18 results about "Grey correlation" patented technology

The application is suitable for the technical field of precision control, and provides a machine tool stand column assembly anti-deformation design and operation intelligent compensation method, which comprises the following steps: in the assembly stage, through parametric finite element analysis and optimization, the optimal pre-stress group of the pull rod that maximizes the static stiffness of the stand column is determined to realize active assembly anti-deformation design; in the operation stage, temperature and spindle box position information are intelligently fused through fuzzy C-means clustering and grey correlation degree analysis, key features are screened, and a high-level correction training strategy optimized Transformer-GRU deep learning model is adopted to establish a high-precision mapping relationship between the features and thermal-mechanical coupling errors, so that real-time prediction and compensation are realized. The application ensures machining precision from two dimensions of source and process, has high prediction accuracy, strong intelligence and self-adaptability, and high engineering practical value.

The application discloses a kind of based on dynamic knowledge graph's relay protection defect diagnosis method and device, belong to relay protection defect diagnosis field, the method is: real-time acquisition first data set in power system, and the first data set is standardized, generates second data set;Second data set is input to MacBERT-BiLSTM-CRF model and is extracted to entity, obtain multiple entities;According to second data set and each entity updates graph structure data, and graph structure data is input to GNN model and is mined to relationship, obtain multiple entity relations;According to each entity and each entity relation updates knowledge graph, based on knowledge graph and the real-time data of target relay protection device carries out rule reasoning, obtains first diagnostic result and is carried out probability reasoning by Bayes network, obtains second diagnostic result;Based on second diagnostic result and real-time data carries out grey correlation degree analysis, obtains consistency evaluation result, when consistency evaluation result is greater than first threshold value, generates and outputs diagnostic report.

The application discloses a planar transformer aging prediction parameter correlation analysis method, system and device, and relates to the technical field of planar transformers. The application receives change data of to-be-analyzed parameters of a planar transformer at different time points; takes data recorded at zero time as a standard value to normalize the rest of the data; processes the normalized data through curve fitting to take parameter data with a slope closest to 1 as a reference sequence; uses a grey correlation degree analysis method to calculate correlation coefficients of the rest of the parameters and the selected reference sequence, and normalizes the correlation coefficients. The application uses the grey correlation degree analysis method to comprehensively apply multiple parameters of the planar transformer, provides a theoretical basis for weight distribution when the influence of the multiple parameters on the prediction process is comprehensively considered, and finally constructs a planar transformer life prediction model according to a mathematical statistics method, thereby improving the accuracy and reliability of planar transformer aging prediction.

This invention discloses an intelligent water quality prediction method for watershed floods based on dynamic weight optimization and deep reinforcement learning. The method comprises the following steps: collecting historical flood water quality observation data and historical flood hydrological data; standardizing the historical flood hydrological data to obtain comparison sequences and reference sequences, and then performing dimensionless processing; calculating dynamic grey relational coefficients and weighted grey relational degrees using the dimensionless comparison sequences and reference sequences, and sorting them to form a weighted grey relational degree sequence; using the weighted grey relational degree sequence to filter out effective historical flood fields, and performing dynamic weight optimization based on a long short-term memory network and a reinforcement learning agent based on the TD3 algorithm; and calculating the final predicted flood water quality data using the historical flood water quality observation data and the historical flood weights. This invention's prediction method enables intelligent water quality detection during flood processes, providing accuracy, real-time performance, and adaptability.

The application relates to a data trader credit determination method and device, computer equipment, a storage medium and a computer program product. It relates to the technical field of big data. The method comprises the following steps: based on a pre-established evaluation index system, a combination weighting method is used to fuse the subjective weight and the objective weight of each to-be-evaluated index respectively, and the comprehensive weight of each to-be-evaluated index is determined; credit evaluation data of a plurality of data traders is obtained based on a grey correlation analysis method, and an evaluation index data column corresponding to the plurality of credit evaluation data is constructed; based on the to-be-evaluated data corresponding to the data trader, the grey correlation coefficient of the to-be-evaluated data of the trader and a pre-determined reference data column is obtained, and the grey correlation degree of the to-be-evaluated index is obtained in combination with the comprehensive weight; based on the grey correlation degree, the to-be-evaluated index is sorted to determine the credit of the data trader. The method can determine reasonable evaluation indexes in the data transaction process to determine the credit of the data trader.

The present application relates to the technical field of power system planning evaluation, and more particularly to a method and system for dynamic evaluation of energy storage based on scenario joint debugging and risk early warning, the method comprising: constructing an evaluation index system; obtaining original index data of the energy storage technology to be evaluated under standard working conditions and performing dimensionless processing; calculating the first weight of each evaluation index using a grey correlation degree analysis method, calculating the second weight of each evaluation index using an entropy weight method, and obtaining a basic fusion weight by fusion; determining a scenario correction coefficient based on the demand proportion of grid frequency modulation and ramp service, and performing a first dynamic correction on the original index data; determining an environmental risk correction coefficient based on an extreme temperature environment, and performing a second dynamic correction on the original index data; and performing weighted calculation on the original index data and the basic fusion weight, and outputting a comprehensive utility score. Through the present application, the problems of lack of scene adaptability and environmental risk early warning and the problem of fuzzy data quantization in the existing energy storage evaluation method are effectively solved.

The application provides a container terminal operation index correlation parameter evaluation method, device and equipment. In the method, the indirect indexes are filtered according to the acquired index causal relationship data, the index value sequence of the direct index and the index value sequence of each indirect index, to obtain first indexes, and the grey correlation degree and the correlation sign of each first index. Further, the correlation weight of each first index is obtained by processing the index value sequence of each first index by using the entropy weight method; the correlation parameter of the first index and the direct index is obtained by multiplying the grey correlation degree of the first index with the correlation weight and combining with the correlation sign; and the correlation parameter of each indirect index other than the first index and the direct index is determined as 0. The grey correlation degree, the correlation weight and the correlation sign are determined to calculate the correlation parameter, thereby improving the evaluation accuracy of the correlation parameter.

This invention discloses a method for identifying anomalies in tailings dam seepage monitoring data based on a grey model and neural network. First, monitoring data is acquired, preprocessed using the arithmetic square root, and generated by a single accumulation. Then, a GM(1,1) grey model is established, and a dynamic adaptive threshold interval is constructed to coarsely screen anomaly data. Data is updated using a metabolic process; data exceeding the threshold is considered anomaly. Next, the grey correlation degree between the anomaly data and related measuring point data is calculated, and the input variable for the radial basis function neural network is selected accordingly. The network is trained to obtain the neural network's seepage prediction value, and the relative error is used to determine whether the anomaly is caused by environmental factors. Unidentified anomalies are retested and verified, and instruments are checked. If the retest is normal, the measurement error data is removed or corrected; if the retest is abnormal, it is determined to be a structural change and an early warning is issued. This invention combines a grey model and a neural network to achieve efficient and accurate identification of anomaly data and automatic identification of environmental factor responses.

The application discloses a mixed driving proton exchange membrane fuel cell degradation prediction method, comprising the following steps: obtaining monitoring data; constructing a multivariate grey model; performing grey correlation analysis and selecting data sequences with strong correlation with output voltage sequences; performing Gaussian transformation; obtaining prediction values based on the grey multivariate model; obtaining the predicted output of the proton exchange membrane fuel cell by inversely transforming the prediction values through the Gaussian transformation; constructing a voltage recovery model; modeling the cell stack voltage through polarization parameters, thereby establishing a time-dependent degradation model; introducing a nonlinear recovery framework to utilize the multivariate grey model to establish the voltage recovery model; and obtaining the mixed driving proton exchange membrane fuel cell degradation prediction based on the voltage recovery model and the multivariate grey model. The application focuses on breaking through three major problems, namely, dynamic health index failure, degradation-recovery mechanism coupling and online prediction real-time performance, and realizes high-precision, low-cost and real-time prediction of the degradation trajectory.

The application discloses a kind of intelligent power supply reliability index classification prediction method, it is related to power grid operation and maintenance management field.The related historical data of power supply reliability of power grid system is obtained, the power supply reliability index value in fixed period is calculated, the power supply reliability index value and historical data are analyzed by grey correlation degree, the data category within the correlation degree threshold is filtered as the important influence factor of power supply reliability index, the real-time data of the important influence factor of power supply reliability of power grid system is collected, the homogeneity between the mean value of real-time data and the mean value of historical data is calculated by matching model, the historical data of the important influence factor within the homogeneity threshold range is filtered as homogenization parameter as input, with the corresponding power supply reliability index value calculation result as output to train and obtain neural network prediction model, the prediction value of the power supply reliability index in real-time data is output by prediction model.

The application relates to a wind turbine monitoring data validity evaluation method, which comprises the following steps: obtaining the linear correlation of monitoring data under the normal working state of a wind turbine; obtaining the monitoring data of the wind turbine in real time; preprocessing and shearing the monitoring data, and calculating the Pearson correlation coefficient and the grey correlation degree between different data type monitoring data; comparing the Pearson correlation coefficient between the monitoring data and the grey correlation degree between the monitoring data sets, obtaining and recording the linear correlation between the monitoring data; comparing the linear correlation obtained in real time with the linear correlation under the normal working state, if the linear correlation changes, it is determined that the monitoring data is abnormal, and an alarm is given, if the linear correlation does not change, it is determined that the monitoring data is valid. Compared with the prior art, the application combines the Pearson correlation coefficient and the grey correlation degree, and can more accurately determine the validity of the monitoring data.

The application relates to the technical field of green product analysis, and discloses a green product whole life cycle analysis system based on big data. The system comprises data acquisition, preprocessing, environmental impact assessment, resource consumption optimization, correlation modeling and result generation and visual interaction modules. The system collects data of each stage in the whole life cycle and related element data such as environment, resources and carbon emissions, carries out preprocessing such as abnormal value elimination and entropy weight assignment, dynamically assesses environmental impact through a fuzzy membership function, optimizes multi-objective resource consumption through a genetic algorithm, determines key stages through a grey correlation degree and generates analysis results in combination with a multi-objective decision model, and finally presents the results in a three-dimensional graph and a dynamic heat map. The system realizes efficient analysis and comprehensive evaluation of whole life cycle data of green products, and provides scientific support for product environmental performance optimization and decision making.

The application discloses a kind of geogrids to enhance the performance of comprehensive evaluation method of macadam bed, including determining the performance of several evaluation indexes for evaluating geogrid to enhance the effect of macadam bed;Obtain the value of each evaluation index of several different geogrids to be evaluated;Each evaluation index value is preprocessed, and each evaluation index value after preprocessing is normalized;Based on each evaluation index value after normalization, the weight of each evaluation index is calculated using entropy weight method;The optimal evaluation sample comprising each evaluation index is constructed;Based on optimal evaluation sample, the comprehensive correlation degree of each geogrid to be evaluated is calculated using grey correlation method;The comprehensive correlation degree of each geogrid to be evaluated is substituted into the preset judging rule, and the comprehensive evaluation result is obtained.The application is used to solve the problem that the performance of geogrid to enhance the macadam bed cannot be effectively evaluated in the prior art, and the performance of geogrid to enhance the macadam bed is realized to carry out comprehensive evaluation of the purpose of progress.

This invention discloses an intelligent quality evaluation method for prefabricated beam bridges on highways based on grey relational analysis, belonging to the field of bridge engineering quality evaluation. The method includes: constructing a three-level quality evaluation index system for the beam bridge and obtaining the percentage scores for each secondary index; based on the historical percentage scores of all secondary indices, identifying the key quality parameters of each primary index through grey relational analysis, and calculating the relative correlation strength between each secondary index and the key quality parameters; constructing and training a grey relational-guided weighted neural network; and based on the optimized network weights and the percentage scores of each secondary index of the bridge to be evaluated, obtaining the comprehensive quality score of the bridge through weighted summation, and determining the quality level of the bridge. This invention significantly improves the accuracy, objectivity, and reliability of prefabricated beam bridge quality evaluation, providing a scientific and efficient technical solution for intelligent quality evaluation of prefabricated beam bridges on highways.