123 results about "Grey correlation" patented technology

The invention discloses a power grid fault characteristic determination and positioning method, system, device and storage medium, and relates to the field of power grid fault processing, and the method comprises the steps: synchronously collecting the voltage, current, frequency and other electrical quantities and temperature, humidity and other environmental quantities of a power grid node through a multi-mode sensor, and forming an original time sequence data set; separating electrical quantity frequency domain fault features by using improved variational mode decomposition, and analyzing the correlation between the environmental quantity and the electrical quantity through a grey correlation degree to suppress noise; mapping the dynamic deviation of each order of derivative of the voltage / current into a spatial-temporal characteristic matrix, and constructing a distribution map in combination with power grid topology; based on the dynamic graph neural network, inputting node features and edge features, and establishing a fault-environment association model through feature propagation and a graph attention mechanism; and carrying out fine trimming on a positioning result by combining double-end traveling wave coarse positioning with an environment correlation degree correction coefficient, and verifying connectivity of a fault point and a power grid topology.

The invention relates to the technical field of data processing, in particular to an edge computing method for mass data analysis. According to the method, when massive Web server access logs are processed, a log field structure, field complexity and data magnitude are combined, resource configuration requirements are defined, accurate estimation of threads, memories and IO calling is achieved, a behavior sequence is further constructed on the basis of response time delay, the completion rate and the interruption condition in server behavior characteristics, and the service life of the server is prolonged. A dynamic evaluation standard for scheduling capability is formed, a grey correlation scoring method between resource requirements and behavior capability is fused in candidate node screening, and sorting optimization is performed in combination with node real-time resource occupancy states, so that a task allocation path has better balance in the aspect of multi-dimensional resource utilization; and parallel processing is realized through log fragment mapping after task scheduling, and structured analysis output is formed through edge side summarization, so that the fitness of edge node scheduling and the precision of task assignment are improved.

The invention discloses a ceramic firing performance prediction method based on a GA-BP neural network, and the method comprises the steps: data collection and preprocessing, gray correlation analysis and screening of key process parameters, construction of a GA-BP neural network model, model training and verification, prediction model testing, and firing performance index prediction. By means of unique intelligent algorithm fusion, the problems of high experience dependence, high experiment cost, long period, low efficiency, time consumption, energy consumption, high efficiency and the like of a method for judging the ceramic firing performance through artificial experience in actual ceramic firing production are effectively solved. The technical problem that multi-target collaborative optimization is difficult to realize at the same time in the prior art is solved, remarkable advantages are shown in the aspects of prediction precision, intelligent degree and the like, the requirements of intelligent, green and high-quality ceramic firing process optimization are met, and the method has wide application prospects and important practical value.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment method and device, electronic equipment and a storage medium. Preprocessing the monitoring data to obtain preprocessed monitoring data; and in combination with the Pearson correlation coefficient, mutual information and grey correlation degree between each piece of monitoring data and the effluent quality index, screening target features strongly correlated with the effluent quality from the preprocessed monitoring data. According to the method, through a multi-dimensional feature screening mechanism, key influence factors are accurately identified, and the adaptability of the model under different working conditions is remarkably improved; by constructing a comprehensive objective function fusing water quality, cost and robustness and embedding process constraints, multi-objective collaborative optimization of pollution reduction and carbon reduction is realized; an optimal control strategy is efficiently solved by adopting an improved intelligent algorithm, and the core problems of insufficient model generalization ability, mechanism and data disjunction and the like in sewage treatment are effectively solved.

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.

An LSTM-Attention-OOA photovoltaic power prediction method based on similar daily clustering comprises the following steps: firstly, integrating historical photovoltaic power data and meteorological monitoring data (including irradiance, temperature, cloud cover and the like), and analyzing and screening key meteorological characteristics by using grey relational degree; similar daily clustering analysis is carried out through K-means, and an optimal clustering number is determined by adopting an elbow rule. And for different weather clusters, similar days are selected by using cosine similarity. And an LSTM-Attention-OAA prediction model is constructed: an LSTM layer captures a time sequence dependency relationship, an Attention mechanism dynamically allocates each time step weight to highlight an important meteorological period, an OOA is introduced to automatically adjust and optimize hyper-parameters, and autonomous optimization of model performance is realized. According to the method, through a progressive architecture of'feature screening-data clustering-intelligent prediction ', the prediction precision under different weather conditions is remarkably improved, and the method is of great significance in improving stable operation of a power system.

The invention discloses a multi-dimensional quantitative analysis method for comprehensive efficiency of power grid construction, and the method comprises the steps: generating a time sequence operation scene set containing a plurality of typical days through employing a clustering algorithm based on a pre-constructed multi-dimensional quantitative analysis index system; determining the comprehensive weight of each quantitative analysis index by adopting a new game theory combination weighting method; calculating the score of each construction scheme in the static fitness index layer and the score of each construction scheme in the dynamic robustness index layer; a new TOPSIS and grey relational degree combined model is adopted to calculate the comprehensive efficiency index of each construction scheme; performing comparison and robustness test on the schemes, and outputting a quantitative analysis result; according to the method, a quantitative analysis index system covering six dimensions is constructed, all aspects of the novel power system are covered, and the quantitative analysis dimensions are comprehensive; according to the method, the index comprehensive weight is determined by adopting a new game theory combination weighting method, and the comprehensive efficiency index is synthesized by utilizing a new TOPSIS and grey relational degree combination model, so that the scientificity of quantitative analysis is improved, and refined and scientific comparison and selection of power grid construction schemes are realized.

The application relates to a power data fusion method, comprising the following steps: acquiring time series data from multiple data sources respectively; calculating the similarity between each time series data to obtain a similarity matrix; accumulating the matrix elements in the same row or the same column in the similarity matrix to obtain the similarity accumulation value of each data source and other data sources; performing longitudinal parameter fusion on each data source with a similarity accumulation value higher than a first threshold value, comprising the following steps: performing data preprocessing on the time series data; calculating the grey correlation degree between each two time series data of the time series data after the data preprocessing; filtering out the time series data with a grey correlation degree lower than a third threshold value; performing data fusion on the time series data by using a BP neural network to obtain a first fusion result; and performing horizontal parameter fusion on each data source with a similarity accumulation value lower than the first threshold value.

The invention discloses a relay protection defect diagnosis method and device based on a dynamic knowledge graph, and belongs to the field of relay protection defect diagnos.The method comprises the steps that a first data set in an electric power system is collected in real time, standardization processing is conducted on the first data set, and a second data set is generated; the second data set is input into a MacBERT-BiLSTM-CRF model for entity extraction, and a plurality of entities are obtained; updating graph structure data according to the second data set and each entity, and inputting the graph structure data into a GNN model for relationship mining to obtain a plurality of entity relationships; updating a knowledge graph according to each entity and each entity relationship, performing rule reasoning based on the knowledge graph and real-time data of the target relay protection device, and performing probabilistic reasoning through a Bayesian network after a first diagnosis result is obtained to obtain a second diagnosis result; based on the second diagnosis result and the real-time data, grey correlation degree analysis is conducted, a consistency evaluation result is obtained, and when the consistency evaluation result is larger than a first threshold value, a diagnosis report is generated and output.

The application provides a force-vibration-noise monitoring and prediction system for wet milling, which comprises an externally-mounted cutting fluid transmission system, a numerical control machine tool and workpiece system, a sound pressure and vibration measurement system, and a three-way force measurement system, can monitor and obtain three-way milling force signals, three-way vibration signals and milling sound pressure signals under given milling parameters. Based on experimental data and grey correlation theory, the correlation degrees of force, vibration and noise about milling parameters during wet milling are better than those during dry milling, which shows that the system has a certain optimization effect on the cutting state. Based on the response surface analysis method, the prediction models of force, vibration and noise about milling parameters and the comprehensive model of noise about force-vibration-milling parameters are established. The results show that the noise comprehensive prediction model established based on milling force-vibration is more reliable, and the correlation between milling force, vibration and milling noise is higher. The application can provide scientific guidance for the monitoring and prediction of the milling processing state.

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 application discloses a kind of power transformer health state determination methods, including obtaining the attribute data of power transformer;Attribute data is normalized and corresponding grey correlation coefficient is calculated;The weight value of attribute data information is calculated using analytic hierarchy process, and grey correlation degree value is calculated;The attribute of power transformer is divided into health grade and the priority of each health grade is determined by comparison;Health grade is associated with grey correlation degree value to construct fuzzy logic control rule;The health state determination of actual power transformer is carried out using fuzzy logic control rule.The application also discloses a kind of system for realizing the power transformer health state determination method.The application obtains and processes the data of power transformer, and predicts the state of power transformer based on analytic hierarchy process and grey correlation analysis scheme, so that the application can not only realize the health state determination of power transformer, but also has high reliability and good accuracy.

The application relates to the technical field of navigation, and particularly provides a multi-attribute quantitative evaluation method and system for an inertial satellite combined navigation device, which comprises the following steps: collecting corresponding observation sample data of the inertial combined satellite according to multiple evaluation indexes, and extracting characteristic values and characteristic vectors corresponding to the evaluation indexes from the observation sample data; screening multiple core indexes from the multiple evaluation indexes according to the characteristic values of the evaluation indexes by using a cumulative contribution rate inequality; generating evaluation data of the core indexes according to the characteristic vectors corresponding to the core indexes and the observation sample data; determining the weights of the core indexes based on the Euclidean distances between the evaluation data of the core indexes and corresponding preset standard values; calculating a grey correlation coefficient matrix of the multiple core indexes based on the evaluation data, the weights and the preset standard values of the core indexes, and calculating quantitative evaluation values of the core indexes based on the grey correlation coefficient matrix. The application significantly improves the accuracy and scientificity of the evaluation results.

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 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 invention discloses an interpretable anomaly detection method and device for a top drive drilling device, equipment and a medium, which are applied to the field of drilling engineering, and are used for acquiring operation data of a to-be-detected mechanical part in the top drive drilling device, and dividing the operation data into various working condition data sets based on working condition parameters, extracting feature data of each feature parameter from each working condition data set; determining a working condition sensitivity index and a conflict index based on a grey correlation degree coefficient and a correlation coefficient between the characteristic parameters, and optimizing memory matrix input parameters by utilizing sensitivity analysis and correlation analysis; a clustering algorithm is used for screening sample data to construct a spatial memory matrix, and a multivariate state estimation method and a sequential probability ratio test method are combined to carry out anomaly detection on the mechanical part to be detected; furthermore, interpretability analysis of the abnormal parameters is carried out, and the abnormal key parameters are visually revealed. The self-adaptive judgment of the running state of the equipment and the abnormal traceability of each mechanical part in the top drive well drilling device are realized.

The application relates to the technical field of environmental science and ecological engineering, and provides an evaluation method for an influence mechanism of cultivated land expansion on a water-carbon trade-off relationship. The method is based on land use data of a preset time period in a dry research area to obtain each cultivated land expansion patch; the CASA model and the PT-JPL model are used to obtain carbon fixation amounts and evaporation amounts of the cultivated land expansion patches, and a water-carbon trade-off degree is evaluated; a water-carbon trade-off degree is used as a dependent variable for detrend processing to obtain a detrended dependent variable; expansion sources, location characteristics and landscape indexes are used as a group of independent variables, the independent variables are processed through multiple collinearity and principal component analysis to obtain a global independent variable group; sorting analysis and grey correlation degree analysis methods are used to evaluate independent influence degrees of each independent variable in the global independent variable group on the dependent variable and comprehensive influence degrees among multiple independent variables. The method realizes accurate evaluation of the influence mechanism of cultivated land expansion in a dry area on a water-carbon trade-off.

The present application relates to the field of corn root lodging resistance evaluation, and particularly relates to a method for screening corn post-silk root lodging resistance indexes. Different corn varieties or hybrid combinations with different root lodging resistance characteristics are selected, and at the silking stage, root system configuration traits, root anchorage, and plant inclination recovery degree indexes are investigated. Through principal component analysis, grey correlation degree analysis, and cluster analysis, indexes reflecting root lodging occurrence rate are screened, and according to the index coefficient of variation normalization value and the correlation degree weight value, the indexes are sorted, combined with the root system configuration characteristics of the corn varieties with root lodging resistance, the interval range of the corn root lodging resistance indexes and key indexes is identified, and the root lodging resistance characteristics are classified. The present application clarifies the root system index characteristics of the corn varieties with root lodging resistance and the root lodging resistance evaluation method, and provides a basis for quickly screening corn varieties with root lodging resistance and mining root lodging resistance genes.

The application discloses a heating load prediction method based on similar day clustering and convolution gate cycle, relates to the technical field of heating load prediction of central heat exchange station, and comprises the following steps: obtaining side heat supply load historical data and meteorological data, preprocessing the side heat supply load historical data and the meteorological data, slicing the side heat supply load historical data and the meteorological data according to days, and dividing the side heat supply load historical data and the meteorological data into a training set and a verification set; according to the change of the heat supply load, performing rough classification of weather types on the training set, performing fine classification on the weather types, and obtaining characteristics of different weather types; combining grey correlation degree analysis and Pearson product-moment correlation coefficient to obtain the maximum value of the correlation degree between a target day and each weather type, and selecting corresponding data sets as inputs; training through a combined neural network model, and obtaining a heat supply load prediction result. Therefore, the heating load prediction method based on similar day clustering and convolution gate cycle can improve the prediction accuracy of the heat load of the central heat exchange station and has stronger flexibility and universality.

The invention discloses a reservoir dam monitoring and early warning method and system based on multi-source information fusion, and relates to the technical field of early warning and monitoring. The method comprises the following steps: acquiring seepage flow, displacement rate and pore water pressure data through a sensor array; generating standardized data through Kalman filtering noise reduction; extracting key monitoring parameters through grey correlation analysis; aligning a main frequency amplitude sequence with acceleration variation to generate a deformation feature vector; and when the risk reaches a threshold value, inputting logistic regression to generate a coupling early warning level and feeding back to the terminal. According to the method, through multi-source data Kalman filtering noise reduction standardization, grey correlation screening high correlation parameter construction features, Fourier transform extraction of dominant frequency and acceleration alignment, fuzzy evaluation fusion of dominant frequency attenuation and accumulated acceleration quantization deformation, logic regression based on mutation and fluctuation grading early warning, and space-time coupling, hidden danger is captured. Non-linear mapping breaks through empirical limitation, and multi-dimensional synchronization improves evaluation precision and reduces false alarm and missing alarm.

The application discloses a kind of milling surface topography distribution characteristics analysis methods of aviation structural parts, analysis vibration displacement signal distribution characteristics;Milling surface topography is obtained and the construction of milling surface topography feature distribution curve;Analysis vibration displacement signal distribution characteristics and the relevance of surface topography feature distribution characteristics, with milling surface topography curve time frequency as reference sequence.Overefficient milling experiment, obtain milling surface topography, extract milling transition surface residual feature curve, analyze the time-frequency characteristics of milling transition surface feature distribution curve.Using the relevance analysis between milling vibration displacement signal and surface topography feature, using grey correlation analysis method, assess the close degree of the influence of milling vibration on processing surface topography, and the correlation characteristics of vibration and processing surface topography characteristics are clear.

The invention discloses an airport delay level evaluation method based on space-time correlation. The method comprises the following steps: constructing an associated airport set according to associated airport index analysis; analyzing the time relationship of the delay influence of the associated airport on the target airport by using the grey correlation degree; establishing an airport delay evaluation index system; an entropy weight method is adopted to carry out weight assignment on each airport delay evaluation index weight; and carrying out quantitative grading on the airport delay index based on a one-dimensional kernel density clustering algorithm so as to realize airport delay level evaluation. According to the method, the airport flight delay level can be objectively measured according to the operation characteristics of the target airport and the associated airport.

The invention discloses a probability model-driven smelting furnace life prediction method and system. The method comprises the following steps of collecting multi-source alarm and process data of smelting key components; feature engineering is carried out, degradation features are extracted, and discrete health states are divided; the current health state of the component is accurately recognized under the small sample condition by using the gray correlation degree theory; constructing a state transition probability matrix in combination with a Markov chain, and quantifying the randomness of the degradation process; and predicting the remaining service life of the component by solving an equation set taking failure as an absorption state. According to the method, the problem of life prediction caused by lack of historical failure data is effectively solved, dynamic, quantitative and interpretable evaluation of the residual life of key components such as an oxygen lance, a furnace lining and a flue is realized, and reliable technical support is provided for predictive maintenance decision-making of the smelting furnace.

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.

The invention belongs to the field of petroleum gas field development, and discloses a tight oil horizontal well productivity influence factor and measure well selection quantitative evaluation method, which is based on a productivity main control factor evaluation model of multiple mathematical methods: integrating dynamic and static data in the aspects of geology, development, engineering and the like, and applying mathematical methods such as partial correlation analysis and a grey correlation method to evaluate the productivity main control factor evaluation model. Calculating the correlation coefficient of the main control factors of the tight oil reservoir horizontal well, calculating the geological, engineering and yield scores according to the evaluation result of the productivity main control factors and the correlation coefficient in combination with the actual oil testing and production data, and adding the three scores to obtain the total score, thereby realizing the quantitative evaluation of the main control factors, the comprehensive evaluation of the reservoir transformation effect and the quantification of measure well selection. According to the method, the problems of large development effect difference and unclear productivity main control factors of the tight oil fractured horizontal well are solved, and the method has guiding significance on new well deployment and later development adjustment of a tight oil field.

The application discloses a method for evaluating flow regulation effect of fracture-vug type oil reservoirs, comprising the following steps: collecting dynamic single-well data of a well to be evaluated, determining each key parameter related to current flow regulation effect evaluation, and calculating actual evaluation coefficients of each key parameter; establishing a flow regulation effect evaluation coefficient network according to fracture-vug type oilfield development databases of different regions, wherein the evaluation coefficient network comprises evaluation standard coefficients of each key parameter under different evaluation grades; based on the flow regulation effect evaluation coefficient network and the actual evaluation coefficients of each key parameter, constructing a multivariate coefficient initial matrix, and using a grey correlation method to normalize the coefficients under different evaluation grades in the multivariate coefficient initial matrix, thereby forming a weighted matrix composed of comprehensive influence coefficients under each evaluation grade, so as to obtain single-well evaluation coefficients for representing the flow regulation effect of the well to be evaluated. The application can quantitatively evaluate the flow regulation effect of flow regulation channels of fracture-vug type oil reservoirs.

This invention relates to a multi-level early warning method, equipment, and medium for substation carbon emissions based on PSR theory and grey relational analysis. It constructs a three-layer early warning index system comprising economic, energy efficiency, and environmental layers based on PSR theory; calculates the optimal combined weight vector using an intuitionistic fuzzy IDEMATEL-entropy weighting method; and calculates the early warning index using grey relational analysis. It establishes multi-level early warning threshold intervals and performs graded early warnings based on the calculated early warning index. Compared with existing technologies, this invention deeply integrates PSR theory with the grey relational decision-making model and ensures the reliability of the early warning results through a sample separation degree verification mechanism.

The invention relates to the technical field of bulk commodity operation, in particular to a data sharing and analysis method and system based on multi-dimensional association, and the method comprises the steps: collecting multi-source heterogeneous data through a mixed mode of interface synchronization and manual reporting, and quantifying an unstructured text into a standardized index through a bidirectional LSTM semantic analysis algorithm; a cosine similarity and grey relational degree fusion algorithm and an LSTM dynamic weight optimization model are adopted, association rules are mined in combination with an improved Apriori algorithm, and market pre-judgment is achieved through three sub-models of supply and demand, price and risk; and finally, establishing a closed-loop mechanism, and outputting an inventory adjustment scheme and a futures hedging strategy. The system correspondingly comprises four functional modules, full-process data processing and decision support are achieved, and scientificity and operability of operation decision are improved.

The invention provides a machine tool spindle thermal error modeling prediction and compensation method based on physical-data hybrid drive, and the method comprises the steps: S1, synchronously collecting key position data in a process that a machine tool spindle operates from a cold machine state to a thermal balance state; s2, carrying out collinearity clustering analysis and grey relational degree analysis on the acquired temperature data of the measuring points, and selecting a plurality of sensitive temperature measuring point data; s3, constructing a physical-data hybrid drive neural network model based on a thermal expansion physical equation, inputting the sensitive temperature measurement point data as an independent variable and the corresponding strain data as a dependent variable into a neural network for training, and performing strain prediction; and S4, performing multiple linear regression fitting on strain data predicted by the neural network model and thermal error data acquired by experiments, establishing a thermal error prediction model of the machine tool spindle, and performing thermal error compensation according to a prediction result.