77 results about "Discrete particle" patented technology

Disclosed in the present application are an integrated circuit routing method and system, and a device and a medium. The method comprises: acquiring an integrated circuit layout model; performing routing area generation processing on the integrated circuit layout model on the basis of a particle swarm optimization algorithm, so as to obtain a routing area; performing routing processing on the routing area on the basis of an improved A* search algorithm, so as to obtain a routing path; on the basis of the routing path, removing a redundant segment, so as to obtain a post-processed path; and performing dictionary conversion processing on the post-processed path, so as to obtain a routing layout. In the embodiments of the present application, a discrete particle swarm optimization algorithm can be used to reduce a routing search space, and a detailed routing area is determined by means of an improved A* search algorithm, thereby further enhancing routing speed, and improving the routing efficiency of an integrated circuit. The embodiments of the present application can be widely applied to the field of integrated circuits.

The invention provides a simulation method and system for interaction of dust collection airflow and abrasive dust of a steel rail grinding wagon, and the method comprises the steps: firstly, constructing an internal and external flow field model of a grinding dust collection device through a computational fluid dynamics method; secondly, establishing a discrete particle model capable of reflecting morphological characteristics of the abrasive dust by using a discrete element method, and defining deposition judgment criteria of the abrasive dust in the vehicle body and on wall surfaces such as a track plate; finally, on the basis of a discrete element and computational fluid dynamics coupling method, continuous phase and discrete phase information is interactively updated, and interaction of abrasive dust and dust collection airflow is simulated. According to the method and the system provided by the invention, the migration, agglomeration and deposition behaviors of the grinding dust under the action of the dust collection airflow are accurately captured for the first time; the dynamic real-time adjustment of the flow field characteristics under the influence of abrasive dust agglomeration and deposition is realized for the first time; macro-micro multi-scale simulation of interaction of dust collection airflow and abrasive dust under the real working condition of the grinding dust collection unit of the steel rail grinding wagon is realized for the first time.

The invention relates to the technical field of electronic digital data processing, and discloses a fluidization system digital twinning construction and state evaluation method, which comprises the following steps: carrying out CFD (Computational Fluid Dynamics) solution on various gas-solid fluidization working conditions, and establishing a numerical simulation database; according to the characteristics of the gas-solid fluidization system, determining a core reward function and physical constraints, training a reinforcement learning agent, and establishing a fluidization system digital twinborn model base; constructing a fluidized system environment according to the twin targets, and calculating multi-physical field distribution and discrete particle state evolution at subsequent moments; and using a multidisciplinary fusion degradation model to analyze the stress condition of the material at each position in a selected time period, calculating the fatigue condition and progressive loss rate of the material, performing system state evaluation, and outputting a result. The problem that in the prior art, simulation precision and efficiency of a fluidization system are difficult to consider at the same time is solved, and the purposes of efficient and accurate simulation, high operation reliability, comprehensive evaluation and analysis, convenient maintenance and high safety are achieved.

The invention discloses an in-port container truck scheduling optimization system and method based on dynamic time matrix prediction, and the method comprises the steps: building a dynamic time matrix which introduces a bridge crane rhythm and a lane impedance factor through collection of TOS and vehicle monitoring data, and carrying out the feedback correction of the matrix through a task result; self-adaptive adjustment of a container truck resource pool threshold value is realized based on a task density function, and multi-target scheduling optimization is carried out by adopting a tabu search and discrete particle swarm interaction algorithm; and when the storage yard is congested or rhythm deviates, task priorities and key road marks of operation roads are considered, task sequence rearrangement is triggered, and algorithm parameters are automatically updated according to execution results. According to the system, a real-time closed loop of prediction-scheduling-feedback is realized, and the port operation efficiency and the resource utilization rate are improved.

The invention relates to the technical field of big data mining, and particularly discloses a discrete particle swarm optimization-based influence maximization method on a hypergraph, which comprises the following steps of: S1, constructing a hypergraph model and a propagation rule: defining a hypergraph which is a node set and a hyperedge set, and satisfying hyperedges; a threshold model is adopted to describe the propagation process, a seed node set is activated at the beginning, other nodes and all hyperedges are not activated, the non-activated hyperedges are traversed, if the proportion of the activated nodes in the hyperedges is larger than or equal to a threshold value, all the nodes in the hyperedges are activated, and the process is repeated until no new hyperedges are activated; s2, initializing a particle swarm; s3, performing two-layer local influence evaluation; s4, particle speed and position updating; s5, local search optimization; and S6, iteration is terminated. By adopting the technical scheme of the invention, the global search capability, the convergence speed and the evaluation precision can be balanced, and efficient and accurate identification of high-influence nodes in the hypergraph is realized, so that the influence propagation effect and the algorithm expandability are improved.

Aiming at the construction of an automatic warehouse logistics system of a digital workshop production line, the invention provides a single-load automatic guided vehicle workshop path planning method based on directed acyclic graph modeling, and the method comprises the functions of task selection, task execution sequence planning and the like. The method comprises a fusion algorithm framework based on a binary discrete particle swarm optimization algorithm and a genetic algorithm component, and can solve and fully consider the diversity of task selection strategies in a short time according to information such as known task arrival time and transportation starting point, so that normal production is ensured; and the task execution sequence with the shortest total driving distance is the optimal (or close to the optimal) task execution sequence.

A VR virtual simulation system for simulating a multi-scene dust diffusion process belongs to the technical field of dust visualization experiment devices, and comprises an integrated helmet, a handheld controller and multi-scene software which are in coupling connection; the multi-scene software achieves dust diffusion simulation based on the gas-solid two-phase flow physical principle, physical simulation calculation is conducted on dust diffusion behaviors by solving a control equation set describing gas-solid two-phase movement, and the control equation set comprises a drag force item, a gravity item and a turbulence coupling item. The calculation process considers momentum transfer, energy exchange and interaction force between the continuous fluid phase and the discrete particle phase; a VR virtual simulation experiment of a multi-scene dust diffusion process can be realized, and formation, diffusion and distribution characteristics and the like of dust in tunnel blasting, fully-mechanized excavation, lifting and typical transfer processes can be visually researched.

The object of the present invention is to provide a method for predicting the discrete particle trajectories of a pipeline centrifugal separator based on a surrogate model, which comprises the following steps: planning the experimental matrix of the structural parameters and flow field distribution of the pipeline centrifugal separator; obtaining the corresponding matrix between each structural parameter and flow field characteristic parameter according to the simulation experimental data; conducting a sensitivity analysis of the input variables, selecting a modeling technique, and constructing a surrogate model for the key input parameters and response values; comparing the results of the surrogate model with the experimental data; constructing a force model for discrete particles in the swirling flow field; establishing a transient motion trajectory equation for discrete particles by using the Euler method, and coupling and solving the flow field calculation model and the particle force model to obtain a discrete particle motion prediction model. The present invention can predict the motion trajectories of discrete particles in the separator under conditions of different physical property parameters, different structural parameters, and different flow parameters, so as to provide guidance for the structural design and application of the pipeline centrifugal separator.

The invention belongs to the field of water pollutant treatment, particularly relates to a garbage retention area analysis method and system based on a two-dimensional shallow water equation model, and aims to solve the problem of inaccurate garbage retention area prediction. The method comprises the steps of performing grid division on a target water area, and constructing a hydrodynamic model of the target water area in a preset time period; according to the hydrodynamic model, simulating the motion trail of discrete garbage in the target water area through a discrete particle motion equation; based on the shoreline gradient and the river curvature of the target water area, determining the stranding probability of the discrete garbage under the motion trail; and in a preset time period, determining a garbage retention index of each grid based on the water flow speed, the discrete garbage retention time in each grid and the stranding probability, so as to screen a garbage retention area in the target water area. According to the method, the prediction deviation caused by simplifying the garbage into an ideal tracer in the prior art is overcome, and the prediction accuracy of the garbage transportation path and the final gathering position is remarkably improved.

The present invention relates to a physical design method of flow layers based on discrete particle swarm under a continuous microfluidic biochip, which comprises the following steps: Step S1: Construct a particle coding scheme based on sequence pairs according to the characteristics of the physical design problem of the continuous microfluidic biochip; Step S2: Search the solution space of the physical design of the flow layers of the continuous microfluidic biochip based on the particle update strategy; Step S3: Generate a design scheme that meets the preset requirements according to the particle coding scheme and the solution space obtained in Step S2. The present invention can effectively improve the efficiency of the physical design of flow layers.

The application relates to the technical field of software function decision, in particular to a software function iteration priority decision method and system based on reinforcement learning, which comprises the following steps: obtaining a software function set to be decided; obtaining a sub-function module set contained by a first software function to an Nth software function, and the sub-function module sets are respectively recorded as a first sub-function module set to an Nth sub-function module set; calculating the overall coincidence degrees of the first sub-function module set to the Nth sub-function module set, and the overall coincidence degrees are recorded as a first overall coincidence degree to an Nth overall coincidence degree; obtaining a basic software function; taking the basic software function as a starting point, adopting a discrete particle swarm optimization algorithm to obtain an optimal development sequence of the first software function to the Nth software function; and developing the software function according to the optimal development sequence of the first software function to the Nth software function. The optimal development sequence is solved by the discrete particle swarm optimization algorithm, and global optimization of the software function development sequence is realized.

The application provides a new energy control parameter self-adaptive identification method based on double-layer particle swarm optimization, and belongs to the technical field of power system simulation analysis. Typical control strategies of heterogeneous new energy units are comprehensively used to construct a strategy database and perform verification; test data are acquired for processing and feature extraction, so that high and low threshold values, current initial states and test feature data are obtained; a fitness function is constructed based on the strategy database, a discrete particle swarm algorithm is used in the global optimization layer to select an optimal strategy combination from the strategy database, and in the local optimization layer, parameters of the selected strategy are continuously optimized in combination with a momentum factor and a learning rate. The application uses actual unit test data characteristic values as inputs of the double-layer particle swarm algorithm, determines an optimal strategy-parameter pair through a total error index weighting, verifies the accuracy of the strategy-parameter pair, and effectively improves the efficiency and precision of modeling of heterogeneous new energy equipment.

The invention provides a remote sensing satellite image ship target association method and system, and relates to the technical field of satellite remote sensing image processing, and the method comprises the steps: S1, constructing a ship multi-source satellite remote sensing image training sample set; s2, based on a deep convolutional neural network, learning coupling and correlation of multi-source satellite remote sensing images, mapping different types of ship target satellite remote sensing images to a common depth feature space, and learning in the depth feature space to obtain a metric function; s3, inputting a ship target multi-source satellite remote sensing image test set into the depth measurement learning network obtained by training, and carrying out feature similarity measurement calculation; s4, designing an association cost matrix ACM; and S5, through a binary discrete particle swarm optimization algorithm DPSO, acceleration association cost matrix ACM optimal scheme solving is carried out, and multi-source remote sensing image ship multi-target association is carried out. According to the method, fuzziness caused by single feature association can be reduced, and the accuracy of ship multi-target association is improved.

This invention discloses a numerical simulation method for particle migration in lead-bismuth fast reactor fuel assemblies. 1. A fluid domain mesh model is established, including the wire rod bundle and the assembly box wall, and user-defined memory is allocated to the wall surface elements to store deposition layer information. 2. Based on CFD solutions of the flow and temperature fields, a discrete particle model is used for Lagrangian tracking to construct a criterion for particle collision with the wall, deposition rebound, and wall-attached sliding. 3. The deposition quality is statistically analyzed using the user-defined memory, and the deposition layer thickness and thermal resistance are calculated, with feedback used to correct the wall heat transfer boundary and the wire rod bundle heat source. 4. A deposition layer peeling and release model is established, including shear-induced buoyancy and rolling desorption and particle impact-induced desorption, and attribute inheritance and re-injection at the next time step are achieved using a wall particle information stack and a release event queue. 5. The wall normal displacement is updated according to the deposition layer thickness through a dynamic mesh, forming geometric feedback to the flow field, achieving iterative prediction capabilities for deposition evolution and heat transfer effects.

The invention discloses a cross-scale discrete Boltzmann method suitable for calculation of an interference flow field of a hypersonic shock wave boundary layer, and the method comprises the steps: building an evolution equation of a cross-scale discrete Boltzmann model based on a Boltzmann-BGK equation, the discrete speed selection of the discrete particle speed space meets the kinetic moment constraint determined based on Chapman-Enskog multi-scale analysis; based on the kinetic moment constraint and the fluid macroscopic physical quantity, a discrete equilibrium state distribution function is solved by adopting a matrix inversion method; obtaining an initial discrete distribution function based on a local equilibrium state hypothesis; performing numerical solution on the evolution equation based on the initial discrete distribution function and the equilibrium state distribution function to obtain an evolved distribution function; and calculating a high-order non-conservation momentum between the evolved distribution function and a discrete equilibrium state distribution function corresponding to the evolved distribution function to obtain a physical quantity for quantifying the thermodynamic unbalance characteristic of the flow field.

The application provides a continuous micro-fluidic biochip high-level synthesis method considering volume management, is based on a discrete particle swarm algorithm with high solving efficiency, and comprises the following key technologies: 1) a particle coding scheme suitable for the high-level synthesis problem considering volume management is provided; 2) an automatic scheduling scheme generation method is provided, which can accurately generate a corresponding operation scheduling scheme according to the particle coding; and 3) according to the characteristics of the problem, a corresponding particle updating strategy is designed, and the convergence of the algorithm is accelerated. The application can obtain a high-level synthesis scheme satisfying volume constraints and having high quality in a shorter time.

The application discloses a kind of topological optimization methods of support position and structure topology simultaneous optimization, and relates to the technical field of topological optimization.The method specifically includes: determining the initial structure design domain of topological optimization, support design domain and boundary condition;Using variable density method to establish the topology optimization model of minimum compliance;The sensitivity information of objective function and constraint is calculated;Based on gradient optimization method and discrete particle swarm algorithm, structure design domain and support design domain are solved;The effectiveness of the method is verified by numerical case.The present application can effectively determine the optimal support position by discrete particle swarm algorithm;Using variable density method to establish structure topology optimization model, density variables in design domain can be efficiently processed.

The application provides an interface calculation method and device based on a discrete particle-finite element grid coupling model. The interface calculation method describes the substances on both sides of the interface by using a discrete particle and a finite element grid method respectively, establishes a coupling calculation model of energy transfer and conversion processes at the interface, and performs computer simulation on diffusion, adsorption, desorption, reaction or heat transfer processes near the interface, so as to study the transfer and conversion processes of energy and substances at the interface or study material performance or catalytic performance, significantly improves the universality of interface calculation, has small prediction deviation of calculation simulation, and has wide application prospect.

The invention discloses a container multimodal transport path optimization method based on discrete particle swarm optimization, and belongs to the technical field of logistics transportation. According to the transportation contract, determining a starting station, a target station and transportation container information; determining a multimodal transport route map according to the station set, the starting station and the target station, and obtaining a transport distance and a shift opening time interval in each station; constructing a container multimodal transport path optimization model by taking multiple transport modes and paths as decision variables and taking the minimum comprehensive cost as an optimization objective function; and designing a discrete particle swarm-simulated annealing algorithm, and solving the container multimodal transport path optimization model by using the discrete particle swarm-simulated annealing algorithm to obtain an optimized path. According to the method, the container multimodal transport path is recommended, so that the transport efficiency of container multimodal transport is improved, and the sustainable development of the logistics transportation industry is promoted.

This invention discloses a method for modeling the delay characteristics of main steam temperature during dynamic operation of a power plant boiler. By collecting dynamic operating data of the power plant boiler, the method determines the set of input variables affecting the prediction model of the boiler's main steam temperature. Considering the historical sequence length of the input variables, an LSTM network model is constructed. The historical sequence length is determined using discrete particle swarm optimization, thereby characterizing the delay time of the boiler's main steam temperature with respect to each input variable. This invention is of significant importance for the design of control strategies for controlling the main steam temperature of thermal power plant boilers.

To reduce calculation time.SOLUTION: An analysis method is used to analyze a composite magnetic member. The composite magnetic member contains multiple soft magnetic particles. The analysis method includes a prescription step and a calculation step. In the prescription step, magnetic circuits for each two-particle system including two arbitrary, adjacent particle models 2 from among multiple particle models 2 are prescribed on the basis of arrangement states of the multiple particle models 2 corresponding to multiple soft magnetic particles in a composite magnetic member obtained by modeling a composite magnetic member by using a discrete particle method. In the calculation step, magnetic reluctance between centers 21 of the two particle models 2 is calculated by using a magnetic circuit prescribed in the prescription step.SELECTED DRAWING: Figure 2

The application discloses a kind of distributed task scheduling method, device and electronic equipment.The method comprises: obtaining the quantity of current task to be scheduled and the total quantity of node server corresponding to distributed node server;Based on the quantity of tasks, the task scheduling vector to be optimized is constructed;Based on discrete particle swarm optimization mode, task resource constraint condition and the total quantity of node server, with the optimization target of maximum load balancing degree, the task scheduling vector is regarded as particle position and global position iterative optimization is carried out, and the target global optimal position after iteration is obtained;Based on the target task scheduling vector corresponding to the target global optimal position, the target scheduling strategy is determined.Through the technical scheme of the application, the problem of discrete task scheduling optimization can be solved, and the accuracy of task scheduling is guaranteed.

The present invention relates to a method for estimating the intake boundary layer and its characteristic values of a mushroom-shaped water intake structure in a power plant, including: establishing a three-dimensional flow field model of the water intake sea area including the mushroom-shaped water intake structure; verifying the flow field model based on the results of physical model experiments; analyzing the intake flow characteristics and flow field of the mushroom-shaped water intake structure; analyzing the boundary layer of the mushroom-shaped water intake structure based on discrete particle tracing; identifying the key factors affecting the intake boundary layer of the mushroom-shaped water intake structure; constructing a rapid estimation formula for the characteristic values and key design parameters of the intake boundary layer of the mushroom-shaped water intake structure. When using the mushroom-shaped deep water intake structure for water intake, the present invention estimates the intake boundary layer for the influence range of the water body in the intake environment, determines the characteristics of the influence of the mushroom-shaped deep water intake structure on the environmental water body, and comprehensively considers multiple factors such as the intake flow characteristics of the mushroom-shaped water intake and the changes in the environmental flow field, providing a basis for the influence range of the mushroom-shaped water intake structure on the water body.

The invention provides a simulation method and device for a cement clinker firing process of a rotary kiln and electronic equipment. The simulation method comprises the following steps: setting initial parameters of material particles in a simulation area in the rotary kiln; simulating a mixed flow process of the material particles to reach a steady state, and obtaining an initial state of the material particles; and simulating the reaction and fusion process of the material particles to obtain the state of the material particles at any moment. The simulation method provided by the invention is based on a discrete particle simulation method, the sintering process in the rotary kiln is simulated from the particle size, simulation can be carried out for different reaction sections according to the characteristics of the rotary kiln, the particle size reaction process of each section is finely calculated, and simulation of local details such as radial distribution in the kiln is realized.

This invention relates to a method for simulating the distribution of magnetofluid injection based on the coupling of particle tracking and fluid dynamics, belonging to the field of magnetic nanoparticle movement and particle tracking. The method includes: constructing a two-dimensional geometric model containing healthy tissue, abnormal tissue, and an injection syringe, and setting parameters; constructing a fluid model based on Darcy's law, and calculating the fluid velocity and pressure distribution in the interstitial tissue; coupling the fluid velocity, and solving the diffusion-convection-reaction equation using the finite element method to obtain the macroscopic concentration distribution of the magnetofluid after injection; establishing a particle tracking module, setting discrete particles with the same properties as the magnetic nanoparticles; coupling the interstitial fluid velocity, calculating the drag force and gravity acting on the particles according to Newton's second law, and simulating the particle motion to reveal the microscopic trajectory of the magnetic nanoparticles. This method, by coupling the macroscopic concentration field with the microscopic particle motion, can more realistically simulate the distribution behavior of magnetofluid within complex porous biological tissues.

The invention relates to a network partition and voltage coordination control method for a high-permeability distributed photovoltaic power distribution network, and belongs to the field of reactive compensation voltage control of a power system. According to the method, a power distribution network partition model based on a reactive power sensitivity matrix is established, and meanwhile, the influence of voltage control in one partition on other partitions is reduced; identifying voltage weak nodes in each partition by adopting an ancient typical all-pure embedded voltage weak node identification method, and generating a candidate node set of the reactive power compensation device; an upper layer takes comprehensive operation performance optimization as a target, and adopts a non-dominated sorting genetic algorithm to solve the optimal site selection and capacity of a compensation device; the lower layer calculates node voltage through a discrete particle swarm algorithm by taking the minimum sum of squares of the total network loss of the power distribution network and the partition voltage deviation as a target, and feeds back the network loss value and the voltage deviation to a target function of the upper layer; the global coordination control of the multi-region voltage is realized by constructing a double-layer interaction optimization mechanism of an upper-layer model and a lower-layer model.

The invention discloses an oxygen-enriched combustion simulation method and system for long-strip-shaped straw particles, and belongs to the technical field of oxygen-enriched combustion simulation, and the specific scheme comprises the following steps: step 1, modeling the long-strip-shaped straw particles by using EDEM software in combination with geometric characteristics of the long-strip-shaped straw particles; fine mesh modeling is carried out on the hearth of the circulating fluidized bed boiler by using Fluent software, and a boiler mesh model conforming to the characteristics of the long-strip-shaped straw particles is constructed; 2, aiming at the boiler grid model, solving a fluid phase equation and a particle phase equation of an internal three-dimensional space by adopting a multiphase flow discrete particle model in Fluent software to obtain a corresponding mathematical model; and 3, simulating the oxygen-enriched combustion process of the long-strip-shaped straw particles in the circulating fluidized bed boiler by utilizing the mathematical model and combining a chemical reaction formula involved in the oxygen-enriched combustion process to obtain a corresponding oxygen-enriched combustion simulation model. According to the invention, accurate simulation of the combustion process of the long-strip-shaped straw particles in the oxygen-enriched environment is realized.

The present disclosure relates to the field of geotechnical numerical simulation technology, and proposes a discrete element porosity calculation and interpolation optimization method and system based on a graphic algorithm. First, the Delaunay triangulation meshing technology is used to perform triangular face / volume meshing with discrete particle point set coordinates as nodes, and then the Voronoi algorithm is used to construct Thiessen polygons. At the same time, a graphic algorithm is used to calculate the porosity of the current polygon, and it is determined whether further interpolation optimization processing is required, thereby achieving uniform continuous processing and smooth display of the entire discrete element calculation data. The present invention can overcome the problems of poor particle display effect in current discrete element calculations, automatically calculate local porosity based on the Voronoi polyhedron graphic algorithm, and quantitatively determine whether interpolation optimization processing is required, which can achieve uniform continuous optimization processing of non-uniform discrete element particle information, and the applicability and accuracy are greatly improved.

The invention specifically relates to a large-scale space antenna assembly sequence planning method considering structure fundamental frequency maximization, and the method comprises the steps: building a dynamic model of a large-scale space antenna assembly process based on the structural characteristic that a geometric configuration is gradually increased in the assembly process and in combination with a modeling thought of a life-death element method in a finite element method; determining optimization variables and constraint conditions of the assembly sequence in the large space antenna assembly process; based on the dynamic model, considering the time-varying characteristic of the dynamic characteristic of the space antenna in the assembly process, and constructing an evaluation index of the assembly sequence; based on constraint conditions and evaluation indexes of the assembly sequence, an improved self-adaptive discrete particle swarm algorithm is provided, an assembly sequence optimization problem with maximized structural fundamental frequency is solved, and a feasible and optimal assembly sequence is obtained. According to the method, the structural rigidity in the assembling process can be improved, structural vibration in the assembling process is reduced, and the design redundancy of the orbit attitude control system is improved.

The application provides a deep hole blasting parameter optimization method, device, equipment and storage medium, and relates to the technical field of blasting engineering. The method comprises the following steps: acquiring a simulation working condition set; wherein the simulation working condition set comprises multiple working conditions, and each working condition is a group of candidate blasting parameters; adopting a nonlinear dynamic finite element method and a discrete particle cohesive model, a whole process simulation of blasting is carried out for each working condition to obtain physical evolution data of each working condition; based on the physical evolution data of each working condition, multi-dimensional blasting quality indexes of each working condition are calculated; multi-objective comprehensive evaluation is carried out on the multi-dimensional blasting quality indexes of each working condition, and an optimal working condition is selected from the simulation working condition set as an optimal blasting parameter combination output. The application can significantly improve the comprehensiveness and reliability of blasting design, effectively reduce the cost of field test, and realize fine design of deep hole blasting.