8 results about "Scaled correlation" patented technology

The application discloses a method for extracting middle layer top temperature multi-period characteristics and analyzing influencing factors, and belongs to the technical field of atmospheric detection and meteorological data analysis. The method first extracts time series data of the middle layer top temperature based on the 90km height and the lowest temperature point standard, then completes time domain trend and mutation analysis through seasonal departure and Mann-Kendall test, adopts discrete wavelet decomposition to obtain four main oscillation periods of 3 years, 7 years, 11 years and 22 years, then uses continuous wavelet to obtain the length of the fine-calibrated period, and finally performs cross wavelet analysis on the quasi-biennial oscillation, the El Nino effect and the solar activity index to obtain the final correlation and time lag results. The application realizes joint time-frequency domain analysis of discrete wavelet transform, continuous wavelet transform and cross wavelet transform, effectively improves the cycle recognition accuracy, quantifies the multi-scale correlation and time sequence hysteresis of each factor, and provides a new research idea for high-altitude atmospheric climate influencing factor analysis.

The application discloses a rock and outcrop cross-scale correlation method and system based on multi-modal data, belongs to the technical field of three-dimensional data platforms and geological learning, and comprises the following steps: S1, geological outcrop data acquisition and geological outcrop modeling are carried out, a three-dimensional geological outcrop model is obtained, multi-modal data are obtained by simultaneously collecting basic rock sample data, rock sample analysis and test data and rock sample explanation information; S2, the multi-modal data are managed by using a database and a file management mode, and the rock sample and the outcrop are correlated to obtain correlation data. Through the management of the multi-modal data, the deficiency of single expression mode is solved, and the fusion and integration of the multi-modal data are realized. Through the correlation mechanism of the rock and the outcrop, the deficiency of the existing system in the data correlation is solved.

This invention discloses a fault location method for power cables compatible with both high and low resistance, comprising: connecting to a test terminal to acquire voltage and current responses, determining the initial impedance state, and setting scanning excitation parameters; applying an exponentially increasing scanning excitation signal, calculating the complex impedance gradient characteristics, and determining the threshold energy level range; expanding a fractal excitation sequence within the threshold range, collecting propagation response data, and constructing a response matrix; normalizing the propagation response matrix data, calculating the cross-scale correlation strength, and forming a consistency curve; constructing a propagation consistency vector field, performing density clustering and topology analysis, and determining the fault stability region; and calculating the fault location based on the propagation time at the center of the stability region and the propagation velocity. This invention achieves stable identification and accurate location of faults in both high-resistance and low-resistance power cables through exponentially increasing scanning excitation and fractal energy spectrum multi-scale propagation analysis.

The application discloses a method for constructing a three-dimensional geological body, which comprises the following steps: establishing a three-dimensional seismic attribute body about a work area to be studied; based on the three-dimensional seismic attribute body, combining conventional logging data and imaging logging data, respectively dividing rock types under geological scale, logging scale and imaging scale; according to the rock type division results under different scales, combining core data, establishing a multi-scale correlation model for sequentially performing scale coarsening and attribute prediction from core, imaging, logging to geology; and updating the original three-dimensional geological body according to the multi-scale correlation model. The application can apply fine scale data to the construction of a macroscopic geological body.

The application discloses an intelligent data analysis method and system based on big data, relates to the technical field of data analysis, and comprises the following steps: sequentially unifying multi-source heterogeneous business data to obtain a unified sequential business data set; extracting features according to the unified sequential business data set to obtain an evolution feature set; compressing the feature space according to the evolution feature set to obtain key analysis features; performing multi-scale correlation analysis according to the key analysis features to obtain a business correlation structure; quantifying the correlation degree according to the business correlation structure to obtain a correlation weight result; and comprehensively reasoning according to the correlation weight result to obtain an intelligent analysis result. The application converts the key analysis features from independent individuals into a mutually correlated overall structure, enables the correlation relationship to simultaneously have short-term and long-term business explanation capabilities, enhances the time semantics of the correlation analysis result, and improves the stability and explainability of the correlation analysis result.

The present application relates to the technical field of intelligent prediction, in particular to a red tide occurrence probability intelligent prediction method based on neural network and key factor identification, comprising S1: obtaining historical red tide event data, multi-station marine environment monitoring data, red tide emergency monitoring data and station continuous hydrological and meteorological observation data of a target sea area, and processing to obtain a standardized monitoring data set; identifying a red tide prediction key area, extracting a multi-period monitoring sequence to form a key area time sequence sample set; S2: performing multi-scale correlation analysis, sensitivity analysis and causal correlation identification to obtain a key factor sorting result; extracting a target key factor affecting red tide occurrence, determining a threshold boundary range of each target key factor, and generating a key factor threshold representation set; S3: inputting a probability prediction network and outputting a red tide occurrence probability result; and generating red tide occurrence early warning information of the target sea area within a prediction period. The present application improves the accuracy and stability of red tide prediction.

This invention provides a method and system for intelligent geological identification and processing scheme generation at tunnel excavation faces, relating to the field of tunnel engineering technology. The method includes parsing geological image data by setting multi-size perception windows, establishing a cross-scale correlation bridging mechanism to integrate rock strata features, and constructing a unified feature description; based on this, classifying surrounding rock grades and geological types; constructing processing schemes using dynamic rule matching and multi-objective optimization; and continuously calibrating feature alignment parameters through digital twin monitoring. This invention achieves accurate tunnel geological identification and intelligent generation of safe construction schemes, improving construction safety and work efficiency.

This invention provides a fruit counting method based on region focusing and scale correlation, comprising the following steps: acquiring images of the fruits to be counted; inputting the fruit images into a pre-trained region-scale coordination network to generate a corresponding fruit density map; performing an integral operation on the fruit density map to obtain the number of fruits; wherein, the region-scale coordination network includes an encoder, a region focusing module, a scale correlation module, and a decoder. The region focusing module performs local and global collaborative attention modeling on deep semantic features, combining spatial gating to enhance fruit response and suppress background interference; the scale correlation module captures multi-scale fruit morphology through multi-expert deformable convolutional pooling and dynamic weighted fusion; the decoder fuses multi-level features to restore resolution step by step. This method achieves synergistic optimization of background interference suppression and multi-scale representation at the feature level, effectively solving the problem of low fruit counting accuracy in complex scenes such as dense, occluded, and changing lighting conditions.