Digitization method for key design parameter configuration of stadium stand bowls

Abstract

The invention discloses a digitization method for key design parameter configuration in stadium building stand bowl design, and belongs to the technical field of building design. The method comprises the following steps: firstly, based on a pre-established court case database and a machine learning model, automatically predicting key characteristic values such as a stand layer number and a court core contour type according to design requirements such as a target total seat number and a court type; secondly, sequentially and automatically calculating field core contour standardization parameters, radial axis net parameters and stand section parameters according to the prediction result and expert experience rules; thirdly, generating and checking a circumferential axis net spacing sequence through an iterative optimization algorithm, and ensuring the structural rationality; and finally, synthesizing all parameters to output a complete design scheme. Through data driving and rule guidance, standardization and automation of parameter configuration are achieved, the design efficiency and the scientificity of the scheme are remarkably improved, and meanwhile excessive dependence on individual experience is reduced.

Description

Technical Field

[0001] This invention relates to the field of computer-aided architectural design and generation technology, specifically to a digital method for configuring key design parameters of stadium grandstand bowls. Background Technology

[0002] Sports buildings are large in scale, expensive, and have strong landmark status, playing an important role in cities. The grandstand is the core component of a sports stadium, and its design directly affects the overall quality of the building. Design parameters are important indicators for controlling the rationality of the grandstand design. The experience of architects is precipitated and reused through specific parameter value rules, preserving intuitive understanding in a more solidified way.

[0003] Since the rise of parametric design, sports architecture designers have increasingly adopted Rhino / Grasshopper modeling software, fully leveraging the freedom, flexibility, and efficiency of node-based programming to replace traditional modeling methods. This trend has led to a greater number and broader coverage of key design parameters in stadium design, making parameter configuration an increasingly important technical skill. However, configuring a reasonable combination of parameters remains a perplexing problem for many architects.

[0004] Currently, there is a lack of quantifiable, standardized, and automated professional methods for configuring design parameters in sports building grandstands, resulting in low design efficiency and designs that fail to meet the growing demands of sporting events and the evolving experience requirements of spectators. With increasing demands for refined design and the rapid development of the sports industry, establishing a set of demand-compliant and procedural methods for configuring key design parameters in sports building grandstands has become an urgent need for the industry.

[0005] Therefore, this invention came into being.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] To address the aforementioned issues, a digital method for configuring key design parameters of stadium grandstand bowls is provided, comprising the following steps: Step S1, Feature value prediction: In response to the design requirements input by the user, based on the pre-built sports stadium case database and machine learning model, predict multiple key feature values ​​of the grandstand bowl. The key feature values ​​include at least the grandstand layer number feature value, the stadium core outline type feature value, and the bowl edge cutting type feature value. Step S2, overall size prediction of the field core: Based on the design requirements and the case database, predict the overall size of the field core profile; Step S3, Field Core Profile Parameterization: Based on the field core profile type feature value predicted in step S1 and the overall field core size predicted in step S2, a standardized parameter set for describing the field core profile is calculated according to preset expert experience rules, and other commonly used parameters of the field core profile are calculated from the standardized parameter set. Step S4, Radial Grid Generation: Based on the core profile obtained in step S3, it is geometrically segmented, and the number of segments in each segment is calculated to generate radial grid configuration parameters; Step S5, grandstand profile parameterization: Based on the key feature values ​​predicted in step S1 and the field core profile obtained in step S3, calculate the design parameters of the grandstand profile, and adjust the design parameters according to the target total number of seats and the feature values ​​of the bowl edge cutting type. At the same time, calculate the standardized parameter set of the bowl edge profile. Step S6, Circumferential Grid Optimization: Based on the profile design parameters obtained in Step S5, the column spacing size is selected from the preset column spacing set through an iterative algorithm to form a circumferential grid candidate scheme, and the rationality of each candidate scheme is checked until a circumferential grid spacing sequence that meets all the check conditions is obtained. Step S7, Detailed parameter synthesis: Integrate the radial grid configuration parameters from step S4 and the grandstand section design parameters from step S5, calculate and output the detailed design parameters of the grandstand bowl.

[0008] Preferably, in step S1, the design requirements include the target total number of seats and the venue type; the key feature values ​​also include the feature values ​​of the number of VIP grandstand tiers, the feature values ​​of the horizontal distribution of VIP grandstands, and the feature values ​​of the location of VIP grandstands.

[0009] Preferably, in step S1, the machine learning model is a random forest model; the case database contains geometric parameters and attribute data of multiple sports venue cases.

[0010] Preferably, the geometric type referred to by the field core contour type feature value and the bowl edge cutting type feature value is one selected from the group consisting of chamfered rectangle, chamfered arc, chamfered rectangle, four-center circle, six-center circle, eight-center circle and circle.

[0011] Preferably, in step S3, the standardized parameter set of the field core profile includes: field core profile type feature value, overall size in the X-axis direction, overall size in the Y-axis direction, subdivision size in the X-axis direction, subdivision size in the Y-axis direction, and corner radius; the other commonly used parameters include at least one of the following: radius, perimeter, boundary point coordinates, total perimeter, and area of ​​the segmented arc of the field core profile.

[0012] Preferably, in step S5, the calculation of the design parameters for the grandstand cross-section specifically includes: Based on the grandstand tier number feature value, VIP grandstand tier number feature value, VIP grandstand horizontal distribution feature value, and VIP grandstand location feature value, a grandstand feature sequence is generated; Calculate the floor elevation sequence after the grandstand; Calculate the number of rows, row width, starting and ending heights, and starting and ending moving distances for each tier of the stands.

[0013] Preferably, in step S5, the adjustment based on the target total number of seats includes: if the current total number of seats does not meet the target, then increasing the number of rows in the last tier of stands until the total number of seats meets the requirement.

[0014] Preferably, in step S6, the iterative algorithm performs the following operations: Select column spacing dimensions from the preset permissible column spacing set C and the initial column spacing set C0 to form a circumferential grid alternative scheme ξ=[c0, c1, ..., ck-1]; For each alternative scheme ξ, its rationality is examined. The examination rules include: for each grandstand, whether the difference between its initial movement distance and the final movement distance and the corresponding column span value exceeds the allowable cantilever distance. If the alternative scheme ξ fails the test, the column spacing size is reselected until a circumferential grid spacing sequence that satisfies all the rationality tests is obtained.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the method described above.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described above.

[0017] The beneficial effects of this invention are: 1. Significantly improves design efficiency and automation. This invention transforms the traditional grandstand bowl design process, which relies on the designer's personal experience and involves repeated trial and error, into a systematic, data-driven, automated process by constructing a standardized parameter configuration flow. Parameters are linked between steps; for example, the feature values ​​output in step 1 directly serve as inputs for steps 3 and 5, achieving seamless transfer and iterative optimization of design data. This effectively avoids the tedious process of global adjustments required for local modifications in traditional parametric modeling, significantly reducing manual intervention and repetitive work. Designers can quickly generate multiple reasonable design schemes for comparison, improving overall design efficiency by several times.

[0018] 2. Effectively ensures the rationality and scientific nature of the design scheme. The beneficial effects of this invention are not vague assertions, but inevitable technical effects based on specific technical features. First, the case data support and machine learning methods introduced in steps 1 and 2 ensure that the initial configuration of key parameters is based on the statistical regularities of a large number of historical excellent cases, rather than purely subjective judgments, thereby improving the scientific nature of the design and its alignment with the design goals from the source. Second, expert experience rules are incorporated into steps 3, 5, and 6, ensuring that the calculation results of key elements such as the stadium core outline, grandstand section, and grid spacing meet the requirements of engineering practice and visual aesthetics, significantly reducing the risk of design rework due to improper parameter configuration.

[0019] 3. Achieve the accumulation and standardization of design knowledge, reducing over-reliance on personal experience. This invention transforms the tacit experience knowledge scattered in the minds of senior designers into reusable and executable standardized parameter configuration logic and algorithms. This method solidifies personal experience into digital assets for enterprises or industries, helping to solve the problem of knowledge loss caused by personnel turnover, and enabling junior designers to produce high-quality design solutions based on this foundation, thereby improving the overall design capabilities and collaboration level of the team.

[0020] 4. Enhance the interactivity of the design process and the optimization capability of the solution. The calculation of circumferential axis parameters based on expert experience and evaluation iteration mentioned in step 6, and the mechanism for adjustment based on the target total number of seats in step 5, embody a human-computer interactive optimization design cycle. The system can generate alternative solutions according to preset rules, and designers can make decisions and fine-tunes based on real-time feedback evaluation results. This interactive process enables the final solution to achieve the best balance between automated calculation and artificial intelligence, helping to discover better solutions that are difficult to obtain using traditional methods. Attached Figure Description

[0021] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the overall parameter configuration process in this invention. Figure 2 A schematic diagram of the field core profile type provided by the present invention.

[0022] Figure 3 A schematic diagram illustrating the standardized description of the field core profile provided by this invention.

[0023] Figure 4 This is a schematic diagram of the radial axis grid parameter arrangement provided by the present invention.

[0024] Figure 5 This is a schematic diagram illustrating the calculation principle of the grandstand cross-sectional parameters provided by the present invention.

[0025] Figure 6 This is a schematic diagram of the circumferential grid parameter arrangement provided by the present invention.

[0026] Figure 7A A diagram of the software operation interface provided for this invention.

[0027] Figure 7B A diagram of the software operation interface provided for this invention.

[0028] Figure 8 The grandstand bowl was generated under the guidance of the parameter matching results provided by this invention. Detailed Implementation

[0029] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] The hardware environment upon which this invention is implemented is a conventional computer workstation, and the software environment is an electronic device equipped with Python 3.12 and a custom-developed design assistance system for this invention. This system integrates all the algorithm modules of the method of this invention.

[0032] Reference Figures 1 to 8 As shown, the digital method for configuring key design parameters of the stadium's grandstand bowl includes the following steps: Step S1, eigenvalue prediction.

[0033] Specifically, in response to user input design requirements, based on a pre-built sports stadium case database and machine learning model, multiple key feature values ​​of the grandstand bowl are predicted. These key feature values ​​include at least the grandstand layer number feature value, the stadium core outline type feature value, and the bowl edge cutting type feature value.

[0034] Furthermore, design requirements include the target total seating capacity and venue type. Preferences for reference cases may also be included, such as location, construction date, architect, competition standards, and stadium rating.

[0035] In addition to basic features such as the number of grandstand tiers, the type of stadium core outline, and the type of bowl edge cutting, key feature values ​​also include the number of VIP grandstand tiers, the horizontal distribution of VIP grandstands, and the location of VIP grandstands.

[0036] This key feature value is the parametric standard form of the sports venue case database, which is based on a collection of cases of various types of sports venues worldwide, containing geometric parameters and attribute data of multiple (e.g., at least 500) sports venue cases. These include various design styles, scales, and design eras.

[0037] Furthermore, the machine learning model is a random forest model, and predictions are made using a pre-trained random forest machine learning model. The model uses "target total number of seats" and "venue type" as core input features and outputs key feature values. For example: Grandstand tier characteristic value: number of grandstand tiers; Field core profile type feature value: Select one from the group consisting of chamfered rectangle, chamfered arc, chamfered rectangle, four-center circle, six-center circle, eight-center circle and circle; Bowl rim cutting type characteristic values: parallel cutting, etc.; VIP Grandstand Level Characteristic Value: Number of VIP Grandstand Levels; Horizontal distribution characteristics of the VIP stands: uniform distribution or central distribution, etc.; VIP grandstand location characteristics: symmetrical arrangement on the east and west sides, etc.

[0038] Exemplary implementation: Please see Figure 7A and Figure 7B Users input design requirements through the system's human-computer interaction interface: the target total number of seats is 40,000, and the venue type is "multi-purpose athletics stadium".

[0039] Upon receiving the design requirements, the system accesses a pre-built database of stadium case studies. This database contains detailed data on numerous domestic and international sports venues. Based on this data, the system uses a trained random forest machine learning model to make predictions. The model takes "target total seating capacity" and "venue type" as core input features, and outputs key feature values ​​such as the following: Grandstand tier characteristic value: 2 tiers; VIP Grandstand Level Characteristic Value: 1 level; The horizontal distribution characteristic of the VIP stands is uniform. VIP grandstand location characteristics: symmetrical arrangement on the east and west sides; Field core profile type characteristic value: four-centered circle; Bowl edge trimming type feature value: parallel trimming.

[0040] Step S2, prediction of overall core size.

[0041] Specifically, based on the design requirements and the case database, the overall dimensions of the field core profile are predicted. The design requirements and case data in step 2 are the same as in step 1; the overall dimensions of the field core profile refer to the length and width of the bounding rectangle of the field core profile geometry, that is, the dimensions in the X-axis direction and the Y-axis direction, which can usually be intuitively understood by using the intercepts of the field core profile geometry on the X-axis and Y-axis.

[0042] Exemplary implementation: The system continues to analyze the statistical patterns of the core dimensions of similar cases matched in step S1 (such as European professional football stadiums with a capacity between 35,000 and 45,000 people), and recommends the overall dimensions of the core outline as follows: X-axis dimension (XLength): 195,500 mm; Y-axis dimension (YLength): 130,900 mm.

[0043] This dimension represents the length and width of the rectangle circumscribed in the core profile.

[0044] Step S3: Parameterize the field core profile.

[0045] Specifically, based on the field core profile type feature value predicted in step S1 and the overall field core size predicted in step S2, a standardized parameter set for describing the field core profile is calculated according to preset expert experience rules, and other commonly used parameters of the field core profile are calculated from the standardized parameter set.

[0046] Furthermore, the six standardized parameters describing the field core profile include the field core profile type feature value, the overall dimension (XLength) in the X-axis direction, the overall dimension (YLength) in the Y-axis direction, the subdivision dimension (XDistance) in the X-axis direction, the subdivision dimension (YDistance) in the Y-axis direction, and the corner radius (Radius). Other commonly used parameters of the field core profile include the radius, circumference, and dividing point of the segmented arcs of the field core profile, as well as the circumferential area of ​​the field core profile.

[0047] Specifically, the field core profile can be viewed as a whole connected by multiple circular arcs or straight line segments. Excluding mutually symmetrical segments, its characteristics can usually be represented by three segments: long side, short side, and corner side.

[0048] The aforementioned expert experience rules refer to the procedures and empirical values ​​used in engineering to fit a curve using a series of arcs. For example, how to accurately fit a given geometric profile (such as a four-centered circle) using a series of smoothly connected arcs. This includes mathematical methods for determining the center position, radius, and tangent relationships of each arc segment.

[0049] Exemplary implementation: The system calculates parameters based on the "four-center circle" type predicted in step 1 and the overall dimensions (195,500 mm x 130,900 mm) predicted in step 2, using preset expert experience rules. Different types of field core profiles are as follows: Figure 2 As shown. Given a "four-center circle" type and overall dimensions (XLength=195,500 mm, YLength=130,900 mm), the expert rule base pre-stores empirical coefficients for the radius proportions of four-center circle outlines with different aspect ratios. The system calls these coefficients and combines them with the overall dimensions to quickly calculate the specific radius values ​​of the four arc segments without requiring designers to manually try and fail.

[0050] First, the system calculates six standardized parameters of the field core profile, the meanings of which are as follows: Figure 3 As shown: Field core profile type characteristic value: four-centered circle; Overall dimension in the X-axis direction (XLength): 195,500 mm; Overall Y-axis dimension (YLength): 130,900 mm; X-axis subdivision dimension (XDistance): 37,374 mm; Y-axis subdivision dimension (YDistance): 0 mm; Corner radius: 0 mm.

[0051] Subsequently, based on these standardized parameters, the system automatically calculates common parameters such as the radius of each arc segment, central angle, and coordinates of the dividing point of the "four-center circle" using geometric algorithms, and generates an accurate geometric shape of the field core profile.

[0052] Step S4: Radial grid generation.

[0053] Specifically, based on the core profile obtained in step S3, it is geometrically segmented, and the number of segments in each segment is calculated to generate radial grid configuration parameters.

[0054] Furthermore, the segment values ​​of each segment of the outline are each segment of the combined curve fitted by the arc. The total number of segments is usually no more than 8 segments, and each segment may be an arc or a straight line segment. The number of segments refers to the number of column spans that are suitable for matching the arc or straight line segment of each segment. The total length of the segments is the target. Since the grid of the stadium usually has a radial feature and the curvature of different segments is not the same, it is necessary to fully coordinate the overall rationality.

[0055] Exemplary implementation: The generated core profile is geometrically segmented. For a "four-center circle" profile, it is typically divided into segments such as long-side arcs, short-side arcs, and corner arcs. Then, based on the radius of curvature and length of each segment, and considering structural rationality principles, the system calculates the appropriate number of column spans for each segment. The principle of radial grid configuration is as follows... Figure 4 As shown.

[0056] For example, in this embodiment: Matching of long side arc segments: 13 column spans (i.e., the long side segment is divided by 13 radial axes). Matching of short-side circular arc segments: 23 column spans (i.e., the short-side segment is divided by 23 radial axes); Corner arc matching: 0 column spans (each corner); Based on this division, the system automatically generates the angle and position parameters of all radial axes.

[0057] Step S5: Parametricize the grandstand profile.

[0058] Specifically, based on the key feature values ​​predicted in step S1 and the field core profile obtained in step S3, the design parameters of the grandstand profile are calculated, and the design parameters are adjusted according to the target total number of seats and the feature values ​​of the bowl edge cutting type. At the same time, the standardized parameter set of the bowl edge profile is calculated.

[0059] Furthermore, the specific design parameters for calculating the grandstand cross-section include: Based on the grandstand tier number feature value, VIP grandstand tier number feature value, VIP grandstand horizontal distribution feature value, and VIP grandstand location feature value, a grandstand feature sequence is generated; Calculate the floor elevation sequence after the grandstand; Calculate the number of rows, row width, starting and ending heights, and starting and ending moving distances for each tier of the stands.

[0060] The adjustment based on the target total number of seats includes: if the current total number of seats does not meet the target, increasing the number of rows in the last tier of stands until the total number of seats meets the requirement.

[0061] Furthermore, the standardized parameters describing the bowl edge profile shape are identical, including bowl edge profile type feature values, overall size in the X-axis direction, overall size in the Y-axis direction, subdivision size in the X-axis direction, subdivision size in the Y-axis direction, and corner radius; the bowl edge profile and the field core profile may or may not be parallel.

[0062] The stands include the general audience stands (T). ga And VIP stands T vip .

[0063] The configuration of the grandstand profile parameters also includes the starting and ending heights and the starting and ending travel distances for each grandstand level. The height refers to the vertical distance from the elevation of the starting and ending points to the horizontal plane of the sports field, denoted as H = [h...]. 11 ,h 12 ,h 21 ,h 22 ,...,h n1 ,h n2 The movement distance refers to the horizontal distance from the starting and ending points to the nearest point on the field core profile, denoted as D=[d]. 11 ,d 12 ,d 21 ,d 22 ,...,d n1 ,d n2 ]. By h i1 Calculate h i2 The main reference indicator is the achievement of the elevation difference CValue.

[0064] The main steps for configuring the grandstand profile parameters are as follows: Based on the predicted grandstand floor number characteristic values, VIP grandstand floor number characteristic values, VIP grandstand horizontal distribution characteristic values, and VIP grandstand location characteristic values ​​in step 1, calculate the grandstand characteristic sequence Order=[o1,o2,...,on]; calculate the floor elevation after the grandstand is built, and represent it with a sequence; calculate the number of rows, row width, start and end heights, and moving distance of each grandstand, as well as the special row number and aisle width, etc.; determine the design parameters of the last grandstand based on the target total number of seats, and take into account the bowl edge profile shape according to the bowl edge cutting type characteristic values ​​in step 1.

[0065] Exemplary implementation: This step is crucial for forming the three-dimensional shape of the grandstand bowl. The system first generates a grandstand feature sequence Order based on the feature values ​​predicted in step S1, such as [regular grandstand, VIP grandstand, regular grandstand], and determines that the VIP grandstand is located on the second layer.

[0066] Next, the system calculates the cross-sectional parameters of each tier of the grandstand based on ergonomic standards and structural specifications, such as C-Value (line-of-sight elevation). The calculation principle is as follows: Figure 5 As shown.

[0067] Taking the first-floor ordinary grandstand as an example, the system calculates its: Starting height: H11 = 1.2m (measured from the site level); Starting movement distance: D11 = 15m (horizontal distance from the field center outline to the edge of the stands); Number of rows: 16; Width: 0.85 meters; The system calculates the cumulative number of seats in real time. If the total number of seats in the three tiers of the stands is less than 40,000, the number of rows in the third tier will be automatically increased to 35 until the requirement is met. Finally, based on the "parallel cut" type, the standardized parameters of the bowl rim profile are calculated to ensure that it is parallel to and does not intersect with the core profile of the stadium.

[0068] Step S6, Circular axis network optimization.

[0069] Specifically, based on the profile design parameters obtained in step S5, the column spacing dimensions are selected from the preset column spacing set through an iterative algorithm to form a circumferential grid alternative scheme, and the rationality of each alternative scheme is checked until a circumferential grid spacing sequence that meets all the check conditions is obtained.

[0070] In this step, the iterative algorithm performs the following operations: Select column spacing dimensions from the preset permissible column spacing set C and the initial column spacing set C0 to form a circumferential grid alternative scheme ξ=[c0, c1, ..., ck-1]; For each alternative scheme ξ, its rationality is examined. The examination rules include: for each grandstand, whether the difference between its initial movement distance and the final movement distance and the corresponding column span value exceeds the allowable cantilever distance. If the alternative scheme ξ fails the test, the column spacing size is reselected until a circumferential grid spacing sequence that satisfies all the rationality tests is obtained.

[0071] Furthermore, the circumferential grid parameters in this step refer to the spacing sequence formed between multiple grid rings surrounding the stadium. The grid rings surrounding the stadium are typically parallel curves, and this parallelism is manifested in that the normal direction of any point on a continuous closed curve is equal to the normal direction of the intersection point of its normal ray and another continuous closed curve.

[0072] Configure the circumferential grid in step 6 as follows: From the allowable column spacing set C={c i | i=0,1,...,n-1} and the initial column spacing set C0={c i Randomly select column spacing dimensions from | i=0,1,...,m-1} to form an alternative circumferential grid scheme ξ=[c0,c1,..,c k-1 ], where the value of c0 comes from C0, and the other values ​​come from C. For each ξ, its rationality is verified by the following method: For each grandstand, verify whether the difference between its initial moving distance and the corresponding column span value exceeds the allowable cantilever distance; verify whether the difference between its final moving distance and the corresponding column span value exceeds the allowable cantilever distance; verify the special requirements of the first and last grandstands, etc. Repeat this process until a reasonable ξ is obtained.

[0073] Exemplary implementation: This step predetermines the permissible column spacing set C = {6000, 6300, 6600, ..., 10500} mm and the initial column spacing set C0 = {10500, 10200, ..., 8700} mm.

[0074] The system begins iteration: First, a starting column spacing c0 = 9000mm is randomly selected from C0, and then subsequent column spacings are randomly selected from C to form a circumferential grid alternative scheme ξ = [9000, 8400, 8100, 7800, ...].

[0075] Subsequently, the system conducted a rigorous rationality check on the solution, with the following logic: Figure 6 As shown.

[0076] Check whether the start and end points of each grandstand are within the allowable cantilever distance (e.g., 4.5 meters) of the corresponding column span.

[0077] Check whether the height of the first-floor grandstand meets the functional requirements of the space below, and whether the cantilever of the top-floor grandstand is too large.

[0078] If the current scheme ξ fails the test, the system generates a new scheme and tests it again. This process is repeated until a circumferential grid spacing sequence that simultaneously satisfies all structural safety and functional requirements is found. The significance of this step is that, through automated iterative optimization, it solves complex multi-constraint problems that are difficult to coordinate manually, ensuring the structural rationality and economy of the scheme.

[0079] Step S7: Detailed parameter synthesis.

[0080] Specifically, the radial grid configuration parameters from step S4 and the grandstand section design parameters from step S5 are integrated to calculate and output the detailed design parameters of the grandstand bowl.

[0081] The system finally integrates the radial grid parameters from step S4 and the grandstand section parameters from step S5, supplementing detailed parameters such as railing height and step details using empirical formulas. Ultimately, it outputs a complete and coordinated set of grandstand bowl parameter configurations. These empirical formulas can be incorporated into expert experience rules. In this embodiment, expert experience rules refer to transforming the implicit knowledge, design principles, and best practices regarding grandstand bowl shape control formed by experienced sports architecture designers through long-term practice into explicit, structured, and computable algorithmic logic that can be recognized and executed by computers, through mathematical formulas, logical judgments, geometric constraints, and parametric relationships. Its essence is the encoding of design knowledge, transforming the intuitive, qualitative "design feeling" into rational, quantitative "design rules."

[0082] Importing this compiled parameter set into parametric modeling software allows for the rapid and automatic generation of a 3D digital model of the grandstand bowls of a 40,000-seat professional football stadium, as illustrated in the diagram below. Figure 8 As shown. The entire process reduces the traditional design cycle of several weeks to several hours, and the solution is scientifically sound and reasonable.

[0083] Embodiments of this application also 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 steps in the above-described embodiment of the digital method for configuring key design parameters of stadium grandstand bowls, including: Step S1, Feature value prediction: In response to the design requirements input by the user, based on the pre-built sports stadium case database and machine learning model, predict multiple key feature values ​​of the grandstand bowl. The key feature values ​​include at least the grandstand layer number feature value, the stadium core outline type feature value, and the bowl edge cutting type feature value. Step S2, overall size prediction of the field core: Based on the design requirements and the case database, predict the overall size of the field core profile; Step S3, Field Core Profile Parameterization: Based on the field core profile type feature value predicted in step S1 and the overall field core size predicted in step S2, a standardized parameter set for describing the field core profile is calculated according to preset expert experience rules, and other commonly used parameters of the field core profile are calculated from the standardized parameter set. Step S4, Radial Grid Generation: Based on the core profile obtained in step S3, it is geometrically segmented, and the number of segments in each segment is calculated to generate radial grid configuration parameters; Step S5, grandstand profile parameterization: Based on the key feature values ​​predicted in step S1 and the field core profile obtained in step S3, calculate the design parameters of the grandstand profile, and adjust the design parameters according to the target total number of seats and the feature values ​​of the bowl edge cutting type. At the same time, calculate the standardized parameter set of the bowl edge profile. Step S6, Circumferential Grid Optimization: Based on the profile design parameters obtained in Step S5, the column spacing size is selected from the preset column spacing set through an iterative algorithm to form a circumferential grid candidate scheme, and the rationality of each candidate scheme is checked until a circumferential grid spacing sequence that meets all the check conditions is obtained. Step S7, Detailed parameter synthesis: Integrate the radial grid configuration parameters from step S4 and the grandstand section design parameters from step S5, calculate and output the detailed design parameters of the grandstand bowl.

[0084] The memory can be used to store software programs and models, such as the random forest model and parametric modeling software in this embodiment. The processor executes various functional applications and data processing by running the software programs and models stored in the memory, thereby realizing the aforementioned digital method for configuring key design parameters of stadium grandstand bowls. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0085] Electronic devices can also be terminal devices such as smartphones, tablets, PDAs, and mobile internet devices.

[0086] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the digital method embodiment for configuring key design parameters of stadium grandstand bowls as described above: Step S1, Feature value prediction: In response to the design requirements input by the user, based on the pre-built sports stadium case database and machine learning model, predict multiple key feature values ​​of the grandstand bowl. The key feature values ​​include at least the grandstand layer number feature value, the stadium core outline type feature value, and the bowl edge cutting type feature value. Step S2, overall size prediction of the field core: Based on the design requirements and the case database, predict the overall size of the field core profile; Step S3, Field Core Profile Parameterization: Based on the field core profile type feature value predicted in step S1 and the overall field core size predicted in step S2, a standardized parameter set for describing the field core profile is calculated according to preset expert experience rules, and other commonly used parameters of the field core profile are calculated from the standardized parameter set. Step S4, Radial Grid Generation: Based on the core profile obtained in step S3, it is geometrically segmented, and the number of segments in each segment is calculated to generate radial grid configuration parameters; Step S5, grandstand profile parameterization: Based on the key feature values ​​predicted in step S1 and the field core profile obtained in step S3, calculate the design parameters of the grandstand profile, and adjust the design parameters according to the target total number of seats and the feature values ​​of the bowl edge cutting type. At the same time, calculate the standardized parameter set of the bowl edge profile. Step S6, Circumferential Grid Optimization: Based on the profile design parameters obtained in Step S5, the column spacing size is selected from the preset column spacing set through an iterative algorithm to form a circumferential grid candidate scheme, and the rationality of each candidate scheme is checked until a circumferential grid spacing sequence that meets all the check conditions is obtained. Step S7, Detailed parameter synthesis: Integrate the radial grid configuration parameters from step S4 and the grandstand section design parameters from step S5, calculate and output the detailed design parameters of the grandstand bowl.

[0087] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0088] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A digital method for configuring key design parameters of stadium grandstand bowls, characterized in that, Includes the following steps: Step S1, Feature value prediction: In response to the design requirements input by the user, based on the pre-built sports stadium case database and machine learning model, predict multiple key feature values ​​of the grandstand bowl. The key feature values ​​include at least the grandstand layer number feature value, the stadium core outline type feature value, and the bowl edge cutting type feature value. Step S2, overall size prediction of the field core: Based on the design requirements and the case database, predict the overall size of the field core profile; Step S3, Field Core Profile Parameterization: Based on the field core profile type feature value predicted in step S1 and the overall field core size predicted in step S2, a standardized parameter set for describing the field core profile is calculated according to preset expert experience rules, and other commonly used parameters of the field core profile are calculated from the standardized parameter set. Step S4, Radial Grid Generation: Based on the core profile obtained in step S3, it is geometrically segmented, and the number of segments in each segment is calculated to generate radial grid configuration parameters; Step S5, grandstand profile parameterization: Based on the key feature values ​​predicted in step S1 and the field core profile obtained in step S3, calculate the design parameters of the grandstand profile, and adjust the design parameters according to the target total number of seats and the feature values ​​of the bowl edge cutting type. At the same time, calculate the standardized parameter set of the bowl edge profile. Step S6, Circumferential Grid Optimization: Based on the profile design parameters obtained in Step S5, the column spacing size is selected from the preset column spacing set through an iterative algorithm to form a circumferential grid candidate scheme, and the rationality of each candidate scheme is checked until a circumferential grid spacing sequence that meets all the check conditions is obtained. Step S7, Detailed parameter synthesis: Integrate the radial grid configuration parameters from step S4 and the grandstand section design parameters from step S5, calculate and output the detailed design parameters of the grandstand bowl.

2. The method according to claim 1, characterized in that, In step S1, the design requirements include the target total number of seats and the venue type; the key feature values ​​also include the feature values ​​of the number of VIP grandstand tiers, the feature values ​​of the horizontal distribution of VIP grandstands, and the feature values ​​of the location of VIP grandstands.

3. The method according to claim 2, characterized in that, In step S1, the machine learning model is a random forest model; the case database contains geometric parameters and attribute data of multiple sports venue cases.

4. The method according to claim 2, characterized in that, The geometric shape type referred to by the field core contour type feature value and the bowl edge cutting type feature value is selected from the group consisting of chamfered rectangle, chamfered arc, chamfered rectangle, four-center circle, six-center circle, eight-center circle and circle.

5. The method according to claim 1, characterized in that, In step S3, the standardized parameter set of the field core profile includes: field core profile type feature value, overall size in the X-axis direction, overall size in the Y-axis direction, subdivision size in the X-axis direction, subdivision size in the Y-axis direction, and corner radius; the other commonly used parameters include at least one of the following: radius, perimeter, boundary point coordinates, total perimeter, and area of ​​the segmented arc of the field core profile.

6. The method according to claim 2, characterized in that, In step S5, the specific design parameters for calculating the grandstand cross-section include: Based on the grandstand tier number feature value, VIP grandstand tier number feature value, VIP grandstand horizontal distribution feature value, and VIP grandstand location feature value, a grandstand feature sequence is generated; Calculate the floor elevation sequence after the grandstand; Calculate the number of rows, row width, starting and ending heights, and starting and ending moving distances for each tier of the stands.

7. The method according to claim 6, characterized in that, In step S5, the adjustment based on the target total number of seats includes: if the current total number of seats does not meet the target, then increasing the number of rows in the last tier of stands until the total number of seats meets the requirement.

8. The method according to claim 1, characterized in that, In step S6, the iterative algorithm performs the following operations: Select column spacing dimensions from the preset permissible column spacing set C and the initial column spacing set C0 to form a circumferential grid alternative scheme ξ=[c0, c1, ..., ck-1]; For each alternative scheme ξ, its rationality is examined. The examination rules include: for each grandstand, whether the difference between its initial movement distance and the final movement distance and the corresponding column span value exceeds the allowable cantilever distance. If the alternative scheme ξ fails the test, the column spacing size is reselected until a circumferential grid spacing sequence that satisfies all the rationality tests is obtained.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 8.

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