6results about How to "High precision prediction" patented technology

The invention discloses a method for predicting the residual fatigue life of a titanium alloy component based on frequency mixing nonlinear ultrasound. The method comprises the following steps: firstly, building a detection system; the detection system is controlled to generate low-frequency f1 and high-frequency f2 sinusoidal signals to carry out frequency mixing excitation on the titanium alloy component, and ultrasonic response signals are collected; performing frequency domain analysis on the ultrasonic response signal, extracting a characteristic amplitude and calculating a nonlinear modulation parameter beta m; then, a Nazarov-Sutin equation and an Elber equation are combined, and a theoretical prediction model of the nonlinear modulation parameter beta m changing along with the fatigue life percentage f is constructed; finally, actual measurement parameters are substituted into the model to reversely derive the consumed fatigue cycle number N, and the residual fatigue life Nr is predicted by combining the critical failure total cycle number Nt of the component. According to the method, the quantitative mapping relation between the nonlinear acoustic parameters and fatigue damage accumulation is revealed by establishing the physical model, and accurate quantitative prediction of the residual fatigue life of the titanium alloy component is achieved.

The invention discloses an SAA-DoA high-precision direction finding method based on LSTM time sequence prediction, and the method comprises the following steps: sequentially switching a plurality of antenna units through a sequential sampling mode of a switch antenna array, and obtaining the signal sampling data of a target direction; performing phase processing on the signal sampling data, and extracting an instantaneous frequency time sequence representing carrier frequency drift; inputting the instantaneous frequency time sequence into a pre-trained long short-term memory network model, learning a dynamic change rule of carrier frequency drift through the model, and predicting an instantaneous frequency value of an unsampled time slot; based on the predicted instantaneous frequency value, reconstructing a complete parallel sampling data matrix through phase integral operation; based on the reconstructed parallel sampling data matrix, an array signal processing algorithm is adopted to calculate the arrival angle of the signal; according to the method, the precision of DoA estimation under the SAA architecture and the robustness in a real wireless environment are remarkably improved.

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 application provides an intelligent machining control method and system based on a physical information neural network, and belongs to the field of precision machining technology, comprising the following steps: constructing a physical kernel by coupling the physical laws of machining process force, heat and vibration, combining the physical kernel with a constructed gated residual compensation network, and forming a hybrid intelligent model; establishing a digital twin with the model as a core prediction engine, and synchronously updating the digital twin by using multi-source state data; in the digital twin, using the hybrid intelligent model to perform rolling time domain forward-looking simulation based on the current synchronization state, predicting the machining results of the force, heat and vibration coupling in the future period, generating process parameter adjustment decision instructions based on the results, and delivering the instructions to a physical machining entity execution mechanism; and performing online incremental learning on the gated residual compensation network based on the machining data after the execution of the instructions, so as to realize dynamic self-adaptive optimization. The scheme realizes high-precision forward-looking prediction of the machining process and real-time active closed-loop control based on the prediction.

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.