Building general layout parameterized design method, system and electronic equipment based on shortest parking path

Abstract

The application provides a building general layout parameterized design method and system based on the shortest parking path, and electronic equipment. The method comprises the following steps: importing a general layout range and a building base range in a parameterized design platform; dividing a remaining area into random parking areas and setting a main building entrance; calculating the shortest path of the geometric center of each parking area from a candidate entrance to the main building entrance, determining an optimal entrance, and calculating a total distance estimate and an area variance; taking the shortest total distance and the smallest area variance as double targets, optimizing the parking area division by using a genetic algorithm, and obtaining a Pareto optimal solution set; manually selecting a final scheme and determining a second entrance; and dividing roads and green areas according to preset parameters to complete the general layout design. The system comprises corresponding function modules. The application automatically generates a building general layout scheme with the shortest parking path and balanced partition through parameterized design and multi-objective optimization.

Description

Technical Field

[0001] This invention relates to the application of computer-aided design in architectural design, specifically to a parametric design method, system, and electronic equipment for architectural site plans based on the shortest parking path. Background Technology

[0002] Site planning is a crucial part of architectural design. Its core task is to rationally plan and delineate the building footprint, parking areas, road areas, and green areas within a defined project site. Traditional site planning processes rely heavily on the architect's manual work and subjective experience. Architects need to manually sketch the boundaries of different functional areas on paper or using computer-aided design software, drawing upon their professional knowledge and past project experience. This experience-based design method reveals its inherent limitations when dealing with complex terrain or large-scale projects.

[0003] A particularly critical issue is that the length and accessibility of the walking path from parking spaces to the main building entrance directly impacts the user experience. Excessive walking distances can lead to fatigue and inconvenience, lowering the overall quality of the project. This problem is especially important for building types with large site areas and high parking demands, such as transportation hubs, large commercial complexes, and sports stadiums. However, under traditional manual design models, designers find it difficult to accurately quantify and optimize this path indicator, resulting in significant uncertainty and potential flaws in the final design regarding parking path efficiency.

[0004] Furthermore, traditional manual design methods also face the challenge of inefficiency. Due to a lack of clear, systematic optimization methods and tools, designers often need to go through numerous trials and modifications when creating site plans to arrive at a solution that barely meets functional, regulatory, and subjective aesthetic standards. This process is not only time-consuming and labor-intensive, but also makes it difficult to effectively guarantee the scientific rigor and objectivity of the design results. The fundamental reason is that existing technologies have failed to provide architects with an effective solution for parking path optimization problems that integrates parametric modeling, algorithm optimization, and performance-driven approaches. Summary of the Invention

[0005] This invention provides a parametric design method, system, and electronic device for building site plans based on the shortest parking path, aiming to solve the problems of difficulty in optimizing parking paths, low design efficiency, and insufficient objectivity of design results caused by reliance on human experience in the prior art.

[0006] In a first aspect, embodiments of this application provide a parametric design method for building site plans based on the shortest parking path, comprising the following steps:

[0007] Step 1: In the Grasshopper visual programming environment of the Rhino platform, based on the imported site plan boundary line and building foundation boundary line, the remaining parking area is initially randomly divided to generate multiple parking areas, and the main entrance point of the building is set.

[0008] Step 2: For each parking area, calculate the shortest path from its geometric center through a series of candidate entrance / exit points along its edge to the main entrance of the building, and determine the optimal entrance / exit point for each parking area based on the shortest path; based on the optimal entrance / exit points of all parking areas and their corresponding shortest path distances, as well as the area of ​​each parking area, calculate the estimated total distance D from all parking spaces to the main entrance of the building.

[0009] Step 3: Calculate the area variance of all parking areas;

[0010] Step 4: Using the variables used in Step 1 to control the random division of parking areas as independent variables, and taking the minimum total distance estimate D and the minimum area variance as dual optimization objectives, the genetic algorithm is used for iterative optimization to obtain the Pareto optimal solution set.

[0011] Step 5: Manually select a final solution from the Pareto optimal solution set and determine the location of the second entrance / exit point in each parking area;

[0012] Step 6: Based on the final determined parking area division, building base range, site plan range, and entrance / exit points of each parking area, divide the road area and green area according to the preset road width and green width parameters;

[0013] Step 7: Integrate all the division results to complete the site plan design.

[0014] Furthermore, step 1 specifically includes:

[0015] Step 1.1: On the Rhino 3D modeling platform, import the site plan boundary curve and the building foundation boundary curve to generate the site plan surface and the building foundation surface; the Grasshopper work interface has multiple Grasshopper battery packs with combination functions predefined in it, which are used to perform subsequent parametric design steps.

[0016] Step 1.2: Set the number of parking areas 'a' according to the overall area, and use the Substrate battery pack to randomly divide the overall surface into 'a' parking areas. The random variables of the Substrate battery pack include Count and Seed.

[0017] Step 1.3: Delete the area occupied by the building base Surface from the division results of Step 1.2, and filter and retain the division scheme with a number of parking areas of 'a'.

[0018] Step 1.4: Set the main entrance point Point X on the building base outline.

[0019] Furthermore, step 2 specifically includes:

[0020] Step 2.1: Using the Divide Distance battery pack, draw points at equal intervals on the edge of each parking area to obtain the candidate point set for the entrance and exit of each parking area;

[0021] Step 2.2: Input the parking area boundary line Curve, the building base boundary line Curve, and the site plan boundary line Curve into the Split battery group to obtain all feasible paths d;

[0022] Step 2.3: Use the Area battery pack to find the geometric center Z of each parking area. i point;

[0023] Step 2.4: Using the Shortest Walk battery pack, calculate the Z-axis from the geometric center of each parking area. i Point X is the shortest path from the candidate entrance / exit points of the parking area to the main entrance point Point X via the feasible path d. The candidate entrance / exit point corresponding to this shortest path is determined as the optimal entrance / exit location Y of the parking area. i Let d be the distance of the shortest path to the point. i ;

[0024] Step 2.5: Calculate the sum of distances D from all parking spaces to the main entrance of the building. The formula is as follows:

[0025] D = ∑(D ZiYi +D di )×S Pi / 50, i=1, 2, 3……a

[0026] in,

[0027] D ZiYi Let Z be the geometric center of the i-th parking area. i Optimal entrance / exit location Y of the parking area i The distance;

[0028] D di The optimal entrance / exit location Y for the i-th parking area i The distance of the shortest path to the main entrance of the building, Point X;

[0029] SPi Let S be the area of ​​the i-th parking area. Pi / 50 represents the number of parking spaces in the i-th parking area, and 50 is the estimated average area of ​​a single parking space.

[0030] Furthermore, in step 3, the area variance of all parking areas is calculated using the Area battery pack and Grasshopper's built-in mathematical algorithm.

[0031] Furthermore, step 4 specifically includes:

[0032] Step 4.1: Use the Wallacei battery pack to set up a genetic algorithm. The two optimization objectives of the genetic algorithm are: minimizing the variance of the area of ​​all parking areas and minimizing the sum D of the distances from all parking spaces to the main entrance of the building; the independent variables of the genetic algorithm are the Count and Seed parameters in the Substrate battery pack.

[0033] Step 4.2: Run the genetic algorithm to perform calculations and monitor the optimization process until convergence, and obtain the last solution set that is Pareto optimal;

[0034] Step 4.3: Use Decode Phenotype and Decode Genome to analyze the Pareto optimal solution set, and select several solutions with the most balanced fitness values ​​as candidate optimal solutions.

[0035] Furthermore, step 5 specifically includes:

[0036] Step 5.1: Based on the project requirements, the architect manually selects a final solution from the candidate optimal solutions obtained in Step 4.3;

[0037] Step 5.2: Based on the final solution, for each parking area, extend the line connecting its existing optimal entrance / exit point and the geometric center of the parking area to the edge of the parking area, and use the intersection point as the second entrance / exit point of the parking area.

[0038] Furthermore, step 6 specifically includes:

[0039] Step 6.1: Extract the parking area boundary lines, building foundation boundary lines, site plan boundary lines, and entrance / exit reference lines for each parking area from the final design;

[0040] Step 6.2: Set the parameters for road width, road chamfer radius, green space width, and parking area entrance / exit width;

[0041] Step 6.3: Based on the parameters set in Step 6.2, use Offset Curve, Fillet, and BoundarySurface batteries to integrate the battery pack, generate road area edges and green area edges with chamfers, and complete the division of road and green area.

[0042] Secondly, embodiments of this application provide a parametric design system for building site plans based on the shortest parking path, including:

[0043] The data import and initialization module is used to import the site plan boundary lines and building foundation boundary lines into the parametric design platform, and to set the location of the main building entrance.

[0044] The parking area division module is used to initially and randomly divide the remaining parking areas based on the boundary lines of the overall map and the boundary lines of the building base, generating multiple parking areas.

[0045] The optimal path calculation module is used to calculate the shortest path from the geometric center of each parking area to the main entrance of the building via candidate entrance / exit points on its edge, determine the optimal entrance / exit point for each parking area, and calculate the estimated total distance D from all parking spaces to the main entrance of the building.

[0046] The area variance calculation module is used to calculate the area variance of all parking areas;

[0047] The multi-objective optimization module uses the random variable controlling the division of parking areas as the independent variable, and takes the minimum total distance estimate D and the minimum area variance as the dual optimization objectives. It uses a genetic algorithm for iterative optimization to obtain the Pareto optimal solution set.

[0048] The scheme selection and second entrance / exit generation module is used to select the final scheme from the Pareto optimal solution set and generate the second entrance / exit point in each parking area;

[0049] The road greening division module is used to divide road areas and green areas based on the final determined parking area division, building base range, site plan range, and entrance and exit points of each parking area, according to preset road and greening parameters.

[0050] The site plan generation module is used to integrate all the division results and output the final architectural site plan design.

[0051] Furthermore, the specific formula for calculating the total distance estimate D by the optimal path calculation module is: D = ∑ (D ZiYi +D di )×S Pi / 50, i=1, 2, 3……a

[0052] in,

[0053] D ZiYi Let Z be the geometric center of the i-th parking area. i Optimal entrance / exit location Y of the parking area i The distance;

[0054] D di The optimal entrance / exit location Y for the i-th parking area i The distance of the shortest path to the main entrance of the building, Point X;

[0055] S Pi Let S be the area of ​​the i-th parking area. Pi / 50 represents the number of parking spaces in the i-th parking area, and 50 is the estimated average area of ​​a single parking space.

[0056] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the parametric design method for building site plans based on the shortest parking path as described in the first aspect.

[0057] Compared with the prior art, the present invention achieves the following beneficial technical effects:

[0058] This invention transforms the traditional site plan design process, which relies on subjective experience, into an objective decision-making process based on algorithm optimization. By automatically generating multiple optimized solutions through parametric modeling and genetic algorithms, it systematically solves the problem of parking route optimization and significantly improves the scientific nature of the design. At the same time, it frees designers from repetitive manual adjustments, greatly improves design efficiency through human-computer collaboration, and ensures the quality of the design solutions in terms of parking convenience and zoning rationality. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0060] Figure 1 This is a flowchart of a parametric design method for building site layout based on the shortest parking path, provided as an embodiment of the present invention.

[0061] Figure 2 This is a schematic diagram showing the result of dividing the site plan into parking areas in Grasshopper in step 1 of a parametric design method for building site plans based on the shortest parking path provided in an embodiment of the present invention.

[0062] Figure 3 This is a schematic diagram showing the result of selecting the optimal entrance / exit location of the parking area based on the shortest distance to the main entrance of the building in step 2 of a parametric design method for building site layout based on the shortest parking path provided in an embodiment of the present invention.

[0063] Figure 4 This is a schematic diagram illustrating step 3 of a parametric design method for building site plans based on the shortest parking path provided in an embodiment of the present invention, which calculates the variance of the parking area in Grasshopper.

[0064] Figure 5 This is a schematic diagram illustrating the result of using the bi-objective genetic algorithm Wallacei to obtain the optimal solution set for parking area partitioning in step 4 of a parametric design method for building site layout based on the shortest parking path provided in an embodiment of the present invention.

[0065] Figure 6 This is a schematic diagram illustrating the result of using reference lines to improve the second entrance / exit of the parking area in step 5 of a parametric design method for building site plans based on the shortest parking path provided in an embodiment of the present invention.

[0066] Figure 7 This is a schematic diagram showing the result of dividing the road and green space areas in step 6 of a parametric design method for building site layout based on the shortest parking path provided in an embodiment of the present invention.

[0067] Figure 8 This is a schematic diagram showing the result of completing the parametric design of the building site plan based on the shortest parking path in step 7 of a parametric design method for building site plan based on the shortest parking path provided in an embodiment of the present invention.

[0068] Figure 9 This is a schematic diagram of a parametric design system for building site layout based on the shortest parking path, provided in an embodiment of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] It should be noted that the batteries and battery packs mentioned in the embodiments of this invention are functional modules in the Grasshopper visual programming environment, and their specific implementation depends on the Grasshopper plugin of the Rhino platform. The specific parameters, values, software environments, etc., described in the following embodiments are merely examples, and those skilled in the art can adjust them according to actual project needs without departing from the core ideas of this invention.

[0071] Example 1

[0072] like Figure 1 As shown in the figure, this embodiment describes in detail a parametric design method for building site plans based on the shortest parking path. The process includes seven steps, which will be explained in detail below with reference to the accompanying drawings.

[0073] Step 1, Divide the parking area in Rhino-Grasshopper: In the Grasshopper visual programming environment of the Rhino platform, based on the imported site plan boundary line and building foundation boundary line, perform preliminary random division of the remaining parking area to generate multiple parking areas, and set the main entrance point of the building;

[0074] See Figure 2 The goal of this step is to perform preliminary, random zoning of the remaining areas available for parking within the given site plan, excluding building foundations.

[0075] Step 1.1: Set the design scope and work platform. On the Rhino 3D modeling platform, the operator imports pre-drawn site plan boundary lines (represented as curves) and building foundation boundary lines (also curves). Using these boundaries, the site plan Surface and building foundation Surface are generated. Then, the Grasshopper workspace is opened; all subsequent parametric design steps are completed within this interface by connecting different functional cells (cell packs). This step ensures the accuracy of the design foundation and the consistency of subsequent operations.

[0076] Step 1.2: Preliminary Random Division of Parking Zones. First, estimate the number of parking zones 'a' based on the overall site area. A feasible estimation method is: assuming that after deducting the area of ​​roads, green spaces, etc., the net parking area is calculated at approximately 50 square meters per parking space (including the parking space, shared driveway, and green space). If the target number of parking spaces is 400, the net parking area would be approximately 20,000 square meters. Divide this net parking area by a preset parking zone area benchmark (e.g., 20,000 square meters) to obtain the number of parking zones 'a', for example, 'a' = 5. Next, using the Substrate battery pack in Grasshopper, randomly divide the overall site Surface into 'a' parking zones, denoted as P1, P2, ... Pa Substrate can generate random Voronoi diagrams or similar patterns for spatial partitioning. This randomness is controlled by two variables: Count (the number of elements, ranging from 1 to a+5) and Seed (the seed, ranging from 0 to 50000). By adjusting these two variables, countless different random partitioning schemes can be generated, providing a rich initial population for subsequent optimization algorithms.

[0077] Step 1.3: Delete the building base area and filter the number of parking areas. The partitioning generated in Step 1.2 covers the entire site plan, including the building base. Therefore, it is necessary to remove the area occupied by the building base Surface from the partitioning results. This can be achieved using a Dispatch cell in conjunction with relevant geometric operation cells, with the goal of obtaining the pure remaining parking area Surface of the site plan. Simultaneously, it is necessary to ensure that after removing the building base, the number of remaining parking areas remains 'a'. This may require conditional judgment and filtering, such as using area thresholds or connectivity analysis to retain valid parking areas.

[0078] Step 1.4: Define the main entrance point, Point X, on the building's base outline. The architect, based on the design scheme, designates one or more points on the building's base outline as the main entrance, denoted as Point X. This point is the endpoint for all subsequent parking path calculations and is one of the core elements of the path optimization model.

[0079] Step 2: Based on the shortest distance to the main building entrance, select the optimal entrance / exit points for each parking area (see...). Figure 3 For each parking area, calculate the shortest path from its geometric center through a series of candidate entrance / exit points along its edges to the main entrance of the building, and determine the optimal entrance / exit point for each parking area based on the shortest path; based on the optimal entrance / exit points of all parking areas and their corresponding shortest path distances, as well as the area of ​​each parking area, calculate the estimated total distance D from all parking spaces to the main entrance of the building.

[0080] The purpose of this step is to find an optimal entrance / exit location for each parking area generated in step 1, such that the overall path from inside the parking area to the main entrance of the building is minimized.

[0081] Step 2.1: Define the set of possible points for the entrances and exits of each parking area. For each parking area P i By using the Divide Distance battery pack to mark points at fixed equidistant intervals (e.g., 50 meters) along its boundary line, a series of candidate entrance / exit points are generated, denoted as P. i Y1, P i Y2, Pi Y3...... These points constitute the set of all possible entrances and exits for this parking area. The choice of equidistant lengths needs to balance computational accuracy and efficiency; too dense a spacing will increase the computational load, while too sparse a spacing may miss the optimal solution.

[0082] Step 2.2: Delineate the parking area entrance / exit P i All walkable paths d from Y to the main building entrance Point X. To calculate the shortest path, the feasible walking area needs to be explicitly defined. The parking area boundary curve, the building footprint boundary curve, and the site plan boundary curve are input into the Split cell group. These curves interact to divide the walkable area within the site plan (typically the area excluding the building footprint) into a series of path units or networks. These paths constitute the set of all possible walking paths d, which is a set of lines representing all paths accessible from parking. This step ensures that the path calculation is based on an actual, accessible spatial network.

[0083] Step 2.3: Determine the geometric center of the parking area. Calculate P for each parking area using the Area battery pack. i The area of ​​the geometric center is obtained, denoted as Z. i The geometric center will be used as a simplified model point representing the average location of all parking spaces within the parking area. This simplification is based on statistical principles, assuming that parking spaces are uniformly distributed within the area, thus using the center point to represent the starting point of the average walking distance.

[0084] Step 2.4: Determine the optimal entrance / exit location Y i A point and its corresponding shortest path d i This is the core of this step. Calculate the battery using the Shortest Walk battery pack (or a similar shortest path calculation). For each parking area P... i Calculate from its geometric center Z i Starting from point X, the path travels sequentially through each candidate entrance / exit point on its boundary line, then follows the walkable path d defined in step 2.2 to finally reach the main entrance point, Point X. The Shortest Walk battery will find the shortest path among all these paths. The candidate entrance / exit point corresponding to this shortest path is determined as the optimal entrance / exit location for the parking area and named Y. i Point. Meanwhile, from Y i The path from point X to the main entrance of the building is the shortest path d for this parking area. i The distance is denoted as D. di At the geometric level, determine the shortest path d. iThe process involves finding the shortest path from a set of lines representing all possible walking paths d. This step finds the globally optimal external connection point for each parking area.

[0085] Step 2.5: Optimize the sum of distances D from all parking spaces to the main building entrance. To evaluate the parking path efficiency of the entire site layout, an overall metric is needed. Directly calculating the path for each parking space is impractical; therefore, a simplified model is used. The formula for calculating the total distance estimate D is:

[0086] D = ∑(D ZiYi +D di )×S Pi / 50, i=1, 2, 3……a

[0087] in,

[0088] D ZiYi Z: Geometric center of the i-th parking area i Click on its optimal entrance / exit Y i The straight-line distance between points. This represents the average walking distance within the parking area.

[0089] D di : Optimal entrance / exit location Y of the i-th parking area i The shortest path distance from Point X to the main building entrance (obtained from step 2.4). This represents the common path distance from the parking area entrance / exit to the building entrance.

[0090] S Pi : The area of ​​the i-th parking area.

[0091] S Pi / 50: Divide the area of ​​the parking area by the average area of ​​a single parking space (50 square meters) to estimate the number of parking spaces in the parking area.

[0092] The significance of this formula lies in simplifying the total walking distance of each parking zone into (average distance within the zone plus distance via public paths outside the zone), multiplying it by the number of parking spaces in that zone, and finally summing the contributions of all parking zones to obtain an estimated total distance D from all parking spaces to the main entrance. A smaller D indicates higher parking path efficiency for the entire layout. This model, while ensuring computational feasibility, approximates the actual total walking load quite well.

[0093] Step 3: Calculate the variance of the parking area.

[0094] like Figure 4The screenshots of the software operation process show that the purpose of this step is to calculate an index that measures the balance of parking area division. Excessive area variance means that some parking areas are overcrowded (too many parking spaces), while others are too spacious, which is detrimental to unified management and efficient space utilization. The Area battery pack is used to obtain the area values ​​of all parking areas, and then Grasshopper's built-in mathematical operation batteries (such as the series statistical operation battery) are used to calculate the variance of these area values. The smaller the variance, the closer the areas of the parking areas are, and the more balanced the division. This helps to avoid situations where some areas are overcrowded while other areas are underutilized.

[0095] Step 4: Using the variables used in Step 1 to control the random division of parking areas as independent variables, and taking the minimization of the total distance estimate D and the minimization of the area variance as dual optimization objectives, a genetic algorithm is used for iterative optimization to obtain the Pareto optimal solution set. The final result is as follows: Figure 5 As shown;

[0096] This step is the core of the method optimization. It uses a multi-objective genetic algorithm to automatically find a parking area division scheme that performs well in both the objectives of "shortest total distance D" and "minimum area variance".

[0097] Step 4.1: Setting Objectives and Independent Variables. Use the Wallacei battery pack in Grasshopper (a multi-objective genetic algorithm plugin). Set the two optimization objectives of the genetic algorithm as follows: Objective 1, minimize the variance of the parking area calculated in Step 3; Objective 2, minimize the total distance estimate D calculated in Step 2.5. Simultaneously, set the two parameters controlling the randomness of parking area division, namely Count and Seed in the Substrate battery, as the independent variables (i.e., genes) of the genetic algorithm. Each (Count, Seed) combination corresponds to a unique parking area division scheme. Wallacei will iteratively evolve these genes to find the optimal solution.

[0098] Step 4.2: Monitoring the Computation Process. Configure the parameters of the Wallacei genetic algorithm, for example, setting the population size to 100 and the number of generations to 50. After starting the algorithm, Wallacei will perform iterative calculations and display the optimization progress curve in real time (such as the evolution of the Pareto front). Designers can determine whether the algorithm has converged based on the curve. After the calculation is completed, the algorithm will output all Pareto optimal solutions (usually 100) in the last generation (e.g., the 49th generation). These solutions constitute a solution set in which any improvement in one objective necessarily leads to the deterioration of the other objective, representing the best trade-off between the two objectives.

[0099] Step 4.3: Result Analysis to Obtain the Optimal Solution. Using the Decode Phenotype and Decode Genome batteries provided by Wallacei, each solution in the Pareto optimal solution set can be reduced to a specific parking area division geometry and its corresponding Count and Seed values. Then, using tools such as the Average set battery, the five solutions with the most balanced fitness values ​​(i.e., the achievement of two objectives) are selected from the Pareto optimal solution set. These solutions achieve a good balance between the two optimization objectives and are excellent candidate solutions for subsequent manual selection. This step transforms the algorithm's output into design schemes that architects can intuitively understand and further refine.

[0100] Step 5: Manually select the optimal solution and refine the second entrance / exit: Manually select a final solution from the Pareto optimal solution set, and determine the location of the second entrance / exit in each parking area. The final result is as follows: Figure 6 As shown.

[0101] This step combines the algorithm's optimization results with the architect's professional judgment and meets design specifications.

[0102] Step 5.1: Based on the needs of other aspects of the project, the architect comprehensively evaluates the layout and shortest path length of the results and manually selects the optimal choice from the results in 4.3. The algorithm provides multiple optimized solutions, and the architect needs to consider factors that the algorithm cannot quantify, such as site topography, landscape orientation, surrounding environment, fire access requirements, underground pipeline distribution, and visual effects, to ultimately select the solution that best meets the overall needs of the project from the five candidate equilibrium solutions. This step demonstrates the advantages of human-computer collaboration; the algorithm is responsible for providing solutions with excellent quantifiable performance, while the architect is responsible for integrating them into the overall design and regulatory requirements.

[0103] Step 5.2: Determine the second entrance / exit for each parking area. According to building design codes, outdoor parking lots with 300-500 vehicles require at least two entrances / exits. Therefore, based on the selected final design, a second entrance / exit is added to each parking area. A simple and effective method is as follows: For each parking area, connect its existing optimal entrance / exit point Y~i~ with its geometric center Z~i~ to form a straight line. Then extend this line inwards into the parking area until it intersects with the other side of the parking area. This intersection point is determined as the second entrance / exit point for that parking area. This method ensures a good geometric relationship between the second entrance / exit and the first entrance / exit and the area center, helping to disperse traffic flow and improve the accessibility of the area.

[0104] Step 6, Delineate road and green areas: Based on the final determined parking area division, building base area, site plan area, and entrance / exit points of each parking area, delineate road areas and green areas according to preset road width and green width parameters.

[0105] See Figure 7 This step generates a specific road and greening system based on the optimized parking area layout.

[0106] Step 6.1: Extract the filtered schemes to the Grasshopper interface. Extract the geometric elements from the scheme finally selected in Step 5, including: the boundaries of all parking areas, the boundaries of the building foundation, the boundaries of the site plan, and the reference lines for the optimal entrances and exits and the second entrance / exit of each parking area (usually lines connecting the entrance / exit points to the geometric center). These elements constitute the basic framework for generating the road and greening system.

[0107] Step 6.2: Set road width and green space width. Based on the specific project design requirements, set a series of parameters. For example: the width of parking area entrances and exits (e.g., 7 meters), the width of main roads (e.g., 10 meters), the road turning radius (e.g., 6 meters), the width of roadside green belts (e.g., 3 meters), and the turning radius at entrances and exits (e.g., 1 meter). These parameters should be determined according to relevant design specifications (e.g., the "Urban Road Engineering Design Code," "Parking Lot Planning and Design Rules," etc.) and the specific level and scale of the project, and can be flexibly adjusted as variables in Grasshopper.

[0108] Step 6.3: Based on the data set in 6.2, draw the chamfered road area boundaries and green area boundaries. Using the Offset Curve battery pack, offset the parking area spacing lines and building foundation boundary lines inward or outward according to the set road width to generate the initial road boundaries. Then, use the Fillet battery pack to add chamfers (rounded corners) to road intersections or turns, setting the radius to the preset road turning radius. This conforms to the physical requirements of vehicle turning and also makes the design more aesthetically pleasing. For green belts, they can be generated by offsetting (Offset) along the outside or inside of the road boundary lines. Finally, using battery packs such as Boundary Surface, these processed curves are converted into closed surfaces, thus clearly defining the road area surface and the green area surface. This step realizes the transformation from conceptual zoning to specific engineering design.

[0109] Step 7: Complete the parametric design of the building site plan based on the shortest parking path.

[0110] See Figure 8The invention integrates the results of all the above steps: the building foundation area, the optimized parking areas, the rationally designed road network, and the green belts. All these elements together constitute the final architectural master plan design. This plan not only meets the design requirements visually, but more importantly, ensures the efficiency of parking paths and the balance of zoning areas in terms of performance. The final result can be viewed directly in the Rhino interface, exported as CAD drawings, or used for further detailed design. This invention's method, through systematic parametric processes and algorithm optimization, ensures the superiority of the design results in key performance indicators and significantly improves design efficiency.

[0111] Example 2

[0112] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the parametric design method for building site plans based on the shortest parking path as described in Embodiment 1. This electronic device can be a workstation, personal computer, server, etc., with Rhino and Grasshopper software installed on it for executing the parametric design process. The memory can be used to store input site plan data, intermediate calculation results, and the final site plan design scheme. The processor is responsible for executing the logical operations and algorithmic processes defined in Grasshopper, completing all calculation tasks from data import to site plan generation. This electronic device provides the hardware foundation for the implementation of the design method, enabling architects to automatically and efficiently complete complex site plan optimization designs using computing devices.

[0113] Example 3

[0114] like Figure 9 As shown, this embodiment provides a parametric design system for building site plans based on the shortest parking path. This system is used to implement the aforementioned parametric design method for building site plans based on the shortest parking path. It can be a customized battery pack collection in Grasshopper, a standalone plugin developed based on the Rhino Common SDK, or a functional module integrated into other BIM or CAD platforms. The system includes:

[0115] Data Import and Initialization Module: This module is used to import site boundary curves and building footprint boundary curves into parametric design platforms (such as Grasshopper), and allows users to set the main building entrance point (Point X). This module provides a graphical interface or data input port to ensure accurate import of basic data.

[0116] Parking Area Division Module: Connected to the data import and initialization module, this module performs preliminary random division of the remaining parking areas based on the imported site plan and building footprint, generating multiple parking zones. The core of this module integrates a Substrate database, and the randomness of the division is controlled by the parameters Count and Seed.

[0117] The optimal path calculation module, connected to the parking area division module and the data import and initialization module, calculates the shortest path from the geometric center of each parking area through candidate entrance / exit points along its edges to the main building entrance. It determines the optimal entrance / exit points for each parking area and calculates the estimated total distance D from all parking spaces to the main building entrance. This module integrates battery packs such as Divide Distance, Area, and Shortest Walk, and incorporates the aforementioned distance estimation algorithm.

[0118] Area Variance Calculation Module: Connected to the parking area division module, this module calculates the area variance of all parking areas. It uses the Area battery pack to acquire area data and calculates the variance using a built-in mathematical statistics battery.

[0119] The multi-objective optimization module, connected to the parking area division module, optimal path calculation module, and area variance calculation module, uses the random variables Count and Seed controlling the parking area division as independent variables, and minimizes the total distance estimate D and the area variance as dual optimization objectives. It employs a genetic algorithm (e.g., using a Wallacei battery pack) for iterative optimization to obtain a Pareto optimal solution set. This module manages the optimization process, including genetic operations such as population initialization, fitness evaluation, selection, crossover, and mutation.

[0120] The scheme selection and second entrance / exit generation module, connected to the multi-objective optimization module, is used to select the final scheme from the Pareto optimal solution set (usually through manual interaction) and generate second entrance / exit locations for each parking area. This module provides a scheme visualization and comparison function and executes a second entrance / exit generation algorithm based on the extension of the geometric center line.

[0121] The road and green space delineation module, connected to the scheme selection and second entrance / exit generation module and the data import and initialization module, is used to delineate road and green areas based on the final determined parking area divisions, building footprints, site plan scope, and entrance / exit locations of each parking area, according to user-preset parameters such as road width, turning radius, and green space width. This module integrates battery packs such as Offset Curve, Fillet, and Boundary Surface to achieve parametric road and green space generation.

[0122] Site plan generation module: Connected to the road and green space division module, this module integrates all division results (building foundation, parking area, road, green space) and outputs the final site plan design. This module is responsible for combining the geometric surfaces or curves of each area into a complete design, and supports functions such as drawing export and data report generation.

[0123] Furthermore, the specific formula for calculating the total distance estimate D by the optimal path calculation module is: D = ∑ (D ZiYi +D di )×S Pi / 50, i=1, 2, 3……a

[0124] in,

[0125] D ZiYi Let Z be the geometric center of the i-th parking area. i Optimal entrance / exit location Y of the parking area i The distance;

[0126] D di The optimal entrance / exit location Y for the i-th parking area i The distance of the shortest path to the main entrance of the building, Point X;

[0127] S Pi Let S be the area of ​​the i-th parking area. Pi / 50 represents the number of parking spaces in the i-th parking zone, and 50 is an estimated average area per parking space. This formula is embedded in the module's algorithm logic to ensure consistency and accuracy in calculations.

[0128] The system in this embodiment, through modular design, materializes the method and process of Embodiment 1, providing an integrated and automated design tool that greatly facilitates architects' application and improves the standardization and efficiency of the design process.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A parking shortest path based building general layout parameterized design method, characterized in that, The method comprises the following steps: Step 1: in the Grasshopper visual programming environment of Rhino platform, based on the imported total plan range boundary line and building base range boundary line, the remaining parking area is preliminarily randomly divided, a plurality of parking areas are generated, and a building main entrance point is set; Step 2: for each parking area, the shortest path from the geometric center thereof to the building main entrance point via a series of candidate entrance point positions on the boundary line thereof is calculated, and the optimal entrance and exit point position of each parking area is determined according to the shortest path; based on the optimal entrance and exit point positions of all parking areas and the corresponding shortest path distances, and the areas of the parking areas, the total distance estimate D of all parking spaces to the building main entrance is calculated; Step 3: the area variance of all parking areas is calculated; Step 4: taking the variable used to control the random division of the parking areas in step 1 as the independent variable, taking the minimum total distance estimate D and the minimum area variance as the double optimization objectives, the genetic algorithm is used for iterative optimization to obtain a Pareto optimal solution set; Step 5: a final solution is artificially selected from the Pareto optimal solution set, and a second entrance and exit point position is determined in each parking area; Step 6: based on the finally determined parking area division, building base range, total plan range and entrance and exit point positions of the parking areas, the road area and green area are divided according to the preset road width and green width parameters; Step 7: all division results are integrated to complete the building general plan design.

2. The method of claim 1, wherein, The step 1 specifically comprises: Step 1.1: importing the total plan range boundary line Curve and the building base range boundary line Curve on the Rhino three-dimensional modeling platform to generate the total plan Surface and the building base Surface; a plurality of Grasshopper battery groups with combination functions are predefined in the Grasshopper working interface to execute subsequent parameterized design steps; Step 1.2: setting the number of parking areas a according to the total plan area, and using the Substrate battery group to randomly divide the total plan Surface into a parking areas; the random variables of the Substrate battery group include Count and Seed; Step 1.3: deleting the area occupied by the building base Surface from the division result of step 1.2, and screening and retaining the division scheme with a number of parking areas; Step 1.4: setting the building main entrance point Point X on the building base range contour line.

3. The method of claim 2, wherein, The step 2 specifically comprises: Step 2.1: using the Divide Distance battery group to draw points on the boundary line of each parking area at equal intervals to obtain a set of entrance and exit candidate points of each parking area; Step 2.2: inputting the parking area boundary line Curve, the building base range boundary line Curve and the total plan boundary range boundary line Curve into the Split battery group to obtain all feasible paths d; Step 2.3: Find the geometric center Z of each parking area with the Area battery i point; Step 2.4: Calculate the shortest path from the geometric center Z of each parking zone to the main entrance point X via the entrance candidate points of the parking zone by using the Shortest Walk battery, and determine the entrance candidate point corresponding to the shortest path as the optimal entrance position Y of the parking zone i i i ;​​ Step 2.5: calculating the sum D of the distances from all parking spaces to the building main entrance, and the calculation formula is: D = ∑ (D ZiYi + D di ) x S Pi / 50, i = 1, 2, 3...a wherein, D ZiYi Geometric center Z of the ith parking area i Distance to the optimal entry position Y of the parking area i of the ith parking area D di Yoptimal exit position of the i-th parking area i distance to the shortest path of the main entrance point of the building Point X S Pi S i is the area of the ith parking zone, Pi / 50, i.e. the number of parking spaces in the ith parking zone, 50 being an estimated average area of a single parking space.

4. The method of claim 3, wherein, In step 3, the Area battery and the Grasshopper built-in mathematical algorithm are used to calculate the area variance of all parking areas.

5. The method according to any one of claims 2 to 4, characterized in that, Step 4 specifically includes: Step 4.1: Set the genetic algorithm using the Wallacei battery, with two optimization objectives: the minimum area variance of all parking areas, and the shortest total distance D from all parking spaces to the main entrance of the building; the independent variables of the genetic algorithm are the Count and Seed parameters in the Substrate battery; Step 4.2: Run the genetic algorithm for calculation and monitor the optimization process until convergence, obtaining a set of solutions represented by the last generation, which is the Pareto optimal solution set; Step 4.3: Use the Decode Phenotype and Decode Genome batteries to analyze the Pareto optimal solution set and select several solutions with the most balanced fitness values as candidate optimal solutions.

6. The method of claim 5, wherein, Step 5 specifically includes: Step 5.1: The architect manually selects a final scheme from the candidate optimal solutions obtained in step 4.3 according to project requirements; Step 5.2: Based on the final scheme, for each parking area, the line connecting its existing optimal entrance and exit point and the geometric center of the parking area is extended to the edge of the parking area, and the intersection point is taken as the second entrance and exit point of the parking area.

7. The method of claim 6, wherein, Step 6 specifically includes: Step 6.1: Extract the parking area edge lines, building base range edge lines, total plan range edge lines, and entrance and exit reference lines of each parking area from the final scheme; Step 6.2: Set the road width, road chamfer radius, green width, and parking area entrance and exit width parameters; Step 6.3: Based on the parameters set in step 6.2, use the Offset Curve, Fillet, and Boundary Surface batteries to generate road area edge lines with chamfers and green area edge lines, completing the division of road and green ranges.

8. A parking shortest path based building general layout parameterized design system, characterized in that, It includes: A data import and initialization module for importing total plan range edge lines and building base range edge lines in the parametric design platform and setting the main entrance point of the building; A parking area division module for preliminary random division of the remaining parking area based on the total plan range edge lines and building base range edge lines, generating multiple parking areas; An optimal path calculation module for calculating the shortest path from the geometric center of each parking area to the main entrance point of the building via the candidate entrance and exit point on its edge line, determining the optimal entrance and exit point of each parking area, and calculating the total distance estimate D of all parking spaces to the main entrance; An area variance calculation module for calculating the area variance of all parking areas; A multi-objective optimization module for using genetic algorithm to iteratively optimize the random variables controlling the division of parking areas, with the minimum total distance estimate D and the minimum area variance as dual optimization objectives, to obtain a Pareto optimal solution set; A scheme selection and second entrance and exit generation module for selecting a final scheme from the Pareto optimal solution set and generating a second entrance and exit point in each parking area; The road greening division module is configured to divide road areas and greening areas according to preset road and greening parameters based on the finally determined parking area division, the building base range, the general plan range, and the entrance point of each parking area. The general plan generation module is configured to integrate all the division results and output a final building general plan design.

9. The system of claim 8, wherein, The specific formula of the optimal path calculation module calculating the total distance estimate D is: D=∑(D ZiYi +D di )×S Pi / 50, i=1, 2, 3 … a In the method, D ZiYi Geometric center Z of the ith parking area i Distance to the optimal entry position Y of the parking area i of the ith parking area D di Optimal entrance and exit position Y for the ith parking area i Distance to the shortest path of the main entrance point of the building Point X S Pi S Pi / 50 i.e. number of parking spaces in the ith parking zone, 50 being an average area estimate for a single parking space.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the parking shortest path-based building general plan parameterized design method of any one of claims 1 to 7 when executing the program.

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