3results about How to "Fully capture" patented technology

This invention relates to the field of time series forecasting technology and discloses a stock index forecasting method based on 1DCNN-ResBiLSTM, comprising the following steps: S1, data acquisition and sample construction; S2, data preprocessing; S3, local feature extraction; S4, long-term dependency learning; S5, residual feature fusion: extracting the feature vector of the last time step output by the 1DCNN module in S3, linearly mapping it to the same dimension as the output feature of S4 through a fully connected layer, and then adding it element-wise with the output feature of S4 to achieve residual feature fusion; S6, prediction result output; S7, model training and optimization. By adopting a technical solution that combines the 1DCNN module with time-dimensional sliding convolution and ReLU nonlinear activation, the method accurately captures the local fluctuation features and short-term trend information of stock time series, solving the problems of insufficient characterization of short-term stock price changes and inability to effectively mine local time series patterns.

The application relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographical weighting, which comprises the following modules: a feature decoupling module for constructing a double-channel input structure of geographical features and non-geographical features, realizing feature decoupling and fusion through a multi-scale spatial weight matrix; a bandwidth allocation module for dynamically determining a bandwidth scale in an XGBoost tree splitting process and establishing dynamic weights of tree levels; a constraint gain module for generating a splitting point marked with a scale; a contribution decoupling module for extracting splitting features and correlating geographical features and non-geographical features, realizing salted prediction and contribution index extraction; and a verification optimization module for optimizing model parameters through multi-scale heat maps and spatial autocorrelation analysis. The model can capture geographical spatial effects of different scales, improve spatial local prediction accuracy, and be applied to a scene with spatial heterogeneity in soil salinization analysis.

The application relates to the technical field of dust removal devices, in particular to a reinforced dust removal electric dust collector and electric dust removal system. A plurality of dust removal areas are arranged in the electric dust collector and are spaced apart along the airflow direction to process dust-containing gas in stages, so that the dust can be fully charged and captured when passing through each electric field. Even if part of the dust cannot be completely captured in the front-stage electric field, the dust can be efficiently separated through re-charging in the subsequent electric field. The overall performance of the electric dust removal device is significantly improved through the step-by-step optimization mode. At least one dust removal area is connected with a reinforced dust removal assembly. The assembly can further enhance the electric field strength or improve the particle charging effect according to the actual working condition requirements (such as dust properties, airflow speed, etc.). In this way, the shortcomings of the traditional electric dust removal device in processing high-concentration dust or difficult-to-capture particles can be compensated for, and the dust removal requirements in different scenes can be flexibly adapted to, so that the applicability and performance of the equipment are significantly improved.