An assembled integrated wallboard automatic pouring control method and system

Abstract

The application relates to the technical field of building prefabricated component control, and discloses an automatic pouring control method and system for fabricated integrated wallboards. The method comprises the following steps: obtaining wallboard design data to be poured, identifying a steel bar dense area and constructing an initial simulation model; determining the positions of potential intersection points caused by the obstruction of steel bars, and generating a distribution map of a confluence confusion area; adjusting flow control parameters according to the distribution map, and optimizing a flow path through finite element simulation; coordinating the confluence sequence of material flow through a flow path distribution map, determining a dynamic confluence coordination scheme, and performing structural integrity simulation analysis to obtain an integrity index; judging whether the manufacturing quality meets the standards based on the integrity index, identifying key areas of filling defects if the standards are not met, extracting a key path and re-running finite element simulation to obtain an updated flow path distribution map; optimizing the pouring inlet position and flow ratio according to the updated distribution map, and generating a final control configuration. The application improves the pouring quality of wallboards.

Description

Technical Field

[0001] This application relates to the field of prefabricated building component control technology, and in particular to an automated pouring control method and system for prefabricated integrated wall panels. Background Technology

[0002] With the rapid development of industrialized construction, prefabricated buildings have become an important part of modern building systems due to their advantages such as high construction efficiency, controllable quality, energy saving, and environmental protection. As a core component of prefabricated buildings, the manufacturing quality of prefabricated wall panels directly affects the structural safety and service life of the building. Especially in the concrete pouring stage of the wall panels, the flow behavior of concrete within the mold cavity is extremely complex due to the dense steel mesh embedded inside. This can easily lead to uneven filling, local voids, and even structural weak points due to the obstruction of the steel bars, seriously affecting the mechanical properties and durability of the wall panels. In existing technologies, the concrete pouring of prefabricated wall panels typically employs a combination of pre-set pouring points and manual experience control. Operators determine the pouring inlet location, pouring rate, and sequence based on design drawings and construction specifications, and make rough adjustments to the pouring process through on-site observation and experience. However, this traditional control method has many limitations: First, the spatial structure of densely reinforced areas is complex. As a non-Newtonian fluid, concrete's flow behavior within narrow reinforced channels is difficult to predict accurately based on experience. Especially at the confluence of multiple material flows, improper timing of the convergence or differences in flow resistance can easily trigger localized turbulence, velocity disturbances, and even backflow, leading to excessive material accumulation in some areas and insufficient filling in others, creating voids. This chaotic convergence is one of the main causes of internal defects in wall panels, but existing methods lack the ability to simulate and dynamically control the convergence process. Second, traditional pouring control parameters such as pouring rate, viscosity, and inlet location are usually determined once before production, making real-time feedback and dynamic adjustment based on actual flow conditions impossible. When abnormal flow occurs during pouring, operators often cannot identify the root cause and take effective measures in a timely manner, frequently having to passively accept the pouring result, leading to increased scrap rates or unstable quality. Furthermore, existing technologies lack a closed-loop mechanism for pre-pouring quality prediction and post-pouring evaluation. After the wall panel is poured, its internal density and structural integrity are difficult to accurately judge through simple visual inspection. It usually requires destructive testing or post-production flaw detection, which not only increases manufacturing costs but also makes it impossible to optimize process parameters in a timely manner during production.

[0003] In summary, how to accurately simulate and dynamically coordinate the merging behavior of multiple concrete flows in complex environments with dense reinforcement, achieve real-time optimization and adaptive control of the flow path, and establish a closed-loop feedback mechanism from process parameters to structural quality has become a key technical challenge that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides an automated casting control method and system for prefabricated integrated wall panels, which improves the casting quality and production control precision of wall panels.

[0005] In a first aspect, this application provides an automated pouring control method for prefabricated integrated wall panels, the method comprising: Step S1: Obtain the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity; Step S2: Based on the initial simulation model, determine the potential intersection points where material flow converges and conflicts due to the obstruction of steel bars, simulate material flow behavior at the potential intersection points, and generate a distribution map of the convergence and chaos area. Step S3: Based on the distribution map of the chaotic confluence area, adjust the flow control parameters and optimize the flow path of the concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained. Step S4: Coordinate the material flow convergence sequence through the flow path distribution map, determine the dynamic convergence coordination scheme, and perform structural integrity simulation analysis on the cast wall panel based on the dynamic convergence coordination scheme to obtain integrity indicators. Step S5: Determine whether the manufacturing quality meets the standard based on the integrity index. If it does not meet the standard, identify the key area of ​​filling defects according to the integrity index, extract the critical path from the key area, and rerun the finite element simulation to obtain the updated flow path distribution map. Step S6: Based on the updated flow path distribution map, optimize the pouring inlet location and flow ratio to generate the final control configuration that minimizes path obstruction.

[0006] Secondly, this application provides an automated pouring control system for prefabricated integrated wall panels, the system comprising: The construction module is used to acquire the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity; The generation module is used to determine the potential intersection points where material flow convergence and conflict occurs due to the obstruction of steel bars, based on the initial simulation model; simulate material flow behavior at the potential intersection points; and generate a distribution map of the convergence and chaos area. The simulation module is used to adjust the flow control parameters according to the distribution map of the chaotic confluence area, and optimize the flow path of concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained. The analysis module is used to coordinate the material flow convergence sequence through the flow path distribution map, determine the dynamic convergence coordination scheme, and perform structural integrity simulation analysis on the cast wall panel based on the dynamic convergence coordination scheme to obtain integrity indicators. The judgment module is used to determine whether the manufacturing quality meets the standard based on the integrity index. If it does not meet the standard, it identifies the key area of ​​filling defects according to the integrity index, extracts the critical path from the key area, and reruns the finite element simulation to obtain an updated flow path distribution map. The optimization module is used to optimize the pouring inlet location and flow ratio based on the updated flow path distribution map, and generate the final control configuration that minimizes path obstruction.

[0007] Compared with the prior art, the beneficial effects of the present invention are at least as follows: This application constructs a "reinforcement-resistance field" mapping model, abstracting the complex reinforcement mesh into a three-dimensional spatial resistance field with directional attributes. This significantly reduces simulation complexity while ensuring computational accuracy, achieving efficient simulation of concrete flow behavior in densely reinforced areas. By identifying potential intersection locations and generating a distribution map of chaotic confluence regions, the invisible internal flow problem is visualized as a high-risk area, providing clear targets for subsequent parameter optimization. Furthermore, this application employs a particle swarm optimization algorithm to dynamically coordinate the confluence sequence of multiple material flows, effectively avoiding local turbulence and filling defects caused by improper confluence timing, and significantly improving the uniformity and density of the wall panel casting.

[0008] Furthermore, this application introduces structural integrity simulation analysis, mapping the flow simulation results to the structural mechanics model to obtain quantified structural integrity indicators, achieving closed-loop evaluation from process parameters to final product performance. When the indicators fail to meet the standards, stress cloud maps are overlaid with chaotic region distribution maps to accurately locate key areas of filling defects and perform mesh refinement and recalculation, forming a complete reverse traceability mechanism. Finally, a genetic algorithm is used to optimize the pouring inlet location and flow ratio, generating a final control configuration that minimizes path obstacles and embedding it into the production execution system, achieving closed-loop control from simulation optimization to actual production. This application effectively improves the automation level and manufacturing quality consistency of the prefabricated wall panel pouring process, reduces the scrap rate and subsequent repair costs caused by filling defects, and provides a reliable technical path for the intelligent manufacturing of prefabricated building components. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of an automated pouring control method for prefabricated integrated wall panels according to an embodiment of this application; Figure 2 This is a schematic diagram of the actual three-dimensional distribution of the reinforcing steel mesh in an embodiment of this application; Figure 3 This is a schematic diagram of the obstruction distribution in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an automated pouring control system for prefabricated integrated wall panels according to an embodiment of this application. Detailed Implementation

[0011] This application provides an automated pouring control method and system for prefabricated integrated wall panels. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 The automated pouring control method for prefabricated integrated wall panels in this application includes: Step S1: Obtain the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity.

[0013] Step S1 includes: extracting reinforcement distribution information from the design data of the wall panel to be poured, calculating the local grid density, and marking the spatial area with a grid density value exceeding a preset threshold as a dense reinforcement area; analyzing the main direction of the reinforcement in the dense reinforcement area to obtain the reinforcement grid arrangement parameters; mapping the wall panel pouring cavity into a three-dimensional spatial resistance field with resistance properties according to the grid arrangement parameters, and constructing an initial geometric model based on the resistance field; setting the constitutive model and boundary conditions of the concrete material in the initial geometric model, calculating the velocity field and pressure field of the concrete in the flow domain through transient simulation, and generating a preliminary framework of the flow path based on the simulation results; comparing the preliminary framework with the design data, and if the deviation exceeds the preset threshold, adjusting the grid arrangement parameters and reconstructing the model until an initial simulation model that meets the accuracy requirements is obtained.

[0014] Specifically, the reinforcement distribution information is extracted from the design documents of the wall slab to be poured. These design documents are building information model files containing three-dimensional coordinate information. The geometric coordinates, diameter, and spatial orientation data of the reinforcement are read from these files. Figure 2 The diagram shows the actual three-dimensional distribution of the rebar mesh. The actual rebar mesh is distributed three-dimensionally within the wall panel mold cavity, comprising a dense mesh structure formed by the interweaving of rebars in the X, Y, and Z directions. The local mesh density per unit volume is calculated based on the extracted rebar spatial coordinates. In practice, the three-dimensional space of the wall panel is divided into multiple cubic voxel units with a preset side length. The sum of the lengths of all rebar segments within each voxel unit is counted, and this length value is defined as the local mesh density of that unit. The preset distance is determined based on the wall panel thickness and the minimum rebar spacing, ranging from 20-100 mm, preferably 50 mm. Sets of voxel units whose mesh density values ​​exceed a preset density threshold are identified, and the spatial areas occupied by these units are marked as densely rebar areas. Within the marked densely reinforced areas, the direction vectors of all rebar segments within the area are statistically analyzed, and the principal eigenvector of its covariance matrix is ​​calculated. The direction of this principal eigenvector is determined as the main direction of the rebar arrangement in that area. Simultaneously, the uniformity index of rebar distribution in the plane perpendicular to the principal direction is calculated. This index is used to quantitatively describe the degree of rebar distribution in the plane perpendicular to the principal direction. Specifically, it can be characterized by analyzing the variance or entropy value of the projection points of the rebars in this plane. The smaller the distribution variance or the lower the entropy value, the more uniform the rebar arrangement in the vertical plane. Conversely, it indicates the presence of uneven distribution characteristics such as local over-density or double-layer mesh. Finally, the mesh density value, main arrangement direction, and distribution uniformity index of each densely reinforced area are output as rebar mesh arrangement parameters.

[0015] After obtaining the rebar mesh arrangement parameters, they are mapped to a resistance field in three-dimensional space. When constructing the three-dimensional resistance field, the combined effects of the rebar density and arrangement direction on concrete flow must be considered simultaneously. Specifically, for any point within the cavity of the wall panel casting, the flow resistance coefficient at that point is calculated based on the local mesh density of its corresponding voxel element and the main arrangement direction of the rebar at that point. First, the local mesh density is used as the reference component of the resistance coefficient. The larger the local mesh density, the more contact interfaces between the rebar and concrete per unit volume, and the more rebar obstacles the concrete needs to bypass during flow, resulting in greater frictional loss and kinetic energy loss. Therefore, the baseline value of the resistance coefficient is positively correlated with the local grid density. The denser the reinforcement, the greater the basic resistance to concrete flow. Secondly, the angle between the flow direction and the main direction of the reinforcement is used as the directional modulation component of the resistance coefficient. When the concrete flow direction is parallel to the main direction of the reinforcement, the concrete can flow smoothly along the longitudinal channel between the reinforcements with less obstruction, and the directional modulation component takes a smaller value. When the flow direction is perpendicular to the main direction of the reinforcement, the concrete needs to repeatedly pass through the reinforcement grid. Each passage will generate additional energy loss and flow direction deflection due to bypassing the reinforcement, and the directional modulation component takes a larger value. For flow directions between parallel and perpendicular, continuous interpolation calculation is performed based on the sine or cosine value of the angle.

[0016] Before fusing the aforementioned reference component and directional modulation component, the reference resistance coefficient needs to be dimensionless. Specifically, the reference resistance coefficient of the current spatial point is divided by the maximum reference resistance coefficient that may occur under the same casting conditions to obtain a dimensionless relative value of the reference resistance. Subsequently, the dimensionless relative value of the reference resistance is weighted and superimposed with the directional modulation factor. The weighting coefficients of the two can be calibrated according to configuration parameters such as rebar diameter and spacing, so that the model can adapt to rebar meshes of different specifications. Through the above dimensionless and weighted superposition processing, the flow resistance coefficient of the spatial point is obtained, and it is assigned to the corresponding spatial point in the wall panel casting cavity. The set of resistance coefficients of all spatial points constitutes a three-dimensional spatial resistance field. This resistance field can accurately reflect the obstruction characteristics of the actual rebar mesh on concrete flow, providing a basis for subsequent concrete flow simulation analysis.

[0017] A three-dimensional spatial resistance field is imported as input parameters into computational fluid dynamics simulation software to construct an initial geometric model for flow analysis. Specifically, the casting cavity of the wall panel is defined as the fluid computational domain, and the resistance field data is assigned to the corresponding mesh elements within the computational domain. This ensures that each mesh element carries resistance attribute information at that location, which is used in subsequent simulations to characterize the obstruction of concrete flow by densely reinforced areas. This approach eliminates the need for solid geometric modeling of the reinforcing bars, significantly reducing the complexity of the computational model and the resource consumption required for simulation. A constitutive model of the concrete material is set in the initial geometric model, specifically using the Bingham fluid model. This model is a mathematical model describing the rheological behavior of non-Newtonian fluids, defining the flow characteristics of concrete by inputting two key parameters: the yield stress and plastic viscosity of the material. Simulation boundary conditions are set, specifically including the inlet flow velocity and outlet pressure. The inlet flow velocity is set according to the pumping capacity of the actual casting equipment, and the outlet pressure is set to atmospheric pressure or a slight positive pressure based on the venting state at the top of the wall panel cavity.

[0018] Subsequently, a computational fluid dynamics solver based on the finite volume method was used to perform transient simulation calculations on the initial geometric model. By iteratively solving the mass and momentum conservation equations, the velocity and pressure field data of concrete changing over time within the computational domain were output. Based on the output velocity field data, regions with flow velocities below a preset critical velocity (determined based on the yield stress characteristics of concrete) were identified. These regions were marked as potential obstruction locations on the flow path, and the average pressure gradient across the entire computational domain was calculated as a quantitative indicator of overall flow resistance. The position coordinate data of the concrete filling front at the end of the simulation calculation were extracted, and these coordinate points were connected using a curve fitting algorithm to form the main flow path lines. Data analysis was performed on the flow path lines, statistically analyzing the angle distribution between their direction and the main reinforcement arrangement direction in areas with dense reinforcement, and outputting the distribution characteristic data of the flow path as a preliminary framework of the flow path.

[0019] The preliminary framework data is compared and verified with the theoretical data of the designed pouring scheme. Specifically, the filling completion time obtained from the simulation is compared with the theoretical filling time estimated based on the designed pouring scheme. Simultaneously, the spatial overlap of the identified low-velocity zone location coordinates is analyzed with the location coordinates in historical pouring defect records. If the filling time deviation exceeds a preset threshold or the overlap of the low-velocity zone location is lower than a preset threshold, the current model is deemed insufficiently accurate and unable to accurately reflect the flow characteristics during the actual pouring process. In this case, the mesh arrangement parameters need to be adjusted and the simulation calculation re-executed. Adjusting the mesh arrangement parameters specifically includes modifying the mapping relationship coefficient between mesh density and drag coefficient, such as increasing or decreasing the weighting coefficient in the drag coefficient calculation formula, to make the simulation results closer to the actual pouring conditions. The iterative process of "parameter adjustment - re-simulation - deviation judgment" is repeated until the deviation between the simulation results and the design data meets the preset accuracy requirements. Once the simulation results are verified, the current model parameters and resistance field configuration are locked. The final determined initial geometric model, material parameters, and boundary conditions are used as the initial simulation model that meets the accuracy requirements and stored in the model library of the control system for subsequent path obstacle analysis and casting control parameter optimization.

[0020] The initial simulation model constructed in the above manner can quickly identify the overall flow path of concrete in the wall panel cavity on a macro scale, providing a reliable simulation basis for subsequent accurate identification of potential intersection locations caused by steel reinforcement obstruction.

[0021] Step S2: Based on the initial simulation model, determine the potential intersection points where material flow converges and conflicts due to the obstruction of steel bars, simulate the material flow behavior at the potential intersection points, and generate a distribution map of the convergence and chaos area.

[0022] Step S2, which involves determining the potential intersection points where material flow convergence and conflict occur due to steel reinforcement obstruction, includes: performing spatial analysis on the flow paths in the initial simulation model, identifying path intersection areas and extracting the geometric coordinate data of the path intersection points, calculating the flow resistance values ​​at each path intersection point, and determining the potential obstruction areas; performing mesh generation and path density analysis on the potential obstruction areas, assessing the degree of mutual influence between each path intersection point, and generating an influence degree matrix; selecting key intersection point locations based on the influence degree matrix, and using a particle swarm optimization algorithm to iteratively optimize the key intersection point locations to generate an obstruction distribution map; and superimposing and comparing the obstruction distribution map with the initial simulation model to verify the accuracy of key intersection point identification and confirm the final potential intersection point locations.

[0023] Specifically, after constructing the initial simulation model in the area with dense reinforcement, in order to accurately locate the key locations where conflicts may occur when multiple concrete flows converge, it is necessary to conduct in-depth analysis of the intersection areas of the flow paths, screen out potential intersection points with high convergence risk due to reinforcement obstruction, and provide accurate analysis objects for subsequent convergence chaos simulation.

[0024] When determining the potential intersection points where material flow convergence and conflict occur due to reinforcement obstruction, the flow paths in the initial simulation model are first scanned. Flow paths refer to the spatial curves formed by extracting the position coordinates of the concrete filling front at each time step during the transient simulation calculation of the initial model and connecting these coordinate points sequentially using a curve fitting algorithm. These curves characterize the preferential flow direction and channel of concrete under the constraint of the reinforcement mesh, reflecting the actual trajectory of the material within the mold cavity. Regions where different flow paths intersect in space are identified, and the three-dimensional geometric coordinate data of each path intersection point is extracted from these intersection regions. After obtaining the geometric coordinate data, the flow resistance at each intersection point is simulated by solving the Navier-Stokes equations. The Navier-Stokes equations are a set of partial differential equations describing the continuity of fluid motion and the conservation of momentum. Their physical meaning is that the rate of change of momentum per unit volume of fluid is equal to the sum of all forces acting on it. In this application, the specific form of the equation is: the sum of the partial derivative of concrete density multiplied by velocity with respect to time (characterizing the change of concrete flow velocity with time at the intersection point) and the dot product of concrete density multiplied by velocity and velocity gradient (characterizing the transport of the concrete's own momentum during the flow process), equals the sum of the pressure gradient term (driven by the difference between the pouring inlet pressure and the pressure inside the mold cavity, which is the main source of power for concrete flow), the viscous stress term (including the shear viscosity of the concrete itself and the additional resistance generated by the steel mesh, wherein the shear viscosity of the concrete is described by the Bingham fluid model, which includes two parameters: yield stress and plastic viscosity, while the additional resistance generated by the steel mesh is introduced through the resistance field data constructed in step S1), and the volume force term (mainly referring to the gravity of the concrete, determined according to the spatial height of the intersection point). Each term in the above equation closely corresponds to the physical scenario of this application: parameters such as density, viscosity, and yield stress are determined based on the actual mix proportion of concrete and rheological test results; the resistance field data comes from the mapping of the steel mesh arrangement parameters in step S1; the gravity term is calculated based on the spatial position of the intersection point within the wall panel mold cavity; and the pressure gradient is determined by the pumping pressure of the pouring equipment and the venting conditions of the mold cavity. By solving this equation, the velocity gradient distribution and pressure distribution at each intersection point are obtained. Then, based on the product of the velocity gradient and the plastic viscosity of the concrete, the shear resistance experienced by the concrete flow at that point is calculated. Combined with the additional resistance given by the resistance field data constructed in step S1, the flow resistance value at that intersection point is constituted. This solution process fully considers the rheological characteristics of concrete as a non-Newtonian fluid, the spatial obstruction of the steel mesh, the pouring process parameters, and the influence of gravity, so that the calculated flow resistance value can truly reflect the ease or difficulty of concrete flow at the intersection point under actual pouring conditions.The calculated flow resistance values ​​are compared with preset resistance thresholds, and areas where the resistance values ​​exceed the thresholds are identified as potential obstruction areas. These areas are typically locations with dense reinforcement and narrow flow channels, where concrete flow is easily obstructed, leading to voids or insufficient filling. By combining the general Navier-Stokes equations with the specific scenario of concrete pouring, the calculation of flow resistance has a clear physical basis and engineering feasibility, providing an accurate data foundation for subsequent path density analysis and key intersection identification.

[0025] Subsequently, the potential obstruction area was divided into grids and path density analysis was performed. Specifically, the potential obstruction area was divided into multiple small grid cells. The number of flow paths within each grid cell was counted and the path density was calculated. Each identified flow path was traversed, and it was determined whether each flow path passed through the current grid cell. The total number of flow paths passing through each grid cell was counted as the number of flow paths in that grid cell. The counted number of flow paths in each grid cell was divided by the area of ​​that grid cell to obtain the path density value of that grid cell. The density difference between different grid cells was analyzed to evaluate the various... The degree of mutual influence between intersections is specifically determined by comparing the path density value of the grid cell containing each intersection with the average path density value of all grid cells within a preset neighborhood of that intersection. The larger the difference or ratio, the higher the path aggregation at that intersection, indicating a stronger convergence effect on surrounding flow paths and a higher probability of merging and conflicting with other intersections. Therefore, the greater the influence of that intersection. The preset neighborhood is defined as a cubic region with a side length L centered on the target intersection, where L is determined based on the average spacing of the reinforcing mesh. After calculating the density difference for each intersection in this way, the density differences of all intersections are summarized to generate an influence degree matrix characterizing the degree of mutual influence between intersections. The influence degree matrix is ​​a two-dimensional data table where each element corresponds to a quantified value of the influence degree of an intersection. The row and column indices of the matrix correspond to the spatial location of the intersections, providing a data foundation for subsequent key point selection.

[0026] Key intersection locations are selected based on the influence degree matrix. Specifically, each element value in the influence degree matrix is ​​compared with a preset influence threshold, and intersections with influence degrees exceeding the threshold are selected as initial key intersections. To further improve the identification accuracy of key intersection locations, a particle swarm optimization algorithm is used to iteratively optimize the initially selected key intersection locations. Specifically, each initially selected key intersection is treated as a target point to be optimized. A group of random particles is initialized in its neighborhood space, with each particle representing a potential candidate solution for a key intersection location. The spatial coordinates of the particles are the x, y, and z values ​​of the candidate location. In each iteration, each particle first recalculates the flow resistance value and path density characteristics of the current candidate location to obtain the fitness value of the candidate location. The fitness value calculation process includes: firstly, calculating the flow resistance value and path density value at the current candidate location based on its coordinates. The flow resistance value is obtained by resolving the Navier-Stokes equations of the grid cell to which the location belongs. The process involves substituting the local grid density and the angle between the concrete flow direction and the main direction of the reinforcing steel at the desired location into the resistance coefficient formula to calculate the flow resistance coefficient at that point. The path density value is obtained by re-counting the number of flow paths within a preset radius centered on the candidate location and dividing it by the total area of ​​that region. After obtaining the flow resistance value and path density value, they are normalized to unify the dimensions of the two indicators to the same order of magnitude. The normalized flow resistance value is multiplied by a first preset weighting coefficient, and the normalized path density value is multiplied by a second preset weighting coefficient. The sum of the first and second preset weighting coefficients is 1, and their specific values ​​are set according to the density of the reinforcing steel and the complexity of the path intersections in the wall structure. Areas with denser reinforcing steel have higher flow resistance weights, and areas with more complex path intersections have higher path density weights. The calculated fitness value is used to evaluate the merits of this location as a key intersection point. A higher fitness value indicates a more concentrated flow obstruction and a greater risk of convergence and conflict at that location.

[0027] Each particle records its highest fitness value position from its own iterations as its individual optimal position. The entire particle swarm records the highest fitness value position among all particles as the swarm's global optimal position. Subsequently, each particle adjusts its movement speed and direction based on its individual optimal position and the swarm's global optimal position, generating new candidate positions. The speed update formula comprehensively considers the particle's current speed, the acceleration term towards its individual optimal position, and the acceleration term towards the swarm's global optimal position. Through multiple iterations, the particle swarm gradually converges to the spatial region with the highest fitness value, ultimately yielding an optimized set of critical intersection positions. Based on this set, an obstacle distribution map representing the concentrated obstacle region is generated, such as... Figure 3 As shown, the obstruction distribution map, presented as a grayscale heatmap, visually displays the spatial distribution and impact of key intersections. Darker areas indicate more concentrated flow obstruction and a higher risk of confluence and conflict. Black dots represent the locations of key intersections obtained through iterative convergence using the particle swarm optimization algorithm, with numerical codes corresponding to different key intersections. The map demonstrates a high degree of overlap between areas with higher obstruction levels (darker colors) and densely distributed key intersections, validating the accuracy of the particle swarm optimization algorithm in identifying key intersections.

[0028] After obtaining the optimized set of key intersection locations and the corresponding obstacle distribution map, they are overlaid and compared with the initial simulation model to verify the accuracy of key intersection location identification. Specifically, the obstacle distribution map is overlaid as a layer onto the three-dimensional spatial coordinate system of the initial simulation model, ensuring that the key intersection locations marked in the obstacle distribution map and the actual path intersection areas in the initial simulation model are located in the same spatial reference system. Image processing algorithms are used to calculate the spatial distance deviation between each key intersection location in the obstacle distribution map and the path intersection locations in the initial simulation model, and the average and maximum deviation values ​​of all key intersections are statistically analyzed. The average and maximum deviation values ​​are compared with preset deviation thresholds. If both are less than the corresponding deviation threshold, it indicates that the key intersection identification results highly match the actual path intersection characteristics in the initial simulation model, and the identification accuracy meets the requirements. If any deviation value exceeds the preset threshold, it indicates that the identification results have deviations. In this case, the grid size of the path density analysis, the screening threshold of the influence degree matrix, or the iteration parameters of the particle swarm optimization algorithm need to be adjusted, and the key intersection identification and optimization process needs to be re-executed.

[0029] Through the aforementioned iterative verification, until the matching degree between the identification results and the initial simulation model meets the preset accuracy requirements, the set of key intersection locations at this point is confirmed as the final potential intersection locations, used for subsequent generation of confluence and disorder area distribution maps and optimization of flow control parameters. In this way, this application ensures that the identified potential intersection locations accurately reflect high-risk areas where confluence and conflict may occur during actual pouring, providing a reliable input basis for subsequent flow behavior simulation and control parameter optimization.

[0030] Step S2, generating the distribution map of the confluence and disorder area, includes: establishing a finite element analysis model based on the potential intersection locations and the reinforcement mesh arrangement parameters to simulate the flow state of concrete material at the intersections; calculating the material velocity and pressure distribution through the finite element analysis model to determine the degree of disorder in the confluence area; dividing the confluence and disorder area according to the degree of disorder and generating the corresponding distribution map; analyzing the distribution map to extract the range and average disorder intensity of each disorder area and confirm the key disorder point data; associating the key disorder point data with the potential intersection locations to verify the reliability of the simulation results, and obtaining the final distribution map of the confluence and disorder area based on the verification results.

[0031] Specifically, after determining the final potential intersection locations, it is necessary to further analyze the flow behavior of concrete at these key locations, quantify the degree of chaos generated when multiple material flows converge, and provide accurate input basis for subsequent adjustment of flow control parameters.

[0032] To further refine the analysis of the local flow behavior of concrete at potential intersections and quantify the degree of disorder caused by the convergence of multiple material flows, a detailed finite element analysis model needs to be established for each potential intersection. Specifically, a finite element analysis model is first established based on the location of the potential intersection and the reinforcement mesh arrangement parameters. During model establishment, the three-dimensional coordinate data of each potential intersection is imported into the finite element analysis software. A sub-region containing the local reinforcement mesh is selected as the analysis domain, centered on this point. The size of the sub-region is determined based on the distribution range of the reinforcement around the intersection and the size of the area potentially affected by the convergence. This sub-region is then meshed to generate a three-dimensional finite element mesh. The mesh density is set according to the required analysis accuracy; a denser mesh is used in the core area of ​​the intersection to capture flow details, while a relatively sparse mesh is used in areas far from the intersection to balance computational resources. After establishing the finite element analysis model, simulation calculations are run to simulate the flow state of concrete material at the intersection.

[0033] During the simulation, the flow velocity and pressure values ​​of each grid cell at each time step are calculated by solving the Navier-Stokes equations and the continuity equation. The velocity and pressure field data of the intersection region as a function of time are output. Based on the output velocity field data, the degree of disorder in the confluence region is quantified using a turbulence intensity index. Turbulence intensity is defined as the ratio of the standard deviation of the flow velocity fluctuation to the average flow velocity. A larger ratio indicates a higher degree of flow turbulence and more severe mutual interference between multiple material flows. The calculated turbulence intensity value of each grid cell is compared with a preset disorder threshold, and grid cells with turbulence intensity values ​​exceeding the threshold are marked as confluence disorder regions. Confluence disorder regions indicate that within this spatial area, multiple concrete material flows exhibit significant flow turbulence due to improper confluence timing, velocity mismatch, or reinforcement obstruction. By marking these regions, high-risk locations most likely to cause filling defects during the pouring process can be accurately located, providing clear target areas for subsequent adjustments to flow control parameters and optimization of the confluence sequence.

[0034] Subsequently, when dividing the confluence disorder regions based on their disorder levels, all grid cells marked as confluence disorder regions are spatially connected to form several continuous confluence disorder regions. Data from these regions is extracted from the calculated disorder distribution matrix to generate a confluence disorder region distribution map displayed as a heatmap. The color intensity in the heatmap represents the disorder level at different locations; darker areas indicate higher turbulence intensity and a greater risk of confluence conflict, while lighter areas indicate relatively stable flow. After obtaining the confluence disorder region distribution map, image processing and analysis are performed. The pixel area of ​​each connected disorder region is calculated and converted into an actual spatial area. Simultaneously, the average turbulence intensity value of all grid cells within that region is calculated as the average disorder intensity of that region. A predetermined number of regions with the highest average disorder intensity are extracted from all disorder regions, and their center point coordinates are used as key disorder point data. These key disorder points represent the locations where the risk of confluence conflict is most concentrated and where filling defects are most likely to occur; these are areas that require focused attention during subsequent flow control parameter optimization.

[0035] The reliability of the finite element simulation results is verified by spatially correlating the data of key chaotic points with the locations of potential intersections. Specifically, the spatial distance deviation between each key chaotic point and its nearest potential intersection is calculated. The average distance deviation of all key chaotic points and the proportion of points with distance deviations less than a preset tolerance are used as the correlation matching rate. If the correlation matching rate exceeds a preset reliability threshold, it indicates that the simulation results are in high agreement with the theoretical analysis, and the current chaotic area distribution map can accurately reflect the possible confluence conflicts during the actual pouring process. If the correlation matching rate is lower than the threshold, it indicates that there is a deviation in the simulation results. In this case, the mesh resolution or boundary condition parameters of the finite element model need to be adjusted and the simulation repeated until the correlation matching rate reaches the required level. Through the above iterative verification, the final chaotic area distribution map is obtained. The chaotic area distribution map is a spatial distribution map that visually displays the degree of concrete flow turbulence in the area near each potential intersection within the wall panel mold cavity. Specifically, the distribution map is presented as a heatmap, with different color depths corresponding to different turbulence intensities. Darker areas indicate higher levels of flow turbulence and more intense interference between multiple material flows, while lighter areas indicate relatively stable flow. This distribution map not only marks the spatial location and extent of confluence turbulence areas but also quantifies the turbulence intensity level of each area through color gradients. This allows operators or control systems to intuitively identify high-risk areas most likely to cause filling defects during the pouring process. As core input data for subsequent iterative optimization of flow control parameters and coordination of confluence sequence, this distribution map provides precise spatial basis and target guidance for adjusting pouring rates, material viscosity, and the start-up and shutdown sequence of multiple pouring equipment.

[0036] Step S3: Based on the distribution map of the chaotic confluence area, adjust the flow control parameters and optimize the flow path of the concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained.

[0037] Step S3 includes: analyzing the void probability of each local region in the chaotic region distribution map, determining whether the void probability exceeds a preset threshold, and if so, determining the flow control parameters to be adjusted and the adjustment range; updating the material constitutive relation in the finite element analysis model based on the adjusted flow control parameters, rerunning the flow simulation to obtain a new flow path distribution and the corresponding chaotic region distribution map; extracting the void probability of the same local region in the old and new simulation results, calculating the degree of improvement in uneven filling, and guiding the next round of parameter adjustment based on the degree of improvement until the void probability is lower than the preset threshold, thereby obtaining the optimized flow path distribution map.

[0038] Specifically, after generating the chaotic confluence region distribution map, the flow control parameters need to be dynamically adjusted based on the high-risk areas marked in the map. The flow characteristics of the concrete are gradually improved through iterative optimization until a flow path distribution that meets the uniformity requirements is obtained. When adjusting the flow control parameters based on the chaotic confluence region distribution map, the void probability of each local area in the distribution map is first analyzed. Specifically, the chaotic confluence region distribution map is divided into multiple local analysis areas based on the reinforcement mesh arrangement parameters. Each local area corresponds to a set of simulated mesh cells. For each local area, the number of mesh cells marked as insufficiently filled is counted, and the ratio of this number to the total number of mesh cells in the area is calculated. This ratio is defined as the void probability of that local area. The calculated void probability of each local area is compared with a preset threshold. The preset threshold is determined according to the load-bearing level of the wall panel. Specifically, for load-bearing wall panels, the void probability threshold is set to 5%; for non-load-bearing wall panels, the void probability threshold is set to 10%. If the void probability of any local area exceeds the preset threshold, it is determined that the current flow control parameters cannot meet the filling uniformity requirements, and the flow control parameters need to be adjusted and the simulation repeated.

[0039] When determining the flow control parameters that need adjustment, specifically the material viscosity parameters to be adjusted, as well as the direction and magnitude of the adjustment, must be identified. Material viscosity is a key parameter affecting the flowability of concrete. When an excessively high probability of local voids is detected, it indicates that the flow resistance in that area is too high or the flow velocity is too slow, requiring a reduction in the plastic viscosity of the material to improve flowability. The adjustment magnitude is determined through a preset mapping relationship based on the proportion of void probability exceeding a threshold. The greater the exceedance of the threshold, the larger the adjustment magnitude. A gradual strategy is adopted during adjustment, with each adjustment magnitude controlled within a reasonable range to avoid over-adjustment due to excessive single adjustments. The adjusted viscosity value must ensure that it is not lower than the minimum allowable viscosity value of the material itself, which can be obtained from a material database. Based on the adjusted material viscosity parameters, the material constitutive relation in the finite element analysis model is updated. The material constitutive relation in the finite element analysis model is described by the Bingham fluid model, which includes two core parameters: yield stress and plastic viscosity. The adjustment mainly updates the value of the plastic viscosity parameter. The flow simulation is rerun in the model after updating the material parameters to solve the Navier-Stokes equations and consider the characteristics of concrete as a non-Newtonian fluid. The velocity field, pressure field, and evolution process of the free surface of concrete in the flow channel containing steel reinforcement obstruction are calculated under the new viscosity conditions. The new flow path distribution map and the corresponding confluence disorder region distribution map are obtained by resimulating.

[0040] After obtaining new simulation results, the void probability values ​​of the same local areas in the old and new simulation results are extracted. The degree of improvement in uneven filling is calculated, and the degree of improvement is quantified by the reduction rate of void probability. For each local area, the initial simulated void probability is subtracted from the new simulated void probability, and then divided by the initial simulated void probability to obtain the degree of improvement value for that area. The average of the degree of improvement for all local areas is calculated, or the average of the degree of improvement for areas with initial void probabilities exceeding the standard is focused on as the overall improvement effect evaluation value after this parameter adjustment. The degree of improvement guides the next round of parameter adjustment. If there are still local areas where the void probability is not lower than the preset threshold, the viscosity parameter adjustment range for the next round is adjusted according to the degree of improvement. If the degree of improvement for a certain area is positive but the value is small, it means that the viscosity adjustment has a positive effect on that area but is not sufficient. In the next iteration, a larger reduction in viscosity is made in that area. If the degree of improvement is negative or zero, other parameters or a change in adjustment strategy are considered. Repeat the iterative process of "parameter adjustment - re-simulation - improvement assessment" until the void probability of all local areas is lower than the preset threshold, and obtain the optimized flow path distribution map. The iteration termination condition is: the void probability of all local areas is lower than the preset threshold, and the improvement degree of multiple consecutive iterations is at a low level, that is, it has converged to a stable state. After reaching the termination condition, lock the current flow control parameters and flow path distribution map, and output them as the final optimized result to step S4. The optimized flow path distribution map includes the magnitude and direction distribution of the flow velocity of concrete on each path in the wall panel mold cavity, the filling status mark of each grid cell at the end of the simulation, which is used to identify whether each grid cell is completely filled by concrete, for example, marked as "filled" or "unfilled", as well as the specific void probability value of the unfilled area, the coordinates of the new boundary position of the merged chaotic area, the final void probability value of all local areas under the void probability threshold requirement, and the geometric direction and branch structure of each path. This distribution map serves as the core input data for subsequent coordination of the merging order of multiple material flows, providing accurate flow characteristic information and a reliable optimization basis for determining the dynamic merging coordination scheme.

[0041] Through the above methods, this application can dynamically adjust the flow control parameters according to the distribution map of the chaotic confluence area, gradually eliminate the risk of local voids through iterative optimization, obtain a flow path distribution that meets the uniformity requirements, and provide a reliable data basis for subsequent coordination of the confluence sequence of multiple material flows, thereby effectively improving the control accuracy and filling uniformity of the prefabricated wall panel casting process.

[0042] Step S4: Coordinate the material flow convergence sequence through the flow path distribution diagram, determine the dynamic convergence coordination scheme, and conduct structural integrity simulation analysis on the poured wall panel based on the dynamic convergence coordination scheme to obtain integrity indicators.

[0043] The step S4, determining the dynamic merging coordination scheme, includes: extracting velocity information on each path based on the optimized flow path distribution map, analyzing the merging time and location of multiple material flows, sorting the merging time and location using a particle swarm optimization algorithm, and generating a preliminary coordination scheme; inputting the preliminary coordination scheme as boundary conditions into the fluid dynamics simulation model to simulate the merging process of multiple material flows and calculating the degree of mutual interference among the material flows; adjusting the merging order according to the degree of mutual interference, matching the optimized coordination scheme with the original flow path, verifying the feasibility of the scheme, and outputting the final dynamic merging coordination scheme.

[0044] Specifically, after obtaining the optimized flow path distribution map, it is necessary to coordinate the merging order of multiple material flows based on the flow velocity information of each path in the map. A dynamic coordination scheme is generated through intelligent optimization algorithms, and the integrity of the wall panel structure under the scheme is simulated and evaluated. When determining the dynamic merging coordination scheme, firstly, the flow velocity information, path geometric length, and position coordinates of the preset merging monitoring points on each path are extracted based on the optimized flow path distribution map. The preset merging monitoring points are set near the areas where paths intersect or merge, and are used to monitor the time when multiple material flows arrive at the same spatial location. The length of each path is divided by the average flow velocity of its corresponding material flow to calculate the estimated arrival time of each material flow from its respective pouring inlet to each preset merging monitoring point. The arrival times of all material flows at the same intersection are compared. When the time difference between multiple material flows arriving at the same location is less than the preset time threshold, it is determined that these material flows will merge at that intersection. The smaller the time difference, the higher the risk of mutual interference during merging. The preset time threshold is determined based on the initial setting time and flow characteristics of concrete. When the time difference between multiple material flows arriving at the same location is less than a certain threshold, it is determined that these material flows will merge at that intersection point. This threshold can be adaptively adjusted according to the wall panel thickness and concrete fluidity. For thin-walled panels, due to the narrow flow space, a stricter time threshold is used; for thick-walled panels, with ample flow space, the threshold requirement can be appropriately relaxed. Through this method, the intersection points where all possible merging events may occur and their corresponding time windows are identified, obtaining the merging time and location data of multiple material flows, providing a basic input for subsequent optimization of the merging sequence.

[0045] After obtaining the convergence time and location data of multiple material flows, a particle swarm optimization algorithm is used to sort the convergence time and location, generating a preliminary coordination scheme. Specifically, the arrival order of the material flows at each potential intersection is encoded as a particle, where each element represents the priority order of a material flow at that intersection. The optimization objective is to minimize the time difference between the arrival times of the material flows at the same intersection. The fitness function is defined as the weighted sum of the arrival time differences of the material flows in all convergence events; the smaller the time difference, the less potential conflict there is at the convergence, and the higher the fitness value. A swarm of random particles is initialized, with each particle representing a possible convergence order scheme. In each iteration, each particle adjusts its search direction and step size based on its own historical best position and the global best position of the entire particle swarm. Through multiple iterations, the particle swarm gradually converges to the convergence order scheme that maximizes the fitness value, generating a preliminary coordination scheme that includes the desired convergence order and time window.

[0046] The preliminary coordination scheme is input as boundary conditions into the fluid dynamics simulation model to simulate the merging process of multiple material flows. The fluid dynamics simulation model uses the finite element method to simulate the flow, encounter, and mixing of concrete as a non-Newtonian fluid in multiple paths. A monitoring area is defined around the merging point, and the standard deviation of the velocity vector within this area is extracted as the velocity turbulence index. Simultaneously, the abrupt change in pressure gradient is calculated as the pressure disturbance index. The velocity turbulence index and the pressure disturbance index are normalized separately and then weighted and summed. The weight coefficients are determined based on the degree of influence of the two types of indices on the filling quality. Velocity turbulence directly reflects flow stability and has a greater impact on filling uniformity, so it is assigned a higher weight; pressure disturbance indirectly reflects filling density and is assigned a lower weight. The specific weight values ​​can be calibrated based on engineering experience or determined through orthogonal experiments, ultimately obtaining a comprehensive mutual interference index to quantify the mutual influence of each material flow during the merging process.

[0047] The merging order is adjusted based on the degree of mutual interference. If the interference level simulated in the initial coordination scheme exceeds a preset acceptable threshold, the merging order or local flow velocity parameters are adjusted based on the mechanism of interference. The adjustment information is fed back to the particle swarm optimization algorithm as a new constraint or a redefinition of the fitness function calculation method. The algorithm searches in the new solution space to generate a new sorting scheme. Through multiple iterations, the generated coordination scheme can adapt to the dynamic changes in the flow process. The optimized coordination scheme is matched and verified against the original flow path. Specifically, based on the target arrival time window of each material flow in the optimized coordination scheme, the required flow velocity range on each path is deduced. It is checked whether this flow velocity range is within the capacity of the concrete pumping equipment and whether it is within the critical flow velocity of the path. At the same time, it is checked whether the adjusted merging order will cause some path segments to suffer excessive waiting time, thus triggering the risk of initial concrete setting. After successful verification, the final dynamic merging coordination scheme containing the planned arrival time, merging order, and suggested flow velocity control parameters for each material flow at each key intersection is output.

[0048] By combining intelligent optimization with simulation verification, the above steps can accurately coordinate the confluence sequence of multiple material flows in complex steel mesh, minimize mutual interference during the confluence process, ensure that concrete fills the mold cavity in an orderly manner, significantly improve the uniformity of wall panel casting and structural density, and provide reliable input for subsequent structural integrity assessment.

[0049] In step S4, structural integrity simulation analysis is performed on the cast wall panel to obtain integrity indices, including: extracting the confluence sequence and casting rate data of multiple material flows according to the dynamic confluence coordination scheme, establishing a three-dimensional finite element model of the wall panel and performing transient mechanical analysis, obtaining the stress distribution and equivalent elastic modulus uniformity coefficient inside the wall panel after casting, and performing weighted comprehensive calculation to obtain quantitative structural integrity indices.

[0050] Specifically, after determining the final dynamic convergence and coordination scheme, the structural integrity of the wall panel under this scheme needs to be quantitatively evaluated. Structural integrity indicators are obtained through finite element simulation analysis to determine whether the current coordination scheme can meet manufacturing quality requirements. Specifically, when performing structural integrity simulation analysis on the poured wall panel and obtaining integrity indicators, the convergence sequence of multiple material flows, the pouring rate of each inlet, and the final flow path network data are first extracted based on the dynamic convergence and coordination scheme. The flow path network data includes the geometric orientation and spatial coordinate distribution of each flow path of concrete within the wall panel mold cavity, the magnitude and direction of the concrete flow velocity on each path, the position coordinates of the intersection points of each path, and the filling state marker of each mesh element at the end of the flow simulation. Based on the extracted flow path network data, a three-dimensional finite element model of the wall panel is established. This is a computational method that discretizes a continuum into a finite number of elements for numerical solution. It is used to simulate the mechanical behavior of the hardened concrete wall panel under stress. Corresponding elastic modulus and strength parameters are assigned according to the filling state marker of each element. The strength parameters include the compressive strength and tensile strength of the concrete, used to describe the material's ability to resist failure under stress. Specifically, the filling status markers of each unit are associated with a pre-defined material parameter mapping table. Densely filled units are assigned higher elastic modulus, compressive strength, and tensile strength to reflect their good mechanical properties. Units with void risk are assigned lower elastic modulus, compressive strength, and tensile strength based on a proportional reduction in void probability value to reflect the deterioration of mechanical properties caused by insufficient filling. This allows the model to accurately reflect the impact of actual pouring results on the mechanical properties of the wall panel. The mapping relationship between the reduction ratio and void probability is established based on the theory of concrete damage mechanics. A higher void probability indicates less dense filling, more severe deterioration of material mechanical properties, and a larger reduction margin. The mapping relationship can use linear or exponential reduction; the specific form needs to be calibrated through mechanical tests on concrete specimens with different void ratios to ensure that the reduced material parameters accurately reflect the actual degree of damage.

[0051] In the established finite element model, transient mechanical analysis is performed based on the pouring process set by the dynamic convergence and coordination scheme. Transient mechanical analysis is a numerical calculation method that simulates the change of physical quantities over time. The analysis process simulates the entire process of concrete from pouring completion, initial hardening to demolding and curing. During this process, the stress history data and strain history data of each point inside the wall panel are calculated as stress changes over time, and the stress distribution cloud map inside the wall panel after pouring is obtained. Key mechanical parameters are extracted from the mechanical analysis results. These key mechanical parameters include the maximum principal stress of the wall panel after demolding, the maximum deflection under the preset load, and the equivalent elastic modulus uniformity coefficient calculated based on the stress distribution. The equivalent elastic modulus uniformity coefficient is calculated as follows: the wall panel model is divided into several layers in its thickness direction, the average elastic modulus of each layer is calculated, the standard deviation of the average elastic modulus of all layers is statistically analyzed, and the ratio of the standard deviation to the average value is subtracted from 1 to obtain the uniformity coefficient. This coefficient reflects the uniformity of material stiffness distribution in space; the higher the coefficient, the smaller the stiffness difference caused by uneven filling.

[0052] The extracted maximum principal stress, maximum deflection, and equivalent elastic modulus uniformity coefficient were normalized to eliminate differences between different dimensions, enabling comparison of the indicators on the same scale. Based on the weighting of parameters in the precast wall panel industry standard, the normalized indicators were weighted and summed. For example, the weight of maximum principal stress was set to 0.5, maximum deflection to 0.3, and uniformity coefficient to 0.2, resulting in a quantitative structural integrity index ranging from 0 to 1. A value closer to 1 indicates better structural integrity of the wall panel. The structural integrity index obtained in this way comprehensively reflects the mechanical properties and filling uniformity of the wall panel under the dynamic convergence and coordination scheme, providing a quantitative basis for subsequent judgment of whether the manufacturing quality meets standards.

[0053] Through the above methods, this application can quantitatively assess the structural integrity of wall panels under dynamic convergence and coordination schemes, and obtain structural integrity indicators that comprehensively reflect stress level, deformation capacity and filling uniformity. This provides a reliable quantitative basis for manufacturing quality judgment and subsequent parameter optimization, thereby effectively ensuring the structural reliability and service life of prefabricated wall panels.

[0054] Step S5: Determine whether the manufacturing quality meets the standards based on the integrity index. If it does not meet the standards, identify the key areas of filling defects based on the integrity index, extract the critical path from the key areas, and rerun the finite element simulation to obtain the updated flow path distribution map.

[0055] Step S5 includes: if the integrity index does not meet the preset standard, the stress cloud map obtained from the finite element model is superimposed with the distribution map of the confluence and disordered regions to identify the overlapping areas with stress concentration and void probability higher than the preset threshold as the key areas for filling defects; the dense mesh line segments of the reinforcing bars that cause flow obstruction are extracted from the key areas as the critical paths, the mesh is refined at the intersections on the critical paths, and the finite element simulation is rerun to obtain the updated flow path distribution map.

[0056] Specifically, after obtaining the quantified structural integrity index, it needs to be compared with the preset standard to determine whether the manufacturing quality meets the standard. If it does not meet the standard, it is necessary to further locate the key areas for filling defects, providing precise target locations for subsequent optimization. When judging whether the manufacturing quality meets the standard based on the integrity index, the calculated structural integrity index is first compared with the preset standard. The preset standard is determined according to the design grade and purpose of the wall panel. For example, for load-bearing wall panels, a lower limit value of 0.85 is set. If the structural integrity index is greater than or equal to this value, the manufacturing quality is determined to meet the standard, and the process can directly proceed to the next production process. If the structural integrity index is less than the preset standard, the current manufacturing quality is determined to be substandard, and further analysis of the key areas affecting the index is required. If the structural integrity index fails to meet the preset standard, the stress cloud map obtained from the finite element model is overlaid and compared with the previously generated chaotic region distribution map. The stress cloud map is a visualization result output from the structural mechanics analysis, using color depth to represent the stress magnitude at various locations within the wall panel. Darker areas indicate higher stress concentration and a greater likelihood of cracking or failure. The chaotic region distribution map is a visualization result output from the flow simulation, using color depth to represent the degree of turbulence in concrete flow in each region. Darker areas indicate a higher risk of confluence conflict and a greater likelihood of infill defects. After overlaying the two maps, overlapping areas that simultaneously exhibit stress concentration in the stress cloud map and correspond to a high probability of voids in the chaotic region distribution map are identified. These overlapping areas are marked as critical areas for infill defects; these areas are both high-risk zones during the flow process and weak points in the final structure.

[0057] The critical path is defined as the densely packed rebar mesh segments that impede flow within the critical region. The critical path refers to the rebar mesh segments within that region that primarily hinder concrete flow; these are typically the areas with the narrowest rebar spacing and the densest intersections. The mesh at the intersections on the critical path is refined. Specifically, the finite element mesh size of the critical path and its surrounding area is reduced, for example, from an initial 5 mm to 2 mm, to improve the simulation accuracy of flow details in that area. Based on the refined mesh, the finite element simulation is rerun to solve the Navier-Stokes equations and the continuity equation, calculating the velocity and pressure fields of the concrete under the refined mesh. This yields an updated flow path distribution map, which more accurately reflects the flow details at the critical path, providing more precise input data for subsequent optimization of the pouring inlet location using a genetic algorithm.

[0058] Through the above methods, this application can accurately locate the key areas of filling defects when the structural integrity index fails to meet the standard, and obtain higher-precision flow path data through mesh refinement and re-simulation, providing a reliable basis for subsequent inlet location optimization, thereby effectively improving the closed-loop optimization capability and manufacturing quality of the prefabricated wall panel casting process.

[0059] Step S6: Based on the updated flow path distribution map, optimize the pouring inlet location and flow ratio to generate the final control configuration that minimizes path obstruction.

[0060] Step S6 includes: taking the updated flow path distribution map as input, using a genetic algorithm to jointly optimize the pouring inlet location and the flow ratio of multiple pouring equipment to generate the final pouring control configuration that minimizes path obstruction; verifying the final pouring control configuration through physical pouring or high-precision simulation, and confirming that the filling uniformity and structural integrity of the wall panel meet the preset quality standards, then solidifying the final pouring control configuration into the production execution system as the benchmark control parameter for the manufacturing of similar wall panels.

[0061] Specifically, after obtaining the updated flow path distribution map, the pouring inlet location and flow ratio need to be jointly optimized based on the distribution map to generate the final control configuration that minimizes path obstruction, and then solidified into the production execution system after verification.

[0062] Specifically, when optimizing the pouring inlet location and flow ratio based on the updated flow path distribution map, the updated flow path distribution map is first used as input, and a genetic algorithm is used to jointly optimize the pouring inlet location and the flow ratio of multiple pouring equipment. The genetic algorithm is an intelligent optimization algorithm that simulates the biological evolution process. It iteratively searches for the optimal solution in the solution space through selection, crossover and mutation operations.

[0063] Specifically, the coordinates of the pouring inlet location and the flow rate ratio parameters of each material flow are encoded as chromosomes. Each chromosome represents a candidate pouring control configuration. A population containing multiple chromosomes is initialized, with each chromosome corresponding to a set of inlet location and flow rate ratio parameters. The total path obstruction is defined as the optimization objective function, which is constructed based on the flow path length and the number of critical intersections. The specific expression is: the total path obstruction equals the product of the flow path length and the first weighting coefficient, plus the product of the number of critical intersections and the second weighting coefficient. Here, the flow path length reflects the flow distance of concrete from the pouring inlet to various points in the mold cavity. The longer the distance, the greater the flow resistance and the more energy loss. The number of critical intersections reflects the number of convergence events at the intersection of multiple flow paths. The more critical intersections, the higher the risk of convergence conflict and the greater the possibility of filling defects. The specific values ​​of the first and second weighting coefficients are determined based on the actual influence of the two types of factors on the filling quality. The number of critical intersections directly reflects the risk of convergence conflict and contributes significantly to path obstruction, thus being assigned a higher weight. The flow path length indirectly reflects the flow resistance and is assigned a lower weight. The weighting coefficients can be calibrated through orthogonal experiments or regression analysis of historical data to ensure that the optimization objectives match the actual filling quality requirements.

[0064] Since genetic algorithms tend to retain individuals with higher fitness values, and the optimization objective of this application is to minimize the total path obstacles, the fitness function is defined as the reciprocal of the total path obstacles; that is, the fitness value equals the reciprocal of the total path obstacles. According to this definition, the shorter the flow path length and the fewer the number of critical intersections, the smaller the total path obstacles, the larger its reciprocal, the higher the fitness value, and the greater the probability that the individual will be selected and retained. In this way, the minimization problem is transformed into a maximization problem that genetic algorithms can handle. In each iteration, a roulette wheel selection method is used based on the fitness value to select superior individuals. Crossover operations are used to exchange some genes between two chromosomes, and mutation operations are used to randomly change the gene values ​​of a single chromosome, generating a new generation of the population. This iterative process is repeated until convergence, yielding the optimal chromosome that maximizes the fitness value, corresponding to the optimal pouring inlet location and optimal flow ratio parameters that minimize the total path obstacles, which serve as the final pouring control configuration.

[0065] Based on the final pouring control configuration, physical pouring verification or high-precision simulation verification is conducted to confirm that the wall panel's filling uniformity and structural integrity meet the preset quality standards. Specifically, physical pouring tests are performed according to the inlet location and flow ratio in the final pouring control configuration, or a higher-precision simulation model is used for verification. The filling uniformity and structural integrity indices of the poured wall panel are measured and compared with the preset quality standards. If the verification passes, the final pouring control configuration is solidified into the production execution system as the benchmark control parameters for manufacturing similar wall panels. Subsequent production of similar wall panels can directly call this configuration for automated pouring control. If the verification fails, the parameters of the genetic algorithm are adjusted or the critical path is re-identified and optimized again.

[0066] Through the above methods, this application can intelligently optimize the pouring inlet location and flow ratio based on the updated flow path distribution map, generate the final control configuration with minimal path obstruction, and solidify it into the production execution system after verification, thereby realizing closed-loop control from simulation optimization to actual production, thus effectively improving the automation level and manufacturing quality consistency of the prefabricated wall panel pouring process.

[0067] In summary, this application provides an automated pouring control method and system for prefabricated integrated wall panels. Addressing key technical challenges in existing technologies, such as uneven concrete filling and internal voids caused by dense reinforcement, a closed-loop control system of "perception-prediction-optimization-feedback" is constructed. Through an innovative "reinforcement-resistance field" mapping method, the complex reinforcement structure is abstracted into a three-dimensional resistance field, achieving efficient simulation of concrete flow. By identifying potential intersections and generating a "convergence disorder area distribution map," the invisible internal flow problem is visualized as a high-risk area. Furthermore, flow control parameters are dynamically adjusted, and a particle swarm optimization algorithm is used to coordinate the confluence sequence of multiple material flows, minimizing mutual interference. This application innovatively introduces structural integrity simulation analysis to obtain quantitative structural integrity indicators. When the indicators fail to meet the standards, stress cloud maps are overlaid with the convergence disorder area distribution map to accurately locate defective areas and refine the mesh for recalculation, forming a reverse tracing mechanism. Finally, a genetic algorithm is used to optimize the pouring inlet location and flow ratio, generating the optimal control configuration and solidifying it into the production execution system. The technical solution presented in this application is logically rigorous and clearly structured, effectively improving the automation level of wall panel casting and the consistency of manufacturing quality, and providing a reliable technical path for the intelligent manufacturing of prefabricated building components.

[0068] The above describes an automated pouring control method for prefabricated integrated wall panels according to embodiments of this application. The following describes an automated pouring control system for prefabricated integrated wall panels according to embodiments of this application. Please refer to [link to relevant documentation]. Figure 4 An automated pouring control system for prefabricated integrated wall panels, as described in this application embodiment, includes: The construction module is used to acquire the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity; The generation module is used to determine the potential intersection points of material flow convergence and conflict caused by the obstruction of steel bars based on the initial simulation model, simulate the material flow behavior at the potential intersection points, and generate a distribution map of the convergence and chaos area. The simulation module is used to adjust the flow control parameters based on the distribution map of the chaotic confluence area, and optimize the flow path of concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained. The analysis module is used to coordinate the material flow convergence sequence through the flow path distribution map, determine the dynamic convergence coordination scheme, and perform structural integrity simulation analysis on the cast wall panel based on the dynamic convergence coordination scheme to obtain integrity indicators. The judgment module is used to determine whether the manufacturing quality meets the standards based on the integrity index. If it does not meet the standards, it identifies the key areas of filling defects based on the integrity index, extracts the critical path from the key areas, and reruns the finite element simulation to obtain the updated flow path distribution map. The optimization module is used to optimize the pouring inlet location and flow ratio based on the updated flow path distribution map, and generate the final control configuration that minimizes path obstruction.

[0069] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An automatic pouring control method for fabricated integrated wallboard, characterized in that, The method includes: Step S1: Obtain the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity; Step S2: Based on the initial simulation model, determine the potential intersection points where material flow convergence and conflict may occur due to reinforcement obstruction. Simulate material flow behavior at these potential intersection points to generate a convergence and chaos region distribution map. Step S2, generating the convergence and chaos region distribution map, includes: establishing a finite element analysis model based on the potential intersection points and reinforcement mesh arrangement parameters to simulate the flow state of concrete material at the intersection points; calculating material velocity and pressure distribution using the finite element analysis model to determine the degree of chaos in the convergence region; dividing the convergence and chaos regions according to the degree of chaos to generate corresponding distribution maps; analyzing the distribution maps to extract the range and average chaos intensity of each chaos region and confirm key chaos point data; correlating the key chaos point data with the potential intersection points to verify the reliability of the simulation results; and obtaining the final convergence and chaos region distribution map based on the verification results. Step S3: Based on the distribution map of the chaotic confluence area, adjust the flow control parameters and optimize the flow path of the concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained. Step S4: Coordinate the material flow convergence sequence using the flow path distribution map to determine a dynamic convergence coordination scheme. For the dynamic convergence coordination scheme, perform structural integrity simulation analysis on the cast-in-place wall panel to obtain integrity indices. Specifically, determining the dynamic convergence coordination scheme in Step S4 includes: extracting flow velocity information from each path based on the optimized flow path distribution map; analyzing the convergence time and location of multiple material flows; using a particle swarm optimization algorithm to sort the convergence time and location to generate a preliminary coordination scheme; inputting the preliminary coordination scheme as boundary conditions into the fluid dynamics simulation model to simulate the convergence process of multiple material flows and calculate the mutual interference degree of each material flow; adjusting the convergence sequence based on the mutual interference degree; matching the optimized coordination scheme with the original flow path to verify the feasibility of the scheme; and outputting the final dynamic convergence coordination scheme. Step S5: Determine whether the manufacturing quality meets the standard based on the integrity index. If it does not meet the standard, identify the key area of ​​filling defects according to the integrity index, extract the critical path from the key area, and rerun the finite element simulation to obtain the updated flow path distribution map. Step S6: Based on the updated flow path distribution map, optimize the pouring inlet location and flow ratio to generate a final control configuration that minimizes path obstruction. The final control configuration includes the optimal pouring inlet location and the optimal flow ratio parameters.

2. The automatic pouring control method of the fabricated integrated wallboard according to claim 1, characterized in that, Step S1 includes: Extract the steel reinforcement distribution information from the design data of the wall panel to be poured, calculate the local grid density, and mark the spatial area with a grid density value exceeding the preset threshold as a dense steel reinforcement area. The main direction of the reinforcing bars in the densely reinforced area is analyzed to obtain the reinforcing bar mesh arrangement parameters; The wall panel casting cavity is mapped into a three-dimensional spatial resistance field with resistance properties according to the grid arrangement parameters, and an initial geometric model is constructed based on the resistance field. In the initial geometric model, the constitutive model and boundary conditions of the concrete material are set, and the velocity field and pressure field of the concrete in the flow domain are calculated by transient simulation. Based on the simulation results, a preliminary framework of the flow path is generated. The preliminary framework is compared with the design data. If the deviation exceeds the preset threshold, the grid arrangement parameters are adjusted and the model is reconstructed until an initial simulation model that meets the accuracy requirements is obtained.

3. The automated pouring control method for prefabricated integrated wall panels according to claim 1, characterized in that, Step S2 identifies potential intersections where material flow confluences and conflicts due to reinforcement obstruction, including: Spatial analysis is performed on the flow paths in the initial simulation model to identify the path intersection areas and extract the geometric coordinate data of the path intersection points. The flow resistance values ​​of each path intersection point are calculated to determine potential obstruction areas. The potential obstacle area is divided into grids and path density analysis is performed to assess the degree of mutual influence between the intersections of each path and generate an influence matrix. Based on the influence degree matrix, key intersection points are selected, and particle swarm optimization algorithm is used to iteratively optimize the key intersection points to generate an obstacle distribution map. The obstruction distribution map is overlaid and compared with the initial simulation model to verify the accuracy of the identification of key intersections and confirm the final potential intersection locations.

4. The automated pouring control method for prefabricated integrated wall panels according to claim 1, characterized in that, Step S3 includes: Analyze the gap probability of each local area in the distribution map of the chaotic confluence area, determine whether there is a gap probability that exceeds a preset threshold, and if so, determine the flow control parameters that need to be adjusted and the adjustment range. The material constitutive relations in the finite element analysis model are updated based on the adjusted flow control parameters, and the flow simulation is rerun to obtain a new flow path distribution and the corresponding distribution map of the confluence and disorder region. Extract the void probability of the same local area from the old and new simulation results, calculate the degree of improvement in uneven filling, and guide the next round of parameter adjustment based on the degree of improvement until the void probability is lower than a preset threshold to obtain the optimized flow path distribution map.

5. The automated pouring control method for prefabricated integrated wall panels according to claim 1, characterized in that, In step S4, a structural integrity simulation analysis is performed on the poured wall panel to obtain integrity indices, including: Based on the dynamic convergence coordination scheme, the convergence sequence and pouring rate data of multiple material flows are extracted, a three-dimensional finite element model of the wall panel is established and transient mechanical analysis is performed to obtain the stress distribution and equivalent elastic modulus uniformity coefficient inside the wall panel after pouring, and a weighted comprehensive calculation is performed to obtain a quantitative structural integrity index.

6. The automated pouring control method for prefabricated integrated wall panels according to claim 1, characterized in that, Step S5 includes: If the integrity index does not meet the preset standard, the stress cloud map obtained from the finite element model is superimposed with the distribution map of the converging chaotic region to identify the overlapping region with stress concentration and void probability higher than the preset threshold as the key region for filling defects. Extract the densely packed steel mesh segments that cause flow obstruction from the critical region as critical paths, refine the mesh at the intersections on the critical paths, and rerun the finite element simulation to obtain an updated flow path distribution map.

7. The automated pouring control method for prefabricated integrated wall panels according to claim 1, characterized in that, Step S6 includes: Using the updated flow path distribution map as input, a genetic algorithm is used to jointly optimize the pouring inlet location and the flow ratio of multiple pouring equipment to generate the final pouring control configuration that minimizes path obstruction. Based on the final pouring control configuration, physical pouring verification or high-precision simulation verification is carried out. After confirming that the filling uniformity and structural integrity of the wall panel meet the preset quality standards, the final pouring control configuration is solidified into the production execution system as a benchmark control parameter for the manufacturing of similar wall panels.

8. An automated pouring control system for prefabricated integrated wall panels, used to implement the automated pouring control method for prefabricated integrated wall panels as described in any one of claims 1-7, characterized in that, The system includes: The construction module is used to acquire the design data of the wall panel to be poured, identify the grid distribution in the densely reinforced area based on the design data, and construct an initial simulation model of the flow path of concrete in the wall panel mold cavity; The generation module is used to determine the potential intersection points where material flow convergence and conflict occurs due to the obstruction of steel bars, based on the initial simulation model; simulate material flow behavior at the potential intersection points; and generate a distribution map of the convergence and chaos area. The simulation module is used to adjust the flow control parameters according to the distribution map of the chaotic confluence area, and optimize the flow path of concrete through finite element simulation until a flow path distribution map that meets the preset flow uniformity requirements is obtained. The analysis module is used to coordinate the material flow convergence sequence through the flow path distribution map, determine the dynamic convergence coordination scheme, and perform structural integrity simulation analysis on the cast wall panel based on the dynamic convergence coordination scheme to obtain integrity indicators. The judgment module is used to determine whether the manufacturing quality meets the standard based on the integrity index. If it does not meet the standard, it identifies the key area of ​​filling defects according to the integrity index, extracts the critical path from the key area, and reruns the finite element simulation to obtain an updated flow path distribution map. The optimization module is used to optimize the pouring inlet location and flow ratio based on the updated flow path distribution map, and generate the final control configuration that minimizes path obstruction.

Images