the steps: obtaining multi-source heterogeneous data in real time through a risk early warning platform; constructing a multi-dimensional feature index matrix based on the multi-source heterogeneous data; analyzing the multi-dimensional characteristic index matrix through a preset machine learning model, and generating a risk score of the patient and a high-risk patient portrait; when the risk score exceeds a preset threshold value, an early warning signal is triggered, and a corresponding intervention strategy is automatically matched according to the grade of the risk score; an intervention task is generated according to the intervention strategy, the execution state of the intervention task is continuously tracked, after the execution state is finished, a feedback result is generated and displayed on the risk early warning platform, and the feedback result is used for updating parameters of the machine learning model. The medical service dispute risk early warning efficiency can be effectively improved, the occurrence rate of service disputes is reduced, and the harmonious development of the medical service quality and the doctor-patient relationship is promoted.

This invention discloses a news event prediction method based on cross-time-step high-order correlation modeling, belonging to the field of news event prediction. It includes: constructing an entity graph based on historical news events; performing relation-time-aware message passing within the entity graph to obtain a comprehensive entity representation, capturing cross-time pairwise correlations between entities; passing the comprehensive entity representation through a learnable entity-hyperedge mapper to obtain an indicator matrix describing the membership relationship between entities and hyperedges, and constructing an entity hypergraph to capture cross-time high-order correlations between entities; then performing message passing on the entity hypergraph to update the hyperedge representation and subsequently update the entity representation, used to predict all possible future relationships between entities. This invention effectively models cross-time-step pairwise and high-order correlations between entities in news event prediction tasks, focuses on relational semantic prediction, and adapts to the representation learning requirements for relation-oriented prediction in temporal knowledge graphs for news event prediction.