235 results about "Entropy weight method" patented technology

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 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 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 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 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 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 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 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 discloses a power corridor disaster risk assessment method and system based on an entropy weight method, and particularly relates to the technical field of power corridor disaster risk assessment, and the method comprises the steps: obtaining original multi-source point location data, related to disaster risk assessment, of a power corridor region in the southwest region, carrying out the coordinate unification and time alignment, and carrying out the coordinate unification and time alignment; an original multi-source point location data set is obtained and subjected to quality control processing, the consistency and stability of the original multi-source point location data set are checked, missing values and abnormal values are removed, and the multi-source point location data set is output; on the basis of electric power corridor axis segmentation, double-threshold amplitude limiting conditions of an information amount retention rate and an extreme value order are set, a segmentation optimal interpolation kernel and a time window parameter set are constructed, and an interpolation scale and time aggregation granularity are dynamically backtracked and adjusted, so that risk integral offset caused by terrain fracture and window averaging is eliminated, and the power corridor axis segmentation accuracy is improved. And the credibility of the information amount of entropy weight evaluation is ensured.

The invention discloses a heterogeneous workflow task scheduling method and device and a medium. The method comprises the steps of obtaining original data of sub-tasks under multiple feature dimensions according to a preset period; according to a preset scoring rule of each feature dimension, converting the original data into a quantitative score under the corresponding feature dimension; based on the original data, adopting an entropy weight method and an analytic hierarchy process to calculate the weight of the subtask under each feature dimension; calculating a total feature score of the sub-tasks based on the quantitative scores and weights of the sub-tasks under each feature dimension; and obtaining a scheduling scheme of the heterogeneous workflow task through the total feature score of each subtask. According to the method, the timeliness and adaptability of the scheduling decision are improved.

The invention provides a solid hydrogen storage single tank structure optimization method and device based on a physical information neural network, and relates to the field of intelligent hydrogen storage. The method comprises the following steps: acquiring structural parameters and multi-source data of a hydrogen storage tank body; constructing a full-parameter space training set based on the multi-source data; based on the full-parameter space training set, constructing a multi-physics field agent model by embedding a multi-physics field control partial differential equation set; inputting the structural parameters of the hydrogen storage tank into the multi-physics field agent model, and outputting a Pareto solution set through the multi-physics field agent model based on the optimization decision; and determining a target weight corresponding to each optimization decision through an entropy weight method, and obtaining an optimized target structure parameter combination from the Pareto solution set based on the target weight in combination with a double-base-point method. The problem that a solid hydrogen storage system lacks a refined regulation and control mechanism in structural design due to the fact that the nonlinear effect and the multi-factor coupling phenomenon in the hydrogen storage process cannot be accurately described only by means of a traditional physical model is solved.

The invention relates to the technical field of data processing, in particular to an industry chain subdivision industry state judgment method and device based on dynamic weighted scoring. The method comprises the following steps: firstly, obtaining enterprise data and subdivided industry association strength, an industry reference value and a dimension dispersion coefficient in an industry chain knowledge base; setting a core dimension of an industry corresponding to the data, calculating an objective weight of each dimension based on an entropy weight method, and performing fine adjustment on the weights within a dynamic adjustment range according to manual optimization requirements; verifying the validity of the adjusted data through historical data; and carrying out multi-dimensional numerical calculation processing on the data based on an intelligent scoring model, processing the processed data through a threshold value and a weighted value, and judging that the state of the subdivided industry is normal, and needs a strong chain or a supplementary chain and a delay chain. And precise judgment of subdivided industrial states is realized. According to the method, objectivity and adaptability of industry chain analysis are effectively improved, and precise judgment of subdivided industry states is realized.

The application discloses a multi-radar point cloud weighted fusion system and method based on an entropy weight method, belongs to the technical field of intelligent stereoscopic warehouse perception and point cloud fusion, and aims to solve the problems of low fusion precision, poor real-time performance and unreasonable weight distribution of existing multi-radar point cloud fusion. The method comprises the following steps: synchronously collecting and pre-processing multi-radar point clouds to generate a set; separating dynamic and static points to obtain static point clouds; dividing fusion voxels and detecting changed voxels; extracting features and standardizing; combining prior weights and entropy weight methods to calculate fusion weights; and weightedly fusing to generate a fusion point cloud map. The application can significantly improve the precision and real-time performance of intelligent stereoscopic warehouse point cloud fusion, and provides a reliable three-dimensional perception basis for upper-layer operations of a warehouse.

The invention provides an urban green infrastructure comprehensive benefit applicability evaluation method based on subjective and objective weights. The urban green infrastructure comprehensive benefit applicability evaluation method comprises the steps of establishing a basic evaluation system, screening indexes, calculating index weights, calculating quantitative indexes, calculating qualitative indexes, performing grading evaluation, performing standardized score assignment and performing comprehensive evaluation. The basic evaluation system is of a three-layer structure and covers four criteria of runoff regulation and storage benefits, environmental benefits, economic benefits and social benefits and 19 indexes; a subjective weight is determined through an analytic hierarchy process, an objective weight is determined through an entropy weight method, and a comprehensive weight is obtained in combination with a D-S evidence theory fusion method; quantitative indexes are calculated through methods such as hydrological simulation, and qualitative indexes are rated by public; the indexes are unified into dimensionless scores through standardized score assignment, and finally an evaluation result is obtained through weighted summation. According to the method, the applicability quantitative evaluation of matching of the typical green infrastructure and the site condition construction target is realized, and a scientific and quantitative decision support tool is provided for sponge city planning.

The invention discloses a user demand insight positioning method and system based on AI driving, relates to the field of artificial intelligence and big data analysis, and solves the technical problem that it is difficult to accurately recognize user demands and respond to the user demands in real time through dynamic analysis of multi-source text data and construction of a knowledge graph. Comprising the steps that text data are collected, structured data are generated through preprocessing, and a structured data unit is generated; calculating text vocabulary diversity and syntactic complexity indexes, inputting the text vocabulary diversity and syntactic complexity indexes into a pre-training regression model to obtain a dynamic character threshold value, encoding short / long texts by adopting a lightweight graph neural network and a hierarchical Transform, and extracting core demand elements; a dynamic time sequence knowledge graph is constructed, a time sequence edge weight updating algorithm is introduced to adjust node association strength, and cross-domain association is mined; and calculating a user demand intensity evaluation value based on an entropy weight method in combination with the inventory pressure factor and the group influence factor, thereby realizing inventory scheduling and personalized recommendation.

The invention discloses an energy storage scheme evaluation method and system based on an entropy weight method, which is applied to the technical field of energy technology evaluation, and comprises the following steps: determining a plurality of dimension index values at least comprising a response dimension from each energy storage scheme, wherein the first index value of the response dimension is obtained by firstly extracting charging and discharging output and original load data in a scheme, determining a typical daily net load output curve through data processing, then extracting load characteristic data such as response time, power density and energy density from the curve, and further determining a response index value; the index values of all dimensions are processed through an entropy weight method, weighted data of each energy storage scheme are obtained, finally, comprehensive evaluation results are obtained based on the weighted data through an approximate ideal solution sorting method and sorted, and finally quantitative evaluation results of all the energy storage schemes are output. According to the energy storage scheme evaluation method based on the entropy weight method provided by the invention, a fair and credible quantitative basis can be provided for energy storage scheme evaluation.

The invention provides an equipment quality and supplier intelligent evaluation method based on a space-time coupling Bayesian model and a two-ticket graph database, and belongs to the technical field of intelligent databases. The two-ticket graph database is firstly constructed, and a station environment, equipment full life cycle maintenance and supplier data are integrated; obtaining a supplier initial score by using an entropy weight method and a Sigmoid function; constructing a space-time coupling Bayesian model by means of ST-LSTM and VAE, and clustering equipment health states; introducing an environment coefficient and a KL divergence gradient to correct a score, and separating environment responsibilities from supplier responsibilities; calculating an equipment target score by combining an aging model and cosine similarity; and aggregating scores through a graph attention network, considering quality fluctuation, and generating a supplier global score. According to the invention, dynamic, precise and traceable evaluation is realized, a basis is provided for power grid supply chain management and the like, and the method is suitable for supplier evaluation in electric power and other industrial fields.

The invention discloses an urban ventilation environment simulation and prediction method and system based on different planning objectives, and particularly relates to the field of urban ventilation environment simulation and prediction, and the method comprises the steps: obtaining ventilation characteristic data, land utilization data and regulation information data for many years as data sources; unifying the data sources to the same coordinate system and spatial resolution, and performing rasterization and numerical normalization processing; constructing a land utilization prediction model based on the PLUS based on the land utilization data and the regulation information data; and rasterizing the driving factors of the land utilization change. Regulation information is introduced in a land utilization prediction process, a dynamic land utilization conversion matrix is constructed, and a space-time diagram neural network and an entropy weight method are combined for calculation, so that joint modeling and quantitative evaluation of an urban land utilization pattern and a ventilation environment under a multi-level management and control condition are realized; the problem of systematic deviation between a simulation result and actual execution in the prior art is solved.

The invention belongs to the technical field of virtual power plant operation evaluation, and particularly relates to a virtual power plant operation comprehensive evaluation method and system.The method comprises the steps that a three-level multi-dimensional operation risk evaluation index system is constructed, and three types of weight vectors are obtained through an analytic hierarchy process, an entropy weight method and an inter-index correlation and conflict objective weighting method; carrying out collaborative correction through a non-cooperative game model to obtain an optimal comprehensive weight vector; a standard cloud model of each risk level is constructed based on an extension cloud model, an actual evaluation cloud model is constructed in combination with the score of a professional scoring system and an optimal weight, the score weight of the professional scoring system is determined through a grey correlation degree, an evaluation value is corrected, the actual cloud model is updated, and finally the digital characteristics of the actual and standard cloud models are compared. And quantitatively judging the operation risk level of the virtual power plant. According to the method, the comprehensiveness, reliability and scientificity of evaluation are improved, and support is provided for risk prevention and control of the virtual power plant.

The invention relates to a quantitative collaborative evaluation method and system for power measurement differences and a storage medium. The method comprises the following steps: acquiring quantitative index data and qualitative index data of power measurement; performing normalization processing on the quantitative index data; respectively calculating subjective weights and objective weights of all indexes based on an analytic hierarchy process and an entropy weight method, and carrying out weighted fusion to obtain subjective and objective combined weights; processing the qualitative index data based on fuzzy comprehensive evaluation, and combining subjective and objective combination weights to obtain quantitative scores of qualitative indexes; calculating a correlation coefficient of the normalized quantitative index data and a corresponding reference sequence based on grey correlation analysis, and calculating a comprehensive correlation degree of the quantitative index; and integrating the comprehensive correlation degree of the quantitative indexes and the quantitative scores of the qualitative indexes, and calculating to obtain a comprehensive difference degree. Compared with the prior art, through subjective and objective combination and qualitative and quantitative collaborative evaluation process, accurate and consistent quantitative output of the power measurement difference is provided.

This invention discloses a method and system for dynamically determining the impact of construction projects on hydrological monitoring, relating to the field of hydrological monitoring technology. The specific steps are as follows: constructing an evaluation index system that couples static and dynamic indicators; assigning static weights to static indicators using the analytic hierarchy process (AHP) and dynamic weights to dynamic indicators using the entropy weight method; calculating the total weight based on the static and dynamic weights; constructing and training a machine learning model; inputting the static, dynamic, and total weight data into the trained machine learning model; and outputting the impact level; automatically matching response measures based on the impact level and trend prediction results. This invention proposes a dynamic weight correction algorithm based on project stage and hydrological conditions, applying machine learning algorithms to determine the impact level of construction projects on hydrological monitoring, achieving a closed-loop linkage from level determination to measure matching, and solving the problems of singular and disconnected evaluation in existing technologies.

The invention relates to an SF6 density table health degree assessment method and system based on a normal cloud model, and the method comprises the steps: firstly obtaining three types of evaluation index feature data of the quality reliability, the operation performance and the operation environment of an SF6 density table, and carrying out the standardization processing of positive and negative attribute indexes through employing corresponding formulas, so as to eliminate the dimension interference; then obtaining a subjective weight through an analytic hierarchy process, obtaining an objective weight through an entropy weight method, obtaining a combined weight in combination with a game theory, and inverting a normal cloud combined weight cloud digital feature based on a weight sample statistical feature; selecting the maximum value of the standardized data of each index to form a reference sequence, calculating a grey correlation coefficient, and constructing a comprehensive evaluation cloud in combination with the weight cloud features; and finally, performing similarity calculation on the comprehensive evaluation cloud and a preset grade standard evaluation cloud to determine a health degree grade. Compared with the prior art, the method has the advantages of being high in evaluation accuracy, automatic in process, capable of reducing manual burden, capable of early warning hidden dangers and the like.

The invention relates to the technical field of computer technology, and discloses a real estate project value prediction method and system. The method comprises the following steps: acquiring feature data of a product and feature data of a competitive product; preprocessing the feature data of the product and the feature data of the competitive product to obtain a numerical feature value and a normalized feature value; on the basis of the numerical characteristic value and the normalized characteristic value, an entropy weight method is adopted to calculate and obtain a score of a current item and a score of a competitive item; based on the score of the product item and the score of the competitive product item, predicting to obtain fusion value data of the product item; and correcting the fusion value data of the product project based on the numerical characteristic value to obtain a target value prediction result of the product project. Through the method, the reasonability and credibility of the target value prediction result can be improved.