117 results about "Particle position" patented technology

The invention discloses a multi-algorithm fusion transmission path planning method and device, terminal equipment and a storage medium, and belongs to the field of transmission path planning, and the method comprises the steps: obtaining state data of each node of a power grid, repeatedly executing a position updating operation until a convergence condition is satisfied, and obtaining a target particle position and a target ant position; the position updating operation comprises the step of updating the particle speed and the particle position according to the current particle speed, the particle position and the pheromone; according to the current ant position, the pheromone and the global optimal solution, the ant position and the pheromone are updated; when the convergence condition is not met, taking the updated parameter as the current parameter of the next iteration; otherwise, outputting the updated particle position and ant position; and determining a final transmission path according to the target position. By implementing the method, the advantages of the particle swarm algorithm and the ant colony algorithm can be combined, so that the problem that a single algorithm is easy to fall into local optimum or slow in convergence in transmission path planning in the prior art is solved.

The invention relates to the technical field of data processing, and discloses an AGV real-time path data planning processing method and system based on a particle swarm algorithm. The method comprises the steps of collecting environmental space connectivity, constraint density and dynamic element indexes to calculate topology complexity, adopting two-dimensional search when the complexity is smaller than 2.0, adopting high-dimensional search when the complexity is larger than or equal to 4.0, constructing particle structures of corresponding dimensions, distributing fitness weight coefficients, and updating particle positions and speeds. And when the environmental complexity change exceeds 0.5, switching the dimension and reconstructing a particle swarm, extracting path coordinates from the global optimal particle, verifying a safe distance and space-time conflicts, and outputting path data. The technical problem that a search strategy cannot be dynamically adjusted according to environment complexity due to dimension solidification in an existing AGV path planning technology is solved.

The invention relates to the technical field of data processing, in particular to a control parameter optimizing method and system for an intelligent valve positioner. The method comprises the steps of obtaining a valve position actual feedback value; initializing a particle swarm optimization algorithm, and in each iteration of the particle swarm optimization algorithm, calculating a control performance fluctuation degree based on a valve position set value and an actual feedback value, determining a population evolution state factor by combining an average fitness change of current and historical iterations, and adaptively adjusting an inertia weight according to the population evolution state factor; updating the speed and the position of the particle by using a self-adaptive inertia weight; and after a termination condition is met, outputting an optimal particle position as a PID control parameter combination so as to realize the optimization method of the control parameters of the intelligent valve positioner. According to the method, through real-time dynamic adjustment of the inertia weight, the problem that a traditional static weight strategy is easy to converge too early or oscillation is not convergent under the condition change is solved, and the efficiency and accuracy of parameter optimization are improved.

The invention discloses a security constraint type inverse kinematics solving method and device and a storage medium. The method comprises the following steps: firstly, forming a joint angle-tail end pose pairing data set by students through forward movement of a robot; training an improved RBF (Radial Basis Function) network by using the data set to establish initial mapping from a tail end pose to a joint angle to obtain an initial approximation model; taking the prediction output of the model as an initial population of a PSO algorithm, and updating the speed and position of particles in the initial population through iteration; designing a comprehensive cost function, introducing a joint angle constraint and an obstacle collision constraint, and evaluating the advantages and disadvantages of each particle; and when the PSO algorithm reaches the maximum number of iterations or the comprehensive cost function value is lower than a set threshold value, outputting a global optimal particle position as a final inverse kinematics solution. On the premise of ensuring the solving precision, the joint limit constraint and the obstacle avoidance constraint are explicitly processed, so that an accurate, safe and feasible inverse solution is output, and the reliability and the safety of robot operation are remarkably improved.

The invention relates to the technical field of intelligent loading, and discloses a particle swarm optimization-based three-dimensional loading optimization method and device and a medium, and the method comprises the steps: defining the size and volume of a carriage, generating tray data, and extracting the arrangement sequence and grouping information; calculating the plot ratio of the trays after loading, and analyzing the position vector to obtain a tray arrangement sequence and a grouping scheme; obtaining a single group fitness and an overall fitness; and constructing a particle swarm, initializing particle positions and speeds, then defining position vectors, updating an individual optimal solution and a global optimal solution, and outputting an optimal loading scheme. According to the method, multi-dimensional constraints such as spatial layout, weight distribution and loading and unloading sequence of cargo loading can be effectively fused, and global optimization of a loading scheme is realized. Through a particle swarm intelligent search mechanism, the arrangement and grouping strategy of trays is continuously iteratively optimized, the loading efficiency and the space utilization rate are remarkably improved, meanwhile, the stability and safety in the cargo transportation process are guaranteed, and the good practical application value is achieved.

The invention relates to a three-dimensional flow field drawing method for an ocean flow field. The method comprises the following steps: acquiring target flow field data of an ocean; defining a structural body of the particles according to the target flow field data, and initializing the particles in a three-dimensional flow field of the particle system; calculating a self-adaptive step length according to the speed and viewpoint height of the particle at the current position, calculating a next position after the particle moves for one frame according to a fourth-order Runge-Kutta method and self-adaptive step length integral under the condition that the current position is taken as a starting point, and performing analogy until the particle disappears in a particle system; drawing a streamline according to the moving position of the particles; according to the method, the self-adaptive step length is designed in the fourth-order Runge-Kutta integration process, the particle viewpoint height and speed can be dynamically changed through the step length, errors can be reduced, the particle position calculation precision is improved, and finally the flow field drawing accuracy is improved.

The invention relates to an SPH pollutant transport simulation method based on background grid division particle dormancy. The method comprises the following steps: initializing parallel computing parameters and initializing particle information; copying the to-be-processed data from the CPU end to the GPU end; parallel computing of the SPH method is carried out, wherein the parallel computing comprises the steps of background grid division, particle sorting, background grid state updating, background grid classification, particle searching and computing, dynamic updating grid classification, particle interaction computing solution diffusion, particle position updating, boundary condition processing and repeated iteration; and copying the result data back to the CPU end from the GPU end, and exporting the result data for subsequent analysis and visualization.

The invention discloses a coal rock seepage-stress coupling simulation calculation method, which belongs to the technical field of geotechnical engineering numerical simulation, and comprises the following steps: carrying out gray threshold division on a coal rock sample CT scanning result to obtain a coal rock CT section; identifying particle position information in a coal rock CT section, and establishing a coal rock refined discrete element model; performing mechanical constitutive parameter calibration on the coal rock refined discrete element model; and on the basis of the coal rock refined discrete element model subjected to constitutive parameter calibration, establishing a fluid grid, setting model parameters, and carrying out seepage-stress coupling calculation. According to the method, the real form, size, spatial distribution and mutual inlaying relation of the mineral components in the coal rock can be accurately captured, and the most real basis is provided for accurate simulation of subsequent mechanical behaviors.

The invention discloses a dynamic optimization method based on a particle swarm optimization algorithm, and relates to the technical field of intelligent optimization algorithms, and the method comprises the steps: initializing the particle swarm optimization algorithm; calculating a target vector value of each particle in the particle swarm, executing Pareto comparison, screening the first 15% of particles as elite particles, and updating the particle swarm to join in optimization; adopting Alpha-Stable distribution to generate random disturbance, and executing dynamic mutation operation on particle positions; maintaining and updating the external archive storing the non-dominated solution; s4, performing quantitative evaluation on the current iteration state of the algorithm based on a preset convergence judgment criterion, and if the convergence condition is not met, returning to S3 and S4 to perform loop execution until the convergence condition is met, and terminating the iteration process; according to the method, aiming at the performance defects of the particle swarm, the optimization performance of the algorithm is pertinently improved in different stages through a dynamic variation mode, and a feasible solution is provided for the dynamic variation of the particle swarm algorithm.

The invention discloses a cross-hardware cooperative operator optimization method and system based on a particle swarm algorithm, and relates to the technical field of operator optimization. Aiming at the technical bottleneck existing in the current cross-hardware platform cooperation scheme, the method comprises the following steps: S1, configuring an operator adaptation rule according to hardware characteristics; s2, coding the operator-hardware allocation scheme into a particle position vector based on an operator adaptation rule; s3, collecting real-time performance data of the hardware; designing a fitness function including hardware throughput, hardware calculation delay and hardware utilization rate, and realizing collaborative optimization of the three by minimizing the function value; and S4, taking the particle position vector coded in the S2 as an optimization object, taking the fitness function value designed in the S3 as an evaluation standard, continuously adjusting a hardware allocation result of each operator in the vector through iterative updating of the particle swarm algorithm, finally finding a position vector with the minimum fitness value, and correspondingly outputting an optimal operator-hardware allocation scheme. The method is suitable for a computing cluster formed by heterogeneous hardware groups.

The invention discloses a free metal particle trajectory simulation method and system based on a deformable grid, and the method is characterized in that the method comprises the following steps: 1, building a particle motion model, and building a density function with free metal particles as key points; 2, when the positions of the particles change, updating the positions of the particle nodes in the background grids and the grids, calculating the grid quality and collecting low-quality grids; step 3, based on a Riemannian gradient control method, establishing a grid size field for each reconstruction range, and performing local grid reconstruction by adopting a leading edge propulsion method; and step 4, simulating particle motion according to the boundary condition of the particle motion.

The application discloses a kind of process database construction method and medium based on additive manufacturing, belong to the field of additive manufacturing, method includes: S1, the process data of product obtained by additive manufacturing under different process parameters is acquired by experiment, to construct initial process database;S2, the preset neural network model is optimized and trained;S3, when the target process data of user is acquired, and there is no matching case in the current process database, execute S4-S5;S4, generate target process parameters using particle swarm optimization algorithm and input neural network model, to obtain the corresponding performance data estimate value, and adjust the particle position in particle swarm optimization algorithm according to performance data estimate value;S5, repeat S4 until the performance data estimate value obtained is greater than target performance data, or reach the set repetition number.It has important significance to the transformation of SLM process to standardization, stabilization, batch production.

The application discloses a kind of multi-dimensional evaluation and anchor point constraint scoring method of particle shape, belong to the technical field of particle shape analysis.The method includes: obtaining the three-dimensional grid model of particle sample and pre-processing;Extract the multi-dimensional shape features including corner angle, sphericity, size, symmetry, texture and taper;Filter key features by multi-model voting;Group key features according to preset dimension and carry out adaptive dimension reduction, construct multi-dimensional evaluation space;Determine anchor point sample set for each evaluation axis, construct two-classification sub-problems for the evaluation axis containing intermediate anchor point, determine initial scoring parameter using seven-model performance weighted integration;Based on the score relationship of low-end, intermediate and high-end anchor points, the initial parameter is corrected by ridge regression, and the final scoring parameter is obtained by sequential monotonicity check;Based on the final scoring parameter, the multi-dimensional score of the particle to be scored is carried out and the result is output.The application realizes the multi-dimensional automatic evaluation of particle shape, has the advantages of feature selection objectivity, weight distribution accuracy, intermediate morphological particle position rationality, dimension reduction method adaptivity, visualization richness and the like.

The application discloses a particle screenshot method and related equipment, the method comprises the following steps: acquiring a particle image to be processed, and cutting a field of view region from the particle image based on a preset frame selection range; performing integral projection on the field of view region to obtain a one-dimensional sequence group; performing weighted standardization on the initial one-dimensional sequence based on the field of view size of the field of view region in the projection direction to obtain a weighted one-dimensional sequence; performing smoothing processing on the weighted one-dimensional sequence to obtain a smoothed one-dimensional sequence; performing local peak marking based on the smoothed one-dimensional sequence to obtain a local peak position; converting the local peak positions corresponding to two projection directions of the horizontal direction and the vertical direction to obtain a particle position; and cutting a particle screenshot of a target particle from the field of view region based on the particle position. By introducing the weighted standardization link based on the field of view size, and combining the bidirectional integral projection, sequence smoothing and local peak detection, the particle positioning and screenshot can be efficiently and accurately realized. The application can be widely applied in the field of image processing technology.

The invention discloses a numerical simulation method for soil erosion caused by rainfall. The method comprises the following steps: establishing a numerical model and boundary conditions of a simulation area; modeling a soil particle system by adopting a discrete element method; modeling a wind and fluid-solid interaction system by adopting computational fluid dynamics; modeling rainfall by adopting a discrete element method; performing CFD-DEM coupling solution and erosion process simulation, calculating movement of all soil particles and raindrops under related acting force in each time step, and updating positions and speeds of the movement; according to the updated particle positions, the porosity in each CFD grid unit is calculated; based on the relative velocity of the particles and the fluid; fluid acting force borne by each particle is calculated and aggregated into a source item in a CFD grid unit; substituting the calculated porosity and the source item into the model, and solving the updated fluid velocity field and pressure field; key interaction judgment and implementation are carried out; iteratively circulating until the set total simulation time is reached; and outputting related data of the soil particles in the simulation process.

The application discloses a bridge scour simulation and boundary setting method for SPH water-sand two-phase flow, and comprises the following steps: model construction, including setting a bed sand layer area range and defining an inlet and outlet boundary area range; particle construction, including setting fluid particles and bed sand particles of a flow field area based on a multiphase flow theory, setting soil yield strength based on a HBP model and introducing a DP yield criterion, initializing inlet and outlet boundary particle parameters, setting particle type identification, and setting particle position identification; N-S control equation solving, obtaining corresponding flow force parameter results; entering a sediment solving module, including sediment particle conversion and viscosity updating calculation; entering an inlet and outlet boundary updating module, including inlet and outlet particle position retrieval, particle type conversion, and particle supplement and deletion; completing a calculation cycle and entering the next cycle; through secondary development based on the SPH multiphase flow theory, the application can realize numerical simulation of foundation local scour by applying the inlet and outlet boundaries to the multiphase flow model and introducing various structure and state models.

The invention discloses a particle screenshot method and related equipment, and the method comprises the steps: obtaining a to-be-processed particle image, intercepting a visual field region from the particle image based on a preset frame selection range, carrying out the integral projection of the visual field region, and obtaining a one-dimensional sequence group; performing weighted standardization on the initial one-dimensional sequence based on the view size of the view area in the projection direction to obtain a weighted one-dimensional sequence; smoothing the weighted one-dimensional sequence to obtain a smooth one-dimensional sequence; performing local peak marking based on the smooth one-dimensional sequence to obtain a local peak position; according to the local peak positions corresponding to the transverse and longitudinal projection directions, particle positions are obtained through conversion; and capturing a particle screenshot of the target particle from the view area based on the particle position. The particle positioning and screenshot can be efficiently and accurately realized by introducing a weighting standardization link based on the view size and combining the bidirectional integral projection, the sequence smoothing and the local peak detection. The method can be widely applied to the technical field of image processing.

The present invention discloses a method for simulating weakly compressible fluid smooth particles based on particle position correction, comprising the following steps: 1. Smooth particle modeling of weakly compressible fluid; 2. Smooth particle position correction; 3. Using a computer to input the corrected position vector of the i-th particle, and using a position-based Verlet algorithm to perform time iteration on each particle in the SPH particle model to obtain an updated particle flow field distribution; and 4. Obtaining fluid state parameters from the updated particle flow field distribution. The method of the present invention has simple steps and a reasonable design. By correcting the position of fluid particles, a uniform distribution of simulated particles is achieved without introducing physical parameters, resulting in better applicability to complex flows and improved stability and accuracy of subsequent simulation calculations.

The present invention relates to a numerical calculation method for the long-distance transmission of relativistic charged particle beams, which solves the problems of inaccurate calculation methods of empirical formulas for the long-distance transmission of high-energy particle beams and great difficulty in numerical calculation in the current fields of space magnetic field measurement and accelerator system design. First, set the initial parameters of the particle beam in the laboratory reference frame, and obtain the positions and velocities of all particles after emitting a single pulse under these parameters. Then, according to the Lorentz transformation formula, transform the positions, velocities of all particles and the external electromagnetic field into an inertial reference frame with a certain intermediate velocity of particle motion. Next, perform numerical calculations in the new reference frame to obtain the positions and velocities of the particles in the final state. Finally, according to the Lorentz transformation formula, transform the positions and velocities of the particles in the final state in the new inertial reference frame back to the laboratory reference frame, and the state of the particle beam in the final state of the laboratory reference frame can be analyzed.

The application provides a nondestructive characterization method of explosive small component particle migration distribution based on mu CT, and the method comprises the following steps: step one, establishing a coordinate system for describing the position of component particles; step two, determining the position of a single particle; step three, describing the position of the single particle in different states; and step four, constructing a migration distribution characterization model. The method of the application establishes a position description system of explosive component particles by taking mu CT as a technical means, determines a single particle position description method, and constructs a component migration distribution characterization model by describing the position of different components and different particles under different conditions, so that the migration distribution change of explosive grain internal small component particles is nondestructively and finely characterized, and a foundation is laid for migration mechanism research.

The invention relates to an intelligent pipeline detector based on a positron emission particle tracer technology and a detection method, and the pipeline detector comprises a tracer particle putting module which is used for quantitatively putting tracer particles into a pipeline; the positron camera module is used for collecting positron pair annihilation events released by the tracer particles in a preset time window and generating multiple pieces of coincidence response line data; the tracer particle positioning module is used for calculating a midpoint between the data conforming to the response line, performing weighted aggregation to obtain an approximate intersection point set, and performing weighted clustering processing on the approximate intersection point set to obtain a final estimated position of the tracer particle; and the pipeline state visualization module is used for performing line connection according to the final estimated positions of the tracer particles to form a track curve of each tracer particle, comparing the track curve with the pipeline boundary image, identifying an abnormal area and performing visual display. According to the invention, the corrosion wear or deposition in the pipeline is visualized, and the corrosion wear or deposition degree is accurately measured.

This invention discloses a multi-objective fuzzy flexible job shop scheduling method with multi-layer learning assistance, relating to the field of job shop scheduling technology. The method includes the following steps: constructing and initializing two populations to generate particles; constructing an external shared archive; building a queue for each particle; calculating the position fitness of the particles; enqueuing the particle positions into the queues; and selecting the position of the particle with the best dominant fitness in each population and assigning it the value gBest. q Update the external shared archive; select the position with the best dominant fitness in each particle queue, denoted as spBest. p q The particle position and velocity are updated using a multi-layer learning strategy; crossover and mutation operations are performed on the population to complete the population update; the updated positions are enqueued into the queue; and gBest is updated. q ; Simulated annealing method for gBest q Apply perturbations; update the external shared archive. Repeat the iteration until completion. This ultimately improves the exploration efficiency and convergence speed of multi-objective Pareto fronts.

The application discloses a mobile device path planning method and an electronic device. The mobile device path planning method comprises the following steps: constructing a fitness function of mobile device path planning; initializing a particle swarm population, and a particle position is used for encoding a candidate path composed of multiple intermediate path point coordinates; performing multiple iterations of optimization, in each iteration, after each velocity and position update is completed, performing a dimension-by-dimension optimization operation on the entire population, the dimension-by-dimension optimization operation searches the path point coordinates of the candidate path encoded by all particle positions in the population dimension by dimension, and optimizes the corresponding path point coordinates in the global optimal position; after the iterations are completed, determining an optimal path based on the path encoded by the global optimal position. The application overcomes the defect that the effective local feature is lost due to simple elimination of low fitness particles in the existing PSO algorithm; meanwhile, repeated calculation of full path evaluation is avoided, and the real-time performance and high efficiency of algorithm operation are ensured while the optimization precision is effectively improved.

The invention discloses a QPSO (Quadrature Particle Swarm Optimization) algorithm-based method for determining a two-way memory alloy component ratio, which belongs to the field of memory alloy ratios and is used for determining the memory alloy component ratio and accurately regulating and controlling the temperature sensitivity and elongation error of a material so as to enable a spring to keep stable deformation performance in repeated thermal cycles and prolong the service life of a device. The method mainly comprises the following steps: determining a multi-target fitness function according to a two-way shape memory effect alloy element; the optimized parameter is the proportion of each element, a four-dimensional solution space is formed, a random particle population is generated, the number of particles is N, and the maximum number of iterations is stipulated as S; evaluating a fitness value of each particle based on a fitness function; updating particle positions; and calculating the fitness value of each particle at the new position, and updating the individual optimal position Pb of the particle and the global optimal position Gb of the particle swarm. The method is mainly used for matching the components of the two-way memory alloy.

This invention provides a vision-based method and system for detecting and compensating wafer particle position offset. It generates an initial imaging data set containing the surface microstructure by performing dual-view synchronous imaging on the wafer surface. Parallax matching and feature segmentation are then performed on the initial imaging data set to generate three-dimensional height information and an initial set of particle coordinates on the wafer surface. Based on the initial particle coordinate set and the charge distribution characteristics of the wafer surface, particle displacement is predicted and corrected, generating displacement compensation feature quantities for each particle. The fine-tuning optical system of the wafer inspection machine is invoked to perform optical correction on the displacement compensation feature quantities, generating corrected real-time imaging data. Coordinate registration is performed on the real-time imaging data to generate the final set of wafer particle position coordinates. This invention can improve the spatial dimensional integrity of wafer particle position detection, the comprehensiveness of multi-factor displacement prediction, and the actual physical accuracy of the detection results.

The invention provides an ambiguity resolving method for hybrid differential evolution and improved particle swarm optimization. And determining various parameters of the algorithm according to the ambiguity resolving space range determined by the floating point solution. And the global search capability of the particles is enhanced according to the set inertia weight, so that local optimum is avoided. In order to solve the problem of population homogenization in the later stage of the particle swarm optimization algorithm, a variation-crossover-selection mechanism of differential evolution is integrated, and particle positions are optimized. And adjusting particle positions according to a particle swarm optimization algorithm position updating formula to obtain a new candidate ambiguity solution. And through boundary processing and calculation of weighted least square cost of each particle position, individual optimum and global optimum are updated. And when the number of iterations reaches a set threshold value, outputting global optimum. Compared with a traditional particle swarm optimization algorithm, the method has the advantages that local optimum is avoided, the convergence speed is increased, and the method is more suitable for fast and reliable ambiguity fixing requirements in GNSS high-precision positioning.

The invention discloses a particle position prediction method and system in a particle field based on a deep learning network, and the method comprises the steps: obtaining a particle field hologram, and marking the particle position; constructing a data set according to the particle field hologram of the known particle position; dividing the data set into a training set, a verification set and a test set; inputting the training set into the deep learning network, training the network, verifying the trained network by using the verification set, and storing the trained network; inputting the test set into the trained network to obtain the coordinate position and confidence of each particle; wherein the deep learning network is realized through improved YOLOV5s; according to the improved YOLOV5s, a multi-spectrum channel attention module is added to a backbone network of an original YOLOV5s network, and the multi-spectrum channel attention module is used for improving the capturing capability of the network on particle stripes.

This application discloses a multi-strategy adaptive particle swarm optimization method and apparatus for dynamic parking space scheduling, belonging to the field of parking space scheduling technology. The method includes: cleaning flight and parking space data to construct a flight-parking space compatibility matrix; mapping particle positions using continuous real-number encoding and initializing the population through an OBL (Optimal Boundary Learning) strategy; adaptively adjusting the inertia weight AIW based on population diversity and combining it with a random migration RI (Increase in Randomization) strategy to avoid local optima; applying boundary constraints and conflict resolution to particle positions, and using a DA (Data Determination) mechanism based on congestion distance to preserve non-dominated solutions; and outputting the optimal parking space scheduling scheme through multi-attribute decision-making after the iteration meets the termination condition. This application improves the algorithm's convergence speed and global optimization capability through multi-strategy collaborative optimization, reduces the proportion of infeasible solutions, increases parking space utilization and flight docking rate, and balances passenger travel experience with airport operational efficiency.

The application discloses a kind of PFC system real-time energy efficiency optimization method and system based on dynamic weight particle swarm algorithm, comprising: first, the multidimensional parameter search space of PFC system is constructed, and system operating state parameter is collected in real time. Then, initialize particle swarm population, and iteration optimization is carried out using dynamic weight particle swarm algorithm. Algorithm is updated particle position and speed by dynamically adjusting inertia weight, combining particle own historical optimum and population global optimum information, and finally finds global optimum energy efficiency parameter combination. Finally, according to the combination generation control instruction and issue to each execution unit of PFC system, realize the real-time adjustment of parameter and the closed-loop optimization of system energy efficiency. The application effectively improves the optimization speed and global optimization ability, and guarantees the efficient and stable operation of PFC system.

Technologies for particle scaffold analysis include a computing device that obtains labeled input data indicative of particles positioned in a scaffold domain. The input data may be generated by an imaging system coupled to the computing device or may be generated by simulating a physical system. The computing device determines a cumulative Euclidean distance transform (EDT) for each voxel of void space of the scaffold domain, and determines multiple medial axis landmarks based on the EDT and on a particle configuration. The particle configuration is indicative of a neighboring particle graph. The computing device segments the void space into multiple subunits based on the medial axis landmarks. The computing device may determine multiple scaffold descriptors based on the subunits. Other embodiments are described and claimed.