57 results about "Discrete particle" patented technology

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 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 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.

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 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 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.

The invention relates to the technical field of laser, in particular to a laser Doppler spectrum simulation method. The laser Doppler spectrum simulation method comprises the following steps: representing incident laser as a virtual photon group; obtaining state parameters of the virtual photons at the initial moment; calculating state parameters after the virtual photons and the discrete particle swarms act; counting the frequency shift of the virtual photons emitted from the discrete particle swarms to generate a frequency spectrum; according to the simulation method of the laser Doppler spectrum, in the simulation process, virtual photons are propelled in the space in the area where a particle swarm is located in the movement direction of the virtual photons so as to simulate the transportation process of a laser light field, and photon state parameter changes after interaction of the photons and particles are calculated; and counting photons which leave the regional space where the particle swarm is located and return to be received by the probe, thereby obtaining the distribution condition of power spectrum intensity along with frequency shift, and realizing accurate simulation and simulation of the laser Doppler spectrum in the particle swarm.

The invention discloses a micro-fluidic chip control mode distribution method considering valve switching balance. The method comprises the following steps: acquiring a flow valve switching matrix and a multiplexing combination matrix at different moments as input; according to the flow valve switching matrix and the multiplexing combination matrix, constructing a particle coding mode for control mode distribution, and meanwhile, constraining the number of control valves and the maximum number of valve switching times; initializing the position and speed of the particle swarm, and calculating the initial fitness of each particle based on the number of constraint control valves and the maximum number of valve switching times; and adopting an algorithm based on discrete particle swarm optimization, dynamically grouping particle swarms through a role-based learning strategy, and generating a control mode distribution scheme and the like. According to the method, a control mode distribution scheme with the minimum number of control valves and the minimum switching frequency of the maximum control valve can be obtained.

The present application relates to a kind of intelligent scheduling method of unrelated parallel machine combined preventive maintenance, steps are to obtain scheduling workpiece and machine parameter information;Establish the unrelated parallel machine scheduling integrated model combined preventive maintenance with minimizing maximum completion time as optimization goal;Discrete particle swarm algorithm is combined with multi-neighborhood search algorithm to solve unrelated parallel machine scheduling integrated model;Local optimal particle individual and global optimal particle individual are updated;After updating, multi-neighborhood search operation is carried out, and final solution is obtained;Whether the current iteration number reaches the maximum iteration number of preset particle group is judged, if yes, the final solution obtained is output as optimal solution.The present application can obtain high-quality scheduling allocation scheme between workpiece and machine and the maintenance time point of each machine in a short time, compared with fixed cycle maintenance, can effectively reduce machine repair frequency, while guaranteeing production efficiency, reach the purpose of cost reduction and efficiency improvement.

The present application belongs to the field of edge computing and automation control technology, and particularly relates to a tray conveying line edge computing intelligent control method. The method comprises: constructing a conveying line node flow field topology model, regarding the conveying line as a discrete particle fluid system and establishing an edge computing unit mapping relationship; sensing a local situation in real time through a sensor, calculating a congestion potential energy gradient and a Reynolds number to quantify a congestion risk; executing an edge game decision, using a Nash equilibrium game algorithm to negotiate with adjacent units to solve an optimal conveying speed strategy; and generating a smooth speed change curve instruction based on the decision result and issuing it to a power actuator to eliminate physical impact. The present application combines distributed edge computing with flow field dynamics modeling, realizes microsecond-level response gain, improves the robustness and throughput of the system, reduces the operation load of the central server, optimizes the energy efficiency performance of the system and prolongs the service life of the equipment.

The invention discloses a killing chain construction method based on a particle swarm algorithm, and belongs to the technical field of cooperative combat systems. The method comprises the following steps: dividing a killing chain construction problem into four links of detection, positioning, interference and strike which are executed in sequence, respectively establishing corresponding task allocation models, and taking maximization of efficiency of each link as a target function; and solving each link model by adopting an improved decimal discrete particle swarm algorithm to obtain an optimal task allocation scheme, and sequentially combining all link schemes to complete the construction of the killing chain. According to the method, the modeling process is simplified, the calculation efficiency is improved, the method is suitable for a large-scale combat scene, an optimization scheme can be quickly generated, and the requirements of actual combat for real-time performance and accuracy are effectively met.

The invention discloses a slurry rheological property calculation method and system, electronic equipment and a storage medium, and relates to the technical field of simulation analysis in a lithium battery manufacturing process. The method comprises the following steps: constructing a physical model for slurry simulation, and setting that a computational domain comprises a fluid phase and a discrete particle phase; establishing a fluid-solid coupling mathematical model, solving a fluid phase by adopting a computational fluid mechanics method, and solving a discrete particle phase by adopting a discrete element method; numerical coupling solution is executed, and flow field information and particle movement and stress information are interacted until flowing is stable; and obtaining the shear force of the fluid phase acting on the wall surface and the total shear force of the discrete particle phase lubricating force acting on the wall surface, and calculating the macroscopic apparent viscosity of the slurry according to the sum of the two shear forces. The method aims to accurately quantify the influence of the microcosmic dispersion state of the particles on the macroscopic rheological property through a numerical simulation means, so that the high-efficiency evaluation of the quality of the slurry is realized.

The invention discloses a multiplexer layout and wiring collaborative design method based on discrete particle swarm optimization. The method comprises the steps of obtaining a control mode distribution scheme of a multiplexer, performing valve information preprocessing based on the control mode distribution scheme, and determining a legal layout interval of each control valve; generating a control valve layout scheme by adopting a discrete particle swarm optimization algorithm, wherein the layout scheme represents column coordinates of the control valves by particle codes; based on the layout scheme of the particle coding, control channel wiring is carried out through an A * algorithm, and the total wiring length is calculated to serve as a fitness value and the like. According to the method, a layout area of each control valve is limited through valve information preprocessing, and then a collaborative optimization framework is constructed by adopting a discrete particle swarm optimization algorithm. Meanwhile, in each round of iteration, an A * algorithm is adopted to carry out control channel wiring on a layout scheme represented by particles. The method effectively improves the layout quality of the control valve and the wiring efficiency of the control channel.

The application relates to a compiler automatic tuning method based on a discrete particle swarm algorithm, which improves the particle swarm algorithm to adapt to a discrete solution space of a compiler optimization problem, and proposes a speed updating strategy combining a crowded distance and fitness, balances the moving speed of particles by evaluating the crowded distance and fitness of solution samples in a region where the current particles are located, selects the best optimization sequence, and thus improves the performance during compiler automatic tuning; solves the premature convergence problem existing in many current discrete particle swarm algorithms and the problem of insufficient global search capability when the problem dimension is high, and significantly improves the tuning effect of compiler automatic tuning.