11 results about "Discrete dynamics" patented technology

This invention belongs to the field of electronic digital data processing and discloses a propulsion modeling method for FPSOs based on Modelica, comprising the following steps: S1: Modeling the propulsion coefficient model of the propeller, including calculating the following parameters: thrust coefficient, thrust torque coefficient, thrust gain coefficient, and thrust torque gain coefficient; S2: Modeling the propeller performance model, including calculating the following parameters: wake coefficient, advance number, thrust loss coefficient, and relative rotational efficiency; S3: Based on the results of steps S1 and S2, outputting the thrust and thrust torque of the propeller, thereby obtaining the propulsion efficiency of the propeller. This method can comprehensively simulate the thrust and torque variation characteristics of the propeller under different operating conditions through a discrete dynamic modeling approach, thus providing accurate simulation support for the dynamic response of FPSO systems in complex marine environments.

The invention discloses a permanent magnet synchronous motor preset performance self-adaptive discrete control method and system, and the method comprises the steps: building a universal discrete time dynamic model through employing an Euler discretization method based on a continuous time dynamic model of a permanent magnet synchronous motor; setting an error performance boundary according to a rotating speed control requirement, and converting an actual rotating speed error variable into an auxiliary error variable meeting a preset performance constraint; performing on-demand updating on the neural network weight updating process by adopting an event triggering mechanism so as to reduce unnecessary calculation amount; and designing a discrete controller to realize rotation speed control of the permanent magnet synchronous motor, so that a rotation speed error meets a preset performance requirement. According to the invention, by constructing the discrete dynamics model, presetting the performance boundary, the event triggering updating mechanism and the neural network approximation, the dual purposes of reducing the calculation amount and improving the control precision can be realized.

This invention provides a method for analyzing the dynamic characteristics of a corrugated sidewall belt lifting system for a ship unloader. The method includes: constructing a fractional derivative viscoelastic model of the corrugated sidewall conveyor belt and establishing the basic dynamic equations of the corrugated sidewall conveyor belt; establishing discrete dynamic equations for the conveyor belt, the drive device, and the tensioning device, and establishing a discrete dynamic model of the corrugated sidewall belt lifting system; establishing continuous dynamic equations for the conveyor belt, setting boundary conditions and initial conditions, and establishing a continuous dynamic model of the corrugated sidewall belt lifting system; solving the discrete dynamic model or the continuous dynamic model of the corrugated sidewall belt lifting system to obtain the acceleration, velocity, and displacement of the corrugated sidewall belt lifting system, thus obtaining the dynamic response of the corrugated sidewall belt lifting system. The use of a fractional derivative viscoelastic model, and the further establishment of a dynamic model, more realistically reflects the dynamic characteristics of the corrugated sidewall belt lifting system at each stage.

The application relates to a diffusion reinforcement prediction control method for supply and demand coordination of a supply chain, belongs to the technical field of intelligent control and supply chain management, and solves the problems of insufficient modeling precision and weak generalization ability in supply chain control in the prior art, and comprises the following steps: S1: modeling of a supply chain network system, including establishing a discrete dynamic model of the supply chain network system, setting random disturbance in the discrete dynamic model, and establishing a control target to obtain a controlled network system model; S2: based on the controlled network system model, a robust stochastic model prediction control model is established to generate a control strategy for supply and demand regulation of the supply chain; S3: strategy generation and training based on a diffusion model, the robust stochastic model prediction control model is trained to obtain a robust regulation strategy model meeting real-time requirements; and S4: the trained robust regulation strategy model is used in the supply chain to generate a control strategy and is used in an execution program in the supply chain.

The invention discloses a path planning method, device and equipment for underground unknown environment space detection, a medium and a product, and relates to the field of robot autonomous navigation and path planning, the method comprises the following steps: constructing and discretizing a robot dynamic model, and obtaining environment point cloud data and a robot state in real time; on the basis of the environment point cloud data, online fine adjustment is conducted on a unified element learning symbol distance function model obtained through pre-training, the current robot state serves as a starting point, the discrete dynamics model and the current unified element learning symbol distance function model are combined, and an optimal control sequence is solved in a rolling mode through a model prediction control method; and executing the control instruction and updating the state, and circularly executing until the task is completed. According to the method, rapid adaptation and geometric modeling of a new environment are realized by utilizing a meta-learning framework, online optimization is carried out in combination with predictive control, the perception and planning capability of a robot to an unknown irregular underground space is improved, and the adaptability, safety and task completion efficiency of path planning are enhanced.

The invention relates to a discrete time dynamic graph neural network training method and device and an electronic device, and is applied to the field of deep learning, and the method comprises the steps: carrying out the random shuffling algorithm processing of a block of a graph snapshot sequence in a discrete time dynamic graph, and carrying out the random shuffling algorithm processing; allocating block labels determined based on the sampling durations of different blocks in the graph snapshot sequence for the blocks in the graph snapshot sequence; sorting the blocks in the image snapshots based on the block labels to obtain sorted image snapshots; in the first time sequence, taking the blocks in the sorted image snapshots as space blocks, and in a second time sequence before the time of the first time sequence, sampling the blocks in the sorted image snapshots to obtain time blocks; and taking the obtained space blocks and time blocks as training batch data, training the graph neural network to obtain a target graph neural network for predicting the graph snapshots, and solving the problem that the computing power resource of the current computing equipment cannot meet the processing requirement of a large-scale discrete dynamic graph.

The invention specifically discloses a stability prediction method based on a Gaussian-Legendre quadrature rule, and relates to the technical field of milling stability analysis. The method comprises the following steps: S1, acquiring physical parameters of a milling system; s2, constructing a milling kinetic model based on a regeneration effect; s3, introducing a system vector, and converting the time delay differential equation into a state space equation; s4, converting the state-space equation into an integral equation, and calculating discrete time points; s5, converting the integral equation into an algebraic equation based on a Gaussian-Legendre quadrature rule, and performing interpolation approximation processing on a state item and a delay state item in the algebraic equation based on a Newton interpolation formula; s6, constructing discrete dynamic mapping, establishing a state transition matrix, and reducing the dimension of the state transition matrix; and S7, evaluating the stability of the milling process based on the Flokey theory. According to the method, high-precision prediction of the milling stability is realized, and the calculation efficiency is greatly improved.

The invention discloses a fluid-solid coupling calculation method for predicting the vortex-induced motion response of an underwater single anchor system, and the method comprises the steps: dividing a flow field calculation domain based on a multi-region overlapping grid technology, and achieving the data transmission through boundary point interpolation; establishing an anchor chain discrete dynamical model by adopting a concentrated mass method, and iteratively solving an anchor chain form and tension distribution; a six-degree-of-freedom rigid body dynamic model of the mooring body is established by applying a Newton-Euler method, and constraint conditions such as hydrodynamic force and anchor chain tension are integrated; a flow field is solved through a computational fluid dynamics method, and bidirectional data exchange of fluid load, anchor chain tension and structural motion is realized by using a custom function. The method has the advantages that the problem of large-displacement motion simulation of the mooring system is effectively solved through a multi-region overlapping grid and a cross-platform collaborative architecture, the six-degree-of-freedom vortex-induced motion response of the mooring body can be predicted in a high-precision mode, and whole-process visualization of dynamic changes of a flow field is achieved.

The application discloses a kind of discrete dynamic event tree and nuclear simulation program efficient parallel dynamic coupling analysis method, this method includes: constructing discrete dynamic event tree DET simulation model, determine nuclear simulation program simulation time and run, according to nuclear simulation program simulation result analysis obtains the time information of all DET simulation object state transition control TRIP variable change, and according to DET simulation model branch rule obtains the restart time and restart number of nuclear simulation program backtracking restart of all DET simulation objects of current accident sequence, judge whether restart time and parent sequence restart time are identical, update the failure restart file as input, copy necessary restart input file in parent sequence to current folder, according to branch number backtracking executes multi-thread parallel simulation to complete the simulation of all DET failure branches.This method makes up the deficiency of traditional safety analysis method in handling the time sequence dynamic response of nuclear power plant accident process, and multi-thread parallel computing mode also improves the calculation efficiency.

Embodiments of the present disclosure provide a network architecture intelligent planning method and system, which comprises: acquiring network data and constructing a network digital twin, establishing a multi-dimensional network state matrix containing node state, link state and traffic state, designing a local matrix exponential function to construct a discrete dynamic model and a continuous dynamic model, and unifying the two models to the same mathematical framework through a unified transformation operator to form a unified modeling framework. Subsequently, based on the framework, the state space and the action space are defined, a composite reward function is constructed, a deep reinforcement learning model with a fusion Transformer structure is designed, and a multi-agent collaborative learning framework is constructed, and a candidate network architecture scheme set is generated through multi-objective optimization technology. Finally, simulation testing and iterative optimization are carried out in the network digital twin environment, and the optimal network architecture planning scheme is output. Embodiments of the present disclosure realize intelligent planning and continuous optimization of network architecture.

The application relates to a discrete dynamics modeling method for a space solar panel servo motor system, and belongs to the technical field of dynamics modeling of mechanical and electrical systems of space vehicles. The method aims to solve the problems of mechanical and electrical separation, neglect of flexible load dynamic reaction and lack of external disturbance response mechanism in existing modeling. The technical scheme is as follows: a global explicit analytical equation is established by integrating the electromagnetic characteristics of a permanent magnet synchronous motor, the transmission of a harmonic reducer and the load characteristics of a solar panel, and a response model of the solar panel to space disturbance force is constructed. The model can truly reflect the coupling influence of a large-inertia flexible load on the system, realize high-precision mechanical and electrical joint simulation, quantitatively analyze the action of external disturbance and internal dissipation, and derive low-dimensional explicit dynamics equations, which are convenient for combination with a control system and provide a reliable theoretical model basis for servo control algorithm design.