Simulation method, device and equipment for concrete pouring process and storage medium

Abstract

The embodiment of the invention discloses a simulation method and device for a concrete pouring process, equipment and a storage medium, and relates to the technical field of digital data processing. The method is applied to a simulation platform, and comprises the following steps: receiving configuration data corresponding to to-be-simulated pouring concrete; the configuration data comprises particle attributes of concrete particles; inputting the particle attributes into a concrete pitted surface prediction model to obtain a particle distribution result; wherein the concrete pitted surface prediction model simulates the pouring process of the to-be-simulated pouring concrete at least based on the configuration data; the particle distribution result comprises the three-dimensional diagram of the concrete particle distribution, so that the simulation efficiency of the concrete pouring process is improved, the formation mechanism of the concrete pitted surface can be disclosed, and accurate guidance is provided for optimizing the concrete mix proportion.

Description

Technical Field

[0001] The present application relates to the technical field of digital data processing, and in particular to a simulation method, device, equipment and storage medium for a concrete pouring process. Background Art

[0002] During the concrete pouring process, there are small pits on the concrete surface and the surface is not smooth and flat due to unclean air removal or leakage of concrete slurry. This is called concrete pockmarking. The pockmarking mainly affects the appearance of the concrete. The parts that are no longer decorated should be repaired. Therefore, mastering and predicting the formation process of pockmarking during concrete pouring can provide a theoretical basis for optimizing the concrete mix ratio and preventing and eliminating pouring defects.

[0003] Currently, a real concrete pouring model is used to simulate the pouring process, but it requires a lot of manpower and material resources and the simulation efficiency is low. Summary of the invention

[0004] The embodiments of the present specification provide a method for simulating a concrete pouring process to solve the problem of low efficiency in simulating a concrete pouring process in the prior art.

[0005] To solve the above technical problems, the embodiments of this specification are implemented as follows: In a first aspect, a simulation method for a concrete pouring process provided in an embodiment of the present specification is applied to a simulation platform, comprising: Receiving configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles; The particle properties are input into a concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

[0006] In a second aspect, an embodiment of the present specification provides a simulation device for a concrete pouring process, which is applied to a simulation platform and includes: A receiving module, used to receive configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles; A determination module is used to input the particle properties into a concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

[0007] In a third aspect, an embodiment of the present specification provides a simulation device for a concrete pouring process, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the simulation method for the concrete pouring process in scheme one.

[0008] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the simulation method of the concrete pouring process in scheme one.

[0009] An embodiment of the present specification realizes the following beneficial effects: simulating the pouring process on a simulation platform, inputting the received configuration data corresponding to the concrete to be simulated poured into a concrete surface prediction model, obtaining particle distribution results, improving the simulation efficiency of the concrete pouring process, helping to reveal the formation mechanism of concrete surface, and further providing accurate guidance for optimizing the concrete mix ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 A schematic diagram of a flow chart of a simulation method for a concrete pouring process provided in an embodiment of this specification; Figure 2 Another schematic flow chart of a simulation method for a concrete pouring process provided in an embodiment of this specification; Figure 3 A schematic diagram of the structure of a simulation device for a concrete pouring process provided in an embodiment of this specification; Figure 4 A schematic diagram of the structure of a simulation device for a concrete pouring process provided in an embodiment of this specification. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0013] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0014] A simulation method for a concrete pouring process provided in an embodiment of the specification is specifically described in conjunction with the accompanying drawings.

[0015] Figure 1 A flow chart of a simulation method for a concrete pouring process provided in an embodiment of this specification. Applied to a simulation platform, LS-DYNA under ANSYS Workbench (simulation software) can be used as a simulation platform. LS-DYNA is a fully functional geometric nonlinear, material nonlinear and contact nonlinear program. The computer simulates the flow pouring process of concrete mixtures on the simulation platform, providing numerical simulation means for research in the fields of concrete material science and structural engineering.

[0016] like Figure 1 As shown, the process may include the following steps: Step 110: receiving configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles.

[0017] In the embodiment of this specification, the concrete to be simulated poured in the concrete casting mold is discretized into a number of particles, and the configuration data may include particle properties of the concrete particles, the geometric shape and size of the mold, boundary conditions, etc., and parameters are configured for each concrete particle.

[0018] Particle properties can be used to describe the basic physical and chemical properties of concrete materials. Preset the ratio of raw materials such as cement, sand, and aggregate to obtain concrete. Particleize the concrete and set the particle properties based on empirical values.

[0019] Boundary conditions can refer to the interaction conditions between concrete and the external environment or internal structure during the pouring process. Boundary conditions can include temperature boundaries, displacement boundaries, load boundaries, contact conditions, etc.

[0020] A reasonable number of concrete particles are set in the concrete casting mold, and the particles are reasonably distributed to ensure uniform spatial distribution of the particles and avoid local over-crowding or over-sparseness to ensure the accuracy and stability of the simulation.

[0021] Step 120: Input the particle attributes into a concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

[0022] In the embodiments of this specification, the concrete surface prediction model simulates the pouring process of the simulated concrete based on the input particle properties, boundary conditions and pre-set configuration data (such as simulation time, time step, number of particles, etc.). This simulation process simulates the movement and interaction of concrete particles and the deformation and flow behavior of the entire concrete, thereby revealing the formation process of defects such as surface pitting on the concrete surface.

[0023] Through simulation calculation, the model will output particle distribution results, which may include particle distribution diagrams and stress-strain distribution diagrams. The particle distribution diagram may include a three-dimensional diagram of concrete particle distribution, which may intuitively display the distribution of concrete particles during the pouring process.

[0024] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification can be interchanged according to actual needs, or some steps can be omitted or deleted.

[0025] In the embodiments of the present specification, the pouring process is simulated on a simulation platform, and the configuration data corresponding to the received concrete to be simulated is input into a concrete surface prediction model to obtain a particle distribution result, thereby improving the simulation efficiency of the concrete pouring process, helping to reveal the formation mechanism of concrete surface, and further providing accurate guidance for optimizing the concrete mix ratio.

[0026] based on Figure 1 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0027] Optionally, the inputting of the particle attributes into the concrete surface prediction model to obtain the particle distribution result in the embodiments of this specification may specifically include: According to the particle properties, a linked list search method is used to perform field particle search and pairing to obtain adjacent concrete particles; wherein the particle properties include at least initial particle density and initial particle position; Calculating the current particle density of the concrete particles using a continuity density method according to the initial particle density and the initial particle position of the adjacent concrete particles; A particle distribution result is obtained according to the current particle density.

[0028] In the embodiments of this specification, the particle properties may include initial particle density, initial particle position, initial particle velocity, initial particle mass, initial particle viscosity, and the like.

[0029] The linked list search method is used to divide the simulation area of ​​concrete pouring into regions, and pairing search is performed in the target area, which can avoid repeated pairing, save search time, and reduce the amount of calculation. The smooth kernel function is a function that decays with distance. It determines how the particle properties "diffuse" to the surrounding space, and this effect gradually decreases with the increase of distance. The smooth kernel function not only determines the form of the function approximation, but also defines the size of the particle support domain, and affects the consistency and accuracy of the kernel approximation and particle approximation. Usually, the size of the smooth kernel function support domain is set to kh, which is generally k=2. The size of the grid space is consistent with the size of the particle support domain. If the size of the smooth kernel function support domain is 2h, the temporary space grid size is also set to 2h. The particle linked list search method is to set a series of temporary grids in the entire problem domain. The temporary grid can cover all particles in the simulation area. The concrete particle i in a certain grid is specified. When the particle spacing between concrete particle j and concrete particle i is less than or equal to kh, they can be matched. Concrete particle j is the adjacent concrete particle of concrete particle i.

[0030] According to the position and properties of adjacent concrete particles, the density, pressure and other field quantities of concrete particle i are calculated. During the calculation process, the interaction forces between particles (such as pressure, viscosity, etc.) and external forces (such as gravity, etc.) need to be considered.

[0031] The density of any concrete particle i is calculated by summing the continuous density method and then approximating it with the SPH method. The density change rate of concrete particle i is obtained by accumulating the mass and velocity of the concrete particle j interacting with it in the support domain of concrete particle i. The density change rate formula is:

[0032] in, represents the density of concrete particle i, t represents time, It means solving the time derivative of density for concrete particle i, represents the density of concrete particle j interacting with concrete particle i in its support domain, represents the weighted sum of the particles interacting with concrete particle i in the support domain, N represents the total number of particles interacting with concrete particle i in the support domain, represents the mass of concrete particle j, represents the velocity difference between concrete particle i and concrete particle j, Represents the derivative of a smooth function.

[0033] The current particle density is determined by the density change rate, and then the particle distribution result after pouring is obtained.

[0034] Furthermore, the inter-particle force can affect the acceleration of the particle, thereby changing the position and velocity of the particle. The force on the concrete particle i is the resultant force of pressure, gravity, viscosity and the interaction force between particles. The resultant force calculation formula is: , , , , , in, represents the resultant force on concrete particle i, represents the pressure on concrete particle i, represents the viscous force on concrete particle i, represents the gravity on concrete particle i, represents the interaction force between concrete particles (including damping force and repulsive force between particles), ∑ j represents the weighted sum of the particles interacting with concrete particle i in its support domain, , denote the pressure values ​​of concrete particle i and concrete particle j respectively, which are obtained by solving the state equation. represents the square of the distance between concrete particles, represents the gradient of a smooth function, , denote the dynamic viscosity coefficients of concrete particles i and j, respectively, represents the speed of sound, is a constant, represents the initial density.

[0035] The calculation of particle interactions is the basis of time integration and particle state update. It determines the calculation results of particle acceleration, thus affecting the particle motion trajectory and state update.

[0036] In the SPH method, the movement of particles follows Newton's second law, and the acceleration of particles is proportional to the force and inversely proportional to the mass. In order to solve the trajectory of the particle, the particle's dynamic equation needs to be time-integrated. The time integration process is actually a process of gradually deriving the state of the particle at different time points. In each step of the time integration, the particle's acceleration needs to be calculated based on the current particle's position and velocity, as well as the interaction force between particles. Then, the acceleration and time step are used to update the particle's velocity and position. Therefore, the calculation of the interaction between particles determines the calculation result of the particle's acceleration, which affects the particle's trajectory.

[0037] After each time step, the particle's velocity and position need to be updated according to the particle's acceleration and time step to obtain the particle's state at the new time point. The impact of the calculation of particle interactions on particle state updates is mainly reflected in the calculation of acceleration. Since acceleration is determined by the interaction force between particles, the calculation result of particle interactions will directly affect the value of acceleration, and the value of acceleration determines the update amount of particle velocity and position, thereby affecting the particle's state at the new time point. Specifically, if the interaction force between particles is not calculated accurately, then the value of acceleration will be inaccurate, which will lead to inaccurate updates of particle velocity and position, which will affect the accuracy and stability of the entire simulation process.

[0038] Based on the results of the time integration, the particle's position and velocity can be updated, and the updated state information will be used as the initial conditions for the next time step for iterative calculation. During the update process, it is necessary to ensure that the particle's physical properties (such as mass, density, etc.) remain unchanged to maintain the conservation of the simulation.

[0039] When the calculation is completed for the first time step, the density and velocity advance half a time step from the initial state, and the particle position advances one time step. At the beginning of each time step, the particle velocity and density advance half a time step again to obtain the value on the integer time step, so that it can be consistent with the particle position at all times. Every time a time step ends, the particle density, velocity and position advance one time step. By controlling the time step, the stability of the numerical integration is maintained. The formulas for updating the density, velocity and position of concrete particles are: , , , in, v i represents the velocity of concrete particle i, x i represents the position of concrete particle i, n represents the current iteration time step, It means predicting half an iteration time step, n+1 means predicting 1 iteration time step, and Δt is the time step length.

[0040] In practical applications, the prediction results of the model are compared with the experimental results or actual observation data. Based on the verification results, the model can be optimized and adjusted to improve the prediction accuracy and applicability.

[0041] Optionally, the method described in the embodiments of this specification may also include: Determining whether the surface concrete particles in the three-dimensional image are evenly distributed; If yes, it is determined that the concrete to be simulated poured does not contain concrete roughness; If not, then determine the target area where the concrete particles are unevenly distributed, and the target area is the concrete surface.

[0042] In the embodiments of this specification, the concrete surface prediction model can output a visualized three-dimensional concrete pouring process simulation result. By rotating, scaling and moving the three-dimensional model, the distribution of concrete particles can be observed from different angles.

[0043] Uniformity can mean that the number of particles per unit area on the 3D chart layer remains consistent or similar. Carefully check the distribution of the surface concrete particles in the 3D chart. If the concrete particles are distributed relatively evenly on the surface of the 3D chart without obvious aggregation or sparse areas, then the distribution of the concrete particles can be considered uniform, and it can be determined that the concrete to be simulated poured has no concrete roughness.

[0044] If there are obvious particle aggregation areas or sparse areas, or the distribution of particles shows obvious regularity (such as stripes, blocks, etc.), it means that the distribution of concrete particles is uneven, and the target areas of uneven distribution of concrete particles can be further determined. These target areas are the locations of the concrete surface. In practical applications, the particles have a density in their initial state, and they also have a density after filling. If the density of particles after filling is equal to the initial density of particles, it indicates that the particles are evenly distributed. If the density of particles after filling is not equal to the initial density of particles, it indicates that the particles are unevenly distributed.

[0045] The concrete pitting prediction model can output stress-strain distribution diagrams during concrete pouring. By analyzing these distribution diagrams, stress or strain concentration areas can be identified, which are often related to the formation of surface pitting, which is conducive to the determination of concrete pitting.

[0046] In the prior art, the prediction model is established by using the coupling system of the existing finite element method (FEM) and discrete element method (DEM). However, the mesh of FEM is prone to distortion, resulting in reduced calculation accuracy or even simulation failure. Although this problem can be alleviated by techniques such as mesh re-division, it will increase the computational complexity and cost. During the simulation process, DEM needs to iteratively calculate a large number of particles or blocks to track their motion trajectories and interactions, and needs to process a large amount of spatial information and contact judgment between particles. At the same time, the numerical simulation of DEM involves complex data structures, grid retrieval, determination of adjacent blocks, and judgment of the generation or cancellation of block contact, all of which require a large amount of memory and computing resources, especially when dealing with large-scale particle systems, so the requirements for computer hardware are higher. In addition, the coupling algorithm needs to handle the calculation tasks of FEM and DEM at the same time. It not only needs to process the mesh calculation of FEM and the particle calculation of DEM, but also needs to consider the time step selection of FEM and DEM at the same time to ensure the synchronization and stability between the two, which takes up more computing resources and memory resources.

[0047] In order to solve the defects in the prior art, optionally, before inputting the particle properties into the concrete surface prediction model in the embodiments of this specification, the method may also include: Based on the smooth particle hydrodynamics algorithm, a concrete surface prediction model is constructed.

[0048] In the embodiments of this specification, a smoothed particle hydrodynamics (SPH) algorithm is used to establish a concrete surface prediction model. Specifically, (1) a three-dimensional model filled with concrete is built and imported into a simulation platform; (2) the model material is set, with structural steel used in the cavity and concrete used in the particles; (3) a constitutive model is selected for the concrete particles; (4) the cavity and particles are meshed; (5) a keyword file is exported and opened in the LS-Prepost operating platform; (6) the file is imported into the LS-DYNA solver for simulation calculation; (7) after the simulation is completed, the simulation results are extracted using the LS-Prepost operating platform.

[0049] Concrete is described by a series of arbitrarily distributed particles that carry various physical quantities, such as mass, velocity, etc. This point-based approximation can completely or partially eliminate the grid, thereby avoiding the initial division and reconstruction of the grid. Since there is no grid relationship between the particles, the SPH method can avoid problems such as accuracy loss caused by grid distortion under extremely large deformations. When dealing with the interface between the formwork and concrete, this meshless feature enables the SPH method to more accurately capture the deformation and dynamic behavior of the interface.

[0050] The kernel function is used for interpolation, and the overall field is approximated by calculating the local field variables around each particle. This kernel approximation method makes the SPH method highly adaptive and flexible, and can handle various complex physical phenomena. At the same time, because the SPH method does not rely on the grid, it can more conveniently and accurately simulate the interaction and deformation behavior between the formwork and concrete.

[0051] In practical applications, the simulation platform includes the LS-Prepost operating platform and the LS-Run running platform. The concrete surface prediction model is built on the LS-Prepost operating platform. The LS-Prepost and LS-Run platforms provide powerful pre- and post-processing functions and numerical calculation capabilities, supporting high-precision simulation and analysis. Among them, the LS-Prepost platform provides a wealth of graphics processing functions, which can intuitively display simulation results, such as particle distribution diagrams, stress-strain distribution diagrams, etc., and can easily adjust model parameters and view analysis results through an interactive interface. At the same time, the LS-Prepost platform provides advanced data analysis functions, such as calculus, fast Fourier transform, etc., which can conduct in-depth analysis and processing of simulation results. Through data analysis, key information such as stress-strain distribution, particle motion trajectory, etc. can be extracted, thereby improving calculation accuracy.

[0052] The LS-Run platform has efficient numerical computing capabilities and can quickly handle large-scale numerical simulation tasks. Through numerical simulation, the real concrete pouring process can be simulated on the computer, avoiding material waste and labor costs in actual construction, reducing uncertainty and risks in experiments, and improving the reliability and accuracy of research.

[0053] Before or after numerical simulation, experimental verification and calibration can be performed to ensure the accuracy of the simulation results. By comparing and analyzing with actual experimental results, model parameters and algorithms can be adjusted to improve calculation accuracy.

[0054] Figure 2 Another flowchart of a simulation method for a concrete pouring process proposed in an embodiment of this specification.

[0055] Step 210: Establishing a concrete surface prediction model; Step 220: Initialize the design of particles. Discretize the concrete to be simulated into a number of particles, initialize the design of the particles, and input the particle properties into the concrete surface prediction model; Step 230: Establishing the interaction between particles. Use the linked list search method to search and pair the field particles to obtain the adjacent concrete particles. According to the position and attributes of the adjacent particles, the continuity density method is used to calculate the current density of the particles. According to the result of the time integration, the state information such as the position and velocity of the particles is updated.

[0056] Step 240: Output simulation results. Output a visualized three-dimensional concrete pouring process simulation result.

[0057] In practical applications, by discretizing concrete into particles, a mathematical calculation model is established using the smooth particle fluid dynamics method, LS-Prepost is used as the operating platform, and LS-Run is used as the running platform. The pouring process is simulated on the simulation platform, and the configuration data corresponding to the received concrete to be simulated is input into the concrete surface prediction model to obtain the particle distribution result, thereby improving the simulation efficiency of the concrete pouring process. According to the particle distribution result, the position distribution of the concrete surface surface is analyzed. This simulation method uses less equipment, the calculation method is universal and reasonable, the calculation speed is fast, and the simulation results are accurate, thereby more comprehensively simulating the formation process of the concrete surface surface, which helps to reveal the formation mechanism of the concrete surface surface. It is convenient to optimize the concrete mix ratio, improve the vibration technology, avoid the generation of surface roughness, and provide accurate guidance for optimizing the concrete mix ratio.

[0058] Figure 3 This is a schematic diagram of the structure of a simulation device for a concrete pouring process proposed in an embodiment of this specification.

[0059] The simulation device for the concrete pouring process described in the embodiments of this specification may include: The receiving module 302 is used to receive configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles; The determination module 304 is used to input the particle properties into the concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

[0060] Optionally, the inputting of the particle attributes into the concrete surface prediction model to obtain the particle distribution result in the embodiments of this specification may specifically include: According to the particle properties, a linked list search method is used to perform field particle search and pairing to obtain adjacent concrete particles; the particle properties at least include initial particle density and initial particle position; Calculating the current particle density of the concrete particles using a continuity density method according to the initial particle density and the initial particle position of the adjacent concrete particles; A particle distribution result is obtained according to the current particle density.

[0061] Optionally, the device described in the embodiments of this specification may also include: A judgment module, used to judge whether the surface concrete particles in the three-dimensional image are evenly distributed; If yes, it is determined that the concrete to be simulated poured has no concrete roughness; If not, then determine the target area where the concrete particles are unevenly distributed, and the target area is the concrete surface.

[0062] Optionally, before inputting the particle properties into the concrete surface prediction model in the embodiments of this specification, the device may further include: The model building module is used to build a concrete surface prediction model based on the smooth particle fluid dynamics algorithm.

[0063] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method.

[0064] Figure 4 The following is a schematic diagram of a simulation device for a concrete pouring process provided in an embodiment of the present specification. Figure 4 As shown, a simulation device for a concrete pouring process provided in an embodiment of the present specification includes a memory 430, a processor 410, and a computer program 420 stored in the memory, and the processor 410 executes the computer program 420 to implement the simulation method for the concrete pouring process described in any of the above embodiments.

[0065] A simulation device for a concrete pouring process provided in an embodiment of the present specification may include a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the simulation method for a concrete pouring process described in any of the above embodiments.

[0066] An embodiment of the present specification provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the simulation method of the concrete pouring process described in any of the above embodiments can be implemented.

[0067] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. Figure 4 As for the device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0068] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with hardware entity modules. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0069] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0070] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0071] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0072] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0080] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0081] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0082] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for simulating a concrete pouring process, characterized in that: Applied to a simulation platform, the method comprises: Receiving configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles; The particle properties are input into a concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

2. The method according to claim 1, characterized in that The step of inputting the particle attributes into the concrete surface prediction model to obtain the particle distribution result specifically includes: According to the particle properties, a linked list search method is used to perform field particle search and pairing to obtain adjacent concrete particles; the particle properties at least include initial particle density and initial particle position; Calculating the current particle density of the concrete particles using a continuity density method according to the initial particle density and the initial particle position of the adjacent concrete particles; A particle distribution result is obtained according to the current particle density.

3. The method according to claim 1, characterized in that The method further comprises: Determining whether the surface concrete particles in the three-dimensional image are evenly distributed; If yes, it is determined that the concrete to be simulated poured does not contain concrete roughness; If not, then determine the target area where the concrete particles are unevenly distributed, and the target area is the concrete surface.

4. The method according to claim 1, characterized in that: Before inputting the particle properties into the concrete surface prediction model, the method further includes: Based on the smooth particle hydrodynamics algorithm, a concrete surface prediction model is constructed.

5. A simulation device for concrete pouring process, characterized in that: Applied to simulation platforms, including: A receiving module, used to receive configuration data corresponding to the concrete to be simulated poured; the configuration data includes particle properties of concrete particles; A determination module is used to input the particle properties into a concrete surface prediction model to obtain a particle distribution result; wherein the concrete surface prediction model simulates the pouring process of the concrete to be simulated based on at least the configuration data; and the particle distribution result includes a three-dimensional graph of the concrete particle distribution.

6. The device according to claim 5, characterized in that The step of inputting the particle attributes into the concrete surface prediction model to obtain the particle distribution result specifically includes: According to the particle properties, a linked list search method is used to perform field particle search and pairing to obtain adjacent concrete particles; the particle properties at least include initial particle density and initial particle position; Calculating the current particle density of the concrete particles using a continuity density method according to the initial particle density and the initial particle position of the adjacent concrete particles; A particle distribution result is obtained according to the current particle density.

7. The device according to claim 5, characterized in that The device also includes: A judgment module, used to judge whether the surface concrete particles in the three-dimensional image are evenly distributed; If yes, it is determined that the concrete to be simulated poured has no concrete roughness; If not, then determine the target area where the concrete particles are unevenly distributed, and the target area is the concrete surface.

8. The device according to claim 5, characterized in that Before inputting the particle properties into the concrete surface prediction model, the device further includes: The model building module is used to build a concrete surface prediction model based on the smooth particle fluid dynamics algorithm.

9. A simulation device for a concrete pouring process, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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