54 results about "Maximal information coefficient" patented technology

The invention discloses a wind driven generator temperature monitoring method based on a graph space-time attention network, and the method comprises the following steps: carrying out the preprocessing of collected SCADA data, and obtaining a data set; each sensor is used as a node, the top-k neighbor relation of each node is calculated, edge weights among the nodes are calculated through a Gaussian kernel function, a weighted adjacency matrix is constructed based on the neighbor relation and the edge weights of all the nodes, and therefore a multi-sensor time sequence diagram of the wind driven generator is obtained, time sequence characteristics are given to the nodes by time sequence data collected by different timestamps, and a multi-sensor time sequence diagram of the wind driven generator is obtained. Forming a space-time diagram structure, and outputting time sequence characteristics of each sensor; a graph attention network is introduced into wind driven generator temperature state monitoring, and global and connectivity characteristics of a multi-sensor network are modeled. According to the method, the generator temperature prediction precision is remarkably improved by fusing a graph space-time two-dimensional attention mechanism and key variable screening, redundant variables are eliminated by adopting the maximum information coefficient MIC, and noise interference is reduced.

The invention discloses an SNCR outlet NOx concentration prediction method and system based on a random forest, and the method comprises the steps: S1, obtaining the operation data of an SNCR system, selecting a plurality of input variables affecting the outlet NOx concentration, and carrying out the normalization processing of the input variables; s2, carrying out maximum information coefficient analysis on the normalized data set, determining the delay time of each input variable relative to the NOx concentration of the outlet, and reconstructing the data set according to the delay time; s3, dividing the reconstructed data set into a training set and a test set, constructing a random forest model, and initializing model parameters; s4, optimizing parameters of the random forest model to obtain an optimal parameter combination; and S5, predicting by using the random forest model of the optimal parameter combination to obtain a predicted value of the NOx concentration at the outlet of the SNCR system. The method has the advantages of high prediction precision and the like.

The invention discloses a power system probability load prediction method, system and device, and a storage medium, relates to the technical field of hydrogen energy ship power system load prediction, and aims to solve the technical problems that multi-source data and physical information rules are not integrated and multi-scale chaos in an HPV complex operation environment is not considered in the prior art. The method specifically comprises the steps of collecting and preprocessing multi-source data; screening strong correlation data by using the maximum information coefficient, and reconstructing multi-source data into a unified spatial-temporal characteristic matrix; constructing an electrical and environmental parameter chaos model based on a Lorentz dynamic equation as a physical constraint term of a loss function; a BERT-PINN framework with an attention mechanism is established, and multi-step probability prediction is realized through two-stage training. According to the method, the data driving model and the physical information rule of the multi-source data are effectively integrated, the feature fusion problem of the multi-scale chaotic features is solved, and the model prediction performance is improved.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

The invention discloses a multivariable cross-correlation spatial-temporal feature fusion dissolved oxygen content prediction method and system. The method comprises the following steps: acquiring a water quality sample data set; aiming at the water quality sample data set, screening an influence factor sequence of which the correlation with dissolved oxygen is greater than a preset value by adopting a maximum information coefficient, and screening a dissolved oxygen sequence; standardizing the screened influence factor sequence and the dissolved oxygen sequence to obtain standardized data; the standardized data are adopted to train a dissolved oxygen content prediction model, a trained dissolved oxygen content prediction model is obtained, and the dissolved oxygen content prediction model comprises a spatial feature extraction network, a local feature extraction network, a multi-channel attention network and an encoder-decoder long-short-term memory network combined with an attention mechanism; inputting to-be-detected water quality data into the trained dissolved oxygen content prediction model for prediction, and outputting a dissolved oxygen content prediction result at the next moment. The method can effectively improve the prediction precision of the dissolved oxygen content.

The invention discloses a day-ahead electricity price prediction method and system based on similar day adaptive screening and SHAP compensation. The method mainly comprises the following steps: preprocessing historical electricity price, load, new energy output and meteorological data; calculating the maximum information coefficient of the features and the electricity price based on a sliding window, and carrying out the self-adaptive screening of similar days through combining the clustering and grey correlation degree after dynamic weighting; the method comprises the following steps: constructing a prediction model by fusing a conditional variation auto-encoder with a multi-head attention mechanism, capturing historical sequence multi-scale time sequence characteristics and future condition information through a double-path encoder, and generating a day-ahead electricity price point prediction result; and performing feature contribution decomposition on the prediction error by using an SHAP tool, and training an error compensation model to correct an initial prediction value. The method can dynamically adapt to market changes, the prediction precision and interpretability are improved, closed-loop self-optimization is achieved, and reliable decision support is provided for electricity market transactions.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

The invention discloses an electricity price prediction method based on dynamic mode decomposition fusion LSTM + CKDE. The method comprises the following steps of: 1, collecting data, including historical electricity prices containing time sequence characteristics and price fluctuation indexes, power generation parameters, power market supply and demand data and external environment factor parameters; 2, performing data preprocessing, abnormal value processing and missing value interpolation on the collected data; 3, performing feature input variable screening on the data processed in the step 2 by adopting a maximum information coefficient method or a Pearson correlation coefficient analysis method; 4, performing dynamic modal decomposition on the screened feature input variables to obtain dynamic feature vectors; step 5, constructing an electricity price prediction model by using an LSTM algorithm, and inputting a dynamic feature vector; and 6, evaluating the electricity price prediction model by adopting a CKDE method. According to the invention, the accuracy of electricity price prediction is improved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention relates to the technical field of load prediction, in particular to a load prediction method and system combining a maximum information coefficient method and deep learning. The method comprises the following steps: screening main factors influencing the load of the hydraulic turbine set to obtain the maximum information coefficient of each influence factor; taking the preprocessed data as input data of a first model, and determining basic hyper-parameters of the first model according to the input data; optimizing the first model by using an intelligent optimization algorithm to perform feature extraction so as to improve the feature capturing capability; and predicting the load of the hydraulic turbine set, and outputting a load prediction result.

The invention discloses a photovoltaic module performance degradation reason determination method and device, relates to the technical field of new energy, and mainly aims to improve the accuracy of a photovoltaic module performance degradation reason determination result. According to the main technical scheme, the method comprises the steps of obtaining power operation data and infrared imaging data of a photovoltaic module; extracting a first feature vector and a second feature vector from the power operation data and the infrared imaging data respectively; on the basis of a maximum information coefficient algorithm, calculating nonlinear correlation intensity between each electric infrared feature pair in the first feature vector and the second feature vector, and constructing an electric heating correlation matrix according to the nonlinear correlation intensity; based on an interpretable machine learning model, quantifying marginal contribution of each power infrared feature pair in the electrothermal incidence matrix during prediction of performance degradation of the photovoltaic module to obtain contribution values of each feature pair; and according to the contribution value and in combination with a preset attenuation mechanism verification rule, determining a reason for performance attenuation of the photovoltaic module.

The invention discloses a gas insulation cabinet partial discharge monitoring method and device based on edge calculation, and relates to the technical field of gas insulation cabinets. The method comprises the following steps: collecting environmental data and background electromagnetic noise signals of the gas insulation cabinet, calculating an environmental noise correlation coefficient by using a maximum information coefficient method, combining an environmental deviation value to obtain a comprehensive environmental disturbance index, and correcting a preset background noise threshold value to obtain a corrected noise threshold value; if the real-time electric signal amplitude exceeds the corrected noise threshold, marking the data as partial discharge signal data; denoising the partial discharge signal data by adopting an improved ensemble empirical mode decomposition adaptive noise method, and extracting a discharge feature vector; calculating the mahalanobis distance of each feature vector in the discharge feature vector sequence, obtaining the discharge similarity, obtaining a homologous discharge link, extracting the feature data of the homologous link, inputting the feature data into a BP neural network model based on a dung beetle optimization algorithm, outputting the type of partial discharge, and carrying out the early warning, thereby achieving the monitoring of the partial discharge of the gas insulation cabinet.

The embodiment of the invention provides a meteorological data processing method and device and electronic equipment. The method comprises the following steps: acquiring generated power and at least one meteorological factor of N new energy power generation equipment at a historical moment; determining the correlation between each meteorological factor and the generated power according to the symmetry uncertainty; according to the sequence of the correlation from high to low, carrying out descending sorting on the meteorological factors for the first time; sequentially determining a maximum information coefficient value between each meteorological factor after one descending sorting and the generated power, and taking the maximum information coefficient value as a correlation score of the meteorological factors; and according to the correlation score of each meteorological factor and a Markov blanket method, redundant meteorological factors in the meteorological factors are screened out to obtain residual meteorological factors, and the residual meteorological factors are used for predicting the generated power of the new energy power generation equipment. According to the method, the meteorological data is screened, and the screened meteorological data is utilized, so that the prediction accuracy of the new energy power generation power can be improved.

The application discloses a kind of wind-driven generator temperature monitoring methods based on graph space-time attention network, comprising the following steps: the SCADA data collected are preprocessed, obtain dataset;With each sensor as node, the top-k neighborhood relationship of each node is calculated, the edge weight between nodes is calculated by Gaussian kernel function, the weighted adjacency matrix is constructed based on the neighborhood relationship and edge weight of all nodes, so that the wind-driven generator multi-sensor time series graph is obtained, the time series data collected at different time stamps is assigned to node time series feature, form space-time graph structure, and output each sensor time series feature;Graph attention network is introduced into wind-driven generator temperature state monitoring, modeling the globality and connectivity features of multi-sensor network.The application significantly improves the generator temperature prediction accuracy by fusing graph space-time dual-dimensional attention mechanism and key variable screening: redundant variables are removed by using the maximum information coefficient MIC, and noise interference is reduced.

The invention relates to the technical field of thermal power generation, and discloses a coal-fired power plant final-stage superheater wall temperature prediction method and system based on an improved wavelet time convolution network, and the method comprises the steps: obtaining the historical operation data of a plant-level monitoring information system, calculating the maximum information coefficient between each variable and the wall temperature, and obtaining the maximum information coefficient; selecting a variable with the maximum information coefficient greater than a preset threshold value as a key feature; constructing the key features and the wall temperature data at the current moment and the plurality of historical moments into model input features, and taking the wall temperatures at the plurality of future moments as model output features; constructing a wavelet time convolutional network model, wherein the wavelet time convolutional network model comprises an input layer, a wavelet decomposition layer, at least one hybrid expansion convolutional layer and an output layer which are connected in sequence; and training the wavelet time convolutional network model by using the model input features and the model output features to obtain a wall temperature prediction model. According to the method, the wall temperature of the final-stage superheater is predicted, an operator can take control measures in advance, and the overtemperature phenomenon is reduced.

The invention discloses a multi-source data fusion wind-solar-storage combined power generation prediction method. The method comprises the following steps: S1, collecting historical power generation data, real-time meteorological data, energy storage system state data and power grid load data of a wind-solar power station; s2, performing standardization processing on the data, screening key features through a maximum information coefficient, and constructing a feature matrix; s3, fusing multi-source data by adopting a dynamic weight mechanism, and iteratively optimizing the fusion weight of each data source based on a gradient descent method; s4, outputting a wind and light output predicted value by using a multi-modal deep neural network comprising a meteorological feature extraction layer, a time sequence feature extraction layer and a global feature interaction layer; and S5, constructing an optimal scheduling model, and generating a charging and discharging strategy in combination with constraint conditions of the energy storage system. The invention relates to the field of new energy power systems, in particular to a multi-source data fusion wind-solar-storage combined power generation prediction method, which has the following advantages: 1, high-precision prediction; 2, intelligent scheduling; and 3, the adaptability is high.

The application discloses a data-driven traditional Chinese medicine manufacturing process parameter optimization method, which comprises the following steps: calculating the maximum mutual information coefficient (MIC) between the historical process parameters and product quality of a traditional Chinese medicine product production process, constructing a quality prediction model (PM-AdaBoost), calculating an adaptive function according to the maximum mutual information coefficient and the mean square error of the quality prediction model, initializing a particle swarm, calculating the adaptive function, and updating the speed and position of the particle in the process parameter search space through multiple iterations to obtain the key process parameters in the traditional Chinese medicine product production process and the optimized quality prediction model, and further taking the mean square error of the optimized quality prediction model as the adaptive function and utilizing a particle swarm optimization algorithm to obtain the optimized key process parameters. The application measures the linear and nonlinear relationship between variables through the maximum information coefficient, selects the maximum information coefficient and the quality prediction mean square error as the standard for screening the key process parameters and constructing the quality prediction model, and has an absolute advantage in accuracy. Based on the quality prediction model, the key is optimized through the particle swarm algorithm, and the particle swarm has an absolute advantage in convergence speed as the process parameter optimization algorithm.

The invention discloses a wind power short-term prediction method based on feature selection and improved HO optimization. The method comprises the following steps: S1, collecting wind turbine generator data and local meteorological data of a wind power plant; s2, performing normalization preprocessing on the data, and dividing the data into a training set and a test set according to a ratio of 9: 1; s3, screening characteristics with high correlation with the wind power by adopting a maximum information coefficient (MIC); s4, optimizing a hyper-parameter of the Transform-BiLSTM model by using an improved Heima algorithm (IHO); and S5, verifying the optimized model on a test set, and outputting a prediction result by adopting a sliding time window strategy. The method aims at solving the problem that in existing wind power prediction, a traditional optimization algorithm is insufficient in combination model hyper-parameter optimization capacity.

The application belongs to the technical field of sub-industry load forecasting, and provides a sub-industry load forecasting method and system based on type analysis and feature reconstruction, which obtains power load data and meteorological feature data of each industry, and carries out pretreatment; classifies power load of different industries; calculates the maximum information coefficient between each meteorological feature variable and power load of different categories, selects features according to the value of the maximum information coefficient, and reconstructs a meteorological feature matrix; takes historical load data in a set time period of a day to be predicted as model input features, and combines them into the feature matrix; takes the combined feature matrix as input and load value as output, trains a deep learning model; selects a corresponding feature matrix according to the power type of a prediction target, and uses the trained deep learning model to perform day-ahead load forecasting. The application can realize day-ahead load forecasting of different industries.

The invention provides a two-stage fan equipment quality abnormity tracing method based on hierarchical information characterization, and relates to the technical field of fan operation and maintenance quality management and control, and the method comprises the steps: obtaining SCADA monitoring data, operation logs and operation and maintenance records of fan equipment, carrying out data preprocessing through overlapping sampling, and constructing a time sequence database; calculating the maximum information coefficient, related to the fault, of each feature in the time sequence database, and screening key features to form a feature subset; constructing a double-end Transform model, taking the feature subset as the input of the model, introducing a classification mark, and outputting a feature prediction value and fault category probability distribution; the difference between the fault sample and the normal sample is calculated based on the mahalanobis distance, the tracing key features are screened according to the contribution degree of the difference, and multi-dimensional time domain statistical indexes are extracted from the tracing key features to construct a high-dimensional feature matrix; and inputting the high-dimensional feature matrix into a tracing model represented by hierarchical information, judging an abnormal reason through a feature matrix threshold value, and outputting a tracing result.

The application relates to a cross-domain paper recommendation method based on heterogeneous data embedding and belongs to the technical field of big data mining application and information processing. First, a field is divided for a data set, a directed acyclic graph is constructed for each subject, a latent Dirichlet distribution model is used to extract field semantics, and cross-domain correlation is learned through a maximum information coefficient. Then, papers and users are respectively expressed in the form of vectors through heterogeneous data embedding, and the mapping relationship between cross-domain papers is trained through the citation and non-citation relationship between the papers. If a keyword search is provided by a user, the field of the user is divided according to the keyword, and the interest list of the user is the cited literature. Finally, a cross-domain paper recommendation model is used to recommend papers for the user. The application can automatically evaluate the cross-domain correlation of papers, effectively overcome the technical defects that a traditional method only takes the content similarity of papers as the recommendation basis, and greatly improve the recommendation precision and efficiency.

The invention is suitable for the field of time series prediction, and discloses a time series prediction method, system and device based on stream batch fusion and a medium, and the method comprises the steps: obtaining a feature set of a multivariable time series, and obtaining a to-be-verified feature subset through calculating the maximum information coefficient between each feature and a prediction target; performing stationarity test on the to-be-verified feature subset, and analyzing a causal dependency relationship by using Granger causal test to obtain a causal feature subset; based on the causal feature subset, determining an optimal lag order and constructing a vector autoregression prediction model; and based on the trained vector autoregression prediction model, performing parallel processing on historical batch data and online stream data, and performing weighted fusion on a generated batch processing prediction result and a stream processing prediction result to obtain a time sequence prediction value. According to the method, through feature selection and stream batch fusion time sequence prediction, high-precision and low-delay prediction of the time sequence is realized while the model input quality is ensured.

The application discloses a flue gas acid making data cleaning and optimization method based on isolated forest and weighted random forest, which analyzes the flue gas acid making desulfurization process, combines a large amount of production monitoring data, adopts a maximum information coefficient analysis method to perform correlation analysis on process variables such as the O2 concentration at the fan outlet, the flue gas temperature at the fan outlet, the first power wave inlet pressure, the furnace pressure, the fan inlet flow, the converter inlet temperature and the like, and obtains key variables affecting SO2 conversion rate and sulfuric acid production and the like indexes. Then, for the key variables, the original data change trend is analyzed, the isolated forest algorithm is used to identify and eliminate abnormal values and outliers in the data set, and a missing data set is obtained. Finally, the weighted random forest algorithm is used to fit and predict the missing data set, to compensate for the missing data therein, realize cleaning and optimization of the flue gas acid making process data, and thus achieve the purpose of improving the desulfurization efficiency and the sulfuric acid production.

This invention belongs to the interdisciplinary field of uranium geological exploration information technology and big data processing technology. Specifically, it relates to a method for screening mineralization feature sets of sandstone-type uranium deposits based on the maximum information coefficient (MIC). The method includes: Step S1: Extraction and organization of mineralization feature data; Step S2: Data preprocessing; Step S3: MIC calculation and preliminary ranking of feature values; Step S4: Screening feature thresholds based on inflection point analysis to determine key mineralization features. This method can identify key ore-controlling factors with high correlation to sandstone-type uranium mineralization from geoscientific big data, significantly improving the accuracy, efficiency, and interpretability of subsequent machine learning mineralization prediction models, and providing reliable data support for the selection of uranium exploration target areas.

This application discloses a method for handling data anomalies in photovoltaic power prediction acquisition devices, belonging to the field of power technology. The method includes: synchronously acquiring and storing multidimensional meteorological data and actual power generation data of the photovoltaic system to obtain raw data; performing linear correlation and nonlinear correlation analysis of the raw data using the maximum information coefficient to obtain core input features; identifying and marking anomalies in the core input features and removing the anomalies to obtain de-anomaly-removed data; and performing iterative interpolation on the de-anomaly-removed data to obtain imputed data and outputting it. This method solves the problems of insufficient accuracy in photovoltaic power data processing and inaccurate identification of key information in existing technologies, thereby improving the accuracy of photovoltaic power data analysis and processing.

The invention discloses an active power distribution network line loss assessment method based on a BP clustering hybrid model, relates to a power grid line loss assessment method, and aims to solve the problems that an existing BP neural network line loss model is poor in line loss assessment precision and cannot be adaptively updated. The method comprises the following steps: constructing an original measurement data set by acquiring electrical characteristic parameters of a transformer area; performing normalization and filtering smoothing processing on the original measurement data set to form a standardized two-dimensional curve; performing joint clustering on the standardized two-dimensional curve to generate an operation scene label, screening 11-dimensional features with the highest correlation with the line loss rate from the electrical feature parameters of the transformer area based on the maximum information coefficient, and forming an input vector of a BP clustering hybrid model; for the operation scenes, independently training each operation scene into a BP clustering hybrid model; and calling the corresponding BP clustering hybrid model to obtain a line loss prediction value. The method has the beneficial effects that the line loss evaluation precision is remarkably improved, and the model is adaptively evolved online.

The invention discloses a power system inter-provincial tie line unit transaction cost confidence interval generation method. The method comprises the steps of obtaining data information of a target power system and performing data processing; calculating a feature sequence weight based on a generalization theory; screening and constructing a similar day set based on a maximum information coefficient scheme; and based on Tukey distribution, fitting the similar daily error CDF quantile to generate a unit transaction cost confidence interval so as to complete generation of the unit transaction cost confidence interval of the inter-provincial tie line of the target power system. The invention also discloses a system for realizing the power system inter-provincial tie line unit transaction cost confidence interval generation method. According to the method, the unit transaction cost confidence interval of the inter-provincial tie line of the power system can be generated, the reliability is higher, and the accuracy is better.

The invention discloses a data processing method and device for a power source end, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting initial data of the power source end, and carrying out the data preprocessing of the initial data to obtain target data; the types of the target data comprise environmental parameters, equipment state parameters and electrical parameters; generating a target irradiance change rate based on the target data, and determining a target time window based on the target irradiance change rate; determining a target statistical feature and a target fluctuation feature corresponding to the target data based on the target time window, and generating a plurality of initial association features corresponding to the target data based on the target statistical feature and the target fluctuation feature; and determining a maximum information coefficient between each initial association feature and the target power task, and screening out a target association feature from each initial association feature based on the maximum information coefficient, so as to process the target power task based on the target association feature. The data utilization value of the power source end can be improved.

A kind of export-oriented city electric power load forecasting method considering foreign trade prosperity conduction hysteresis, through the cleaning and alignment of multi-source heterogeneous data, time lag analysis based on maximum information coefficient method and through double-channel neural network model prediction, output final future time electric power load prediction value;The double-channel neural network model includes time series fluctuation channel and foreign trade fluctuation channel, mainly rely on time series fluctuation channel when meteorological mutation, and use foreign trade fluctuation channel to adjust load benchmark when external impact is serious.Can adapt to the change of international trade environment, and accurately reflect its hysteresis effect, improve the accuracy of electric power load forecasting result, to help guarantee the safe and stable operation of power system and reliable power supply.