380 results about "Factor analysis" patented technology

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention discloses a bolt loosening automatic acquisition and analysis method and equipment based on a 5G sensing network, and relates to the technical field of bolt state intelligent acquisition. The bolt loosening automatic acquisition and analysis method and device based on the 5G sensing network comprises the steps that S1, multi-source data in the bolt use process is acquired and preprocessed, and a standardized bolt channel state data set is constructed; s2, evaluating the state of a bolt loosening evolution stage in combination with an angular displacement change trend, and driving the sampling frequency to be dynamically switched; s3, analyzing the response intensity change of the three types of channels, evaluating the channel fusion state, and adjusting the monitoring priority; s4, comprehensively evaluating a response offset trend, and recombining monitoring resources; and S5, constructing an attribution process based on the fusion state value and the high-risk transition value, and generating an evolution link structure. The problem that a high-risk section cannot be recognized in advance due to the fact that a multi-stage multi-channel driving factor analysis and cross-time-domain expression mechanism lacks in the evolution process of bolt loosening is solved.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention discloses a case factor intelligent analysis and processing method based on intelligent semantic understanding, and belongs to the field of artificial intelligence, natural language processing and public security judicial informationization, and the method comprises the following steps: 1, preprocessing public security judicial document information, and supporting text analysis, format standardization and structured data conversion; 2, carrying out refined extraction on case elements based on a large language model, and identifying case constituent elements, hazard degrees and processing results; 3, fusing logical reasoning of a public security judicial knowledge graph, and enhancing cross-cause semantic understanding and reasoning accuracy; and 4, performing intelligent auxiliary processing, generating class case recommendation and penalty interval prediction, and performing interactive regulation question and answer. According to the method, efficient, accurate and automatic case factor analysis is successfully realized, and the public security judicial document processing efficiency and decision accuracy are remarkably improved.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention discloses a battery carbon emission influence factor analysis method and device and a storage medium, and belongs to the technical field of data processing. Comprising the following steps: acquiring life cycle data of a battery at the current moment from a data source, constructing a knowledge fusion atlas database of the battery based on the life cycle data through an atlas construction model, generating a carbon emission prediction model according to the knowledge fusion atlas database, and predicting the carbon emission through a feature contribution degree analysis algorithm. Calculating a prediction contribution degree of each factor variable in the carbon emission prediction model, determining a target factor variable according to the prediction contribution degree, constructing a directed acyclic graph of the target factor variable based on a preset electrochemical constraint condition, and calculating conditional probability distribution of the electrochemical constraint condition in the directed acyclic graph, and forming a coupling relation model of the target factor variables, and calculating the sensitivity coefficient of the factor variables in the coupling relation model to the carbon emission. According to the method, the accuracy of the analysis result is improved by quantifying the sensitivity coefficient of each carbon emission factor under the coupling relationship.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a sheep abnormal behavior intelligent monitoring method and system based on multi-source data fusion and deep learning, and the method comprises the steps: collecting the multi-source data, such as the motion trail, limb joint angle, body temperature, sound and environmental parameters, of a sheep through multi-type sensing equipment, and carrying out the time-space alignment to form a feature matrix; encoding and reconstructing the feature matrix by using a variational auto-encoder to generate a reconstructed feature matrix, and calculating errors of the two feature matrixes to form an error sequence; and inputting the error sequence into a local abnormal factor analysis model to obtain an abnormal factor sequence, judging an abnormal behavior through a threshold value, and outputting related information. The system comprises a multi-dimensional parameter acquisition unit, a spatial-temporal feature fusion unit, a feature reconstruction unit, an error sequence generation unit, an abnormal factor analysis unit and an abnormal judgment output unit. The method and the system realize multi-source data fusion, improve anomaly identification accuracy, adapt to dynamic changes and are suitable for large-scale breeding.

The invention provides an investment and research transaction multi-strategy parallel execution method, and relates to the technical field of financial data analysis, and the method comprises the steps: accessing multi-source real-time data from a stock market, a future market and other financial markets, and obtaining a standardized multi-source heterogeneous data set; generating a multi-strategy transaction signal, outputting a strategy coordination instruction set, generating strategy instance operation state data, sending a transaction instruction generated by each strategy to a transaction system of a corresponding market for execution, obtaining transaction execution result data, generating risk open assessment data and a strategy consistency report, and performing risk open assessment based on an optimized strategy parameter set. And executing the steps of data access, factor analysis, strategy generation, independent execution and optimization in a loop iteration manner. Through the method and the device, the technical problems of high transaction delay and instable income caused by high coupling degree among multiple market strategies, poor execution isolation and difficulty in realizing cross-market strategy collaboration and risk unified management and control in the prior art are solved; and the transaction execution efficiency and the strategy stability are improved.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention discloses a slope stability analysis method and system based on data analysis, belongs to the technical field of slope safety analysis, and aims to solve the problems of dependence on hypothesis simplified model and neglect of real-time dynamic change in the prior art. S2, dynamic environment factor analysis; s3, slope stability analysis; s4, establishing an intelligent decision-making and early warning system; s5, system integration and user interaction; s6, performing system optimization and continuous learning; by comprehensively considering complex soil body characteristics such as heterogeneity and anisotropy of the slope and dynamic changes of environmental factors such as external rainfall, meteorology and earthquakes, the system can provide a more real and accurate slope stability analysis result, an intelligent algorithm and a data analysis technology are adopted, and the stability of the slope is improved. The dependence on a simplified physical model and a static hypothesis is reduced, so that the scientificity and the practicability of an analysis result are improved.

The invention discloses an urban flood mapping and driving factor analysis method based on multi-source remote sensing data, and the method comprises the steps: respectively extracting an optical semi-permanent water body and an SAR semi-permanent water body based on a global surface water body data set and a Sentinel-1 image, and taking the intersection of the optical semi-permanent water body and the SAR semi-permanent water body to generate an enhanced semi-permanent water body mask; extracting a water body in a flood period by using the Sentinel-1 image to obtain flood water body distribution, and performing water body extraction precision verification in combination with the Sentinel-2 image; the precision of the SRTM DEM is improved based on a DEM error prediction model of a Transform network, and a corrected DEM is generated; and constructing a multi-dimensional spatial analysis model based on the elevation, gradient and surface coverage type of the corrected DEM, and identifying spatial distribution characteristics and a terrain driving mechanism of the flood water body. According to the method, comprehensive analysis is performed in combination with terrain factors, and the spatial precision and timeliness of flood risk assessment can be improved.

The invention provides an electrical performance prediction and influence factor analysis method and system based on machine learning, and relates to the technical field of photovoltaic cells, and the method comprises the steps: obtaining technological parameters in a photovoltaic cell production process and electrical performance indexes of corresponding production batches; extracting process statistical characteristics of the process parameters; according to the production batch number and the time window identifier, matching the process statistical characteristics with the electrical performance indexes through an automatic alignment mechanism to form a mapping relation between the process statistical characteristics and the electrical performance indexes; on the basis of the mapping relation, an enhanced process feature matrix used for representing the influence of the process on the electrical performance is constructed through a multi-modal feature extension mechanism, and an electrical performance prediction result is generated through a layered weighted regression model; analyzing the marginal influence of each process parameter on an electrical performance prediction result by using an SHAP value theory, and generating an influence matrix; and according to the influence matrix, adjusting process parameters in the production process of the photovoltaic cell so as to optimize the electrical performance of the photovoltaic cell.

The invention relates to the technical field of three-dimensional reconstruction, in particular to an electric power facility surrounding environment three-dimensional reconstruction method and system. The method comprises the following steps: acquiring electric power facility data, performing electric power facility layout structure analysis, and constructing an electric power facility three-dimensional model; obtaining electric power facility environment data, carrying out dynamic environment evolution, and constructing an electric power facility environment dynamic evolution model; performing rendering and model alignment embedding processing on the electric power facility three-dimensional model based on the electric power facility environment dynamic evolution model to generate an environment-electric power facility fusion model, and performing electric power facility corona loss fluctuation detection and environment fluctuation factor analysis; corona environment disturbance area detection and error calculation are carried out on the environment-electric power facility fusion model, disturbance error data are generated, and corona environment disturbance reconstruction processing is carried out on the environment-electric power facility fusion model. According to the invention, the electric power facility environment combined corona loss dynamic three-dimensional model reconstruction can be realized.

The invention relates to the technical field of Bluetooth earphones, and discloses a Bluetooth earphone dynamic power consumption and transmission delay balance control system, which comprises a state acquisition module, a strategy generation module, a factor analysis module, a quantitative evaluation module, a parameter adjustment module and an execution control module. The state acquisition module obtains working current, data packet state and the like to construct an evaluation model; the strategy generation module generates an initial association strategy table; the factor analysis module analyzes historical data to identify interference factors; the quantitative evaluation module calculates a stability deviation index; the parameter adjusting module updates the strategy table according to the index; and the execution control module triggers rate switching. The system is further provided with a data packet comparison unit, a timestamp marking unit and the like to process data transmission conflicts, and the transmission quality is optimized through a three-dimensional transmission quality field model, a genetic algorithm and a three-level response mechanism. The system realizes dynamic balance of power consumption and transmission delay, and is suitable for the field of power consumption and transmission control of the Bluetooth headset.

The invention relates to the technical field of data analysis, in particular to a geological disaster analysis method and system based on cloud, and the method comprises the following steps: obtaining and coding a rainfall intensity, gradient, wind speed and humidity data source of a geological area, recognizing a disaster factor mapping structure to generate a coding group, and quantifying a gradient difference superposition humidity screening high-frequency unit. And performing connectivity analysis to extract an aggregated risk area boundary, extracting a wind speed and humidity ratio to screen out an overrun factor, outputting a high-coupling factor combination, and identifying a trend change to output a risk grading identification group. According to the method, multi-source environment data are synchronously collected and indexed according to time and space, the matching precision of disaster factors and space grids is enhanced, the high-frequency disturbance recognition stability is improved through multi-layer fusion analysis, risk boundaries are extracted through communication modeling to improve the positioning accuracy, and the coupling factor analysis capability is improved through factor ratio fusion; trend matching is combined with a response gradient to improve risk grading perspectiveness, and the overall process enhances early warning efficiency and expands data analysis depth and breadth.

The invention provides a cultivated land non-cultivation data processing and analysis system and method. The system comprises a non-cultivation rate influence factor analysis module and a non-cultivation rate prediction module. The non-farming rate influence factor analysis module integrates a geographic detector model, a panel model, a space model, a space-time geographic weighted regression model and other modules to obtain a significant influence factor result of non-farming in a target area; and the non-farming rate prediction module comprises a panel model prediction module and a time t prediction module to predict the non-farming rate corresponding to each administrative region in the next year. Based on the technical scheme of the invention, multi-source heterogeneous data such as land utilization, climate environment and the like are integrated; index classification is more detailed and comprehensive, so that an analysis result is more effective; time and space factors are fused, and variables of different regions are analyzed more accurately; the factor identification accuracy is remarkably improved, the space prediction precision is remarkably improved, and the time sequence prediction error is remarkably reduced.

The invention belongs to the technical field of business and financial integration, and discloses an intelligent business and financial integration data processing system which comprises a data management module, a factor analysis module, a risk early warning module and an optimization resource configuration module. According to the system, all the modules are integrated, fusion processing and deep analysis of business data and financial data are achieved, the system can accurately recognize key factors influencing operation and fund conditions, the perspectiveness and accuracy of risk early warning are improved, meanwhile, an enterprise is assisted in dynamically adjusting resource allocation and management and control strategies, and the system has high practicability. And the cross-department cooperation efficiency and the risk response capability are enhanced, so that fine management and robust operation of an enterprise can be realized.

The invention relates to the technical field of carbon emission prediction, and discloses a multi-source data driven automobile carbon emission characteristic space-time prediction method and system, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, and constructing a multi-factor carbon emission data set; performing factor analysis on the data set, and extracting three principal component factors; a CNN-LSTM mixed deep learning model containing an attention mechanism is constructed to predict carbon emission; calculating carbon emission intensity of different spatial scales; based on a Gaussian diffusion theory, constructing a carbon emission diffusion model under a multi-wind-speed condition, and calibrating a diffusion coefficient by adopting measured data of an air monitoring station; modeling is carried out on the fused spatio-temporal data, and a carbon emission intensity prediction value of a grid scale in a specified time period in the future is output; performing visual display on the prediction result, and triggering early warning and generating a corresponding ecological driving control strategy based on the prediction value; the problem that an existing carbon emission prediction mode is low in prediction precision is solved.

The invention discloses a shelf life kiwi fruit comprehensive quality quantitative evaluation method based on hyperspectral imaging and a convolutional neural network, and the method comprises the steps: 1, obtaining spectral images of kiwi fruits of different shelf lives through a hyperspectral imaging system, and extracting the original spectral data of a region-of-interest sample; step 2, measuring the soluble solid content, hardness and color parameters of the sample, normalizing the parameters with correlation, extracting a dominant factor combination with an accumulative contribution rate greater than or equal to 80% by using a factor analysis method, and constructing a KCQI (Kiwi berry Comprehensive Quality Index) based on factor load weight; step 3, preferably selecting a characteristic wave band related to the KCQI, and performing sample set division; 4, taking the spectral data of the characteristic wave band as input, and adopting a one-dimensional convolutional neural network to construct a quantitative prediction model of KCQI; and step 5, applying the model to a spectral image of a sample, generating a KCQI spatial distribution visual image pixel by pixel, and visually displaying the comprehensive quality change.

The invention relates to the technical field of industrial electric furnace control, and discloses a graphitization furnace power transmission power curve optimization method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring historical operation data of a graphitization furnace, and carrying out cleaning and standardization processing on the data; the historical operation data comprises a power transmission power curve, in-furnace temperature and pressure data, material initial parameters and finished product quality indexes; s2, key factor analysis: determining key factors influencing the quality of a finished product from historical operation data through correlation analysis and principal component analysis methods; s3, constructing an optimization model; s4, performing model training and verification; s5, generating an optimization curve; and S6, applying and iterating. Compared with the prior art, the method has the advantages that the optimal transmission power curve is automatically generated through data driving and a machine learning model, so that the problems of unstable product quality, high energy consumption and process adjustment lagging caused by dependence on artificial experience are solved.

The invention relates to a disturbance source positioning method based on dynamic mode decomposition, which belongs to the technical field of power system operation and analysis and comprises the following steps of: accurately extracting oscillation mode characteristics of a system by performing matrix decomposition on actually measured data; furthermore, a participation factor analysis model fusing time-space characteristics is constructed, and the spatial distribution characteristics and the time evolution characteristics of the modal are comprehensively considered, so that accurate positioning of the oscillation source is realized. Compared with a traditional method, the method has the advantages of no dependence on a detailed system model, high positioning precision and the like, is realized completely based on measurement data calculation, avoids complex system modeling, and has relatively high practical application value and popularization potential.

The invention relates to the technical field of food safety risk management, in particular to an aquatic product risk analysis system based on multi-factor analysis, which comprises a pollution factor response module, a path aggregation module, a channel decision module, a pollution risk simulation module and a risk mapping collection module. According to the method, by extracting the concentration change direction of the pollution factor in the specific water area in the continuous time period and establishing the sequence combination of the concentration rising section, the time sequence behavior characteristics of the pollution factor can be identified, and the overlapping relation between the risk factor and the biological class group exposure can be effectively judged; in a standard exposure period, contact frequencies are integrated, sensitive time periods are screened, and generality factor grouping is completed by analyzing frequency coincidence conditions of a plurality of aquatic products under the same factors, so that a risk influence map can reflect a factor interaction correlation and category common inductance response relationship; and the comprehensiveness, granularity and prediction capability of risk assessment are improved in a dynamic scene.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention discloses a teaching scheme generation method and system based on student score change analysis. The method comprises the steps of obtaining multiple pieces of historical score data of a target student at multiple historical time points; constructing a plurality of score change curves corresponding to the target student according to the historical score data and the corresponding historical time points; predicting score influence factors corresponding to the target student according to the plurality of score change curves based on a graph neural network; and according to the score influence factors and the score change curve, based on an LLM algorithm model, generating a teaching scheme corresponding to the target student. Therefore, accurate score influence factor analysis and personalized teaching scheme making based on the historical score trend can be realized, and the learning effect of students and the utilization efficiency of educational resources are improved.

The invention belongs to the technical field of battery health degree evaluation, and particularly relates to a battery health degree evaluation method and system, electronic equipment and a storage medium. Based on the original sample data of the battery health degree evaluation indexes, establishing a standardized decision matrix to eliminate the dimensional difference between the indexes; constructing a correlation matrix according to the matrix, and evaluating index correlation; extracting common factors of a correlation matrix, calculating a core quantitative index, determining a main factor by taking standard reaching of an accumulated variance contribution rate as a standard, realizing data dimension reduction and retaining original index core information; and calculating a main factor score in combination with the original data and the rotated factor matrix, performing weighting according to a variance contribution rate, and performing weighted summation to obtain a health degree comprehensive score. And the health difference of the batteries can be judged in an auxiliary manner through clustering analysis, and data support is provided for reasonable configuration and fine management of the batteries. According to the method, dimension reduction weighting is carried out on various related indexes through a factor analysis method, a result is objectively deduced based on data features, subjective influences are reduced, and evaluation scientificity and reliability are improved.

The invention belongs to the technical field of laser processing numerical simulation, and discloses a random forest model laser absorption coefficient efficient prediction method based on factor analysis, and a laser absorption coefficient data set comprises four parameters of laser power density, a forming coefficient, conductivity and thermal diffusivity. According to the method, multiple factors influencing the laser absorption coefficient in laser processing are comprehensively considered, the dimensionality of a data set is reduced, the feature correlation between data is low, and a high-quality training set is provided for subsequent training of a random forest model. Multiple Gaussian distribution linear mixing probability distributions are generated through a Gaussian mixture model to describe original data, new samples are generated through random sampling to form an incremental data set, the training effect of the random forest model is improved, and the over-fitting condition is avoided; through factor analysis, features with strong correlation are divided into one group, generation of a weak decision tree is avoided, the accuracy and convergence speed of the model are improved, and an efficient model training mode is realized.

The invention provides a closed watershed lake water level evolution prediction method based on multi-source data, and relates to the technical field of hydrological resources. The method comprises the following steps: firstly, collecting natural environment and social economic multi-source data of a closed drainage basin, and constructing a normalized multi-source feature input matrix; carrying out total element analysis on the features and extracting a nonlinear response threshold value; and meanwhile, Mantel statistical test is executed on the feature matrix to obtain a statistical correlation coefficient. And judging the characteristics which simultaneously meet the statistical significance and mechanism threshold conditions as real causal driven factors, and eliminating false related factors which are statistical significance but have no physical threshold. And finally, reconstructing a prediction model based on the screened causal-driven feature set, and outputting a water level evolution prediction value. Through'statistics-mechanism 'dual verification, interference of false correlation is effectively eliminated, the problem that a traditional black box model cannot distinguish causal and coincident trends is solved, and physical interpretability and robustness of water level prediction are remarkably improved.

The invention provides a post evaluation simulation method based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: calculating a real-time cognitive load pressure index and an emotion state parameter of a student; obtaining a fault evolution speed parameter and a complexity parameter used for adjusting the fault evolution process; inputting the fault evolution speed parameter and the complexity parameter into an intelligent fault chain generation system, and generating a cascade fault situation conforming to the physical law of the power system through a graph neural network model; in a high-precision simulation environment of a digital twin platform of a power system, students are guided to execute simulation operation based on a cascade fault situation, and multi-dimensional behavior data are collected and recorded; the multi-dimensional behavior data comprises operation time sequence data, operation path deviation data and decision delay data; and inputting the multi-dimensional behavior data into a multi-dimensional human factor engineering evaluation system, and processing the multi-dimensional behavior data through a human factor analysis model to obtain a post evaluation result of the student. According to the embodiment of the invention, the accuracy of post evaluation can be improved.