354 results about "Entropy weight method" patented technology

The invention discloses a hydraulic rammer tamping construction parameter adjusting method based on sensing parameter real-time feedback, and relates to the technical field of hydraulic rammer tamping construction.The hydraulic rammer tamping construction parameter adjusting method comprises the steps that a multi-mode sensing monitoring network is constructed to collect full-amount construction data, a wavelet packet decomposition algorithm is adopted for noise layered suppression, and a multi-mode sensing monitoring network is established; constructing a working condition associated data set in combination with the construction stage labels; based on the working condition associated data set, establishing a dynamic tamping effect evaluation model, and outputting a deviation index moment of time-space distribution; training a parameter adjustment intelligent model based on the deviation index matrix and a transfer learning mechanism, generating a multi-parameter collaborative adjustment strategy, and carrying out working condition adaptation degree scoring and adjustment risk early warning on strategy output; and adjusting the intelligent model based on incremental learning and model distillation technology optimization parameters. According to the method, the multi-modal sensing network, the geological dynamic quantitative model, the improved entropy weight method, the migration and reinforcement learning and the lightweight deployment technology are fused, so that full-chain intelligent dynamic optimization and safe controllable execution of hydraulic rammer construction parameters are realized.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention discloses a laser equipment fault prediction and maintenance method based on digital twinning, and the method comprises the following steps: S1, collecting structure parameters, control logic and historical data, and constructing a unified neural network digital twinning model; s2, collecting multi-source operation data in real time in an operation process, and constructing a standardized state data set; s3, mapping the data to a unified neural network digital twin model to realize state synchronization; s4, predicting a future key parameter trend by using an LSTM model; s5, a health score is calculated through an entropy weight method, and the equipment state is quantitatively evaluated; s6, triggering early warning based on the health threshold value, identifying abnormity and generating a maintenance strategy; s7, pushing the strategy to an operation and maintenance system for closed-loop execution; and S8, maintaining feedback for retraining and self-optimizing the unified neural network digital twin model. According to the method, high-fidelity digital twin modeling is realized, so that a prediction-maintenance-feedback-updating intelligent closed-loop system is formed, and the fault prediction accuracy and the maintenance response efficiency of the laser equipment are remarkably improved.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention relates to the technical field of underground space development, and provides a coastal bedrock type city underground space development geological suitability evaluation method comprising the following steps: analyzing key geological problems of land area and sea area underground space development, and extracting related geological factors; basic geological data are collected and classified, a geological suitability evaluation index system is constructed, and geological evaluation units are divided; subjective and objective weights are determined based on an analytic hierarchy process and an entropy weight method, and an optimal comprehensive weight is obtained through game theory combination weighting; modeling based on a grey correlation analysis method, and calculating and grading suitability scores of the evaluation units; and integrating evaluation results, and generating an underground space development geological suitability partition map of land-sea overall planning. According to the scheme, the underground space development pattern is optimized, comprehensive utilization of land and ocean resources is promoted, and safe, efficient and sustainable development and utilization of the coastal city underground space are powerfully supported.

The invention relates to the technical field of education evaluation, and discloses a course teaching effect evaluation method, which comprises the steps of constructing a two-dimensional evaluation index system containing student ability and teacher teaching as a basic framework of evaluation; based on the two-dimensional evaluation index system, determining an initial weight by adopting an interval intuitionistic fuzzy analytic hierarchy process, and dynamically correcting the initial weight in combination with an entropy weight method to form a combined weight; collecting multi-source data corresponding to the two-dimensional evaluation index system through a triangular verification method, and preprocessing the multi-source data; combining the combined weight with the preprocessed multi-source data, and performing layered fuzzy comprehensive evaluation, thereby realizing quantitative evaluation and weak link diagnosis of the course teaching effect; according to the method, expert knowledge and objective data are fused, so that the evaluation weight is dynamically changed along with actual teaching data, and the problem that evaluation is separated from reality due to weight solidification is solved.

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention provides an SAR (Synthetic Aperture Radar) anti-interference evaluation method and device based on maximum variance combined with layered entropy weight, and relates to the technical field of radar anti-interference. Comprising the following steps: constructing an evaluation index matrix according to a plurality of target SAR echoes; dividing the plurality of evaluation indexes into two layers of indexes by using a Pearson correlation analysis algorithm according to the evaluation index matrix and a preset threshold value; according to the plurality of evaluation indexes, the random number and the normalized evaluation index matrix, constructing a maximum variance evaluation optimization model which takes a maximized weighted evaluation matrix and a utility value matrix as objective functions and takes a difference degree weight row vector as a parameter to be solved; according to the maximum variance evaluation optimization model and the evaluation index matrix, determining a maximum difference evaluation matrix by using an interior point method; and according to the maximum difference evaluation matrix, the evaluation index matrix and the two layers of indexes, determining a plurality of evaluation values corresponding to the plurality of anti-interference methods by using a layered entropy weight method. In this way, the accuracy of anti-interference evaluation of various anti-interference methods is high.

The invention discloses a risk assessment method and system for access of new energy photovoltaic power generation to a power distribution network, and belongs to the technical field of operation safety of the power distribution network, and the method comprises the steps: collecting illumination resource data and load data of the power distribution network, and generating a basic data set covering typical and extreme scenes through empirical mode decomposition, Copula function and improved Latin hypercube sampling; a coupling evaluation model fusing the graph neural network and the risk propagation theory is constructed, an improved CRITIC-entropy weight method is adopted to dynamically empowerly weight the multi-dimensional risk indexes, and a time-varying comprehensive risk level matrix is output; analyzing the risk characteristics, matching a pre-constructed measure risk knowledge graph, and generating a dynamic self-adaptive coping scheme through conflict verification and priority ranking; verifying the scheme in the digital twin power distribution network simulation model, and updating model parameters and a basic data set by incremental data feedback when the scheme does not reach the standard; according to the invention, the comprehensiveness and timeliness of risk assessment are improved.

The invention discloses a threat intelligence credibility evaluation method based on multi-dimensional feature construction, and belongs to the field of network security. In order to solve the problems that an existing evaluation method is based on a single dimension, credibility evaluation is one-sided, and complex threat scenes are difficult to adapt, the invention provides a'multi-dimensional feature dynamic weight distribution model ', and five features of source credibility, content consistency, timeliness, context relevance and feedback verification degree are fused. And combining an entropy weight method and an adaptive learning mechanism to construct a dynamic evaluation framework. Experiments show that the accuracy of the dynamic weight model (mu = 0.2) is improved by 12.9% compared with that of a static entropy weight method, the threat intelligence availability and decision support value can be effectively improved, and the method adapts to complex and changeable network threat scenes.

The invention relates to the field of power system clean low-carbon evaluation, and discloses an RAM-based power system clean low-carbon evaluation method and system. The method comprises the following steps: collecting multi-source heterogeneous data in real time and carrying out exception cleaning and structured processing to construct a standardized data set; dynamically adjusting the weight based on an entropy weight method and expert experience, calculating an efficiency value by using an RAM model, and optimizing a constraint condition; recognizing input redundancy and output insufficiency by decomposing slack variables, and generating a high-value improvement list in combination with principal component analysis and analytic hierarchy process; and finally, matching the optimization strategy library to form a decision report and realizing closed-loop updating of the index library. The system can dynamically evaluate the clean low-carbon level of the electric power system, accurately identify energy efficiency improvement key points, realize evaluation-optimization-feedback full-process automatic management, and significantly improve the accuracy of an evaluation result and the effectiveness of decision support.

The invention relates to an e-commerce live broadcast intelligent item selection method and system. The method comprises the following steps: S1, constructing a multi-dimensional commodity selection criterion system, and collecting original data of a to-be-recommended commodity under each criterion; step S2, subjective weight calculation; s3, objective weight calculation; and S4, carrying out weight fusion and scheme evaluation. According to the method, weight quantification verification is performed through the analytic hierarchy process and the entropy weight method, and the fusion coefficient is dynamically adjusted in combination with the business scene, so that the problems of subjective experience contradiction, poor cold start adaptability and low decision transparency of traditional product selection are solved, and an experience quantification-data verification-weight fusion decision chain is formed; and finally, the product selection scientificity is improved, the scene adaptability is enhanced, and the decision transparency is improved.

The invention discloses a thermal power plant multi-source heterogeneous data quality evaluation method and system, and relates to the technical field of data evaluation, and the method comprises the steps: carrying out the comparison based on an alignment path length, a preset path length threshold value and a deviation threshold value, obtaining a consistent data flow and a conflict data flow, the method comprises the following steps: respectively calculating an accuracy index, an integrity index and a timeliness index for consistent data flow and equipment log data, obtaining a three-dimensional quality index matrix, weighting the three-dimensional quality index matrix through an entropy weight method, and calculating a comprehensive quality score of the multi-source heterogeneous data in combination with a real-time weight value of the multi-source heterogeneous data, and comparing the comprehensive quality score of the multi-source heterogeneous data with a preset score threshold to obtain a quality evaluation result, and directly generating a conflict alarm signal for a conflict data flow. According to the evaluation method based on the data indexes, not only is the sensitivity to data logic contradictions enhanced, but also context awareness and weight optimization in the evaluation process are realized.

The invention discloses a deep reinforcement learning highway variable speed limit control method based on multi-target dynamic weight distribution, and the method comprises the steps: 1, collecting the traffic flow information of a road section, and obtaining the traffic state data; 2, defining an action parameter and a reward function of the deep reinforcement learning method; 3, constructing a network model of deep reinforcement learning, and establishing a playback memory bank storage sample; and 4, reward function weight updating is carried out based on an entropy weight method, an optimized control strategy is obtained based on a sampling training deep reinforcement learning network model, and speed limit control is carried out on each control unit. According to the method, dynamic weight distribution of the traffic efficiency and the safety reward function is achieved, the method can adapt to a complex traffic environment, meanwhile, the operation efficiency of the expressway is effectively improved, and the safety risk is reduced.

The invention discloses a power distribution network electric energy quality comprehensive evaluation method based on a multi-dimensional index system, and the method comprises the steps: firstly constructing the multi-dimensional index system, and taking three dimensions as a basic technical index, a user satisfaction index and other key indexes; secondly, performing standardization preprocessing on the collected index data by adopting an extreme value standardization method, and mapping a standardization value to [0, 1]; then, carrying out weight determination on each index by adopting a combined weighting method, wherein the combined weighting method comprises the steps of determining a subjective weight by adopting an analytic hierarchy process, determining an objective weight by adopting an entropy weight method and a combined weight of the subjective weight and the objective weight; and finally, establishing an excellent evaluation grade, a good evaluation grade, a middle evaluation grade and a poor evaluation grade, and carrying out single-dimensional fuzzy evaluation and comprehensive evaluation to obtain a final score so as to determine the grade of the power distribution network. The method has the advantages that the evaluation range is more comprehensive and effective, and the evaluation result is more professional and reasonable, the electric power service quality can be improved, the sustainable development of an electric power system is promoted, and the requirements of intelligent power grid development and power distribution network big data application technology development are met.

The invention discloses a daily load curve clustering method based on a mixed distance improved FCM algorithm, and belongs to the technical field of data processing, and the method comprises the steps: collecting daily load data of a user at a sampling frequency of 5 minutes, carrying out the corresponding data preprocessing, analyzing the fluctuation characteristics of a daily load curve, and carrying out the clustering of the daily load curve; euclidean distance representing load curve power amplitude information and cosine distance representing load curve power fluctuation direction information are constructed respectively, and then an entropy weight method is adopted to construct a hybrid similarity distance (HSD) fusing the Euclidean distance and the cosine distance as a similarity criterion between daily power load curves; replacing the Euclidean distance in the FCM algorithm with the constructed HSD, then calculating a corresponding membership degree and a clustering center, iteratively updating the membership degree and the clustering center by using a target function based on an intra-class error weighted quadratic sum, and completing daily load curve clustering according to a preset maximum iteration number; and finally, determining the optimal clustering number and the corresponding optimal daily power load curve clustering result by using the sum of intra-class errors (SAE), thereby effectively solving the prominent problems of poor effect, low efficiency and the like of the traditional power load curve clustering method.

The invention is suitable for the technical field of data processing, and provides a news credibility evaluation method based on a multivariate fusion weighting and nonlinear punishment mechanism, and the method comprises the following steps: carrying out the multi-dimensional scoring of a news article through a large language model; determining a subjective weight matrix of the news article through the AHP expert matrix; objective weights are calculated based on an entropy method and a plurality of pieces of historical news of the same type, and weight fusion is carried out; penalty factors are applied to the key indexes, and weighted scores are adjusted; and summing the weighted scores and carrying out nonlinear normalization to output a total score. According to the method, the news credibility analysis process is divided into a plurality of detailed evaluation dimensions, and the score of each dimension can be independently output, so that a solid technical basis is provided for manual rechecking, intelligent auditing and transparent responsibility asking. And a weight distribution mechanism gives consideration to subjective expert experience and historical data statistics, and through fusion of AHP and an entropy weight method, the problem that single subjective configuration is easily influenced by personal experience and prejudice is overcome, and the accuracy is improved.

The invention provides a shipyard power distribution network health degree assessment method and system, and relates to the technical field of health state assessment and management of power systems. The method comprises the following steps: constructing a shipyard power distribution network health degree evaluation system by adopting an analytic hierarchy process, performing grading and scoring according to corresponding national standards, and determining subjective weights of indexes; calculating objective weights of the indexes by adopting an entropy weight method; calculating a comprehensive weight based on the subjective weight and the objective weight; and based on the comprehensive weight and the fuzzy relation matrix, performing shipyard power distribution network health degree evaluation by adopting a double-layer judgment criterion. Quantitative evaluation and hierarchical management of the health degree of the shipyard power distribution network are realized, a double-layer judgment criterion is introduced in evaluation grade judgment, the accuracy and rationality of an evaluation result are ensured, the stability of the power distribution network can be effectively improved, the unplanned shutdown risk is reduced, the power resource configuration is optimized, and the production safety and the product quality are guaranteed.

The invention discloses a pantograph-contact network system operation performance evaluation method and system. The method comprises the following steps: firstly, collecting data in real time, normalizing the collected data, and screening key hidden dangers which have power-off risks and need speed reduction operation through a two-stage risk rapid judgment mechanism; after hidden dangers are eliminated, an analytic hierarchy process is adopted to calculate index weights and initial weights of all parameters, then information entropies of all the parameters are calculated based on an improved entropy weight method to determine the dispersion degree, and real-time dynamic adjustment of the parameter weights is achieved through a combined weight method; and finally, by applying a fuzzy comprehensive evaluation method and combining the dynamically adjusted parameter weights and the membership degrees of the parameters, calculating the operation performance score of each index, then obtaining the comprehensive score of the operation performance of the pantograph-catenary system, and judging the performance grade according to the score interval. The method and the system provided by the invention are suitable for accurate evaluation of the operation performance of the icing galloping section pantograph-catenary system, and can quickly identify the operation risk and scientifically judge the performance grade.

The invention belongs to the technical field of database management technologies, and particularly relates to a dynamic allocation method and system for multi-level storage resources of a database, and the method comprises the following steps: S1, data feature and storage state collection: collecting multi-dimensional feature parameters of target data and state parameters of each storage level in real time; s2, multi-dimensional data value evaluation: constructing a data value evaluation model based on an analytic hierarchy process, determining each index weight in combination with expert scoring and an entropy weight method, and calculating a comprehensive value coefficient of target data; s3, a storage hierarchy matching decision is made, a value adaptation interval of each storage hierarchy is preset, and the optimal storage hierarchy of the target data is decided according to the comprehensive value coefficient of the target data and the real-time state parameters of each storage hierarchy; according to the method, reliable acquisition of data features, accurate evaluation of multi-dimensional data values, intelligent matching of storage resources and advanced pre-migration of data can be realized, and finally, the goal of'sharing high-performance storage by high-value data and controlling storage cost by low-value data 'is achieved.

The invention provides an epoxy curing agent compatibility test and formula system optimization method and system, and relates to the technical field of material testing, and the method comprises the following steps: collecting curing performance data and standardizing; adopting a collaborative entropy weight method to construct a coupling strength matrix to obtain comprehensive evaluation; constructing a performance feature directed acyclic graph, and obtaining a curing agent compatibility index by using a performance association tracking algorithm; taking a compatibility index as an optimization target, and screening a formula through adaptive variation and similarity cross; an optimal formula is obtained through experimental verification. According to the method, the compatibility evaluation accuracy of the epoxy curing agent is improved, and accurate optimization of a formula system is realized.

The invention discloses an ecological assessment method and system based on an analytic hierarchy process and an entropy weight method, and belongs to the technical field of ecological environment assessment, and the method comprises the steps: data collection and preprocessing: collecting evaluation index data related to ecological environment influence, and carrying out cleaning and processing; determining objective weights by an entropy weight method; calculating the objective weights of the indexes according to the data variation degree; determining a subjective weight by an analytic hierarchy process: constructing a judgment matrix in combination with expert opinions, calculating the subjective weight of each index, and performing consistency check; an analytic hierarchy process and an entropy weight method are combined, optimized and weighted; the objective weight and the subjective weight are combined and optimized, and a comprehensive weight is determined; tOPSIS comprehensive evaluation: the original indexes are subjected to forward processing and standardization processing, the distance between each evaluation object and the optimal solution and the distance between each evaluation object and the worst solution are calculated, and a normalized score is obtained. According to the invention, the accuracy and reliability of an ecological assessment result can be improved, and a more scientific and comprehensive solution is provided for ecological assessment.

The invention provides a multi-modal data set-oriented intelligent classification and grading method, which comprises the following steps of: accessing a multi-modal data set through a streaming data source and a batch data source by utilizing a data access module, and judging whether a data set compression process is triggered or not according to the scale of the data set; when the data scale of the data set exceeds a preset threshold value, a Coreset data set is generated through clustering analysis, dimension reduction processing and other means, and efficient compression and representative sample extraction of the data set are achieved; deep processing is carried out on the Coreset data set, data cleaning and feature analysis are carried out, and corresponding processing technologies are adopted for different modal data; performing multi-modal content identification and classification on the processed data set based on a deep learning model to obtain a classification result; a rule engine and an entropy weight method are combined to carry out multi-dimensional evaluation on classification results, weight distribution is determined, and a quantitative basis is provided for data set grading; and finally classifying and grading the data set by adopting an intelligent multi-dimensional evaluation method to obtain a classification and grading result.

A water-rich tunnel permeability dynamic partitioning method based on multi-source data comprises the following steps: acquiring a permeability coefficient, rock compressive strength, an integrity coefficient, RQD and longitudinal wave velocity data of a tunneling area, and constructing a multi-source feature matrix through space-time alignment and standardization processing; dynamically calculating the weight of each index based on an entropy weight method, and adaptively adjusting the contribution degree of the parameter in combination with the geological type; a standard cloud parameter is generated by using a cloud model, the membership degree of real-time data to each penetration grade is calculated through a forward cloud generator, and a final grading result is comprehensively judged; and mapping a grading result to a three-dimensional BIM model to generate a dynamic penetration cloud picture, and triggering a real-time early warning mechanism. According to the method, through multi-source data fusion and dynamic weight distribution, the static and experience dependence of a traditional grading method is broken through, and the complex stratum permeability evaluation precision is remarkably improved; the level boundary fuzziness is quantified by the cloud model, and the risk of misjudgment of a critical interval is avoided; through deep integration of advanced geological forecast data, permeability space prediction of an unexcavated area is achieved, minute-level early warning and grouting parameter intelligent recommendation are combined, the probability of water inrush accidents is effectively reduced, and an intelligent solution is provided for water-rich tunnel safety construction and resource optimization.

The invention relates to a multi-element reliability test data dynamic weight optimization method, which comprises the following steps of: inputting data, including accelerated life test data, fault excitation data, a mean value of historical failure and a standard deviation; calculating entropy values and dynamic weights of various types of data according to a formula; according to the dynamic weight optimization method, weight distribution is performed on existing test data related to various reliability faults through a multi-source data weight distribution method fusing an entropy weight method and grey correlation analysis, so that a verification result is more credible. According to the entropy-gray combined calculation technology, the theory of information entropy and the theory of gray relational degree are integrated, and test data are adjusted through the product term of the information entropy and the gray relational degree to verify the distribution weight. According to the full-automatic weight distribution system, a real-time weight updating mechanism without manual intervention is achieved by updating an input sequence of data.