8 results about "Local regression" patented technology

The invention provides a traffic time-space diagram reconstruction method and system based on neighborhood adaptive regression, and relates to the technical field of computers, and the method comprises the steps: obtaining historical traffic data; performing space-time diagram construction processing according to the historical traffic data to obtain a historical data space-time diagram; performing sample construction according to the historical data space-time diagram, and dividing the down-sampling low-resolution space-time diagram into image blocks with predefined sizes in an overlapping and cutting mode to obtain a training sample pair set; performing neighborhood search on the set based on the training sample to construct a neighborhood set; performing neighborhood regression processing according to the neighborhood set, and fitting to obtain local regression parameters; and performing high-resolution reconstruction processing according to the local regression parameters, predicting high-resolution sub-block values by applying the regression parameters to linear transformation of the to-be-reconstructed image blocks, and integrating all the sub-blocks to obtain a reconstructed traffic space-time diagram. According to the method, efficient reconstruction precision improvement can be realized under a small sample training condition.

The invention discloses a subarea treatment method and device for soil erosion, a storage medium and electronic equipment, and the method comprises the steps: carrying out the preprocessing of collected multi-source data needed by landscape pattern research, and obtaining the preprocessed data; calculating an equation factor of the general soil loss equation by using the preprocessed data, and deducing a soil erosion modulus based on the equation factor inversion; determining a landscape index space layer of the landscape pattern, inputting the landscape index space layer and the soil erosion modulus into a space-time geographically weighted regression model, and outputting a local regression coefficient on each space-time unit; and dividing treatment stages based on the local regression coefficient and generating a partitioning scheme. By means of the method and device, the problem that in the related technology, due to the fact that correlation analysis between the landscape pattern and soil erosion is insufficient, the zoning treatment effect of the soil erosion is poor is solved.

The invention provides a power plant carbon dioxide emission quantification method and system based on satellite observation data. The method comprises the following specific steps: extracting and screening observation XCO2 data with an inversion condition in a set space range around a target power plant, obtaining background XCO2 optimal estimation by adopting robust local regression, further obtaining a background baseline, obtaining robust observation enhanced XCO2 data, and obtaining the optimal estimation of the background XCO2. And inputting the estimated value into an emission inversion model to realize emission rate inversion at the transit moment of the power plant, and repeatedly iteratively operating for at least 1000 times to output an estimated value set of emission intensity to judge that the transit inversion result is effective, so that a matched quality control mechanism is formed. According to the method, manual intervention and experience setting dependence are reduced through automatic screening and robust modeling, inversion stability, calculation efficiency and engineering applicability are improved, and the method has obvious advantages in a large-range and long-time-sequence power plant COemission monitoring and list verification scene.

The invention discloses a multi-school knowledge crack robust inference method based on generative quantile regression. The method comprises the following steps: carrying out tensor modeling and feature construction on multi-school academic data; constructing a local quantile regression function and a local loss function, and training and optimizing the local quantile regression function to obtain a local regression parameter vector of the ith campus; generating an agent sample of the central node; constructing a global quantile regression function, and estimating a global knowledge mastering quantile curved surface; constructing a knowledge crack tensor by adopting a local quantile regression function and a global quantile regression function, and performing comprehensive crack index calculation; performing structured smoothing processing on the comprehensive crack index based on a pre-repair relationship between the knowledge points, performing crack significance test, and forming a crack feature vector with a structural feature in any campus; clustering the crack feature vector of any campus; and a crack matrix is constructed, and a result set of multi-campus knowledge crack key inference is obtained.

The application relates to the technical field of road maintenance, and provides a double-line self-updating method and system for asphalt pavement performance estimation, which comprises the following steps: constructing a general prediction model based on historical data and a local regression model based on measured data; through a short-term adaptive prediction path, model fusion strategy is adopted to fuse the prediction results of the two models, the weight is dynamically adjusted according to the data accumulation condition, and a final performance prediction value is output; through a long-term gradual updating path executed in parallel, new knowledge samples are detected based on a clustering model, when the samples reach a threshold value, incremental learning of the general prediction model is triggered, and self-updating of the model is realized. The application overcomes the limitations of existing static models, can dynamically adapt to different road section characteristics and data changes, and improves the accuracy, self-adaptability and long-term reliability of asphalt pavement performance estimation.

This invention provides a geographically weighted infectious disease risk analysis method and system, comprising: rasterizing multi-source data and constructing a unified grid dataset and analysis indicator library; calculating the Pearson correlation coefficient between grid cases and various indicators in the unified grid dataset, and outputting the final set of influencing indicators; determining the neighborhood range using an adaptive bandwidth method and determining the spatial weights corresponding to neighborhood samples based on a specified kernel function; constructing exposure sequences corresponding to the current analysis period and multiple historical lag periods for each key indicator; forming a cross-basis local sample set based on the cross-basis features constructed based on the target grid cell and neighboring grid cells, constructing a geographically weighted distributed lag nonlinear model based on the cross-basis local sample set, and performing local regression fitting; comparing the effect differences of different exposure levels over all lag periods to obtain the cumulative effect value, and determining the lag period that contributes the most to the infectious disease risk of the target grid cell as the optimal lag period.

This invention provides a method for reconstructing complex surfaces based on white light interferometry data: The method acquires surface topography measurement data of the complex surface to be reconstructed using white light interferometry; constructs a sliding local region centered on each point to be estimated within the measurement area; processes the measurement data within each local region to obtain the height value of the corresponding point to be estimated; and outputs the global reconstruction result of the complex surface after traversing all points to be estimated across the entire region. The process of processing the measurement data within each local region includes the following steps: S1, using MM estimation to perform robust pre-estimation of the measurement data within the current local region; S2, using the absolute value of the residual as input, and through mixing... t Distribution clustering adaptively classifies the measurement data to obtain the retained valid measurement data; S3, repeat S1 to S2 until the outlier removal iteration is completed to obtain the final valid measurement dataset; S4, use weighted Jacobi basis functions to perform local regression to obtain the height value of the point to be estimated.

The invention discloses a cancer prediction model construction method based on a causal network and adaptive feature selection, and the construction process comprises the steps: 1) obtaining multi-omics data, and generating an omics feature matrix through similarity network iterative fusion, chi-square test dimension reduction and diffusion enhancement; 2) introducing a causal diffusion Do-calculus algorithm to construct a gene causal consensus network, constructing a sample specific network in combination with local regression residual analysis, and extracting topological features to obtain a network feature matrix; 3) calculating a modal weight based on balance accuracy, and performing weighted splicing to generate an integrated multi-modal feature; and 4) inputting the integrated multi-modal features into a full connection layer embedded with a feature gating mechanism and sparse group Lasso regularization, obtaining a cancer prediction model through end-to-end training, and screening core markers. The problems that an existing prediction model is poor in multi-omics data integration effect, lacks a causal mechanism and is difficult to explain deep nonlinear interaction of genes are solved, and the precision and generalization ability of the cancer prediction model are improved.