38 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.

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.

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.

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 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 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 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.

This invention discloses a method for constructing a discrete element model (DEM) of pavement structure fracture performance based on the CZM (Concentrated Zymmetric Model), comprising the following steps: constructing a discrete particle model of the pavement structure using a DEM; classifying particle clusters in the discrete particle model and setting material properties; determining the contact type and contact model between the boundaries of different types of particle clusters; for the bilinear cohesive model, introducing two parameters—the bond strength of the pavement structure and the ratio of the slope of the descending segment to the slope of the ascending segment in the bilinear cohesive model curve—to construct a new bilinear cohesive model; rewriting the code and importing it into the DEM, inputting the two parameters to complete the construction of the CZM-based DEM of pavement structure fracture performance; using the DEM for simulation and conducting real experiments simultaneously, comparing the simulated data with the real experimental data to verify the accuracy of the DEM. This invention fills the gap in applying the DEM based on the cohesive model to three-point bending beam tests of pavement structures.

The invention discloses a photovoltaic cluster division method and device and a storage medium, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: obtaining the node electrical information of a power distribution network, and calculating a feature data set which comprises a sensitivity matrix, the maximum / minimum reactive power of the nodes, and an inter-node equivalent impedance matrix; according to the feature data set, constructing three evaluation indexes, including a sensitivity matrix-based cluster modularity index, a node maximum / minimum reactive power-based intra-cluster reactive power balance index, and an equivalent impedance matrix-based intra-cluster electrical distance index; weight distribution is carried out on the three evaluation indexes through an SMART method, and a comprehensive evaluation index is generated; taking the comprehensive evaluation index as a target function, performing iterative optimization through a discrete particle swarm algorithm, and outputting an optimal photovoltaic cluster division result; according to the method, the reasonability and the high efficiency of a cluster division result can be improved, and a solid foundation is provided for intra-cluster autonomous and inter-cluster coordinated control scheduling tasks of subsequent clusters.

The application relates to the technical field of data processing, in particular to an intelligent production scheduling method for robot cable flexible manufacturing. The method extracts process parameters of a robot cable to be processed, generates a directed acyclic process topology graph containing parallel processes and mutually exclusive processes; establishes a device state space vector containing a device processing die remaining life and a device current temperature control interval; inputs the directed acyclic process topology graph and the device state space vector into a discrete particle swarm algorithm, and maps the processes to device nodes meeting the temperature control interval according to the topology graph level depth; for the nodes with mutually exclusive processes, a neighborhood search strategy based on device resource locking is used to update the particle position, and a production scheduling sequence meeting the physical processing boundary and the dependency relationship is output. The application eliminates the device resource deadlock phenomenon caused by parallel process branches, and shortens the material flow stagnation time in a mixed-line production environment.

A kind of discretization method suitable for complex computational domain of lattice discrete particle model, including 1, concrete component three-dimensional entity modeling;2, round spherical concrete aggregate particle generation;3, on the surface and inside three-dimensional entity randomly throw aggregate particle;4, aggregate inclusion generation.The present application introduces three-dimensional entity modeling before aggregate particle generation and throwing, so that LDPM pre-processing method is more complete, in entity modeling stage, various shapes of entity model can be generated by the establishment, transformation and assembly of parts, so that the pre-processing method can process various shapes of concrete test piece, so that it is more flexible and versatility;The uniformity of aggregate particle size distribution and spatial distribution is improved, so that aggregate particle model is close to real concrete material;Delaunay tetrahedron and each face thereon are marked, so that the pre-processing method has the ability to process complex geometric feature concave entity with notch, with hollow and the like.

The invention discloses a battery swap station battery charging and swap optimization method based on an improved discrete particle swarm optimization algorithm, which enhances the global search capability by introducing crossover and mutation operations in a genetic algorithm, and effectively prevents the algorithm from falling into a local optimal solution. Meanwhile, a reinforcement learning mechanism and a discrete particle swarm algorithm are combined, and the convergence speed of the algorithm is increased. According to the scheme, the operation efficiency of the electric bulk cargo ship route and the battery swap station is improved, and coordination and income maximization of the ship and shore-based'user side energy storage 'are achieved; therefore, an efficient and feasible solution is provided for the electric bulk cargo ship charging optimization problem.

The application relates to a method and system for characterizing laser plasma flow field pressure, comprising the following steps: irradiating the surface of discrete particle accumulation by a pulsed laser to generate laser plasma; adjusting the laser parameters to change the generated laser plasma flow field pressure, and capturing the particle jet formed by the laser plasma flow field acceleration; analyzing the captured particle jet to obtain the particle jet speed or kinetic energy parameters; obtaining the laser plasma flow field pressure according to the obtained particle jet speed or kinetic energy parameters under different laser parameters; and characterizing the laser plasma flow field pressure generated under different laser parameters by the particle jet speed. The application directly relates the particle jet speed to the laser plasma flow field pressure, and constructs a method for conveniently, quickly, timely and in-situ characterizing the laser plasma flow field pressure generated when laser ablation of discrete micro-particle accumulation by measuring the macroscopically visible particle jet.

The invention provides a new energy control parameter adaptive identification method based on double-layer particle swarm optimization, and belongs to the technical field of power system simulation analysis. The typical control strategies of the heterogeneous new energy unit are integrated, and a strategy database is constructed and verified; acquiring test data for processing and feature extraction to obtain high and low pass threshold values, a current initial state and test feature data; and constructing a fitness function based on the strategy library, screening an optimal strategy combination from the strategy library by using a discrete particle swarm algorithm in a global optimization layer, and continuously optimizing a parameter set of a selected strategy in combination with a momentum factor and a learning rate in a local optimization layer. According to the method, the characteristic value of the actual unit test data is extracted as input of the double-layer particle swarm algorithm, the optimal strategy-parameter pair is judged and output through total error index weighting, the accuracy of the strategy-parameter pair is verified, and the efficiency and precision of heterogeneous new energy equipment modeling are effectively improved.

The invention discloses an unmanned aerial vehicle cluster collaborative operation path planning method and device, and belongs to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: distributing a subtask sequence and a corresponding starting and ending time constraint for each unmanned aerial vehicle in an unmanned aerial vehicle cluster, and obtaining a preliminary operation plan; constructing a three-dimensional digital elevation map model based on the unmanned aerial vehicle cluster operation environment data, and generating a path planning total potential field according to the three-dimensional digital elevation map model; and generating an initial path network diagram based on the path planning total potential field and the three-dimensional digital elevation map model, performing arc weight calculation on the initial path network diagram, and performing path planning on the initial operation plan, the path planning total potential field and the arc weight of the initial path network diagram according to a discrete particle swarm algorithm to obtain an optimal operation path. The method can overcome the defects of a traditional path planning method in a three-dimensional environment, guarantees the safety and stability of the path, and improves the motion controllability of an unmanned aerial vehicle cluster in a three-dimensional space.

This invention discloses a port-based truck scheduling optimization system and method based on dynamic time matrix prediction. By collecting TOS (Transportation Time of Sale) and vehicle monitoring data, a dynamic time matrix incorporating crane rhythm and lane impedance factors is constructed, and task results are used to correct the matrix. The system adaptively adjusts the truck resource pool threshold based on a task density function and employs tabu search and discrete particle swarm optimization algorithms for multi-objective scheduling optimization. When there is congestion in the yard or rhythm deviation, task priority and key road markers are considered to trigger task reordering, and algorithm parameters are automatically updated based on execution results. This system achieves a real-time closed loop of prediction-scheduling-feedback, improving port operation efficiency and resource utilization.

The present application relates to the technical field of multi-agent system control, and discloses a kind of leader selection method of multi-object discrete particle swarm optimization based on set operation, comprising: obtaining the communication topology data of multi-agent system and constructing fitness function based on system error and convergence rate;Adopt triple guiding mechanism based on set operation, in turn through individual historical optimal set, global optimal set and neighbor set guide to carry out iterative update to candidate leader set;Different weight parameters of fitness function are traversed, and the corresponding global optimal leader set is output;Finally, based on the difference threshold of fitness value and the screening of Pareto non-difference characteristics, the final leader set is obtained.The present application directly processes discrete nodes using set operation, avoiding information loss caused by binary mapping, and effectively avoiding local optimum through neighbor guiding mechanism, achieving efficient collaborative control of multi-agent system.

The logistics park vehicle pickup scheduling method based on the adaptive discrete particle swarm algorithm has the following specific steps: analyzing the operation data related to the logistics park vehicle, performing K-means clustering on the target goods based on the time of each vehicle entering the park and the time of each vehicle leaving the park and the target pickup goods corresponding to each vehicle; encoding the yard allocation part; encoding the pickup goods part; setting the global search to determine the initial population; setting the local search to determine the initial population; setting the random search to determine the initial population; setting the maximum iteration number of the algorithm as iter; initializing the state set and the action set in the reinforcement learning, selecting the action by using the greedy strategy, and calculating the reward according to the state and the fitness value of the chromosome of the population at the current time. The logistics park vehicle pickup scheduling method based on the adaptive discrete particle swarm algorithm has the following beneficial effects: the waiting time of the vehicle in the pickup process can be significantly reduced, the logistics throughput is improved, and the maximum operation time of the yard is indirectly shortened to meet the demand of the logistics park yard for work efficiency.

The invention provides a shaft core and logging curve data verification homing method and system based on DPSO-ANN evaluation, and belongs to the field of data processing. The method aims at solving the problems that a shaft core depth homing method depends on single similarity measurement and lacks overlapping constraints among shaft cores, and local optimum is caused by manual parameter setting. Comprising the following steps: acquiring basic information of a well, logging curve data, a lithology interpretation result and core experiment data; constructing a neural network based on the stratum, the sedimentary environment, the lithologic characteristics, the logging curve and the experimental data of the calibrated well; constructing an offset search space according to wellbore information and a logging curve sampling interval; for an unverified well, constructing an objective function based on reservoir parameters predicted by a neural network, and constructing a discrete particle swarm optimization model by taking shaft parameter and rock core experiment data overlapping as a constraint term; the method is used for depth homing correction of the wellbore core and reservoir parameter core experiment data, and automatic verification and homing of the wellbore core and reservoir parameter core experiment data and a logging curve are achieved.

The application discloses a real-time updating method for discrete particle interface viscoelasticity parameters based on a genetic algorithm, and comprises the following steps: a material stress-strain model is established based on constitutive parameters in discrete element software, and a simulation stress-strain curve is obtained; an experimental stress-strain curve is obtained as benchmark data; the constitutive parameters are updated in real time based on fitting errors of the simulation stress-strain curve and the experimental stress-strain curve, until the fitting result meets preset conditions, and the current simulation stress-strain curve is output as a final simulation prediction curve. The application realizes automation and intelligentization of the parameter calibration process, and greatly improves the precision of the discrete element simulation.