8results about How to "Remove random noise" patented technology

The invention provides a circuit board life prediction method and system based on mass failure welding spot data and a risk matrix, electronic equipment and a storage medium, and relates to the field of welding spot failure. The method comprises the following steps: acquiring massive invalid welding spot data, and extracting welding spot failure mechanism characteristics from the invalid welding spot data based on expert knowledge to obtain welding spot failure mechanism characteristics; based on the welding spot failure mechanism characteristics, welding spot failure characteristic factors are determined; acquiring welding spot failure characteristic factor data and circuit board data of a to-be-predicted circuit board, and determining welding spot failure data of the circuit board based on the welding spot failure characteristic factor data; and determining a welding spot failure type based on the welding spot failure data, and inputting the circuit board data and the welding spot failure type into a preset life prediction model to obtain a life prediction value. By means of the method, mass data are deeply utilized, essential reasons influencing welding spot faults are found out, and accurate evaluation and prediction of the residual life of the circuit board are achieved.

The invention discloses a remote detection method for soot blowing system pipe structure defects, and relates to the technical field of thermal equipment detection. The problems that in the prior art, detection needs to be stopped, real-time monitoring cannot be achieved, and defect information is not visually presented can be at least partially solved. The method comprises the following steps: deploying an electromagnetic ultrasonic guided wave sensor array around a welding seam to perform signal excitation and synchronous acquisition; performing dual noise reduction processing by adopting synchronous average and wavelet transform to extract defect signals; constructing a space model to position defects based on the signal arrival time difference; and generating a defect image by utilizing C-scanning imaging and performing quantitative evaluation. According to the invention, on-line remote detection without shutdown is realized, the signal-to-noise ratio is obviously improved through dual noise reduction, accurate positioning and visual display of defects are realized by combining time difference analysis and C scanning imaging, and safe operation of a unit is guaranteed.

The invention discloses an SVD (Singular Value Decomposition) seismic fracture-cavity body enhancement method based on structural constraint, which comprises the following steps of: taking a seismic data sub-body in a set surface element from single-channel post-stack seismic data, and performing singular value decomposition on the seismic data sub-body to obtain a singular value and a singular vector; performing data reconstruction according to the size sequence of the singular values, performing similarity structure evaluation on the reconstructed data and the original data, gradually determining a fracture-cavity response threshold value based on a similarity coefficient, and determining a noise response threshold value according to an energy retention principle; designing a soft threshold window function according to the fracture-cavity response threshold and the noise response threshold to construct a fracture-cavity enhancement operator; applying a fracture-cavity enhancement operator to the singular value matrix to obtain reconstructed fracture-cavity enhanced seismic data of the single-channel data; and repeating the process on all single-channel seismic data to obtain a final fracture-cavity enhanced seismic data volume. According to the method, the seismic response characteristics of the fracture-cavity body can be effectively enhanced, the weak signals of the reservoir are highlighted, and the interpretation precision is further improved.

The invention provides a positioning and leveling method, system and equipment of an underwater leveling machine and the underwater leveling machine, and relates to the technical field of ocean engineering. The method comprises the following steps: acquiring an original ranging data sequence of a fixed reference node; performing deviation compensation and correction on the original ranging data sequence to generate a target ranging value; performing time sequence filtering processing on the target distance measurement value of the distance measurement link between the underwater leveling machine and each fixed reference node to obtain a smooth distance measurement sequence; selecting data at multiple moments from the smooth ranging sequence to construct a time window input matrix, taking the time window input matrix as the input of the target deep learning model, and outputting the predicted position of the underwater leveling machine; and generating a control instruction based on the predicted position so as to control the underwater leveling machine to carry out positioning and leveling. According to the method, by combining multi-stage data processing and deep learning prediction, distance measurement errors and environmental interference are eliminated, so that the positioning and leveling accuracy of the underwater leveling machine can be improved.

This invention discloses a spectral analysis method, apparatus, device, and storage medium. It includes: acquiring raw spectral data to be analyzed; preprocessing the raw spectral data to obtain preprocessed spectral data; performing multi-peak fitting on the preprocessed spectral data using an iterative residual peak-finding fitting algorithm to obtain a final fitted spectrum and a final residual spectrum; and generating spectral analysis results based on the final fitted spectrum and the final residual spectrum. Preprocessing the raw spectral data effectively eliminates background interference, cosmic ray pseudo-signals, and random noise, improving the signal-to-noise ratio of the spectral data. Using the iterative residual peak-finding fitting algorithm for multi-peak fitting accurately identifies and fits all significant spectral peaks, avoiding underfitting and overfitting problems, and improving the accuracy of multi-peak fitting. The generated spectral analysis results can accurately extract core feature indicators, and the fitting quality can be verified through residual spectroscopy, ensuring that the analysis results are accurate and reliable, truly reflecting the structural characteristics of the material.

The invention relates to the technical field of image display, in particular to a metal metallographic structure image classification display method and system. The method comprises the following steps: preprocessing a metal metallographic structure image, calculating and evaluating an image feature index Tzls, quantifying the overall quality of the metal metallographic structure image, automatically screening low-quality and repeated images, and reducing stored and processed invalid data; classifying the metal metallographic structure images by using two characteristics of structural factors and defect factors of the metal metallographic structure; the metal metallographic structure image is classified and graded by calculating and evaluating a structure characteristic index Jtzs and a defect characteristic index Qtzs; and all grade division results are transmitted to the image classification display module, so that timely and accurate data transmission is ensured, a user searches and filters according to a structure grade or a defect grade, a required image is quickly found, and the working efficiency is improved.

The invention relates to a UWB radar target motion prediction method and system based on a multi-convolutional neural network, and the method comprises the steps: obtaining echo signal features collected by a UWB radar in real time and corresponding target motion parameters, and obtaining an initial data set through time series data division and normalization processing; extracting and splicing the initial data set based on a local mean decomposition algorithm to obtain standard input feature data; constructing a motion parameter prediction model based on the echo signal features, optimizing the motion parameter prediction model by adopting a genetic algorithm, and performing training through standard input feature data; and eliminating multi-path and noise interference from echo signal characteristics corresponding to a to-be-detected target through local mean decomposition, and inputting the echo signal characteristics into the trained motion parameter prediction model to obtain predicted motion parameters of the to-be-detected target. The UWB radar motion parameter prediction precision and generalization ability can be improved through LMD interference removal and CNN combined with attention and gating and genetic algorithm optimization.

The application provides a college major talent outflow trend prediction method based on multi-channel feature fusion and adaptive weighting random forest, relates to the talent flow prediction and machine learning application technical field, and comprises the following steps: collecting college major enrollment data and corresponding macroeconomic data of each year, and preprocessing missing data; adopting a multi-channel feature decoupling and fusion mechanism; the multi-channel feature decoupling and fusion mechanism adopted by the application effectively solves the defects of weak feature expression ability and neglecting external economic driving factors in the prior art, decouples internal trends and external driving factors into three channels, comprehensively extracts enrollment sequence characteristics, regional macroeconomic characteristics and regional economic difference characteristics, realizes deep fusion of internal and external information, enhances the model's recognition ability to complex patterns, can fully capture the influence of macroeconomic variables on professional talent flow, and greatly improves the prediction accuracy.