28 results about "Rank correlation" patented technology

The application discloses a cable branch box grounding fault detection method and system, the method comprises the following steps: calculating the effective value of each return line zero sequence current per power frequency cycle, determining the baseline mean and baseline standard deviation by using a sliding window, and identifying a sharp peak event and recording the occurrence time; obtaining the temperature change rate of the cable connection point of each return line, dividing the statistical period into multiple time windows, forming a sharp peak occurrence rate sequence and a temperature change rate absolute value mean sequence respectively, and calculating the Spearman rank correlation coefficient of the two sequences; according to the comparison result of the median of the rank correlation coefficient of the latest preset number of statistical periods and the determination threshold, it is judged whether there is an intermittent grounding fault in the corresponding return line. The application can utilize the correlation between the zero sequence current sharp peak distribution and the temperature change to identify the fault, and improve the stability and accuracy of the intermittent grounding fault detection.

The application discloses a power plant 5G private network elastic security data analysis method and device based on data mining, and relates to the technical field of power grid security data analysis. The method comprises the following steps: defining a power grid security data analysis target and an analysis range; building a data acquisition and transmission link, and collecting operation parameters, operation logs and fault record data of key equipment of the power grid in real time; transmitting the collected data to a collection server in a safe access area; pre-processing the received data by the collection server to obtain a time series data set of security data; performing preliminary correlation analysis on the time series data set by adopting a Spearman rank correlation method to obtain correlations between a plurality of security data state indexes; obtaining a time series data set corresponding to a strong correlation security data index based on the correlations between the indexes; and mining the association rules of the security data based on the time series data set by adopting a time series FP-Growth algorithm to obtain strong time series association rules. The application can improve the utilization efficiency of security data.

This invention relates to a method and system for decoupling risk characteristics of natural electric fields on the surface based on the principle of order. The method includes: determining the spatial range of an electrode array based on the principle of spatial separability of risk sources and deploying the electrode array; acquiring multi-channel observation time series data of shallow surface natural potential through the electrode array; determining a time window based on the principle of spatial separability of risk sources; calculating the rank correlation coefficient between any two observation channels within the time window; mapping the calculated rank correlation coefficient to the corresponding field source state level according to a preset field source state level classification standard; calculating a decoupling index based on the normalized proportion of each field source state level and its preset risk weight; and directly quantifying the overall orderliness of the field sources through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field, effectively solving the core problem of identifying risk characteristics.

The present application relates to the technical field of dynamic wireless power transfer (DWPT), and especially relates to a DWPT system electromagnetic exposure evaluation method and system considering human activity, aiming at the problem that the existing static evaluation method is difficult to take into account the time-varying characteristics of dynamic system and the difference of human activity distribution, the present application derives the time domain expression of the receiving coil current through establishing a circuit model, quantifies the influence of speed and load on the dynamic change of current; the electromagnetic region is classified into alert zone and waiting zone, and multiple height monitoring planes are set to correspond to the main organs of the human body; the magnetic field data set is obtained by laying measuring points, the regional average magnetic induction intensity, the maximum magnetic induction intensity and the magnetic field energy are calculated as the evaluation target; based on the rank correlation coefficient and the mutual information value, the redundant measuring points are removed, the core measuring point combination is screened by combining the NSGA-II multi-objective optimization, and the multi-objective high-precision synchronous prediction is realized by using the second-order ridge regression, so that the accurate characterization and low-cost measurement of dynamic electromagnetic exposure are realized.

The invention provides a time series data prediction method and device based on dynamic error modeling, and the method comprises the steps: predicting original time series data through an LSTM model, and generating a historical error sequence of a predicted value and a real value; carrying out CEEMDAN mode decomposition on the sequence, and extracting a plurality of error mode components with non-stationary characteristics; clustering the fusion components by using an affinity propagation algorithm to generate a representative error modal cluster after dimension reduction; analyzing the nonlinear correlation between the historical error sequence and each modal cluster based on a Spearman rank correlation coefficient, and obtaining a dynamic weight distribution result through normalization; and respectively predicting a future error value of each modal cluster through the parallel LSTM network, weighting and correcting a prediction result according to a dynamic weight, and generating a final prediction value. According to the time series data prediction method based on dynamic error modeling, parallel collaborative optimization of prediction and error correction is realized through explicit modeling error dynamic characteristics in combination with a CEEMDAN-AP joint optimization algorithm and Spearman correlation weight distribution, and the precision and real-time performance of time series prediction are remarkably improved.

The invention discloses a combined heat and power generation unit load distribution prediction and optimization method, and the method comprises the following steps: S1, obtaining the historical data of a combined heat and power generation unit, carrying out the grouping of two working conditions: medium-pressure air exhaust and low-pressure air exhaust, carrying out the feature extraction through the combination of a Spearman rank correlation coefficient and mutual information, and carrying out the prediction of the load distribution of the combined heat and power generation unit; removing redundant features and low correlation features to obtain a target data set for model training; s2, constructing a TCN-ECA-BiLSTM prediction model, wherein the TCN-ECA-BiLSTM prediction model comprises a time sequence convolutional network layer, an efficient channel attention layer and a bidirectional long and short term memory network layer; according to the method, a TCN-ECA-BiLSTM prediction model and a TD3 reinforcement learning algorithm model are combined to form a prediction and optimization integrated framework, key feature reinforcement of the TCN-ECA-BiLSTM prediction model is matched with bidirectional sequence modeling advantages to reinforce prediction input after feature extraction, and the TD3 reinforcement learning algorithm model carries out distribution by considering a steam coal consumption rate and a power generation steam consumption rate. And real-time and reliable dynamic optimization is realized.

The application discloses a lung nodule classification method based on multi-task learning, comprising the following steps: 1, extracting lung nodule CT data and feature level labels; 2, calculating the Spearman rank correlation coefficient between lung nodule features, constructing and training a neural network so that the cosine similarity between the initial labels embedded in the output lung nodule features is approximately equal to the Spearman rank correlation coefficient; 3, designing an adaptive method to obtain a graph adjacency matrix to describe the correlation between lung nodule features; 4, constructing and training a multi-task model with GCN and 3D U-Net as the backbone network, containing an image fusion module and a cross-channel attention module, outputting the malignant degree classification result of the lung nodule, and giving the feature score and segmentation result to provide auxiliary information for the classification result. The application fully utilizes the correlation between lung nodule features to construct a lung nodule classification model, which can output multi-dimensional analysis results for lung nodules, so that the diagnosis result has high interpretability and credibility.

This invention discloses a multi-objective prediction method for power system sources and loads based on Laguerre polynomial theory, relating to the field of power system prediction and intelligent dispatching technology. The method includes the following steps: using Spearman Rank Correlation Coefficient (SRCC) to analyze the correlation of characteristic influencing factors of wind power, photovoltaic power, and power load; and using Robust Local Mean Decomposition (RLMD) to decompose the time series of wind power, photovoltaic power, and power load into high-frequency and low-frequency components to reduce their fluctuations; using Weighted Permutation Entropy (WPE) to analyze the complexity of the subsequences after RLMD decomposition, merging subsequences with similar complexity to reduce the model's prediction complexity; and constructing a hybrid Laguerre neural network prediction model using Laguerre polynomials. This invention is the first to simultaneously consider both accuracy and stability objectives in source and load prediction, selecting a compromise solution in the Pareto front using the MORUN algorithm, making the prediction results more applicable to power system dispatching scenarios with high robustness requirements.

This invention discloses a multivariate time series classification method based on wavelet-enhanced dual-branch fusion. Addressing the issues of coexistence of multi-scale periodicity and transient abrupt changes in time series and susceptibility to non-discriminatory perturbations such as baseline drift, this method designs an adaptive periodicity discovery mechanism guided by stationary wavelet to identify the dominant period, and constructs a heterogeneous dual-branch encoder module to model frequency-domain periodic patterns and time-domain transient dynamics respectively. Furthermore, a symmetric mutual-enhancing attention module is designed to achieve interaction between dual-domain features. Finally, a graph neural network classification head based on Kendall's rank correlation coefficient is proposed to achieve end-to-end training and effectively capture morphological similarities between samples. This invention significantly improves classification robustness and accuracy while maintaining model interpretability, making it suitable for high-reliability scenarios such as medical monitoring and industrial equipment diagnostics.

This invention discloses a flood consensus zone delineation method based on a machine learning model using consensus clustering, relating to the field of watershed flood characteristic zoning. The method includes: dividing flood records from hydrological stations in the target area into data-sufficient, data-insufficient, and data-free areas according to record length; classifying watershed flood characteristics in data-sufficient areas using hierarchical clustering to obtain consensus clustering results; establishing a multilayer perceptron deep learning model of hydrological categories and flood characteristics to predict hydrological categories in data-insufficient areas; predicting hydrological categories in data-free areas through hydro-meteorological similarity; and determining the main control factors of flood peaks in each area by examining the similarity between flood peaks and hydro-meteorological variables using Spearman rank correlation coefficients, thus obtaining the final watershed flood hydrological zoning results. This invention helps improve the accuracy of engineering hydrological design in data-scarce areas, better addressing watershed flood risk management, and is suitable for widespread promotion and use.

The invention provides a combined temperature forecast correction method and system, and relates to the technical field of weather forecast. According to the method, three kinds of errors are systematically corrected, and the climate mode temperature forecast deviation is corrected. The method comprises the following steps: firstly, considering an error source of temperature forecast, and respectively disassembling observation temperature data and original forecast temperature data in a training period into three independent components, namely a mean term, a trend term and a residual term; and using conditional Gaussian correction to optimize the residual term error, and keeping the rank correlation structure of the original ensemble forecast. And according to requirements, performing mean value correction, trend correction and residual error correction on the original forecast temperature data, combining a mean value correction result, a trend correction result and a residual error correction result of the original forecast temperature data, and outputting a combined temperature forecast correction result. The method is used for temperature forecast correction and has the advantages of being good in correction effect, flexible to use, efficient in calculation and the like.

The invention relates to a method for judging the correlation degree between fetal X chromosome concentration and each physical sign index of a pregnant woman, which comprises the following steps: acquiring prenatal detection data of the pregnant woman, including the proportion of fetal X chromosome free DNA fragments, detection weeks of pregnancy, the BMI value of the pregnant woman and other physical sign indexes of the pregnant woman; according to the range of different indexes corresponding to the obtained prenatal detection data of the pregnant woman, removing abnormal data in the range; the Spearman rank correlation analysis method is applied to the processed prenatal detection data of the pregnant woman to obtain the correlation degree between each sign index of the pregnant woman and the X chromosome concentration of the fetus, and the indexes are subjected to significance test. And performing normalization processing on the pregnant woman sign index data without abnormal values, establishing a correlation model of pregnant woman indexes and X chromosome concentration by adopting a grey comprehensive analysis method, performing cross validation with a Spearman analysis method, and determining a correlation analysis model of the fetal X chromosome concentration and the pregnant woman indexes. The invention aims to solve the problem of inaccurate detection result of a single statistical method.

The application discloses a kind of power line loss analysis system and method based on big data, it is related to power line loss analysis technical field, including real-time acquisition power grid equipment operating state and environmental parameter, constructs power line loss dataset;Each power line loss data and line loss rate's rank correlation coefficient is calculated, and strong correlation factor of line loss is output to be weighted, and the real-time data of each line's strong correlation factor of line loss is analyzed by first line loss constraint, whether to meet constraint is judged, and second line loss constraint analysis command is sent out;The strong correlation factor of line loss of each line is analyzed by exponential smoothing residual error method, and second constraint analysis result is obtained, the first constraint analysis result and the second constraint analysis result are comprehensively, and third constraint analysis result is obtained, to third constraint analysis result is rendered line loss rate distribution in real time.Further analysis residual error, capture line aging, nonlinear factors such as load mutation superimposed influence on line loss, combined with multi-stage constraint analysis improves decision robustness.

The application relates to a kind of multi-time scale based groundwater monitoring data dynamic period management method and system, belong to groundwater monitoring and data analysis technical field.The groundwater data time series is decomposed into different scale periods by db4 wavelet decomposition, the identified period is verified by wavelet power spectrum significance test, and the core period of the groundwater level data in the region is determined according to the number of significant sites.According to the time scale of the core period, the driving factors are classified according to the influence, the water level of each core period of each site is subjected to Spearman correlation test with the main driving factor components, and the rank correlation coefficient of water level and each driving factor component is calculated.A contribution matrix of the region is formed by constructing period-leading factor-contribution weight;Hierarchical management and zoning management are carried out.The application greatly improves the analysis accuracy and regional adaptability.

The application relates to the technical field of intelligent industrial park management, and discloses a water quality intelligent prediction method based on rank correlation and an XGBoost model, which comprises the following steps: a data collection step, which collects multi-dimensional data of an industrial park and pre-processes the data; a correlation analysis step, which analyzes the correlation between the multi-dimensional data and influent water quality by using a Spearman rank correlation coefficient; a key feature variable screening step, which screens multi-dimensional data with correlation meeting preset standards as key feature variables based on the analysis result of the correlation; an intelligent prediction model construction step, which normalizes the key feature variables and constructs an influent water quality intelligent prediction model based on an XGBoost method; and a prediction step, which performs real-time prediction based on the influent water quality intelligent prediction model. The application realizes accurate and timely prediction of influent water quality of an industrial park sewage treatment plant by analyzing multi-dimensional correlation data of the industrial park sewage treatment plant and establishing an intelligent prediction model, improves sewage treatment efficiency, and reduces operation cost.

The invention relates to the technical field of mineral resource quantity calculation, in particular to a polymetallic mineral resource quantity estimation method and system, and the method comprises the steps: carrying out the unified constraint of the spatial correlation of polymetallic grades in adjacent calculation units through a cooperative Kriging method, enabling the consistent relation of different metals in the trend and tendency change direction to be brought into a same judgment system, and carrying out the estimation of the polymetallic mineral resource quantity. The space splitting risk generated when multiple metals are calculated separately is reduced, contribution sorting is conducted on multiple metal grades in the same calculation unit through the Spearman grade correlation coefficient, the relative change relation between the metal grades is converted into a comparable grade sequence, it is avoided that low-contribution metals amplify unstable factors in numerical value conversion, and the calculation efficiency is improved. Mutational metal items are identified and eliminated through spatial correlation constraints, interference of local abnormal grades on an overall result is reduced, and the formed multi-metal resource quantity result is enhanced in the aspects of spatial consistency, numerical comparability and overall reliability.

The invention discloses a carbonate reservoir connectivity characterization method based on path analysis and dynamic data optimization. The carbonate reservoir connectivity characterization method comprises the following steps: counting optimized sensitive basic attributes and drilling fluid leakage values at drilling leakage sample points in a research area; constructing a complex nonlinear correlation between the sensitive basic attribute and the drilling fluid leakage through a neural network, and obtaining a predicted communication fusion attribute; picking up an inter-well optimal communication path through an ant colony optimization algorithm, obtaining path parameters, and calculating a rank correlation coefficient between the path parameters and actual well group communication dynamic data; taking the ratio of the predicted mean square error to the rank correlation coefficient as an objective function, improving the combination mode of the sensitive basic attributes and the deep learning parameters of the neural network through a stepwise regression method, enabling the objective function value to reach a set threshold value, and obtaining the high-precision communication fusion attributes matched with the production materials. The problem that effective means for representing the communication structure and predicting the communication path are lacked in the carbonate fractured-vuggy reservoir is solved.

The invention discloses a multi-index-based sand beach erosion risk assessment method, relates to the technical field of coastal ecological protection and disaster risk assessment, and aims to reconstruct a sand beach erosion comprehensive risk assessment index system aiming at the influence of sand beach erosion on coastal community disaster prevention function weakening, tourism economic benefit reduction and ecological system degradation. Performing standardization processing on the index data, and determining an index weight by adopting a combined weighting method combining a principal component analysis method and an entropy weight method; calculating a comprehensive index of the sand beach erosion risk based on a multi-criterion decision analysis method, and grading the calculation result of the comprehensive risk index by adopting a natural breakpoint method; and establishing a correlation between the risk index and the economic loss through a Spearman rank correlation coefficient method, and checking the rationality of an evaluation result. The method is objective and reliable in process, extensible in prediction, high in universality and capable of recognizing the risk levels of different coastal areas, and scientific support is provided for coastal zone resource optimization configuration and adaptive management.

This invention discloses an automatic scheduling optimization method and system for a deep learning compiler based on adaptive feature weighting. The automatic scheduling optimization method includes the following steps: In the t-th iteration of the cost model, the comprehensive dynamic feature weights are calculated. S i ( t The cost model predicts the Top-K scheduling scheme in the (t-1)th iteration and its actual hardware test ranking; the Spearman rank correlation coefficient between the predicted ranking and the actual test ranking is calculated. R The weighting coefficients are then adjusted, and the overall score of the scheduling scheme is calculated. Score j ; Calculate the time contribution ratio of each scheduling scheme and generate virtual label samples; Validate the virtual label samples, and merge the valid virtual label samples with the historically accumulated real hardware measurement samples to construct a hybrid training dataset, and update and iterate the cost model in a closed loop.

The invention discloses a commercial vehicle engine fault identification method based on hybrid feature optimization, and belongs to the technical field of automobile fault diagnosis. The method comprises the following steps: firstly, performing mixed feature extraction on an engine data sample acquired by an automobile diagnostic instrument by adopting a Spearman rank correlation coefficient and a Kendall grade correlation coefficient; then, the extracted features are screened and verified through a K-fold cross validation method; dividing the screened data into a training set and a test set; and finally, optimizing a BP neural network weight threshold by adopting an improved particle swarm optimization algorithm which introduces a dynamic inertia weight and an adaptive learning factor, constructing an IPSO-BP classification model, and carrying out training and performance testing. According to the method, through collaborative innovation of mixed feature optimization and the IPSO-BP algorithm, the accuracy and robustness of engine fault recognition are remarkably improved.

The invention discloses a construction method of a charging load prediction model, a load prediction method and related equipment, and relates to the technical field of load prediction, and the key points of the technical scheme are that based on the position information of electric vehicle charging stations, a Dijkstra algorithm is adopted to construct a dynamic adjacency matrix representing the association relationship between different electric vehicle charging stations; calculating a Spearman rank correlation coefficient between the historical meteorological data and the historical charging load data, and screening at least one meteorological factor from the historical meteorological data as a meteorological feature according to the Spearman rank correlation coefficient; determining date features based on the date data; and inputting the dynamic adjacency matrix, the meteorological features and the date features into a graph convolutional neural network-Transform model, defining a regularized mean square error loss function, training the graph convolutional neural network-Transform model by adopting an optimizer, and outputting a charging load prediction model when the training is finished. According to the invention, the problem of low load prediction precision when a plurality of charging stations are dispersedly coupled is solved.

The present application relates to the technical field of image recognition and understanding, and more particularly to a model directional confusion detection method based on a confusion matrix, which comprises the following steps: S1, obtaining an image confusion matrix; S2, preprocessing; S3, performing rank correlation test; S4, first determination; S5, obtaining the total number of categories; S6, obtaining a difference index comprehensive evaluation value; S7, second determination of the execution of error distribution entropy test; S8, obtaining the sample balance degree of a pure image confusion matrix; S9, outputting a directional confusion result; and S10, outputting a directional confusion analysis report. Through dynamic early termination, process intelligent trimming and entropy normalization, the present application improves the calculation efficiency of confusion detection and enhances the cross-scene consistency of diagnostic results.

The invention relates to the technical field of communication networks, in particular to a communication network management and control system fault type judgment method and system, and the method comprises the steps: obtaining and carrying out the characterization processing of network operation data of a communication network management and control system, and obtaining network operation parameters; obtaining information entropies of the network operation parameters, and screening the network operation parameters based on the information entropies of the network operation parameters to obtain target network operation parameters; obtaining a fault occurrence moment of the network management and control system, and extracting m groups of data of the target network operation parameters before and after the fault occurrence moment to generate a target network operation parameter sequence; based on the box dimension and the Spearman rank correlation coefficient of the target network operation parameter sequence, obtaining the target network operation parameter sequence after feature extraction; and inputting the target network operation parameter sequence after feature extraction into an SVM fault classification model to obtain a fault type. According to the invention, by simplifying the data set of the network management system, the efficiency of network performance evaluation and fault location is improved.

The application provides a kind of grain security resilience influence factor analysis method based on Bayesian network.The method relates to the field of grain security.The method comprises the following steps: by Spearman rank correlation coefficient method, adjusted R 2 The maximum criterion optimal subset, random forest feature importance evaluation three kinds of methods are used to screen the influence factors of grain security resilience, and the union method is used to determine the index; the natural break point method is used to convert the quantitative index into three types of qualitative index by minimizing the within-group variance and maximizing the between-group variance; the grain security resilience is used as the dependent variable, and the final index is used as the independent variable; the simulated annealing algorithm is used to construct a three-layer Bayesian network; the Bayesian information criterion scoring function is used to evaluate the iteration to obtain the best structure; based on this, the reverse reasoning method is used to calculate the reverse reasoning probability and change of each index, and the influence effect is analyzed. The application can systematically and comprehensively analyze the influence effect of various factors.

This application relates to the field of medical data processing technology, and discloses a method and system for screening differential lipid metabolism biomarkers in human tissues. The method involves calculating the absolute value of the rank correlation coefficient of differential lipid expression sequences in a standardized data matrix; constructing a lipid co-adjacency matrix and a graph Laplacian matrix by fusing node diagnostic penalty factors; extracting Fiedler values ​​and eigenvector centrality parameters to determine core hub biomarker clusters; establishing a diagnostic model, extracting the diagnostic error back gradient when a threshold is not reached; defining an absolute safety perturbation step size threshold by combining eigenvalue offset radius; constructing an optimization model to update the penalty factor and reconstructing the matrix iteratively. This method can at least solve the problem that traditional biomarker screening mostly uses static network unidirectional extraction, which lacks a low-level topology correction mechanism for clinical error feedback, resulting in an extremely high false positive rate in clinical validation.

The invention relates to the field of hydrology and meteorology, and discloses a flood susceptibility identification method and system, and the method comprises the steps: obtaining flood point data, and preliminarily constructing a meteorological factor, topographic factor and environmental factor data set affecting flood; screening factors influencing flood susceptibility through a Spearman rank correlation coefficient and a tolerance and variance expansion factor; the method comprises the following steps: evaluating flood susceptibility of a research area through an information amount model to obtain a disaster susceptibility partition map, selecting non-flood points in a low-susceptibility area and a lower-susceptibility area, and constructing a non-flood point data set; utilizing flood point and non-flood point data to construct a coupling information amount model and a flood susceptibility model of machine learning; constructing a sample set of non-flood data and flood data; identifying an optimal flood susceptibility model; according to the method, the flood susceptibility model is constructed from two perspectives of uncertainty of model simulation and uncertainty of data, and a scientific basis is provided for research on flood control and disaster reduction decisions of different drainage basins.

The invention discloses a daily scale runoff prediction method based on feature screening and interpretable analysis, and belongs to the field of hydrology and water resources. Establishing an SWAT model based on drainage basin spatial data, meteorological data and hydrological data, performing sensitivity analysis and calibration on hydrological parameters, and outputting related hydrological process variables; through meteorological elements and SWAT output variables, screening input factors by adopting a Spearman rank correlation analysis and random forest combined feature selection method so as to design different feature input sets; a BiLSTM model is constructed, and key hyper-parameters are optimized through a Bayesian optimization algorithm; evaluating different model performances by adopting common hydrological statistical indexes, and analyzing simulation performance under an extreme runoff condition; an SHAP interpretability analysis method is introduced, and the behavior mechanism of the coupling model is deeply analyzed. The advantages of a physical mechanism model and a deep learning model are fused, and high-precision simulation of the runoff process and the extreme runoff is achieved.