4 results about "Stochastic partial differential equation" patented technology

This invention relates to the field of medical artificial intelligence and discloses a comprehensive data acquisition and optimization system for CRRT based on artificial intelligence. The system includes: an acquisition and calibration module for acquiring CRRT equipment parameters, physiological signals, and laboratory test data to form multi-source data, and performing time alignment and spatial calibration to obtain calibration data; a modeling and estimation module for constructing a spatiotemporal dynamic model of material transport and biochemical reactions during the CRRT treatment process based on stochastic partial differential equations, and estimating state variables using calibration data to generate high-dimensional state data; and a fusion and inference module for performing dimensionality reduction and adversarial denoising on the high-dimensional state data. Through multi-source acquisition and spatiotemporal calibration of CRRT equipment parameters, physiological signals, and laboratory data, a foundation for treatment data is constructed. Based on stochastic partial differential equations, spatiotemporal dynamic modeling of material transport and biochemical reactions is performed, enabling dynamic characterization of the treatment process.

The invention relates to the technical field of electric energy storage, in particular to a prediction method, device and equipment for generating capacity increment, a storage medium and a product. The method comprises the steps of predicting a wind power generation increment of a wind power station after a future time period based on wind power generation parameter information, predicting a photovoltaic power generation increment of a photovoltaic power station based on photovoltaic power generation parameter information, and predicting a load increment of a virtual power station based on load parameter information, predicting charge and discharge increments of charge and discharge of the clustered resources based on the charge and discharge parameter information; and on the basis of a random partial differential equation, according to the wind power generation increment, the photovoltaic power generation increment, the load increment, the charging and discharging increment, the wind power generation parameter information, the photovoltaic power generation parameter information, the load parameter information and the charging and discharging parameter information, predicting the power generation increment of the virtual power plant to obtain the total power generation increment of the virtual power plant. According to the invention, the problems of poor real-time performance and poor accuracy of prediction of the power generation increment of the virtual power plant can be solved.

The invention relates to the field of seedling cultivation, and discloses a method for fusing water quality regulation and disease control in seedling cultivation, which comprises the following steps of: acquiring multi-field coupling space-time continuous data of a water quality field, a biological and physiological field, a medicament diffusion field and an environment auxiliary field in a seedling cultivation pond; performing fractional order multi-field data preprocessing on the multi-field coupling space-time continuous data to obtain a feature vector containing a long-time memory feature and a coupling feature; and constructing a fractional order LSTM-SPDE coupling prediction model formed by coupling the fractional order LSTM and a random partial differential equation based on the feature vector, and predicting water quality evolution data in a future preset duration by taking the real-time feature vector as input. Multi-field coupling space-time continuous data is acquired, information containing long-term memory features and multi-field coupling features is accurately extracted through fractional order multi-field data preprocessing, and the future water quality evolution trend and disease risk level are pre-judged in advance, so that dynamic cooperation, pre-judgment and long-term adaptation of water quality regulation and control and disease prevention and control are realized.

The application belongs to the technical field of scientific computing and artificial intelligence, and in particular to a random partial differential equation regular structure feature construction method and system. The integral operation of the regular structure feature is transferred from the physical space to the Fourier space. The Fourier diagonalization property of the Laplace operator is used to convert the integral of the partial differential equation coupled at each space point into the accurate solution of the scalar ordinary differential equation independent of each wave number. On the applicable periodic domain or the region processed by the periodization, the spectral accuracy is realized. Further, the serial recursive format is expanded into the causal convolution matrix form in the Fourier space. The integral factor power is pre-calculated and the three-dimensional causal convolution tensor is assembled. Through the parallel pipeline of batch fast Fourier transform, de-aliasing, tensor contraction causal convolution and batch inverse fast Fourier transform, the memory occupation is significantly reduced and the calculation efficiency is improved. The feature calculation efficiency and approximation accuracy of the machine learning system based on the regular structure feature construction are improved.