25 results about "Quartile" patented technology

The invention discloses an interpretable load prediction method based on knowledge distillation and a time self-attention additive neural network, and the method comprises the steps: collecting the historical load and meteorological data of a power grid as the input characteristics of a model, carrying out the detection of a data quartile abnormal value, dividing the data quartile abnormal value into a training set, a test set and a verification set, and carrying out the detection of the data quartile abnormal value; standardization and abnormal value filling are carried out through Z-shaped orthogonalization and linear filling, and finally, a tensor form meeting the model input requirement is converted through a sliding window; designing a knowledge distillation'teacher-student 'framework based on multiple scales and multiple cycles; constructing a time self-attention additive neural network TSA-NAM as a student model; calculating a shape function representing the contribution degree and the characteristic value in the sub-network to obtain the interpretability of the characteristic dimension; exporting the attention weight of the time self-attention module to obtain the interpretability of the time dimension; performing simulation verification; according to the method, high reliability and high precision are guaranteed, and meanwhile, multi-dimensional interpretability is brought to power load prediction.

The invention discloses a traffic flow prediction method based on a multi-scale time window adaptive graph bias neural network, and belongs to the field of deep learning and intelligent traffic. The method comprises the following steps: collecting flow, speed and occupancy data of traffic network nodes, and constructing a historical data set; constructing a time embedding module, and extracting intra-day and intra-week time features; constructing a multi-scale time window trend sensing module, and capturing short-term fluctuation and long-term trend of the traffic flow; a time condition adaptive graph bias module is constructed, a graph bias matrix is dynamically generated according to the time context, and time-varying spatial dependence is modeled; a sparse space attention module is constructed, the calculation complexity is reduced, and spatial-temporal features are fused; and a self-adaptive double-end output module is constructed, a gating coefficient is generated based on the volatility and the quartile distance, and linear and nonlinear branches are fused to output a multi-step prediction result. According to the method, multi-scale spatial-temporal feature fusion and adaptive prediction of the traffic flow are realized, and the prediction precision and robustness are improved.

The invention discloses an electric energy meter metering misalignment judgment method based on multi-source data and consistency check, and belongs to the technical field of intelligent power grid metering monitoring. The method comprises the steps of collecting electric energy meter daily electric quantity, transformer area total electric quantity and user load characteristic data; identifying and correcting data exception by adopting a sliding window and a self-adaptive quartile distance method; constructing a multivariate linear fitting model of the total electric quantity of the transformer area and the electric quantity of each sub-meter, and calculating the average residual contribution degree of each electric energy meter through least square estimation and residual analysis; and calculating a metering deviation index in combination with electric quantity volatility, judging a suspected misalignment electric energy meter based on a confidence interval, and controlling a list length and effectiveness according to an accumulated contribution ratio threshold. Remote, real-time and automatic monitoring of mass electric energy meters is realized, the defects of high cost, long period and high false alarm rate of a threshold value method in traditional manual checking are overcome, and the accuracy, robustness and interpretability of metering misalignment judgment are remarkably improved through multi-source data fusion and statistical checking.

The present application relates to the technical field of civil structure monitoring, and discloses a setting method of dynamic threshold of civil structure monitoring data and a safety warning method. The setting method first collects all monitoring data in a previous preset time period of a sensor, fits the serial number corresponding to each monitoring data with the monitoring data according to the time sequence of data collection, and obtains a fitting curve. Then, the monitoring data is subtracted from the data of the corresponding serial number in the fitting curve in sequence, a group of data without trend items is obtained, the absolute value is taken, the normal distribution is calculated, the data in the upper and lower quartile ranges are selected and the mean value is taken as the accurate value, the data is fitted again through comparison and replacement, and the fitting curve under the influence of external load can be obtained. Finally, the monitoring data of a subsequent preset time period of the sensor is numbered in the same way, the monitoring data center curve of the future time period is obtained, and the dynamic threshold interval is generated accordingly. The present application can set appropriate dynamic thresholds for the civil structure monitoring data of different measuring points.

This application relates to the field of equipment performance testing and evaluation technology, and provides a method and system for equivalent reduction of equipment test sample size. By constructing a data fusion evaluation model based on actual and numerical test data, the fidelity of actual test response estimation under small sample conditions is improved. An equivalent measure is designed and calculated by fusing the mean square error of the evaluation model from a single data source with a preset test confidence level, and a box plot of the equivalent measure from a single data source is obtained. By connecting the lower quartile and upper quartile of each obtained box plot, equivalent composite confidence intervals for actual and numerical simulation tests are plotted. Based on these equivalent composite confidence intervals, the theoretically required actual test sample size is equivalently reduced to the numerical simulation test sample size, achieving an equivalent reduction of the actual test sample size by the numerical simulation test, thus solving the problem of equipment performance evaluation under insufficient actual test samples.

This application relates to the field of model training technology, specifically to a semi-real-time prediction model training method based on hierarchical triggering of data distribution drift. The method includes: extracting feature values ​​from transmission current signals to train a fault prediction model for real-time prediction of fault feature values ​​at all upcoming data collection times within a preset time period; calculating distribution drift evaluation values ​​and persistence evaluation values ​​to assess the significance of data distribution drift persistence within a preset adjacent time period before the current data collection time; statistically analyzing the quartiles of historical persistence evaluation values ​​to perform semi-real-time update training of the fault prediction model using hierarchical triggering; and using the real-time updated fault prediction model to predict transmission line faults. This application aims to improve the real-time accuracy of fault prediction models in predicting transmission line faults, thereby more effectively ensuring the safety and reliability of transmission lines.

The invention belongs to the technical field of rail transit cost assessment, and particularly relates to a rail transit project cost intelligent assessment method based on big data, which comprises the steps of inputting a cost file of a project to be assessed, including sub-items, a list, a quota and labor, material and machine information; performing index physical examination early warning, matching historical sample data, rejecting abnormal values through quartile, calculating a confidence interval, and detecting cost deviation; checking the features, comparing with a standard feature configuration library, and verifying the integrity and normalization of the engineering features; through cooperation of index physical examination early warning and feature examination, the problem of engineering type recognition errors can be quickly responded, so that the defects of a static model are overcome, construction cost files with engineering type labeling errors can be quickly responded, the false alarm frequency can be reduced, and the defects of the construction cost files can be quickly analyzed based on a multi-stage examination mechanism. Compared with manual checking, the method and the device have the advantages that the speed and the efficiency are higher, and the checking is more comprehensive.

The application discloses an online fault diagnosis method for a hydro-generator based on a potential interval distance of a through screw, relates to the technical field of generators, and is suitable for detecting a stator winding ground fault. Voltage collection devices are additionally arranged on a plurality of through screws of the hydro-generator, real-time induced potential is acquired, a historical data matrix is established, and a data buffer matrix is updated at interval time; the data buffer matrix is divided into a data evaluation matrix, a fundamental wave and an odd harmonic phase are extracted through Fourier decomposition, a harmonic phase matrix is constructed, a phase difference matrix is obtained by calculating normalized phase differences of adjacent columns, a quartile distance is solved after being converted into a column vector array, a dynamic quartile distance array is formed, abnormal limit values are determined by combining a box plot principle and an error level of the voltage collection device, and faults are determined by traversing the array. The method does not need additional sensing devices and traditional threshold setting, is convenient to operate, has low implementation difficulty, and can accurately and timely identify faults.

The invention relates to an underground water environment risk assessment method based on big data, and relates to the technical field of underground water monitoring and pollution risk assessment. According to the method, water quality parameter data filling is carried out based on a big data pre-trained water quality parameter filling model, a Bayesian classifier or a K nearest neighbor mean value through adaptive missing rate and data type selection, and the calculation efficiency and prediction precision of different missing rates are balanced; a similarity function is defined by considering sampling sites and water quality parameters, repeated data screening is carried out, and data with similar water quality parameters and non-similar sites are prevented from being deleted. Clustering and quartering anomaly mixed detection is utilized to consider univariate statistical anomaly and multivariate structure anomaly, and true pollution data is prevented from being deleted by mistake. A groundwater monitoring data set is utilized, and a composite groundwater environment evaluation model is trained through a differential evolution optimization fusion weight mode to estimate a groundwater environment index value. Model complementation is utilized to reduce deviation; and the integration benefit of the model is maximized by optimizing the weight through differential evolution.

The invention discloses a meteorological science popularization work evaluation index system construction method. The method comprises the following steps: construction of an expert investigation method and an initial index library: adopting the expert investigation method to construct the initial index library; two-dimensional validity screening: introducing a consensus degree-concentration degree two-dimensional validity screening model, and establishing a two-dimensional screening matrix by calculating an average score M and a variable coefficient CV of each index; aHP-entropy weight method combined weighting: obtaining a subjective weight through an AHP analytic hierarchy process, obtaining an objective weight in combination with an entropy weight method, and then carrying out linear weighting combined weighting; nondimensionalization: performing annual data collection on the quantitative indexes, calculating quartile and range, and performing 10-score questionnaire and linear conversion on the qualitative indexes into 1-5 scores; a Pearson's correlation coefficient matrix is calculated through index correlation analysis, if the two indexes are highly correlated and reflect the same problem, one of the indexes is merged or deleted, and if different problems are reflected, the two indexes are reserved; and performing dynamic adjustment and case verification.

The invention discloses a data set construction method for post-training of a vertical large language model, and belongs to the technical field of vertical large language model training. The technical problem to be solved is that serious long-tail data distribution and noise pollution exist in a training data set after a vertical big language model in the field of hydropower construction. According to the technical scheme, the method is characterized in that a nonparametric outlier detection algorithm, namely a box plot, is adopted to regulate and control data distribution, quartile and quartile distance of a data set sample frequency are firstly calculated, outliers are identified, then quantitative data enhancement is performed on business key long-tail scarce outliers, outliers which cannot be enhanced, such as noise and acquisition errors, are directly cleared, and the accuracy of data enhancement is improved. And finally obtaining an optimized training data set.

The invention discloses a laying hen lossless data processing method based on improved SVR, relates to the technical field of data processing, and adopts an adaptive cap cleaning data based on quartile distance to dynamically adjust an abnormal value elimination boundary, avoid mistaken deletion of effective physiological signals, improve the robustness of data cleaning and improve the accuracy of data cleaning. The defect that data distribution is damaged by a traditional fixed threshold value and a simple filling method is overcome. Through a preprocessing assembly line of KNN topology filling and Yeo-Johnson manifold transformation, missing data are effectively repaired, data skewed distribution is corrected, and the problem of feature space measurement failure is solved. Based on self-adaptive feature screening of F statistics, redundant features can be accurately removed, data dimension reduction is realized, and dimensionality disaster and model overfitting are avoided. According to the established TTR-SVR model and parameter optimization strategy, optimization precision and calculation efficiency are both considered, the problem that traditional SVR hyper-parameter selection is unreasonable is solved, and the accuracy and efficiency of laying hen nondestructive monitoring data processing are integrally improved.

The application provides an unsupervised fault diagnosis method and system based on multi-scale feature enhancement and adaptive feature selection, and belongs to the field of industrial equipment fault diagnosis. In order to solve the problems that the existing feature extraction method has insufficient feature representation ability, single feature scale and serious redundant feature interference, the application extracts multi-dimensional statistical features such as quantile, quartile range and skewness, combines adaptive wavelet base selection of the information maximum redundancy minimum criterion, and significantly improves the representation ability of the multi-scale characteristics of the fault signal. The cosine similarity weighted Laplace score and variance double threshold mechanism are introduced, and the redundant features are effectively eliminated. Through the collaborative design of multi-scale interval enhancement and adaptive wavelet transform, the time-frequency domain features are effectively extracted, the time consumption of high-dimensional data processing is obviously reduced compared with the traditional elastic distance method, and the real-time demand of industrial online monitoring is met.

The invention discloses a credit overdue prediction analysis method and system based on a knowledge graph, and the method comprises the steps: extracting entity network structure and business association two-dimensional features based on the credit knowledge graph, forming a numerical feature set, matching overdue labels, and removing invalid samples; through double-index two-stage screening of core features, feature discretization is completed in combination with quartile and a credit business threshold value; effective association rules are mined by focusing on overdue labels to form a rule base, and a linkage judgment mechanism of'rule matching priority + GNN model understanding 'is adopted. According to the method, the problems of incomplete feature coverage, single screening, poor adaptability and the like in traditional prediction are solved, the prediction accuracy and the coverage are considered, the rule mining efficiency and the feature quality are improved, and reliable technical support is provided for credit decision-making of financial institutions.

The invention relates to the technical field of signal-to-noise ratio detection, in particular to a medical endoscope signal-to-noise ratio detection method, device and equipment and a storage medium. Obtaining brightness channel average quartile difference data, first color difference channel average quartile difference data and second color difference channel average quartile difference data from the image data according to a preset first percentile and a preset second percentile; calculating the average quartile difference data of the brightness channel, the average quartile difference data of the first color difference channel and the average quartile difference data of the second color difference channel according to a preset total noise synthesis formula to obtain total noise data; drawing a signal-to-noise ratio curve according to the total noise data; according to the scheme, multi-frame acquisition is combined with a clinical working distance, a uniform gray scale region and slight focusing blur, the noise data purity is guaranteed, the quartile difference can shield an abnormal value, non-Gaussian noise is adapted, three-channel noise is fused to meet the requirement, the extracted signal noise is traceable, and the evaluation accuracy is improved.

The invention relates to the technical field of network security, in particular to an intrusion detection method based on density divergence clustering, which comprises the following steps: acquiring a data set of network intrusion detection, and performing density divergence clustering to acquire attack data; wherein the density divergence clustering comprises the following steps of: calculating Euclidean distances of all feature data points in a data set in pairs, and taking preset percentage quantiles of all the Euclidean distances as truncation distances; calculating the density of each feature data point according to the Euclidean distance and the truncation distance; according to the density, density divergence clustering is carried out on the feature data points, the feature data points are distributed to corresponding clusters, and a clustering result containing a plurality of clusters is obtained; and for a clustering result, constructing a judgment index through quartile, judging and extracting an abnormal cluster, and obtaining attack data. According to the scheme, abnormal behaviors can be identified more accurately in a complex environment, and the method has higher anti-interference capability so as to effectively detect network attacks.

The invention relates to the technical field of equipment performance test evaluation, and provides an equipment test sample size equivalent conversion method and system. By constructing a digital-real test data fusion evaluation model, the fidelity of real-installation test response estimation under the small sample condition of the real-installation test is improved. The equivalent measure is designed and calculated by fusing the mean square error of the single data source evaluation model and the preset test credibility, and a single data source equivalent measure box diagram is obtained. And drawing a real installation test equivalent composite confidence interval and a digital simulation test equivalent composite confidence interval by respectively connecting a lower quartile and an upper quartile for each obtained box type graph. On the basis of the equivalent composite confidence interval of the real installation test and the equivalent composite confidence interval of the digital simulation test, equivalently converting a real installation test sample size required by a theory into a digital simulation test sample size, so that the equivalent conversion of the digital simulation test to the real installation test sample size is realized; the problem of equipment performance evaluation under the condition of insufficient real installation test samples is solved.

The application discloses a kind of based on robust statistical characteristics of milk powder particle morphology dispersion prediction method, belong to milk powder quality detection and analysis technical field.The method is by using quartile distance and 5% tail cut mean to represent particle morphology, effectively reduce abnormal value interference, compared with traditional mean-standard deviation method can reflect the real characteristics of particle group, specifically, the present application is directed to the quartile distance and tail cut mean of 9 kinds of shape factor parameters of milk powder particle are calculated, and further based on dispersion correlation 14 key features are screened out, the reliability of feature is improved, and then the accuracy of milk powder dispersion prediction is improved possibly;Further, the present application scheme is directed to small sample application scene, using Bootstrap resampling, vertically splicing original data and 3 groups of resampling data, generate enhanced data set, and using CatBoost regression model further improves the prediction accuracy.

The application discloses a kind of dynamic residual threshold adaptive selection space-time filtering method, device, equipment and storage medium, the method includes obtaining the remote sensing time series image data of target area;By model training and predicting the possible change trend of remote sensing time series data obtains the prediction result of remote sensing time series data.Furthermore, the absolute residual between image predicted value and image true value is calculated, and the residual threshold determined based on the absolute residual median, quartile range, skewness and kurtosis is selected as the threshold of filtering method selection, specifically, for high residual area, spatiotemporal weighted filtering strategy combined with Euclidean distance is used;For low residual area, the filtering reconstruction method in time domain is used, so as to ensure the continuity of image while suppressing non-systematic noise.Compared with prior art, it has higher degree of automation and adaptability, and is especially suitable for remote sensing time series data processing in high-altitude, frequent cloud cover or complex terrain area.

The invention provides an intelligent early warning method, system and device for operation data fluctuation and a medium, and belongs to the technical field of data analysis. The method comprises the following steps: extracting continuous transaction data, and generating a standardized data set through time sequence completion optimization; the quartile and the quartile distance of the current period or the cumulative year-on-year ratio are calculated, and a five-level early warning interval is dynamically defined through an IQR coefficient regulator; a three-level decision rule system is constructed, a comprehensive score is generated based on a tree-shaped decision framework and confidence coefficient weighted scoring, and early warning is triggered; the dynamic thermodynamic diagram, the trend radar map and the multi-dimensional drilling-down assembly are utilized to construct a visual billboard, and interactive traceability is supported; and back testing the verification rule, adjusting an IQR coefficient and a trend period parameter through a sensitivity knob, optimizing a threshold value and a weight in combination with an A / B test, and dynamically iterating judgment logic. According to the invention, through data standardization processing, dynamic threshold interval construction, three-level rule weighted determination, visual traceability and backtest optimization, accurate early warning of operation data fluctuation is realized.

The invention relates to the technical field of model training, in particular to a semi-real-time prediction model training method based on data distribution drift hierarchical triggering, and the method comprises the steps: extracting a feature value in a power transmission current signal to train a fault prediction model for predicting fault feature values at all to-be-collected moments in a future preset time period in real time; calculating a distribution drift evaluation value and a persistence evaluation value for evaluating the significance degree of the data distribution drift persistence in a preset adjacent time period before the current acquisition moment; and carrying out statistics on quartile of the historical persistence evaluation value, carrying out semi-real-time updating training on the fault prediction model by adopting a hierarchical triggering mode, and predicting the power transmission line fault by utilizing the fault prediction model after real-time updating training. The invention aims to improve the real-time precision of the fault prediction model for predicting the power transmission fault, thereby ensuring the safety and reliability of the power transmission line more effectively.

Provided is a method for diagnosing a deterioration state of an oil-immersed transformer that is provided with insulating oil and insulating paper, the method comprising: a step for preparing a reference insulating oil, which is the same as the insulating oil, and reference insulating paper, which the same as the insulating paper, and acquiring at least two data groups that each comprise detection amounts of at least two compounds that are contained in the reference insulating oil and the degree of polymerization of the reference insulating paper; a step for multiplying or dividing a detection amount of a compound by a constant, and thereby adjusting the detection amount of the compound to a deterioration index for selection, which is smaller than the detection amount of the compound; a step for selecting, as a deterioration index, at least one type of numerical value selected from the group consisting of the sum, the mean, the median, the first quartile, the third quartile, and the interquartile range of the deterioration index for selection that is based on a data group; a step for creating a calibration curve related to the deterioration index and the degree of polymerization on the basis of the at least two data groups; and a step for diagnosing the deterioration state of the oil-immersed transformer using the calibration curve.

The invention discloses a high-speed train axle box bearing vehicle-mounted online diagnosis method for spectrum peak saliency judgment, and the method comprises the steps: obtaining train speed and vibration data, and carrying out the band-pass filtering and envelope demodulation processing; constructing a search space of various fault characteristic frequencies based on a speed range and a bearing geometric parameter range, and introducing a tolerance coefficient to adapt to parameter uncertainty; spectrum peak saliency judgment is carried out by adopting a box plot extreme anomaly judgment criterion, and robust suppression of noise and interference is realized through quartile statistics; according to the frequency spectrum characteristic modes of the faults of the outer ring, the inner ring, the rolling body and the retainer, a differentiated characteristic frequency outburst judgment criterion is established; determining a fault type by adopting hierarchical progressive diagnosis logic, and designing a correction mechanism for an inner ring fault side frequency effect; according to the method, a rotating speed sensor and clear bearing model information are not needed, and the method has high robustness, low calculation complexity and high interpretability.

The application provides an industrial user missing data filling method based on SA-GAN, which comprises the following steps: 1) constructing the industry correlation degree of the industrial user based on the grey correlation degree algorithm considering positive and negative correlation degrees, using the daily load data of each industrial user at the same time sequence to form a data set A for multiple industrial users with a correlation degree higher than a threshold value, and using historical daily load data to form a data set B for a single industrial user with a correlation degree not higher than the threshold value; 2) determining the abnormal data in the data set A and the data set B by using the quartile method, and setting the value of the abnormal data as 0; 3) converting the daily load data in the data set A and the data set B into RGB color pixel values, so that the data set A and the data set B are imaged to obtain an image A and an image B; 4) inputting the image A and the image B into an SA-GAN model, filling the image A and the image B to obtain an SA-GAN filled image E and an SA-GAN filled image F; and 5) performing an inverse imaging process on the image E and the image F to convert the image E and the image F into daily load data.

The invention discloses a multilevel radar signal sorting method and equipment based on pulse parameter cascading. The method comprises the steps of receiving radar pulse signals, constructing corresponding three-dimensional pulse description words, performing normalization processing, mapping the radar pulse signals into data points in a three-dimensional feature space, and calculating Euclidean distances to establish a distance matrix; calculating the local density of each pulse data point based on K neighbor and natural neighbor, introducing a relative local abnormal factor, and identifying and eliminating false alarm pulses; k-nearest neighbor search is executed again, the DBSCAN algorithm is adopted for primary pulse sorting, the neighborhood radius is constructed based on the third quartile and the quartile distance of the K-nearest neighbor distance, and the minimum point number threshold value is K value; based on inter-cluster K neighbor correlation and boundary natural neighbor connectivity, verification merging is carried out; and according to a preset PRI search range and step length, carrying out two-dimensional plane mapping on the arrival time sequence of each cluster pulse after combination, constructing a comprehensive judgment criterion to identify a real PRI and extract a pulse sequence, and obtaining a sorting result.