17 results about "Solution of equations" patented technology

The application discloses a spacecraft magnetic moment attitude control method based on a non-inverse iteration algorithm, and the method establishes a linear periodic model for a spacecraft attitude control system which only relies on a magnetic moment; on the basis, according to a linear quadratic optimal control theory, an optimal controller design problem is converted into a solution problem of a discrete periodic Riccati equation associated with the optimal controller design problem; on the basis of a gradient descent principle, an error function is constructed, and a non-inverse iteration algorithm based on the gradient descent principle is proposed to solve the discrete periodic Riccati equation, so that a matrix inversion operation in a traditional solution method is avoided, and the calculation efficiency and the numerical stability are effectively improved; the obtained Riccati equation solution is used to obtain an optimal state feedback control law, so that the spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution scheme is provided for an attitude control problem of a limited actuator in a space task.

The application discloses a full-process data processing method and system for fluid mechanics control equations, and local servers are initialized and artificial intelligence agents are used to solve paths related to fluid mechanics control equations, so that the parameter space of a fluid mechanics problem is imported and disassembled; the artificial intelligence agent is used to identify the control equation corresponding to the fluid mechanics problem and batch match example files, and core parameters related to the control equation are extracted; the artificial intelligence agent is used to configure parameters of the control equation; the example file is converted in format and saved locally to the corresponding path; a solver of the control equation is called to perform solving processing, and the result of the solving processing is verified to determine the validity of the result. By constructing a special link for solving the fluid mechanics control equation, automation of the full-process link from problem parameter disassembly, control equation identification, special parameter configuration, format conversion and result verification is realized, and the example processing efficiency and solving accuracy are improved.

The application discloses a numerical simulation method and device for a foreland thrust belt, and the method comprises the following steps: determining and acquiring basic parameters required for numerical simulation; constructing a stratum model and setting the stratum model based on the basic parameters; setting boundary conditions for numerical simulation; solving a mass conservation equation, a momentum conservation equation and an energy conservation equation to obtain field data of a velocity field, a pressure field and a temperature field of the stratum model; solving a topographic diffusion equation and a river erosion rate equation to obtain evolution data of topography and geomorphology of the stratum model; solving a flexure isostatic equation to obtain a basement subsidence amplitude of the stratum model; and outputting the calculated data. The application can better simulate a geomorphology evolution process dominated by river erosion in an actual geological process.

The invention discloses a full-process data processing method and system oriented to a fluid mechanics control equation, and the method comprises the steps: initializing a path and an artificial intelligence agent related to the solution of the fluid mechanics control equation in a local server, so as to import and disassemble a parameter space of a fluid mechanics problem; identifying a control equation and batch matching example files corresponding to the hydromechanics problem through an artificial intelligence agent, and extracting core parameters related to the control equation; performing parameter configuration on the control equation through an artificial intelligence agent; converting the format of the example file and locally storing the example file to a corresponding path; and calling a solver of the control equation to carry out resolving processing, and verifying the result of the resolving processing so as to judge the effectiveness of the result. By constructing the exclusive link for solving the fluid mechanics control equation, an automatic full-process link from problem parameter disassembly, control equation identification, exclusive parameter configuration, format conversion and disintegration verification is realized, and the example processing efficiency and the solving accuracy are improved.

The invention discloses a ship zero-sum game tracking control method based on digital-object dual-drive reinforcement learning. The method comprises the following steps: constructing a data physical dual-drive model; adopting a Lyapunov backstepping method to hierarchically design a ship track tracking feedforward controller; defining a performance index function and a Hamiltonian-Jacobian-Ansaxophone equation based on an error system and a zero-sum game theory, and designing an optimal feedback controller and a worst interference strategy; a fuzzy logic system is utilized to approach an HJI equation solution, and Nash equilibrium of an output estimation value of an optimal feedback controller and a worst interference strategy is obtained; a feedforward controller, an optimal feedback controller and a worst interference strategy are fused, and a final ideal ship controller capable of directly executing an actual control instruction is obtained through constraint processing, so that ship track tracking is realized. The method solves the problems that the existing method generally has the inherent defects of high dependence on high-quality data, weak physical interpretability, insufficient generalization ability to complex working conditions and the like, and is difficult to meet the safety and reliability requirements of ship navigation.

The application discloses a ship zero-sum game tracking control method based on data-physical dual driving and reinforcement learning, which comprises the following steps: constructing a data-physical dual driving model; adopting a Lyapunov backstepping method to design a ship track tracking feedforward controller in layers; defining a performance index function and a Hamilton-Jacobi-Isaacs equation based on an error system and a zero-sum game theory, and designing an optimal feedback controller and a worst disturbance strategy; using a fuzzy logic system to approximate a solution of the HJI equation, and obtaining a Nash equilibrium of estimated values of outputs of the optimal feedback controller and the worst disturbance strategy; and fusing the feedforward controller, the optimal feedback controller and the worst disturbance strategy, and performing constraint processing to obtain a final ideal ship controller which can directly execute an actual control instruction, so that ship track tracking is realized. The application solves the problems of inherent defects of existing methods, such as high dependence on high-quality data, weak physical interpretability, insufficient generalization ability for complex working conditions and the like, and cannot meet the safety and reliability requirements of ship navigation.

The application discloses a kind of gas-spring structure multimode vibration numerical simulation method, terminal equipment and storage medium, based on finite element modeling software and computational fluid dynamics dynamics software Fluent, using mode superposition method to decouple the multi-order vibration equation of system and compile UDF secondary development program embedded solver calculation, realize three-dimensional structure and wind flow field two-way fluid-structure coupling multimode calculation.The application can truly reflect the multimode vibration of actual three-dimensional structure.

The application discloses an optimization design method of interior ballistic charge parameters of an ultrahigh-speed fragment launching device. The method adopts a multi-objective simulated annealing algorithm, combines an ultrahigh-speed fragment launching device interior ballistic calculation program and a Runge-Kutta equation solution program, and solves the problem that in the design of an ultrahigh-speed fragment charge, the ultrahigh-speed fragment launching device charge design meeting certain test target requirements cannot be quickly and accurately found, and is suitable for solving the optimization target requirements of various ultrahigh-speed fragment tests.

The application discloses a kind of cogeneration system digital twin model construction methods, comprising clear trusted parameter, selecting benchmark condition, establishing equation, solving equation correct solution and constructing digital twin model.The complex cogeneration heat system is analyzed, the reliability of thermal characteristic parameter is clear, and the equation set is established for the heat system using the parameter with higher reliability, the correct solution of equation set is solved by iterative correction idea, and finally the digital twin model is established using the correct solution, to accurately reflect the purpose of complex cogeneration heat system characteristics by digital twin model, representing.The method provided by the application can establish high-fidelity digital twin model.

The application discloses a flow field parameter determination method, device and equipment for full-basin gas flow and a storage medium, relates to the technical field of full-basin axisymmetric flow calculation, and comprises the following steps: establishing and decoupling Boltzmann model equations to obtain each sub-equation, i.e., a nonlinear source term sub-equation, a convection motion sub-equation and an axisymmetric source term sub-equation; analytically solving the nonlinear source term sub-equation to obtain a molecular velocity distribution function value; taking the value as an initial value, solving the convection motion sub-equation by using a finite volume method to obtain a first distribution function value; taking the value as an initial value, solving the axisymmetric source term sub-equation by using the finite volume method to obtain a second distribution function value, integrating the second distribution function value to obtain a current flow field macroscopic parameter, and judging whether convergence is achieved; if convergence is not achieved, the nonlinear source term sub-equation is updated, and the above steps are repeated until convergence is achieved, so that the target flow field macroscopic parameter is obtained, and the efficiency of determining the full-basin axisymmetric flow field parameter is improved.

The invention discloses a spacecraft magnetic moment attitude control method based on a non-inverse iterative algorithm, and the method comprises the steps: building a linearized periodic model for a spacecraft attitude control system which only depends on magnetic moment; on the basis, according to a linear quadratic form optimal control theory, an optimal controller design problem is converted into a solving problem of a discrete period Riccati equation connected with the optimal controller design problem; on the basis of the gradient descent principle, by constructing an error function, a non-inverse iterative algorithm based on the gradient descent principle is provided to solve a discrete period Riccati equation, matrix inversion operation in a traditional solving method is avoided, and calculation efficiency and numerical stability are effectively improved; and an optimal state feedback control law is obtained by using the obtained Riccati equation solution, so that spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution is provided for the attitude control problem of a limited actuator in a space task.

The invention relates to the technical field of optimal scheduling of an integrated energy system, and discloses a fully-distributed combined heat and power optimal scheduling method, device and equipment and a medium. In order to solve the problems of linear convergence, low calculation efficiency, dependence on a coordination center and the like of a traditional distributed optimization algorithm in electric heating system scheduling, on the basis of reserving an original electric heating coupling model core architecture, a distributed collaborative interior point conjugate gradient method is introduced as an optimization algorithm, and fully-distributed scheduling with efficient convergence is achieved. According to the method, an electric heating comprehensive energy system optimization model is converted into a convex optimization problem, a correction equation is decoupled, a coefficient matrix is ensured to be symmetrical and positive definite, a distributed conjugate gradient method is adopted for solving, and collaborative optimization of an electric power system and a thermodynamic system can be achieved without a coordination center. Simulation verification shows that the calculation efficiency is improved by more than 90% on the premise of ensuring that the optimization precision is consistent with a centralized algorithm, the privacy of each energy main body can be protected, and the method is suitable for real-time scheduling of electric heating comprehensive systems of different scales.

The application discloses a dynamic calculation method based on a spectral feature coding network, comprising: analyzing a type of a structure dynamic equation corresponding to a to-be-solved dynamic problem and modal parameters; determining input variables of the structure dynamic equation, building a spectral feature coding layer, and mapping the input variables to a frequency spectrum space related to a solution of the structure dynamic equation; constructing a neural network architecture; constructing a loss function based on physical constraint conditions of the structure dynamic equation; training the neural network architecture under the constraint of the loss function, and solving the structure dynamic equation to respond to dynamic calculation. The application projects the input variables to a frequency spectrum space of the structure dynamic equation, a multilayer perceptron fits base functions with clear physical meanings in the space, effectively solves a frequency spectrum deviation problem, improves solving accuracy of a high-frequency structure dynamic equation, reduces nonlinear fitting complexity of the neural network, makes the network quickly converge, reduces iteration times, improves solving efficiency, and provides an efficient solving idea for engineering high-frequency dynamic problems.

The invention discloses a data backfilling time shifting method and device and a storage medium, and belongs to the technical field of oil exploration seismic data processing. The method comprises the following steps: arranging aliasing data into gather data, respectively extracting the time shift amount of each single channel from the header of the single channel of each gather, and respectively moving the data of each single channel to zero time according to the corresponding time shift amount on the premise of ensuring that the data volume of each single channel is unchanged, and backfilling the data moved out of each single channel to the opposite direction of the movement to obtain the data after backfilling time shifting. Compared with a traditional edge expansion time shifting method, the data backfilling time shifting method provided by the invention has the advantages that the memory storage space of data is greatly saved, and the operation speed of subsequent processing is improved. 0 value filling is not carried out, and the data are all effective numerical values, so that the sparsity of the data is reduced, the underqualitative property of an equation solution is reduced in the process of carrying out sparse matrix equation solution in subsequent inversion iteration, and the separation effect of aliasing data is better.

The invention relates to a separated iterative multilevel physical information neural network (PINN) method for solving an incompressible NS equation. Comprising the steps that parameters are initialized, an NS equation is decoupled, PINN decomposition solving is used, and equation coupling is achieved through iteration. The initialization parameters comprise an initialization computational domain, a sampling point, a network parameter and a speed / pressure field. For each physical time step in decoupling the NS equation and using PINN decomposition solution, the NS equation is decoupled into a momentum equation and a pressure-Poisson equation, a multi-stage neural network is introduced for respective solution, and iteration is performed to realize NS equation coupling solution. In each step of sub-iteration, a new-level neural network is used for predicting a speed correction value of the previous step of the momentum equation, and the network structure is optimized and adjusted according to current error distribution. And when the set physical time is reached, determining a final velocity field and a final pressure field. Experimental results show that the method can significantly reduce the residual error of solving the NS equation by the neural network and improve the solving precision.

The present application relates to a kind of physical driving deep learning-based near-field wave numerical simulation method, including sampling step, training step, simulation step;In the sampling step, space-time sample collection is realized using Sobol sequence algorithm, in the training step, artificial neural network model and loss function are constructed, and optimizer mode is optimized, and in the simulation step, the wave equation solution and the solution of each order partial derivative of the near-field wave of any spatial point under any resolution is predicted using the trained artificial neural network.The present application has the advantages of meshless, refinement, wave field side boundary full transmission, etc., for typical working conditions, the neural network formed by training has the generalization ability under different initial conditions, combined with transfer learning, the training efficiency of network can be efficiently improved.

The invention provides a Navier-Stokes equation solving method of an integrated interpretable Poisson neural network for incompressible fluid flow simulation, and belongs to the technical field of computational fluid mechanics, Poisson-NN consistent with a Poisson equation analytical solution form is constructed, and definite physical significance is given; high-fidelity flow field data meeting a pressure Poisson equation is generated with low calculation complexity, and the problem that training data is difficult to obtain is solved; and the trained Poisson-NN is packaged into an incompressible Navier-Stokes equation numerical solution framework, and a prediction result of the Poisson-NN is used as an initial guess of a traditional iteration method, so that the number of iterations is reduced. According to the method, the pressure Poisson equation with the maximum computing resource consumption in the numerical solution process of the incompressible Navier-Stokes equation is solved, the interpretable Poisson-NN is innovatively designed to replace a traditional iteration method, and calculation acceleration is achieved; on the premise of ensuring the same precision as a conjugate gradient family or multi-grid iteration method, the calculation efficiency is improved by 4-8 times, the calculation resource and time cost is greatly reduced, and an efficient solving scheme is provided for high-fidelity fluid simulation.