4results about How to "Improve predictive reliability" patented technology

The invention relates to a fire tracing and diffusion prediction method and system based on multi-source data fusion and a storage medium, and relates to the technical field of disaster monitoring and early warning. The fire tracing and diffusion prediction method comprises the following steps: collecting and cleaning multi-dimensional environment information of a disaster scene, and obtaining multi-modal environment data; constructing a disaster situation multi-dimensional map according to the multi-modal environment data, and dividing a field dangerous area in combination with a preset disaster situation threshold value; analyzing the disaster situation multi-dimensional map according to the real-time environment data collected by the intelligent carrier, determining the disaster situation category, and positioning the disaster situation generation source; monitoring environment change data of a disaster occurrence source, and constructing a disaster time sequence deduction model in combination with disaster categories; according to the disaster situation time sequence deduction model in combination with the disaster situation multi-dimensional map, pre-judging and determining the regional disaster situation level of the field dangerous region, and drawing a disaster situation diffusion rendering graph; and planning a rescue path of a disaster scene according to the position of the intelligent carrier in combination with the regional disaster level, and providing disaster early warning.

This paper presents a method and system for correcting and predicting supersonic internal flow fields by integrating topology consistency evaluation and physical constraint latent space mapping, belonging to the interdisciplinary fields of fluid aerodynamics design and artificial intelligence. The method first employs a topology consistency evaluation based on a self-organizing mapping grid to realize the nonlinear coupling evolution of parameter sets and spatial errors under supersonic conditions in a hexagonal topological space. Gaussian smoothing and dot product operations are used to quantify the topology consistency between parameters and spatial errors, automatically selecting core parameters. Next, a physical constraint latent space mapping architecture is constructed, introducing a composite physical loss function to reduce the dimensionality of high-dimensional flow field features and establish a mapping model from core parameters to the latent space. Decoder weights are frozen to ensure the continuity of physical laws and derivatives. Finally, an error-driven correction mechanism is used to statistically analyze residuals and generate an error feedback matrix to complete the implicit core parameters, achieving closed-loop reconstruction and corrective prediction of the surrogate model. This method can significantly improve the physical fidelity of supersonic internal flow field reconstruction and effectively solve the problem of large prediction deviations in the flow field behind the gate.

This invention discloses a method and system for predicting power supply risks based on multi-source data fusion, belonging to the field of intelligent operation and maintenance technology for power systems. The method includes: acquiring multi-source raw data from prediction units and constructing a standard fusion feature vector; combining the standard fusion feature vector with a pre-trained spatiotemporal risk-constrained prediction model to output a probabilistic prediction result containing multi-step future photovoltaic output and bus load prediction values; analyzing the probabilistic prediction result to construct a comprehensive risk index, and outputting a kilometer-level gridded risk heat map as the risk prediction result based on the comprehensive risk index. This invention achieves the fusion and probabilistic prediction of multi-source heterogeneous data, improving the ability to identify power supply risks and the level of precision in early warning under extreme weather scenarios.

This application provides a dynamic modeling and simulation method and system for the energy efficiency ratio (EER) of a solar-powered seawater desalination system, belonging to the technical field of seawater desalination and system modeling. First, this application acquires data on membrane surface resistance, selective permeability, and DC bus voltage ripple during the photovoltaic electrodialysis process. Second, the ripple data is Fourier transformed and concatenated with membrane parameters to generate an input matrix. This matrix is ​​then imported into a deep belief network, and a restricted Boltzmann machine is used to extract the unsteady-state ion impedance vector reflecting the influence of voltage fluctuations. Subsequently, a nonlinear regression model of this vector and unit water production energy consumption is established using a least-squares support vector machine. Finally, the water production rate and EER are calculated based on the predicted energy consumption and photovoltaic power, generating a dynamic simulation curve. This application can quantify the nonlinear influence of photovoltaic voltage ripple on membrane impedance through deep learning, significantly improving the accuracy of EER prediction for seawater desalination systems under fluctuating power supply conditions.