3results about How to "Effective parsing" patented technology

The invention discloses a multi-modal disambiguation and knowledge graph embedding-based power grid Internet of Things equipment plug and play method and system, and the method comprises the steps: analyzing an ambiguous user instruction through a multi-modal module, and fusing context and sensor data to generate a clear configuration intention; a knowledge graph containing equipment configuration and topological resources is constructed, after the knowledge graph is converted into a vector through embedding, an optimal access scheme and a physical model are matched through machine learning, meanwhile, power grid operation rule verification is conducted on a matching result, and it is ensured that the scheme can be directly executed. According to the method, the problems of inconsistent object model matching, manual configuration migration, weak ambiguous instruction analysis and the like are solved, automatic access of novel switches, photovoltaic equipment and other equipment is realized, and the automation level of the power grid internet of things is improved.

The application discloses an electrochemical multi-component simultaneous quantitative detection system and method based on a one-dimensional convolutional neural network, and belongs to the technical field of electrochemical analysis. The method first collects square wave voltammetry (SWV) signals of a mixed sample; then, the signals are subjected to baseline correction, filtering and normalization pretreatment; then, a one-dimensional convolutional neural network (1D CNN) regression model is constructed and trained, the model can automatically learn and extract deep features related to the concentration of each component from the pretreated complex overlapping signals; finally, the trained model is used to predict the sample to be measured, and the concentration of each component is output. The application innovatively combines 1D CNN with SWV technology, effectively solves the problem of multi-component signal overlap, and significantly improves the detection accuracy, sensitivity and automation degree. The system is suitable for simultaneous rapid detection of various components such as antioxidants, and has wide popularization and application value.

The present application relates to a kind of gamma spectrum based on branch fusion network's spectrum and dose prediction method, using Monte Carlo method to construct the model consistent with actual detection system, obtain the single-energy response spectrum under different energy gamma radiation, and establish the response relationship between energy spectrum and air absorption dose;Through the construction of detector response matrix and energy spectrum superposition principle, generate multi-energy mixed energy spectrum and corresponding dose label data set, while covering point source and body source radiation scene.Based on the data set, establish the spectrum model based on residual network, realize the inversion reconstruction of each energy component in mixed energy spectrum, to construct multi-branch feature fusion neural network to realize the nonlinear mapping from spectrum to dose rate.Model is based on homologous data progressive drive, output energy spectrum structure information is used to output dose information after the information, realize the energy composition and dose level collaborative representation of radiation field.It avoids complex inversion algorithm, improves the applicability and stability of model in complex radiation field.