6 results about "Correlation test" patented technology

The invention belongs to the technical field of landslide evaluation, and particularly relates to a modeling method of a landslide susceptibility evaluation coupling model fusing an auto-encoder and a feedforward neural network, and the method comprises the steps: obtaining slope unit data and historical landslide data, carrying out the preprocessing, extracting landslide-related evaluation factors, carrying out the correlation test and correlation analysis, and obtaining a landslide susceptibility evaluation coupling model. Screening out evaluation factors for subsequent evaluation; constructing a landslide data set, balancing samples by adopting a GPU acceleration oversampling technology, and dividing a training set and a test set; constructing a landslide susceptibility evaluation coupling model based on the auto-encoder and the feedforward neural network; training the landslide susceptibility evaluation coupling model by using the training set and the test set to obtain optimal parameters; and outputting a landslide susceptibility distribution result by using the trained landslide susceptibility evaluation coupling model. According to the method, feature dimension reduction and redundant information compression can be effectively realized, and the evaluation precision and generalization ability of the model are improved.

The invention relates to the technical field of airtightness detection, in particular to a compressed air energy storage salt cavern cavity airtight sealing monitoring method and system.The method comprises the following steps that a deviation sequence is generated by collecting wellhead pressure and layering pressure, brine injection amount is compared to obtain a normalized ratio, and a spatial deviation record is generated; the sampling point rate is calculated, a time record is generated in combination with correlation test, pressure recovery time is extracted to verify abnormity and form a stability record, cross comparison is performed on a multi-dimensional result to identify an abnormal area, a risk result is output, and the sealing risk identification precision and reliability are improved. According to the method, the space distribution is revealed by collecting the multi-depth pressure of the wellhead and the cavity, the injection interference is eliminated through the normalized ratio, the rate and correlation test intensifies the time discrimination, the dynamic abnormity is verified through the recovery time comparison, the space time dynamic cross validation positioning is more accurate, and the detection is expanded from the static state to the three-dimensional comprehensive discrimination. And the sealing risk identification precision is obviously improved.

The application discloses an automatic data exploration method and system based on target label contribution degree, and the method comprises the following steps: associating and integrating feature data with a target label to obtain a data set; inputting the data set into a random forest and a cross-validation model, an information gain rate algorithm and a random forest algorithm respectively to determine a sorting table of the contribution degrees of respective feature fields in the feature data relative to the target label; processing the three sorting tables in a voting manner to obtain a feature contribution degree sorting table; obtaining a preset number of feature fields with high ranking in the feature contribution degree sorting table, performing correlation test on the feature fields two by two, and removing feature fields with correlation exceeding a preset threshold; and performing data distribution analysis on the feature fields remaining after the correlation test according to the basic data types of the fields. Through the technical scheme, automatic screening of the feature fields is realized, and the efficiency of feature screening is greatly improved, and tedious and repetitive manual work is avoided.

This application relates to the field of image processing technology, and discloses a method, apparatus, and computer device for estimating the radial distortion coefficient of an image. The method performs discrete sampling within a preset range of radial distortion coefficients to generate an ordered sequence of distortion coefficients. For each coefficient in the sequence, a parameter traversal operation is performed: trial correction is performed based on the coefficient and the original image size; straight lines are detected and filtered in the corrected image; intersection points of straight lines are randomly sampled and outliers are cleaned; and statistics describing the dispersion of the intersection point distribution are calculated in the X and Y directions. After traversal, two corresponding statistical sequences are formed. A convexity trend test is performed on both sequences sequentially, and a correlation test is performed on the sequences that pass the test. The distortion coefficient corresponding to the global minimum point in the statistical sequences that pass all tests is determined as the optimal estimate. This application achieves automatic estimation without calibration or manual intervention, and has the advantages of strong robustness and wide adaptability.

The application provides a kind of based on one-dimensional simplified water quality model, predicts water quality mutation, calculates real degradation coefficient and pollutant migration speed of the whole year using the monitoring data of water quality monitoring station, analyzes the relationship between degradation coefficient and river pollutant migration speed, builds regression model, and obtains the optimal degradation coefficient, migration speed by using correlation test calculation and mean square error calculation, and brings it into one-dimensional model formula, and calculates time sliding to form final prediction data, output prediction result graph.

This application relates to a method, apparatus, and equipment for analyzing post-meter water leakage based on smart water meter flow data. The method sequentially processes the original time-series-cumulative reading sequence using a data quality assessment algorithm, Boolean matrix and Fisher exact correlation test, robust Mahalanobis distance, and exponential smoothing filtering algorithms to generate a high-quality standard time-series-flow sequence. This ensures the data input to the model is "clean" and complete, laying the foundation for subsequent leakage analysis. The constructed dynamic threshold leakage judgment module automatically adjusts the threshold according to changes in user water usage habits, avoiding the limitations of fixed threshold judgment. The constructed micro-leakage identification model effectively identifies small, persistent leaks. Overall, this approach more accurately identifies post-meter water leakage, effectively avoiding misjudgments and omissions, and improving the reliability of leakage judgment.