Intelligent partition sorting and route planning method for building material distributing machine

Abstract

The invention discloses an intelligent partition sorting and route planning method for a building material distributing machine, which comprises the following steps of: dividing a construction floor plane into a plurality of regular partitions according to grid data of a building general drawing, and constructing a graph model based on a communication relationship between the partitions; using a depth-first search algorithm to search a longest path accessing most vertexes in the graph; for vertexes which are not covered by the path, judging and constructing an Euler path, and intelligently re-segmenting a corresponding partition, so as to realize a global path capable of traversing all partitions without repetition at one time; a genetic algorithm is adopted to plan a bow-shaped operation path meeting the requirements of full coverage, high efficiency and obstacle avoidance for the interior of each subarea; and finally, on the basis of all path results, construction simulation is carried out in a Unity 3D digital twin system, and the advancing speed and the concrete flow of the material distributing machine are dynamically calculated. The problem that a material distributing machine cannot efficiently and continuously pour under complex partition layout is solved, and the construction efficiency and the building quality are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, specifically to a method for intelligent zoning, sorting, and route planning of building concrete placing booms, which is particularly suitable for intelligent division of building floor areas, path optimization, and operation control of concrete placing booms during the concrete pouring process. Background Technology

[0002] In modern building construction, using a concrete placing boom for sectional concrete pouring is a crucial step in completing the main structure of a building. To improve construction efficiency and structural quality, the floor plan needs to be rationally divided into several construction zones according to the design drawings, and a working path should be planned for the placing boom to sequentially cover all zones. Ideally, the placing boom should be able to continuously traverse all zones in one go without repetition, thus avoiding structural quality problems caused by uneven concrete setting.

[0003] However, due to the complex spatial topological relationships between different zones in a building's floor plan, with numerous irregular arrangements and obstacles, finding an optimal path that traverses all zones without repetition becomes extremely difficult. This path planning problem can essentially be abstracted as a "one-stroke drawing" problem or an Eulerian path problem in graph theory. As shown in the classic Seven Bridges of Königsberg problem, not all connected graphs have paths that traverse all edges without repetition; when there are more than two odd vertices in the graph, a one-stroke drawing is impossible. In practical concrete placing boom path planning, directly applying this theory often fails to find a path that meets the conditions due to the inherent characteristics of the zone layout, resulting in incomplete coverage or the need to repeatedly traverse certain areas, severely affecting the continuity and efficiency of construction.

[0004] Existing path planning methods often struggle to simultaneously meet the engineering requirements of full coverage, high efficiency, and no repetitive traversal. They either fail to handle optimal sorting problems under complex partition layouts, leading to path omissions or excessive backtracking; or they do not adequately consider the connection with the global path, obstacle avoidance, and coordinated control of pouring parameters (such as speed and flow rate) when planning paths within a partition. Therefore, there is an urgent need for a comprehensive solution capable of intelligently handling partition sorting and constructing feasible and efficient paths by dynamically adjusting partition topology. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides an intelligent zoning and routing method for concrete placing booms, designed to solve the path planning challenge of achieving one-time, non-repetitive, full-coverage pouring under complex building zoning layouts. This method abstracts physical zoning into a graph model and intelligently reconstructs it, ultimately generating efficient, continuous, and complete global and local operation paths. The method includes the following steps: Step 1: Divide the construction floor plan into multiple regular partitions based on the grid data of the building site plan, and determine the connectivity between the partitions based on the coordinates and obstacle information of the partitions. Take the geometric center of each partition as a vertex of the graph, and establish edges between connected vertices to construct the graph model. Step 2: Based on the graph model constructed in Step 1, use the depth-first search algorithm to find the longest path that can visit the most vertices and visit each vertex only once. Step 3: For the vertices not covered by the longest path obtained in Step 2, divide the adjacent partitions connected to the remaining vertices into a first sub-partition and a second sub-partition. The first sub-partition is close to the adjacent edges of the partitions corresponding to the remaining vertices and the adjacent partitions, while the second sub-partition is far away from the adjacent edges of the partitions corresponding to the remaining vertices and the adjacent partitions. Then divide the first sub-partition into a third sub-partition and a fourth sub-partition. Both the third and fourth sub-partitions are adjacent to the partitions corresponding to the remaining vertices. Using the determination method of the Euler path problem, by adding virtual edges between the second and third sub-partitions, between the third sub-partition and the remaining vertices, and between the remaining vertices and the fourth sub-partition, the longest path is transformed into a global path that can traverse all partitions at once without repetition, thus obtaining a global path sequence composed of the partitions and sub-partitions traversed by the global path in sequence. Step 4: Based on the global path sequence obtained in Step 3, for each partition and sub-partition in the sequence, use a genetic algorithm to optimize the bow-shaped operation path inside it. The path must meet the requirements of full coverage, obstacle avoidance and smooth connection between regions. Step 5: Based on all the paths obtained in the previous steps, perform a visual simulation of the construction process in the Unity3D digital twin system, and dynamically calculate the traveling speed of the concrete placing boom and the concrete flow rate.

[0006] Furthermore, in step 1, the determination of the connectivity between partitions is specifically based on the following conditions: The minimum horizontal gap between two partitions satisfies: ,in Represents grid coordinates; These represent the width and length of the two partitions, respectively. This is the preset gap tolerance parameter; The vertical alignment deviation between the two partitions satisfies: ,in Represents grid coordinates; These are the preset alignment tolerance parameters; The proportion of obstacles on the edges where the two partitions meet is lower than the preset obstacle proportion threshold. .

[0007] Furthermore, in step 2, the depth-first search algorithm uses a main stack and an auxiliary stack to manage the search state and backtracking process, specifically including: Initialization phase: Push the starting node onto the main stack and empty the auxiliary stack; Depth-first search phase: When the main stack is not empty, the top node of the main stack is taken as the current node, and its adjacent node set is searched. If there are unvisited adjacent nodes, one of them is selected and pushed onto the main stack, and the remaining list of unvisited adjacent nodes is pushed onto the secondary stack. If the current node does not have any adjacent nodes that can be visited, a backtracking operation is performed, the current node is popped from the main stack, and the unvisited adjacent list of the previous node is taken from the secondary stack. Path recording phase: When the top node of the main stack is the same as the target node, the current node sequence in the main stack is recorded as a feasible path.

[0008] Furthermore, in step 3, the corresponding physical partition is further divided, specifically by using a T-shaped cutting method to divide the partition into three parts, or by using a horizontal and vertical cutting method to divide the partition into two parts.

[0009] Furthermore, in step 4, the optimization of the internal paths of the partition using a genetic algorithm aims to maximize a comprehensive evaluation function. : In the formula, Step size weight, Efficiency weighting; Indicates coverage completeness. , , Indicates the effective coverage area; Indicates the total area of ​​the partition; Indicates the total path length; Indicates the working width of the equipment; Indicates average working speed. , This indicates the total amount of materials required for the partition.

[0010] Furthermore, in step 4, the specific iterative steps of the genetic algorithm include: ① Initialize the population: Randomly sample from the predefined set of step size parameters to generate an initial population containing multiple individuals, each representing a step size scheme; ② Calculate fitness: Calculate the comprehensive evaluation function value of the path choices for each individual in the population. As fitness; ③Elite selection and retention: Select the top 10% of individuals with the highest fitness in the current population and store them in the elite population; ④ Crossover and mutation: For non-elite individuals, parents are randomly selected from the elite population to perform crossover and mutation operations to generate offspring; among them, the crossover operation is to exchange part of the step size value, and the mutation operation is to apply a random perturbation to the step size value. ⑤ Survival of the fittest selection: The newly generated offspring are compared with their original parents. If the offspring are more fit, the original parents are replaced. ⑥ Forming a new population and cycling: Merge elite individuals and the updated individuals into the next generation population, and repeat steps ② to ⑤ until the maximum number of generations is reached.

[0011] Furthermore, in step 4, the planning of the bow-shaped work path requires determining the starting edge and ending edge of the partition; the starting edge is selected on the side away from the obstacle, and the ending edge is selected on the side closer to the next partition to be visited.

[0012] Furthermore, in step 5, the dynamic calculation of the concrete placing boom's travel speed and concrete flow rate is performed by combining path length, pouring area, and partition size parameters.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. Solved the path planning problem under complex topology: This invention abstracts physical partitions into a graph model and creatively combines depth-first search with Eulerian path determination and construction methods, effectively solving the fundamental problem of not being able to find a non-repeating traversal path due to the complexity of building partition layout. It realizes the generation of a globally continuous pouring path under any partition layout, filling the gap in the existing technology.

[0014] 2. Achieved truly efficient full coverage: Through the strategy of "longest path priority + Euler path completion + intelligent re-segmentation of zones", it is ensured that the concrete placing boom can traverse all construction zones in one go without omission, avoiding the problems of repeated coverage, area omission or construction interruption caused by improper path planning in traditional methods. Thus, while ensuring the uniformity of pouring and structural quality, construction efficiency is significantly improved.

[0015] 3. Optimized the collaborative efficiency of global and local paths: This invention adopts a hierarchical planning approach, optimizing the partition access sequence at the macro level and using a genetic algorithm to generate high-quality internal operation paths for each partition at the micro level. This collaborative planning ensures that the concrete placing boom remains highly efficient at both the global movement and local pouring levels, resulting in shorter total path lengths, less equipment idle time and turning waste, and a significant improvement in overall operational smoothness.

[0016] 4. Achieved precise control and forward-looking verification of the construction process: By integrating the Unity3D digital twin system, the planned path is dynamically linked with the speed and flow control of the concrete placing boom, enabling visualized simulation and precise parameter calculation of the construction process. This allows for the verification and optimization of work plans before actual construction, and the early identification and avoidance of potential problems, providing solid data support and decision-making basis for the automation, intelligent control, and high-quality management of construction. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the working environment; Figure 2 A diagram showing how to determine connectivity. Figure 3 A schematic diagram showing the division of the overall plan into several regular and constructible zones; Figure 4 The corresponding diagram obtained based on the building zoning; Figure 5 This is a graph showing the longest path obtained by the depth-first search algorithm. Figure 6 This is a schematic diagram of the Euler road structure obtained through segmentation; Figure 7 This is the final path result diagram; Figure 8 Here are schematic diagrams of four possible start-end point combinations; Figure 9 This is a schematic diagram of a digital twin system for a building cloth laying machine based on the Unity3D engine. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0019] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0020] Example 1 A method for intelligent zoning, sorting, and route planning of concrete placing booms is provided to solve the path planning problem of concrete placing booms being unable to achieve one-time, non-repetitive, full-coverage pouring under complex building zoning layouts. The method includes the following steps: Step 1: Divide the site plan into m rectangular zones and number them (1, 2, ..., m). Figure 3 The system searches for interconnected partitions based on their coordinates and obstacle information. Each partition contains no insurmountable obstacles. Using the geometric center of each partition as a vertex in the overall architectural plan, edges are established between corresponding vertices of connected partitions to construct a graph model, such as... Figure 5 On the left side of the middle.

[0021] The overall building plan is composed of grids, and the grid information of each grid includes the following information: grid coordinates, pouring depth, obstacle information, and slab number. The obstacle information is used to indicate whether the grid is an obstacle.

[0022] Partitions that are horizontally connected to each other satisfy the following conditions: The minimum horizontal gap between two partitions satisfies: (It is permissible for the two rectangles not to fit perfectly together, depending on the actual situation.) (Preset gap tolerance parameters); The vertical alignment deviation between the two partitions satisfies: , This is the preset alignment tolerance parameter.

[0023] If the proportion of all obstacles in the edges where two partitions are adjacent is less than a threshold. For example, if Then the distance between the overlapping points is The number of obstacles in the overlapping area is m, and the proportion of obstacles is: ;like Then the distance between the overlapping points is The proportion of obstacles is: .

[0024] Because cement spreads during the pouring process, if there is an obstacle between two zones but the minimum width of the pouring path is met, then the two zones are still considered adjacent. This determines whether two partitions are adjacent, even if there are obstacles between them, as long as the path is passable.

[0025] In this real-world case The value of 2 is based on the grid data, which corresponds to 0.2m and 0.4m in practice. The value is 4. The value is 0.95, and this setting is based on the pouring speed of the concrete placing boom in the project. For example, if the pouring speed of the concrete placing boom is... Then the interval between paths is rice.

[0026] Partitions that are vertically connected to each other satisfy the following conditions: The minimum vertical gap between two partitions satisfies: (It is permissible for the two rectangles not to fit perfectly together, depending on the actual situation.) (Preset gap tolerance parameters); The horizontal alignment deviation between the two partitions satisfies: , This is the preset alignment tolerance parameter.

[0027] If the proportion of all obstacles in the edges where two partitions are adjacent is less than a threshold. For example, if Then the distance between the overlapping points is The number of obstacles in the overlapping area is m, and the proportion of obstacles is: ;like Then the distance between the overlapping points is The proportion of obstacles is: .

[0028] The above connectivity relationships are constraints, indicating that the concrete placing boom can pass through during the pouring process, mainly to obtain... Figure 4 The connectivity of an undirected graph like the one on the right.

[0029] ( , ), ( , () represents the grid coordinates of the lower left corner of each of the two partitions; These represent the width and length of the two partitions, respectively.

[0030] Step 2: In the graph model, use a depth-first search algorithm to find all connected paths. The longest path among all connected paths is taken as the output path. Each vertex in a connected path is traversed only once by the found path, and adjacent vertices on the path are connected to each other. Figure 5 As shown, the depth-first search algorithm uses a main stack + auxiliary stack approach to effectively manage the search state and backtracking process.

[0031] The main steps of the algorithm are as follows: 1) Initialization Phase: Push the starting node onto the main stack and set the secondary stack to empty. At this point, the main stack represents the starting point of the current path, and the secondary stack has not yet stored any adjacency information. Adjacent nodes must satisfy the conditions in step 1.

[0032] 2) Depth-first search phase: When the main stack is not empty, the top node of the main stack is taken as the current node, and its adjacent node set is retrieved. If there are unvisited adjacent nodes, one of them is selected and pushed onto the main stack, and the list of the remaining unvisited adjacent nodes of the current node is pushed onto the secondary stack. If the current node has no further traversable adjacent nodes, a backtracking operation is performed, the current node is popped from the main stack, and the list of unvisited adjacent nodes of the previous node is retrieved from the secondary stack, the search state is restored, and the traversal continues.

[0033] 3) Path Detection Phase: Whenever the top node of the main stack before the backtracking operation is the same as the target node obtained after the backtracking operation, it indicates that a feasible path from the starting point to the target node has been found. At this time, the node sequence stored in the main stack can be recorded as the path result, and backtracking can be performed to continue exploring other possible paths. The target node is the endpoint of the search process. When the top node of the main stack is the same as the target node, it means that a complete path has been found. At this time, the node sequence in the main stack is a valid path and can be recorded and saved. Figure 6 In (1), the last top node in the main stack is 11. 11 has no remaining unvisited adjacent nodes, so a backtracking operation is performed. The unvisited adjacent list of the previous node 10 is retrieved from the auxiliary stack. The node in the unvisited adjacent list of node 10 is 11, which is the target node.

[0034] Pushing a vertex into the main stack refers to adding a vertex from the node graph to the top of the main stack, where the top node is the currently visited node. Since stacks follow the "last-in, first-out" principle, the top node is always the last node pushed into the search path and has not yet been backtracked. During the search, the algorithm only starts from the top node of the main stack each time, checking its adjacent nodes for any unvisited parts.

[0035] The auxiliary stack is used to store the list of unvisited adjacent nodes of the current node, and pushing onto the auxiliary stack means saving the remaining list of unvisited adjacent nodes of the current node into the auxiliary stack. The purpose of the backtracking operation is to restore the search state. When the current node has no unvisited adjacent nodes, the current node is popped from the main stack, the unfinished search continues, the unvisited adjacent list of the previous node is retrieved from the auxiliary stack, a new adjacent node is selected and pushed back onto the main stack, and the search continues, ensuring that all possible paths are explored, rather than getting stuck in an infinite loop.

[0036] like Figure 5 As shown, the longest path that can be found by the algorithm is: 6->3->4->1->2->8->9->15->7->12->5->14->10->11, with only 13 not in the path.

[0037] Step 3: For vertices not reached by the output path, use the Eulerian path problem's determination method to construct an Eulerian path between the not reached vertex and its adjacent nodes. For example... Figure 6 As shown, the path planned by the placing machine does not connect all vertices, but vertices 1 and 13 are adjacent. If an edge is added between vertices 1 and 13, it will be impossible to draw a path through all vertices in one stroke. Therefore, two edges are first added between vertices 1 and 13 to make it a path that can be drawn in one stroke. However, since region 1 cannot be traversed twice, region 1 is divided into three smaller regions. During this process, whenever the longest path cannot cover all regions (i.e., all vertices), a virtual edge is added and the region is divided again. The adjacent regions of the remaining vertex regions corresponding to the vertices not traversed by the output path are divided into a first sub-region and a second sub-region. The first sub-region is close to the adjacent edges of the remaining vertex regions and adjacent regions, and the second sub-region is far away from the adjacent edges of the remaining vertex regions and adjacent regions. The first sub-region is then divided into two sub-regions. In this way, each sub-region and other regions can be traversed once and only once, which meets the route requirements of the intelligent placing machine.

[0038] The reason for adding two edges between vertices 1 and 13 in the above operation is because, for example... Figure 4 In an undirected connected graph, 1 and 13 are connected. However, in engineering project regulations, like... Figure 4 Points like 13 and 2, which are diagonally adjacent, are considered non-adjacent, i.e., disconnected. The approach to handling uncovered points is to use adjacency interpolation, which involves inserting points not covered by the longest path based on their adjacency relationships.

[0039] When segmentation is required, how to select the target partition to be segmented? In this embodiment, we take "Region 1" as an example to illustrate why its generation path is 6->3->4->1->2->8->9->15->7->12->5->14->10->11.

[0040] Based on the adjacency interpolation described above, 13 is only adjacent to 1. Therefore, there are two possible insertion positions for 13, corresponding to the paths 6->3->4->1->13->2->8->9->15->7->12->5->14->10->11 and 6->3->4->13->1->2->8->9->15->7->12->5->14->10->11. The selection rule for these two paths is as follows: for 6->3->4->13->1->2->8->9->15->7->12->5->14->10->11, the disconnected path is 4->13, and the shortest path for 4->13 is 4->1->13. Therefore, the final irrigation path is 6->3->4->1->13->1->2->8->9->15->7->12->5->14->10->11. It can be seen that 1 is a partition that needs to be visited repeatedly in the irrigation path, so 1 is selected as the target partition to be segmented. For 6->3->4->1->13->2->8->9->15->7->12->5->14->10->11, the final irrigation path generated according to the above judgment method is 6->3->4->1->13->1->2->8->9->15->7->12->5->14->10->11, which is the same as the irrigation order obtained above. Only one path is selected for display in the illustration.

[0041] The determination of an Eulerian path in a directed graph is as follows: In an Eulerian path, there are at most two vertices whose in-degree is not equal to their out-degree, the starting point's out-degree is 1 greater than its in-degree, and the ending point's in-degree is 1 greater than its out-degree.

[0042] The area is divided into three parts in a T-shape or into two parts in a horizontal and vertical manner (this project only uses this method) to satisfy the Euler path.

[0043] When an undirected graph is very large or its complexity is too high, this method can be used to process local parts of the undirected graph so that the paths can be connected to the greatest extent.

[0044] Step 4: Based on the constructed Eulerian path, refine the working path of the cloth placing machine using a genetic algorithm, such as... Figure 7 As shown.

[0045] When planning the work path within any sub-section of a concrete placing boom, the path is not simply a connection of lines, but must simultaneously meet several stringent engineering conditions to ensure efficient, safe, and complete operation. These core conditions include: Full coverage: The primary task of the path is to completely cover the entire sub-section to avoid any missed or untreated blank areas.

[0046] Efficiency: The path should be as efficient as possible, which means optimizing the total path length while ensuring complete coverage. For a given volume of cement to be poured, a shorter path means a higher average work speed and lower energy consumption.

[0047] Safety and obstacle avoidance: The path must be able to completely avoid all obstacles within the zone and maintain a safe distance from the edge of the obstacle at all times during the journey. The speed of the concrete placing machine during operation must be lower than the set threshold.

[0048] Path simplicity: To ensure the smoothness and logic of the operation, the path should not intersect itself within the sub-partition to avoid duplicate operations and unnecessary complex turns.

[0049] Smoothness of inter-partition connections: The starting and ending points of a path need to be planned reasonably so that they can be smoothly and shortly connected to the end point of the previous partition and the starting point of the next partition, thereby optimizing the global path.

[0050] To meet the above requirements, the path is planned using a bow-shaped (or ox-plowing) pattern. During planning, the starting and ending edges of each partition are identified sequentially according to the access sequence. The edge furthest from obstacles within each partition is selected as the starting edge, and the edge closest to the next partition within each partition is selected as the ending edge. The next partition is the partition that is sequentially adjacent to the current partition according to the access sequence.

[0051] Select a grid cell from a column or row of grid cells near the starting edge of the partition as the starting point of the partition path. The bow-shaped path is planned starting from the starting edge of the partition, proceeding parallel to the starting edge. Upon reaching the boundary of that row, the concrete placing boom turns and enters the adjacent row, continuing in the opposite direction, row by row, until the entire partition is covered and the final endpoint is reached at the termination edge. This forms a winding partition path composed of interconnected path segments, with the spacing between adjacent rows being the step size. This path pattern is suitable for the working characteristics of the concrete placing boom, namely, simultaneous cement application during its movement. Since cement naturally flows to both sides after spraying, choosing a bow-shaped path ensures that each movement is parallel and adjacent to the previous row, reducing omissions and overlaps, and helping to cover the entire partition.

[0052] like Figure 7 and 8 As shown, by adjusting different step sizes and different directions, there are different combinations of partition paths. The purpose of this step is to select a suitable partition planning mode.

[0053] Adjacent path segments at bends in a partition path are perpendicular to each other, and the partition path covers the entire partition without intersecting within the partition.

[0054] Obtaining the partition path that covers the entire partition within each partition includes: using a genetic algorithm to select the step size between several row path segments parallel to the starting edge.

[0055] In the current partition Within the construction equipment, the zonal path planning of the concrete placing boom involves the selection of the step size l, which directly affects the coverage completeness and smoothness of the boom within the zonal area. Therefore, a genetic algorithm is introduced to find the optimal step size l* through its population search capability.

[0056] In the process of step size optimization, the first step is to define the step size range. [Take multiple discrete step size values ​​l1, l2, ..., l] n Each step size generates a partition path. The fabric placing machine follows these partition paths, moving from the partition... The starting point Initially, it moves in a reciprocating motion until it covers the entire zone.

[0057] To find the optimal step size, the steps using a genetic algorithm are as follows: ① Initialize the population: Starting from a predefined, reasonable step size set {l1, l2, ..., l n A set of step sizes is randomly selected as the initial population, and each step size represents a candidate partitioning path scheme. For each step size l i Create partition paths based on the partition path generation rules of the fabric placing machine, and calculate their fitness functions. .

[0058] ② Fitness function evaluation: To ensure good coverage and smooth inter-regional connectivity in the partitioned paths, the fitness function... Taking into account the following factors: coverage completeness and average work speed; The formula for this fitness function is: In the formula, Step size weight, Efficiency weights (where , (These are algorithm parameters that need to be set by the user). Indicates coverage completeness, used to measure the path's completeness to the target partition. The extent of coverage; This represents the average operating speed, used to measure the equipment's performance in a given area. The average operation speed within the path reflects the execution efficiency of the path.

[0059] The more comprehensive the coverage of a solution, the higher its fitness function value. Coverage completeness is calculated as the ratio of "effective coverage area" to "total area of ​​the zone": Among them, effective coverage area It is achieved by measuring the total length of the partition path. Operating width of the equipment Multiplying them together gives: The indicator is used to measure the equipment in the region The average job speed within the area reflects the execution efficiency of the path. Assuming within the region... The total amount of material to be laid is Therefore, the operating efficiency of the equipment needs to be dynamically adjusted according to the material demand. The calculation method is to divide the "total amount of material required" by the "total path length". ③ Selection operation: In each generation of the genetic algorithm, individuals in the current population are sorted from largest to smallest fitness value, and the top 10% of individuals in the fitness value ranking are selected as superior individuals and placed into the elite population Q; while retaining individuals with better step size, individuals with low fitness value are moderately retained to maintain population diversity.

[0060] ④ Crossover and Mutation Operations: Crossover: Randomly select a superior individual from the elite population Q as the parent and perform crossover and mutation operations with the current individual in the current population to generate a new individual. Exchange the step lengths between some path segments parallel to the starting edge in the superior individual and the current individual. The crossover operation utilizes existing gene combinations to generate new step lengths, potentially leading to better solutions. Mutation: Perform a small mutation on the step lengths between some path segments parallel to the starting edge in the superior individual and the current individual, i.e., apply a small-probability random perturbation to the step length value. This perturbation follows a Gaussian distribution with a perturbation amplitude of (0, 1). This introduces randomness into the search space, preventing the algorithm from getting trapped in local optima.

[0061] ⑤ Termination Condition and Step Size Determination: After multiple generations of genetic evolution, the algorithm terminates when the population fitness value tends to converge or reaches the maximum number of iterations. At this point, the step size l* with the highest fitness is selected as the optimal step size. This step size performs excellently in terms of coverage completeness and regional connectivity, and is the optimal working path for the concrete placing boom within this partition.

[0062] Step 5: Develop a digital twin system based on Unity3D, such as... Figure 8As shown, the speed and flow rate of the concrete placing boom are calculated. Based on the aforementioned path planning and area division results, a digital twin system for a building concrete placing boom based on the Unity3D engine is constructed to realize the visualization simulation and dynamic parameter calculation of the construction process. This system uses 3D modeling technology to restore the floor structure and the motion state of the concrete placing boom, and can simulate the operation process of the concrete placing boom along the planned route in real time in a virtual environment.

[0063] During system operation, the system automatically calculates the concrete placing boom's travel speed and concrete flow rate at each stage, taking into account parameters such as path length, pouring area, and zone dimensions. The concrete flow rate is user-defined, and the speed calculation is controlled within the user-defined range. For example, the concrete flow rate is set to a certain value per hour. Based on the total volume of concrete required for this area, the required time can be calculated, thereby calculating the average speed of the concrete placing boom through this area. By dynamically adjusting the speed and flow rate, precise control of the working rhythm of the concrete placing boom in actual construction can be achieved, ensuring uniform distribution and continuous pouring of concrete.

[0064] Furthermore, the system can intelligently optimize the operation strategy of the concrete placing boom based on input construction parameters (such as pouring strength, concrete viscosity, and zone area), and display the construction status, flow distribution, and time progress through a visual interface, providing intuitive decision support for construction personnel. This digital twin system not only realizes the visualization and controllability of the construction process, but also provides data support for subsequent automated control of the concrete placing boom and construction quality assessment.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent zoning, sorting, and route planning of a building concrete placing machine, characterized in that, Includes the following steps: Step 1: Divide the construction floor plan into multiple rectangular partitions based on the grid data of the site plan. Determine the connectivity between the partitions based on their coordinates and obstacle information. Using the geometric center of each partition as a vertex of the site plan, establish edges between the corresponding vertices of the connected partitions to construct the graph model. Step 2: Based on the graph model constructed in Step 1, use the depth-first search algorithm to find the longest path that can visit the most vertices and visit each vertex only once. Step 3: For the remaining vertices not covered by the longest path obtained in Step 2, divide the adjacent partitions connected to the remaining vertices into a first sub-partition and a second sub-partition. The first sub-partition is close to the adjacent edges of the partition corresponding to the remaining vertex and the adjacent partition, while the second sub-partition is far away from the adjacent edges of the partition corresponding to the remaining vertex and the adjacent partition. Then divide the first sub-partition into a third sub-partition and a fourth sub-partition. Both the third and fourth sub-partitions are adjacent to the partition corresponding to the remaining vertex. Using the determination method of the Euler path problem, by adding virtual edges between the second and third sub-partitions, between the third sub-partition and the remaining vertex, and between the remaining vertex and the fourth sub-partition, the longest path is transformed into a global path that can traverse all partitions at once without repetition, thus obtaining a global path sequence composed of the partitions and sub-partitions traversed by the global path in sequence. Step 4: Based on the global path sequence obtained in Step 3, for each partition and sub-partition in the sequence, use a genetic algorithm to optimize the bow-shaped operation path inside it. This path must meet the requirements of full coverage, obstacle avoidance, and smooth connection between regions. Step 5: Based on all the work paths obtained in the previous steps, perform a visual simulation of the construction process in the Unity3D digital twin system, and dynamically calculate the traveling speed of the concrete placing boom and the concrete flow rate.

2. The method according to claim 1, characterized in that, In step 1, the connectivity relationship between partitions is determined by the following conditions: The minimum horizontal gap between two partitions satisfies: ,in Represents grid coordinates; These represent the width and length of the two partitions, respectively. This is the preset gap tolerance parameter; The vertical alignment deviation between the two partitions satisfies: ,in Represents grid coordinates; These are the preset alignment tolerance parameters; The proportion of obstacles on the edges where the two partitions meet is lower than the preset obstacle proportion threshold. .

3. The method according to claim 1, characterized in that, In step 2, the depth-first search algorithm uses a main stack and an auxiliary stack to manage the search state and backtracking process, specifically including: Initialization phase: Push the starting node onto the main stack and empty the auxiliary stack; Depth-first search phase: When the main stack is not empty, the top node of the main stack is taken as the current node, and its adjacent node set is searched. If there are unvisited adjacent nodes, one of them is selected and pushed onto the main stack, and the remaining list of unvisited adjacent nodes is pushed onto the secondary stack. If the current node does not have any adjacent nodes that can be visited, a backtracking operation is performed, the current node is popped from the main stack, and the unvisited adjacent list of the previous node is taken from the secondary stack. Path recording phase: When the top node of the main stack is the same as the target node, the current node sequence in the main stack is recorded as a feasible path.

4. The method according to claim 1, characterized in that, In step 3, the corresponding physical partition is further divided, specifically by using a T-shaped cutting method to divide the partition into three parts, or by using a horizontal and vertical cutting method to divide the partition into two parts.

5. The method according to claim 1, characterized in that, In step 4, the genetic algorithm is used to optimize the internal paths of the partition, and the optimization objective is to maximize a comprehensive evaluation function. : In the formula, Step size weight, Efficiency weighting; Indicates coverage completeness. , , Indicates the effective coverage area; Indicates the total area of ​​the partition; Indicates the total path length; Indicates the working width of the equipment; Indicates average working speed. , This indicates the total amount of materials required for the partition.

6. The method according to claim 5, characterized in that, In step 4, the specific iterative steps of the genetic algorithm include: ① Initialize the population: Randomly sample from the predefined set of step size parameters to generate an initial population containing multiple individuals, each representing a step size scheme; ② Calculate fitness: Calculate the comprehensive evaluation function value of the path scheme for each individual in the population. As fitness; ③Elite selection and retention: Select the top 10% of individuals with the highest fitness in the current population and store them in the elite population; ④ Crossover and mutation: For non-elite individuals, parents are randomly selected from the elite population to perform crossover and mutation operations to generate offspring; among them, the crossover operation is to exchange part of the step size value, and the mutation operation is to apply a random perturbation to the step size value. ⑤ Survival of the fittest selection: The newly generated offspring are compared with their original parents. If the offspring are more fit, the original parents are replaced. ⑥ Forming a new population and cycling: Merge elite individuals and the updated individuals into the next generation population, and repeat steps ② to ⑤ until the maximum number of generations is reached.

7. The method according to claim 1, characterized in that, In step 4, the planning of the bow-shaped work path requires determining the starting edge and ending edge of the partition; the starting edge is selected from the side away from the obstacle, and the ending edge is selected from the side closer to the next partition to be visited.

8. The method according to claim 1, characterized in that, In step 5, the dynamic calculation of the concrete placing boom's travel speed and concrete flow rate is performed by combining path length, pouring area, and partition size parameters.

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