6 results about "Conservation law" patented technology

The invention provides an electric power system carbon potential tracking and predicting method based on physical and data dual drive. The method comprises the following steps: constructing a time sequence characteristic data set; constructing a source-side unit double-layer collaboration map, and generating a normalized adjacent matrix; constructing a physically guided source side graph neural network model, and outputting a predicted source side dynamic carbon emission factor and a node carbon injection amount sequence; constructing a high-dimensional input tensor through a space-time diagram attention mechanism; constructing a whole-network space-time diagram neural network model to realize dynamic tracking of carbon potential; the method comprises the following steps: introducing a Kirchhoff carbon flow conservation law as a physical regularization term in a space-time law deduction process, constructing a mixed loss function containing the physical regularization term, and executing an optimal sentinel mechanism in a back propagation process of whole-network space-time diagram neural network model training, and finally, outputting a dynamic carbon potential prediction result of the load side after physical verification. According to the invention, the precision and credibility of carbon potential prediction can be improved.

The application provides a physical mechanism coupling data-driven fast prediction method, and belongs to the technical field of disaster prediction.The application extracts an invariant topological feature vector through topological data analysis driven phase space reconstruction and inputs a physical constraint identification model to obtain a conservation law deviation vector and a physical feasible region boundary parameter, adopts a multi-resolution adaptive grid technology to perform multi-scale decomposition, extracts long-term evolution trend features and spatial local features through a full connection layer network and a convolution layer network, combines the physical feasible region boundary parameter to perform projection gradient descent iterative optimization to generate a preliminary prediction field, performs Bayesian uncertainty quantization, adjusts an artificial intelligence model regularization coefficient according to a disaster precursor identification function value, and outputs a graded early warning result, so that the technical problem of insufficient prediction accuracy caused by insufficient coupling of a physical constraint and a data-driven model in a disaster prediction system is solved.

This application relates to a satellite orbit prediction method. The method includes: constructing an expression for the gravitational force acting on the satellite based on its mass and distance from Earth; constructing a differential equation for the satellite's acceleration based on Newton's second law and the expression for the gravitational force; solving the differential equation based on the conservative force field characteristics of mechanics, calculating the gravitational potential energy function and its derivative with respect to time; transforming the derivative expression based on the mechanical conservation law that the sum of the derivatives of kinetic energy and gravitational potential energy with respect to time is zero, to obtain the derivative expression for acceleration; converting the time relationship in the derivative expression of acceleration to a radial position relationship based on the principle of conservation of angular momentum; integrating the radial position relationship to obtain the satellite's orbit equation. This method enables satellite orbit prediction.

The invention discloses a multi-modal fusion and reasoning enhanced boiler four-tube intelligent monitoring method and system, and the method comprises the steps: collecting unstructured asynchronous event flow data through a sensor group disposed on the inner wall of a boiler, carrying out the processing of the asynchronous event flow data, generating a sparse pulse feature tensor, and carrying out the processing of the sparse pulse feature tensor; the sparse pulse feature tensor is mapped to a high-dimensional potential space, the future physical state of the boiler system is dynamically predicted in the high-dimensional potential space, the physical conservation law constraint is introduced in the training process of an industrial world model, it is ensured that prediction of the future physical state conforms to hydromechanics and thermodynamic equations, and the prediction accuracy is improved. By predicting a set of states in a physical state parameter set, a future physical state is comprehensively predicted, so that an artificial intelligence system conforms to the pre-judgment capability of a physical law and obtains key dynamic details for multiple physical states, a perception-decision-action closed loop is realized, and the boiler monitoring efficiency is greatly improved.

The invention relates to the field of boiler prediction, and discloses a boiler real-time data prediction system which is used for improving the accuracy of boiler fault prediction. According to the boiler real-time data prediction system, intrinsic characteristics of combustion fluctuation, convective heat transfer, heat conduction and the like of a flow field in a boiler are extracted through computational fluid mechanics characteristic modal decomposition, a dynamic state evolution model is built by combining mass, energy and momentum conservation laws, and coupling prediction of a temperature field, a pressure field and a flow velocity field is achieved; and a non-equilibrium thermodynamic entropy generation principle is introduced to carry out physical consistency correction on a prediction result, so that high-precision prediction under the constraint of a thermodynamic law is ensured. And finally, mapping the risk indexes to a three-dimensional boiler model through a digital twinning technology, and generating an interactive early warning interface with spatial positioning and risk level visualization. According to the invention, a complete technical chain of data acquisition, feature analysis, state prediction and risk early warning is formed, and early accurate early warning and space positioning capabilities are provided for safe operation of the boiler of the power plant.

The application provides a method and system for predicting particle settling, collision and flow characteristics in a pump, comprising: constructing a multi-phase flow training sample library of real-time collected environment and virtual-real fusion; after pre-processing the sample library data, using a double-flow deep neural network architecture to extract multiple source feature vectors in parallel; cross-fusing the multiple source feature vectors to obtain a multi-modal fusion feature vector, and establishing an Euler-Lagrange two-way coupled partial differential control equation; inputting the multi-modal fusion feature vector into a physical information neural network based on a gradient-aware adaptive physical loss function, and outputting particle instantaneous settling velocity, particle collision contact force, local entropy production rate and global reconstructed three-dimensional flow field; based on the output calculation index, hierarchical regulation is executed accordingly. The application breaks the feature mismatch problem, replaces the complex grid iteration with millisecond-level forward reasoning conforming to the conservation law of fluid mechanics, and realizes high-precision intelligent online prediction of complex flow field characteristics.