2results about How to "High precision prediction" patented technology

The present application is suitable for the technical field of building energy consumption analysis, and provides a building energy consumption dynamic response analysis method and system under meteorological evolution, which comprises the following steps: integrating a multi-source meteorological data set and constructing a building energy consumption reference model; generating sample combinations of uncertain parameters by using a sampling method, running the building energy consumption reference model, obtaining energy consumption output results under different climate scenarios, and statistically analyzing the energy consumption output results; based on the energy consumption output results, using a global sensitivity analysis method to analyze the contribution of each uncertain parameter to the energy consumption, and identifying key driving parameters; based on the sample set generated in the uncertainty analysis step, using a machine learning algorithm to train a building energy consumption prediction model, and outputting building energy consumption prediction results for target meteorological conditions. The present application solves the problem that the traditional method is inaccurate in evaluating under non-stationary climate, and can provide quantitative and robust decision support for climate-adaptive building design and energy-saving strategy formulation.

The invention provides a bone age prediction method based on multi-modal feature fusion, a storage medium and electronic equipment. The method comprises the following steps: predicting a plurality of key anatomical points in a wrist X-ray image based on a pre-trained key point detection network, and segmenting a plurality of regions of interest; extracting global feature vectors of the regions of interest based on a pre-trained backbone network, extracting local depth feature vectors of a plurality of regions of interest based on two pre-trained lightweight networks sharing weights, and splicing and fusing the local depth feature vectors and gender feature vectors, obtaining significant feature vectors of the local features; calculating an attention weight based on the splicing of the global feature vector and the local feature apparent feature vector, and obtaining a fusion feature vector based on the attention weight; and inputting the fusion feature vector into a preset regression device, and outputting a bone age prediction value. According to the method, high-precision prediction can be achieved, high interpretability is achieved, and bone age prediction adapting to individualized differences is achieved.