79 results about "Degree matrix" patented technology

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are improved.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention provides a computing power resource partitioning method and device based on hypergraph clustering, equipment and a medium. The method comprises the following steps: constructing a computing power resource hypergraph in a computing power network resource side scene; determining an equipment degree matrix and a hyperedge degree matrix of the computing power resource hypergraph, and converting the computing power resource hypergraph into an equipment Laplacian matrix required by hypergraph clustering by using the equipment degree matrix and the hyperedge degree matrix; carrying out eigendecomposition on the equipment Laplacian matrix to obtain an equipment eigenvector; and in combination with the graph cutting target function, performing hypergraph clustering processing on the computing power resource hypergraph based on the feature vector of the device Laplacian matrix to obtain a target resource partitioning result corresponding to the computing power resource hypergraph. According to the method, the problems of low resource utilization rate, high scheduling complexity, poor resource dynamics, lack of effective partitioning strategies and the like existing in the existing computing power resource partitioning can be obviously improved.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention belongs to the technical field of power consumption prediction, and discloses a fuzzy power consumption prediction method and system based on multi-scale dual supervision driving, and the method comprises the steps: carrying out the multi-scale feature reconstruction of a data sample, calculating reconstruction errors, aggregating the reconstruction errors, obtaining a sample structure guide index, and synthesizing the structure guide index and entropy contribution to screen the sample; performing fuzzy division on the screened samples, and arranging the membership degrees of all the samples in sequence to obtain a fuzzy membership degree matrix; fusing the structure supervision signal and the target supervision signal, and adjusting the membership matrix by using the fusion guide signal; and taking the membership degree in the new membership degree matrix as a weight, and carrying out weighted summation on the prediction result of each local regression model to obtain a final prediction result. The invention designs a fuzzy division method combining a multi-scale structure adaptive mechanism with a structure and target dual supervision signal, and improves the modeling accuracy of the model for complex energy consumption data, the system adaptive capability and the sample utilization efficiency.

The invention discloses a safety early warning system, which relates to the technical field of early warning regulation and control, and comprises an acquisition and processing module, an analysis and prediction module and an early warning regulation and control module, the acquisition processing module measures the temperature and the pressure in the current period, and processes the temperature and the pressure in the historical period into a standard data tensor; the analysis and prediction module establishes a matching degree matrix through dynamic time warping, introduces physical regular constraint into principal component analysis based on an ideal gas equation, extracts and generates a coupling feature sequence, splices the coupling feature sequence with a standard data tensor, inputs the coupling feature sequence into a prediction model, outputs a prediction data tensor, and performs fitting to generate a trend function of temperature and pressure; and the early warning regulation and control module dynamically judges a dangerous moment and dynamically determines a regulation and control time length, performs model prediction control based on an equipment regulation and control model, defines a regulation and control objective function, solves an optimal regulation and control problem in a limited time length, obtains an optimal parameter adjustment quantity vector, and executes regulation and control. And accurate prediction and regulation and control considering the temperature and pressure coupling relationship and the adjustable and controllable time length are realized.

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 engineering suitability evaluation, in particular to a qualitative and quantitative multi-type index full-life-cycle comprehensive fuzzy evaluation method and a product, and the method comprises the steps: constructing a mutual influence degree matrix in each evaluation time period, constructing a comprehensive evaluation coefficient matrix, and employing a qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method. The method comprises the following steps: generating a fuzzy comprehensive evaluation value, then calculating a time period membership degree vector and an influence degree membership degree matrix, finally obtaining a comprehensive fuzzy evaluation result in a whole life cycle by weighting and summarizing the fuzzy comprehensive evaluation value of each evaluation time period, and sorting a plurality of schemes.

The invention discloses an artificial intelligence-based interactive question-answering method and system, and relates to the technical field of interactive question-answering, and the method comprises the steps: converting a user input text question into vector representation, constructing a relation graph by combining a dependency relation tree of a text entity, capturing a dependency relation in the question as an edge weight, forming an adjacent matrix, and constructing a node degree matrix; and determining a standardized graph Laplacian matrix, performing feature decomposition, determining an optimal clustering number, clustering language phrases of the text question, and determining task fragment sets of different clusters. According to the method, semantic association between language phrases is achieved by constructing the dependency relationship graph, meanwhile, syntactic relationships are fused, the integrity of text structure information is ensured, the degree of nodes serves as a quantitative index of local strength in the graph, the information directly supports subsequent Laplacian matrix normalization processing, a unified scale is provided, and the method is suitable for being applied to the field of text processing. And the imbalance problem of the data range is avoided.

The invention provides a time-space prediction method and device for settlement along a railway. The prediction method comprises the following steps: identifying an unstable target region based on monitoring data obtained by monitoring a railway line; obtaining time sequence settlement data of each monitoring point in the unstable target area, and performing noise reduction processing by using the optimal noise reduction model to obtain the time sequence settlement data of each monitoring point after noise reduction processing; obtaining an adjacent matrix and a degree matrix based on a preset distance threshold and the distance between every two monitoring points in the unstable target area; and based on the time sequence settlement data, the adjacency matrix and the degree matrix of each monitoring point after noise reduction processing, utilizing a GCN-LSTM prediction model, and adopting a rolling prediction method to carry out advanced prediction on the time sequence settlement data of each monitoring point to obtain an advanced prediction set of each monitoring point. According to the method, the time sequence settlement data of the monitoring points are predicted in advance by constructing the GCN-LSTM prediction model with the space-time sequence coupling characteristics, and the method has the advantage of being high in prediction precision.

The invention provides a method for intelligently extracting key geometric features of a target object through a three-dimensional laser scanner, and belongs to the technical field of actual measurement of an internal forming surface of a wind tunnel body. Constructing an association degree matrix by using a minimum spanning tree algorithm in a graph theory, calculating three clustering center values based on K-means clustering analysis, determining a boundary threshold value, starting a scanning precision adaptive strategy, dynamically adjusting scanning parameters through a feature extraction optimization model, extracting key geometric features by using a geometric feature recognition algorithm, and carrying out feature extraction on the key geometric features; spatial context information is automatically extracted by using a Transform attention mechanism, and finally a key geometric feature extraction result of the target object is output through sequential processing of six parallel computing units, so that the technical problem of insufficient geometric feature extraction precision of three-dimensional laser scanning point cloud data in a complex environment is solved.

This application discloses a data-driven energy dispatch fault diagnosis method, device, and medium, mainly relating to the field of fault diagnosis technology. It addresses the problems of existing solutions neglecting the multimodal distribution characteristics of energy data, failing to effectively capture the complex coupling relationships between the source-grid-load-storage links, lacking awareness of dispatch strategies, and being sensitive to noise interference. The method includes: obtaining a graph convolutional hidden representation matrix and then calculating a sparse dictionary matrix; optimizing the graph convolutional sparse coding objective function, solving for the sparse coding coefficients, and outputting a feature mining data matrix; calculating a dispatch strategy matching degree matrix based on the feature mining data matrix, using introduced dispatch plan data and real-time operational constraints; fusing the operational status classification vector and the dispatch strategy matching degree matrix to obtain predicted fault categories; and iteratively training a diagnostic model based on the predicted fault categories and labeled fault categories until a well-trained diagnostic model is obtained.

The invention discloses a geographic national condition comprehensive index monitoring method, and relates to the technical field of geographic national condition comprehensive indexes. The method comprises the following steps: collecting a geographic remote sensing image map, and segmenting the geographic remote sensing image map through a MaskR-CNN model instance to obtain a geographic semantic entity; reasoning through a causal discovery method based on an evolutionary algorithm to obtain a first causal intensity matrix; calculating a spherical distance between geographic semantic entities to obtain an entity distance matrix, generating an entity comprehensive influence degree matrix in combination with the first causal intensity matrix, and further constructing a geographic knowledge graph; inputting the geographic knowledge graph into the adversarial graph attention network model, and outputting a second causal intensity matrix; and based on the second causal intensity matrix, reversely traversing the geographic knowledge graph by using a breadth-first search algorithm to extract a change causal chain, calculating a feature contribution ratio of each geographic semantic entity in the chain, obtaining a geographic national condition comprehensive index, and realizing monitoring of the geographic national condition comprehensive index.

The application discloses a TBM tunneling parameter abnormal data identification method and system, and belongs to the technical field of data processing. The application constructs a polynomial chaos expansion regression model through polynomial chaos expansion, identifies abnormal data in TBM tunneling parameter data by using data clustering and polynomial chaos expansion regression error, depicts the correlation between TBM tunneling parameters through polynomial chaos expansion, compares the difference between data by using the correlation between TBM tunneling parameters, constructs a clustering objective function based on polynomial chaos expansion regression error, optimizes and solves the clustering objective function by using a Lagrange multiplier method, and obtains a TBM tunneling parameter membership degree matrix. Whether the data is abnormal is determined by using the TBM tunneling parameter membership degree matrix, and accurate identification of abnormal data of TBM tunneling parameters is realized.

A distributed photovoltaic cluster rapid clustering method, comprising: S1, extracting the historical meteorological data of the grid-connected point of the distributed photovoltaic power station and the operating characteristic parameter value of the distributed photovoltaic power station, and normalizing the same; S2, performing rapid filtering on the distributed photovoltaic power station based on the approximate k-means vector quantization technology, and obtaining representative typical distributed photovoltaic power stations; S3, screening out the distributed photovoltaic power stations with similar relations between each other through the KNN algorithm; S4, obtaining the adjacent matrix and degree matrix information of the typical distributed photovoltaic power stations by means of the Gaussian kernel calculation method, and constructing a Laplace graph; S5, adopting the "average cut" graph cutting method, obtaining the clustering result of the typical distributed photovoltaic power stations; S6, calculating the distance from each distributed photovoltaic power station to all typical distributed photovoltaic power stations, and distributing to a similar class according to the distance size, and completing the clustering. The scheme realizes rapid and accurate clustering of the distributed photovoltaic cluster.

The invention discloses a trademark batch retrieval method and system based on big data and AI assistance. The method comprises the steps that a batch to-be-retrieved trademark set and multiple trademark retrieval condition texts are obtained; utilizing the trained multi-modal trademark analysis model to respectively extract retrieval condition features of the retrieval condition text and graphic attribute features of the trademark main body to be retrieved; constructing a retrieval condition feature matrix and a graph attribute feature matrix, and calculating the multi-modal correlation degree in batches through matrix operation to obtain a batch correlation degree matrix; and determining a retrieval target according to the batch association degree matrix and generating a batch retrieval result containing association degree sorting. According to the method, efficient batch retrieval and accurate sorting of trademarks are achieved through multi-modal feature fusion and matrix operation, the retrieval efficiency and accuracy are effectively improved, and the actual application requirement is met.

The invention provides an equivalent circuit analysis method and system for soil resistance heating efficiency, and relates to the technical field of soil remediation. Resistivity distribution data and salt content distribution data of a pile space are obtained; calculating the ionic conductivity according to the salt content distribution data, and calculating the target conductivity of each grid unit; constructing a topological graph, and constructing a weighted adjacent matrix based on the topological graph; constructing a degree matrix according to the degree of each graph node to obtain a feature value sequence and a feature vector corresponding to each feature value; extracting a second small feature value from the feature value sequence as a connectivity index, and marking a mutation position as a weak connection interface; the blocking factor is determined by counting the number of graph nodes located at the weak connection interface, the equivalent circuit transmission efficiency representing the soil resistance heating efficiency is calculated based on the negative correlation between the connectivity index and the blocking factor, and accurate quantitative evaluation of the heterogeneous soil resistance heating efficiency is achieved.

The invention discloses a space-time enhanced aviation network flight delay prediction method and system, and belongs to the technical field of data mining. The method comprises the following steps: constructing an aviation network dynamic congestion degree matrix; processing a congestion degree matrix by adopting a graph embedded network of an encoder-decoder structure, and extracting a dynamic congestion mode through short-term time enhancement; extracting a spatial dependency relationship through long-term spatial enhancement by using the hidden state of the encoder and the static characteristics of the airport; fusing short-term and long-term features to generate node embedding, and constructing an adaptive adjacency matrix for graph convolution; multi-step prediction is performed based on an encoder-decoder framework in combination with time attention and graph convolution. According to the method, the implicit relationship of delay propagation between airports can be effectively captured, the space-time modeling capability is enhanced, and the accuracy and practicability of flight delay prediction are improved.

The application discloses a heterogeneous network node representation method and system based on biased walk of edge information entropy, and comprises the following steps: receiving an input heterogeneous network containing multiple types of nodes and edges, obtaining an adjacency matrix and a degree matrix, and calculating an edge information entropy matrix; for each target node, normalizing the edge information entropy between the target node and different types of neighbor nodes; determining multiple meta-paths according to the semantic information composition of the heterogeneous network nodes; for each target node, obtaining the next node type according to the node sequence set by the meta-path, and performing biased sampling according to the normalized edge information entropy; taking the next node as the target node after obtaining the next node, and repeating the above steps until the set path length and walk times are reached, so that a walk path set of the node is finally obtained; finally, inputting the obtained node walk path set as the context semantic association sequence of the target node into a classical Skip-Gram model for training, so that the vector representation of each target node is finally obtained.

The invention provides an automatic polishing roughness control method and system for the inner wall of a fire extinguisher tank. The method comprises the following steps: acquiring three-dimensional point cloud data of the inner wall of a fire extinguisher tank body, generating a local height deviation characteristic value and a curvature gradient characteristic value through curvature analysis, and quantifying to obtain a surface concave-convex degree matrix; the scanning frequency and angle of a rotating laser contour scanner at the front end of the polishing rod are adjusted based on the matrix; scanning the inner wall in the polishing process to generate a real-time morphology thermodynamic diagram for identifying a geometric mutation area; in combination with the surface concave-convex degree matrix and the thermodynamic diagram, a pressure correlation value of the inner wall geometrical characteristics and the polishing pressure is calculated, and a polishing pressure correlation vector corresponding to the geometrical mutation area is determined; and the axial path and the rotating speed of the polishing head are adjusted based on the vector, and the roughness difference of the geometric mutation area of the inner wall is eliminated. Dynamic elimination of the roughness difference of the wall of the inner tank body of the fire extinguisher is achieved, the polishing uniformity is improved, and the machining efficiency is improved.

This invention provides a landslide area detection method based on dual-channel graph convolution, applicable to landslide area detection in remote sensing images. The specific steps are as follows: First, a fully convolutional network is used to extract deep-level features from the landslide area image in the remote sensing image; triple information of the graph structure is constructed from the deep-level features, including nodes, edges, and distances between nodes; adjacency matrix, degree matrix, feature matrix, and weight matrix are obtained; the adjacency matrix, degree matrix, feature matrix, and weight matrix are input into a dual-channel graph convolution to obtain new feature information; the new feature information is upsampled to obtain upsampled feature information; the upsampled feature information is mapped through dimensionality reduction and softmax function operations to output the final landslide area detection result in the remote sensing image. This invention can effectively improve the automation and intelligence level of landslide area detection in remote sensing images and can meet the actual on-orbit requirements of satellites for detecting landslide areas in remote sensing images.

The application discloses a cold-rolling friction coefficient prediction method based on a physical graph topology and a graph convolutional neural network, and comprises the following steps: defining a graph topology structure establishment rule for friction coefficient analysis in plate strip cold continuous rolling; selecting analysis parameters according to plate strip cold continuous rolling process characteristics and production experience, determining a node set, a physical relationship function set and an edge set in the graph topology structure; constructing an adjacency matrix and a corresponding self-adjacency matrix of the graph convolutional network with physical weights; introducing a degree matrix to perform normalization processing on the self-adjacency matrix to generate a normalized self-adjacency matrix; constructing a physical feature graph convolutional neural network based on the above settings and a basic form of the graph convolutional network; selecting cold-rolling product samples, obtaining analysis parameters and friction coefficient prior values, and training the physical feature graph convolutional neural network; and adopting a determination coefficient, a mean square error, a mean absolute error and a mean absolute percentage error to evaluate the performance of the physical feature graph convolutional neural network.

The invention provides a graph data processing method and device, equipment and a medium, relates to the technical field of artificial intelligence, can be applied to scenes such as cloud technology, artificial intelligence, intelligent traffic and auxiliary driving, and comprises the following steps: if a data structure of to-be-processed graph data is a directed graph, obtaining first matrix data and first graph attribute characteristics of the to-be-processed graph data; the first matrix data comprises an out-degree matrix and an in-degree matrix, elements of the out-degree matrix are used for representing an outward connection relationship between a corresponding node in the to-be-processed graph data and other nodes, and elements of the in-degree matrix are used for representing a connection relationship between the corresponding node in the to-be-processed graph data and the other nodes; the out-degree matrix and the in-degree matrix are symmetrical matrixes; performing data analysis of the first matrix data and the first graph attribute features based on the target graph neural network to obtain a graph analysis result; by means of the conversion method, direction information of the directed graph data can be reserved, and meanwhile complex conversion is not introduced.

The invention provides a science popularization content intelligent recommendation method and system based on deep learning. The method comprises the following steps: acquiring science popularization image / video data, performing space-time decoupling processing to generate a multi-scale space-time feature tensor, and constructing multi-level feature representation of a local scene, a dynamic process and an overall context; utilizing a multi-head cross-modal attention network to calculate a semantic association degree matrix of the features and knowledge graph entities; a time sequence causal chain extraction module is introduced to generate a causal path constraint vector, a dynamic semantic alignment constraint is formed through matching with a knowledge graph relation path, and a semantic incidence matrix is optimized; and generating a uniform space-time semantic fusion vector by adopting an adaptive mapping decoder, and outputting a structured semantic tag set through a deep semantic classifier. According to the method, the limitation of traditional shallow recommendation is broken through, understanding of deep semantics and causal logic of scientific contents is realized, and the accuracy, semantic depth and interpretability of science popularization content recommendation are remarkably improved.

The application relates to the technical field of computers and discloses an AI monitoring method for performance prediction and scheduling of a multi-core CPU on a MODT mainboard. The method comprises the following steps: collecting multi-core running states and mainboard physical topology data, and constructing a communication delay-dynamic power consumption joint feature map; performing space-time modeling through a graph neural network to generate a multi-core state embedding vector; fusing task features to calculate a task-core matching degree matrix; establishing a multi-objective optimization model with the minimum communication delay, balanced power consumption and controllable temperature as constraints, and solving the model by an reinforcement learning agent to output optimal scheduling instructions. The application reduces the standard deviation of communication delay and temperature rise and improves the energy efficiency ratio through physical perception and AI collaborative scheduling, and application code does not need to be modified.

The invention provides a fault-tolerant processing method and device for incomplete data, and the method comprises the steps: recognizing the incomplete data with errors and missing, and determining an abnormal data sample set containing errors and non-random missing in the incomplete data; determining error distribution and missing probability density distribution of data key feature dimensions in the abnormal data sample set; determining a dynamic association degree matrix of characteristic variables between the financial association data and the incomplete data, and determining an adaptive characteristic weight vector of the incomplete data according to the dynamic association degree matrix, the error distribution and the missing probability density distribution; determining reconstructed complete data of the incomplete data in combination with historical financial core data related to financial risk perception; and performing distributed fault-tolerant verification on the reconstructed complete data, performing homomorphic encryption processing on a fault-tolerant verification result, and transmitting the fault-tolerant verification result to a financial risk perception data storage end. By adopting the scheme of the invention, fault-tolerant processing can be carried out on the data in combination with errors and non-random missing of the data.

The application discloses a kind of methods for determining reposition frame, device, vehicle and storage medium.The method comprises: obtaining feature map;Determine the image key frame of current image, according to the global feature of the image key frame and the global feature of the map key frame, determine current difference degree matrix;According to the current difference degree matrix, determine reposition mark according to preset dynamic programming algorithm;Based on the reposition mark, determine target reposition frame in the feature map.The technical scheme of the embodiment of the application is smaller in memory resources occupied by the established feature map, solves the problem that great computing resources are consumed in searching reposition frame in the global range of semantic map, is suitable for the scene with high scene similarity and poor communication signal, improves the accuracy and robustness of reposition frame.

The present invention discloses a safety early warning system, which relates to the field of early warning and control technology, including an acquisition and processing module, an analysis and prediction module and an early warning and control module; the acquisition and processing module measures temperature and pressure in the current cycle, combines the temperature and pressure of the historical cycle, and processes them into a standard data tensor; the analysis and prediction module establishes a matching degree matrix through dynamic time regularization, introduces physical regularity constraints in principal component analysis based on the ideal gas equation, extracts and generates a coupling feature sequence, splices it with the standard data tensor, and then inputs it into a prediction model, outputs the prediction data tensor and fits to generate a trend function of temperature and pressure; the early warning and control module dynamically determines the dangerous moment and dynamically determines the adjustable time, performs model predictive control based on the equipment control model, defines the control target function and solves the optimal control problem within a finite time, obtains the optimal parameter adjustment vector and executes control, and realizes accurate prediction and control considering the temperature, pressure coupling relationship and adjustable time.

The invention discloses a protection action curve visualization engine based on an IED (Intelligent Electronic Device) model difference atlas, which relates to the technical field of relay protection teaching, training and testing of a power system and comprises an IED model difference atlas construction module and a WebGL (Web Graphics Library) visualization engine module. According to the technical scheme, data are analyzed and a JSON equation is constructed based on a to-be-processed SCD file through an IED model difference atlas construction module, a boundary gradient vector is calculated based on the JSON equation to obtain a difference degree matrix, and a protection action curved surface is constructed and rendered in a unified parameter space based on the difference degree matrix through a WebGL visualization engine module. And a visual result of the protection action curve is displayed, so that students can understand protection action conditions and critical regions more visually, and the teaching efficiency and the practical operation conversion rate are remarkably improved.

The invention provides a yield prediction method and device of a produced product, a storage medium and a program product, and relates to the technical field of data processing, and on the basis of obtaining reference product information of each reference product and target product information of a target product, clustering processing is performed on each reference product to obtain at least two reference product clustering sets; and for each reference product cluster set, performing multi-head self-attention fusion processing to generate a corresponding product relation degree matrix. And matching a corresponding target reference product cluster set according to the target product information of the target product, and determining the target reference product. In each production time window, training the initial yield prediction model to obtain a target yield prediction model; inputting the product relation degree matrix corresponding to the target reference product cluster set, the target product information of the target product and the future time feature vector into a full-connection neural network layer, and outputting a target predicted yield; the problem of inaccurate yield prediction of newly developed products is solved.