Stadium model generation method, form classification method, device and medium

Abstract

The application discloses a gymnasium model generation method, a form classification method, equipment and a medium, and the generation method comprises the following steps: acquiring gymnasium requirement information input by a user; generating an activity site according to the activity site requirement; generating a bowl-shaped space of a seat area based on the outer boundary of the activity site and the audience seat requirement; acquiring a first random variable related to the outer boundary of the fixed seat; generating a roof plane contour curve according to the first random variable; projecting the roof plane contour curve to the bowl-shaped space, and cutting the bowl-shaped space through the audience seat boundary curve obtained by the projection, to obtain an audience seat shape; determining a roof form according to the audience seat boundary curve and a second random variable related to the roof parameter; and constructing a gymnasium model based on the roof form and the audience seat shape. The application can quickly generate a large number of gymnasium models of various scales and various architectural forms and meet the input requirements of the user.

Description

Technical Field

[0001] This invention relates to the field of building modeling technology, and in particular to a method for generating a stadium model, a morphological classification method, equipment, and medium. Background Technology

[0002] As an important public building, stadiums need to host various large-scale sports, cultural and entertainment events, and are often regarded as landmark buildings of the city. A stadium is generally composed of a bowl-shaped space formed by the competition venue and the spectator seating area as the lower part, and a large-span roof as the upper covering structure. The stadium has distinct characteristics, and its scale directly affects the form of the stadium.

[0003] In existing technologies, designers typically need to draw stadium models in relevant drawing software according to design requirements, which results in low model generation efficiency and further affects designers' analysis and selection of different design schemes. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for generating stadium models, a method for classifying morphology, equipment and medium, which can quickly generate a large number of stadium models that meet design requirements, and classify the generated models to assist users in analyzing and selecting different models.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for generating a stadium model, comprising:

[0006] Obtain the stadium requirement information input by the user; wherein, the stadium requirement information includes the activity venue requirements and the spectator seating requirements;

[0007] The event venue is generated according to the event venue requirements, and a bowl-shaped space for the seating area is generated based on the outer boundary of the event venue and the audience seating requirements.

[0008] Obtain a first random variable related to the outer boundary of the fixed seat, and generate a roof plan profile curve based on the first random variable;

[0009] The roof plan outline curve is projected onto the bowl-shaped space, and the bowl-shaped space is cut by the audience seating boundary curve obtained by the projection to obtain the audience seating shape;

[0010] The roof shape is determined based on the audience seating boundary curve and the second random variable related to the roof parameters.

[0011] The stadium model is constructed based on the roof shape and the seating arrangement of the audience.

[0012] As an improvement to the above solution, the step of generating the roof plan profile curve based on the first random variable specifically includes:

[0013] Based on the first random variable, determine several boundary curve control points, boundary curve types, and the symmetry of the fixed seats relative to the activity area.

[0014] Determine the interpolation curve formed by several boundary curve control points;

[0015] Based on the boundary curve type and the symmetry, the interpolated curve is modified to obtain the roof plan profile curve.

[0016] As an improvement to the above scheme, the second random variable includes the height parameters of several boundary curve control points, the ridge curve type in the X and Y directions, and the spatial net height of the center point of the activity area.

[0017] The step of determining the roof shape based on the audience seating boundary curve and the obtained second random variable related to the roof parameters specifically includes:

[0018] Set corresponding height parameters for each boundary curve control point on the audience seating boundary curve to generate the roof structure boundary curve;

[0019] Based on the roof structure boundary curve and the space clear height, a first X-direction ridge curve and a first Y-direction ridge curve are generated;

[0020] Based on the ridgeline curve type, the first X-direction ridge curve and the first Y-direction ridge curve are modified respectively to obtain the second X-direction ridge curve and the second Y-direction ridge curve.

[0021] The roof shape is determined based on the roof structure boundary curve, the second X-direction ridge curve, and the second Y-direction ridge curve.

[0022] As an improvement to the above solution, the construction of the stadium model based on the roof shape and the spectator seating shape specifically includes:

[0023] Based on the roof shape, set the horizontal projection dimension parameters on the structural grid to generate the first single-layer structural grid;

[0024] Based on the first single-layer structure network and the preset structural parameters, a second single-layer structure network is generated superimposed on the first single-layer structure network to obtain a double-layer structure network.

[0025] A stadium model is constructed based on the shape of the audience seating and the first single-layer structural net; or, a stadium model is constructed based on the shape of the audience seating and the double-layer structural net.

[0026] The first single-layer structural net is used to construct a single-layer reticulated shell structure, and the double-layer structural net is used to construct a double-layer spatial grid structure or a spatial truss beam structure.

[0027] As an improvement to the above solution, the step of obtaining a first random variable related to the outer boundary of the fixed seat and generating a roof plan profile curve based on the first random variable includes:

[0028] Generate an initial roof plan profile curve based on the first random variable;

[0029] Based on the area enclosed by the initial roof plan contour curve, the area of ​​the activity area, and the number of seats per unit, the number of fixed seats is calculated, and it is determined whether the number of fixed seats is greater than a preset fixed seat threshold.

[0030] If so, the initial roof plan profile curve is used as the generated roof plan profile curve;

[0031] If not, the first random variable is retrieved again to generate a new initial roof plan profile curve until the calculated number of fixed seats is greater than the fixed seat threshold.

[0032] As an improvement to the above scheme, the seating requirement includes the maximum number of seats for the target sporting event;

[0033] The process of generating a bowl-shaped seating area based on the outer boundary of the event venue and the audience seating requirements specifically includes:

[0034] Based on the outer boundary of the activity area, the maximum number of seats, and the preset first system parameters, a bowl-shaped space for the seating area is drawn using a drawing method; wherein, the first system parameters include the standard specifications within the seating area.

[0035] This invention also provides a morphological classification method for stadium models, including:

[0036] Obtain multiple stadium models generated according to the stadium model generation method described in any of the above-mentioned methods;

[0037] Design variables were extracted from each of the described stadium models;

[0038] By using self-organizing mapping, based on the design variables of each stadium model, the stadium models are clustered to obtain several stadium morphology clusters; wherein, each stadium morphology cluster includes at least one stadium model.

[0039] Furthermore, the extraction of design variables from each of the stadium models specifically includes:

[0040] Extract several initial design variables from each of the described gymnasium models;

[0041] For each stadium model, the initial design variables are scaled, and the scaled initial design variables are used to form the design variables of the stadium model.

[0042] This invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method described in any of the above embodiments.

[0043] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the method described above.

[0044] Compared with the prior art, the beneficial effects of the method for generating a stadium model, the morphological classification method, the equipment and the medium provided by the embodiments of the present invention are as follows: it can quickly generate a large number of stadium models of various sizes and architectural forms that meet the user's input requirements, and can accurately and efficiently classify the large number of stadium models generated, thereby facilitating users to analyze and select stadium models generated by different design schemes. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a preferred embodiment of a method for generating a stadium model provided by the present invention;

[0046] Figure 2 This is a schematic diagram of a preferred embodiment of a morphological classification method for a gymnasium model provided by the present invention;

[0047] Figure 3 This is a schematic diagram of a preferred embodiment of a terminal device provided by the present invention;

[0048] Figure 4 This is a schematic diagram of a preferred embodiment of the bowl-shaped space of the seating area provided by the present invention;

[0049] Figure 5 This is a schematic diagram of a preferred embodiment of the present invention for cutting a bowl-shaped space. Detailed Implementation

[0050] 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, and 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.

[0051] Please see Figure 1 , Figure 1 This is a flowchart illustrating a preferred embodiment of a method for generating a stadium model provided by the present invention. The method for generating the stadium model includes:

[0052] S11, Obtain the stadium requirement information input by the user; wherein, the stadium requirement information includes activity venue requirements and spectator seating requirements;

[0053] S12, generate an activity venue according to the activity venue requirements, and generate a bowl-shaped space for the seating area based on the outer boundary of the activity venue and the audience seating requirements;

[0054] S13, obtain a first random variable related to the outer boundary of the fixed seat, and generate a roof plan profile curve based on the first random variable;

[0055] S14, Project the roof plan outline curve onto the bowl-shaped space, and cut the bowl-shaped space using the audience seating boundary curve obtained by the projection to obtain the audience seating shape;

[0056] S15, determine the roof shape based on the audience seating boundary curve and the obtained second random variable related to the roof parameters;

[0057] S16, Construct a stadium model based on the roof shape and the spectator seating shape.

[0058] Specifically, this embodiment of the invention is implemented in the Grasshopper parametric platform of Rhino (3D modeling software), where the stadium requirements information input by the user is used as the input variable.

[0059] In step S12, see Figure 4 Centered on the outer boundary of the activity area, a bowl-shaped space is generated by extending outward (i.e., the process from A to B as shown in the diagram).

[0060] In step S14, see Figure 5The two-dimensional curve parallel to the activity area below the bowl-shaped space is the roof plan outline curve. Projecting the roof plan outline curve upwards onto the bowl-shaped space yields the intersecting three-dimensional audience seating boundary curves. Using the audience seating boundary curves as the outer boundary and the outer boundary of the activity area as the inner boundary, the bowl-shaped space is cut to obtain the audience seating shape.

[0061] It should be noted that the activity venue requirements include the size of the activity venue (0 represents activity venue type I (36M*44M), 1 represents activity venue type II (36M*54M), and 2 represents activity venue type III (40M*70M)).

[0062] This embodiment can quickly generate a large number of stadium models of various sizes and architectural forms that meet user input requirements.

[0063] In another preferred embodiment, generating the roof plan profile curve based on the first random variable specifically includes:

[0064] Based on the first random variable, determine several boundary curve control points, boundary curve types, and the symmetry of the fixed seats relative to the activity area.

[0065] Determine the interpolation curve formed by several boundary curve control points;

[0066] Based on the boundary curve type and the symmetry, the interpolated curve is modified to obtain the roof plan profile curve.

[0067] For example, the first random variable includes at least one of the following: the length of the outer boundary of the fixed seat in the X (minor axis of the activity area) direction, the symmetry of the fixed seat relative to the activity area, the corner position of the outer boundary of the fixed seat in the four quadrants, and the boundary curve type of the outer boundary of the fixed seat.

[0068] The symmetry of the fixed seats relative to the activity area includes: the asymmetry ratio of the outer boundary of the fixed seats along the X (minor axis of the activity area), the length of the outer boundary of the fixed seats along the Y (major axis of the activity area), and the asymmetry ratio of the outer boundary of the fixed seats along the Y (major axis of the activity area).

[0069] It should be noted that when the corner point position value is 0, it means that it coincides with the corner point of the activity area, and when it is 7, it means that it coincides with the corner point of the outer rectangle defined by ST_LenX and ST_LenY; when the boundary curve type value is 1, it represents a polyline, and when it is 3, it represents a curve.

[0070] The asymmetry ratio is randomly selected from 0 to 10. When it is 0, it means that the outer boundary of the fixed seat is symmetrical along the X-axis or along the Y-axis; when it is 10, it means that the outer boundary of the fixed seat is asymmetrical along the X-axis or along the Y-axis. Asymmetry is equivalent to the seat being completely biased to one side relative to the activity area.

[0071] In another preferred embodiment, the second random variable includes the height parameters of several boundary curve control points, the ridgeline curve types in the X and Y directions, and the spatial net height of the center point of the activity area.

[0072] The step of determining the roof shape based on the audience seating boundary curve and the obtained second random variable related to the roof parameters specifically includes:

[0073] Set corresponding height parameters for each boundary curve control point on the audience seating boundary curve to generate the roof structure boundary curve;

[0074] Based on the roof structure boundary curve and the space clear height, a first X-direction ridge curve and a first Y-direction ridge curve are generated;

[0075] Based on the ridgeline curve type, the first X-direction ridge curve and the first Y-direction ridge curve are modified respectively to obtain the second X-direction ridge curve and the second Y-direction ridge curve.

[0076] The roof shape is determined based on the roof structure boundary curve, the second X-direction ridge curve, and the second Y-direction ridge curve.

[0077] Specifically, corresponding height parameters are set at each of the boundary curve control points, which is equivalent to raising the audience seating boundary curve in the vertical direction according to the principle that different points have different relative heights, thereby raising the audience seating boundary curve to the roof structure boundary curve, and the roof structure boundary curve has a relative height; whereby the relative height refers to the height relative to different points on the shape of the audience seating.

[0078] It should be noted that the clear height of the space is ≥18m; the height parameter is ≥3m to ensure the minimum height, thereby ensuring the clear height of the last row; the value of the height parameter is from 0 to 10, where 0 represents the minimum height and 10 represents the maximum height less than the clear height of the space; the value of the ridge line curve type is from 1 to 3, where 1 represents a broken line and 3 represents a curve.

[0079] For example, the roof shape includes a flat surface, a single curved surface, a dome surface, and a saddle surface.

[0080] In another preferred embodiment, the construction of the stadium model based on the roof shape and the spectator seating shape specifically includes:

[0081] Based on the roof shape, set the horizontal projection dimension parameters on the structural grid to generate the first single-layer structural grid;

[0082] Based on the first single-layer structure network and the preset structural parameters, a second single-layer structure network is generated superimposed on the first single-layer structure network to obtain a double-layer structure network.

[0083] A stadium model is constructed based on the shape of the audience seating and the first single-layer structural net; or, a stadium model is constructed based on the shape of the audience seating and the double-layer structural net.

[0084] The first single-layer structural net is used to construct a single-layer reticulated shell structure, and the double-layer structural net is used to construct a double-layer spatial grid structure or a spatial truss beam structure.

[0085] Specifically, this embodiment of the invention is implemented using the Karamba software in Rhino (a 3D modeling software). By defining the horizontal projection dimension parameters of the structural mesh, a single-layer structural mesh can be generated. By defining the structural height parameters (included in the structural parameters) at the center of the roof and the boundary of the roof, a second-layer structural mesh can be generated above the single-layer structural mesh.

[0086] It should be noted that the single-layer reticulated shell structure is generated using the grid lines in the first single-layer reticulated structure as the center lines of the structural members;

[0087] The double-layer space frame structure has an upper chord member generated based on the first single-layer structure net and a lower chord member generated based on the second single-layer structure net. Vertical and diagonal web members are provided between the upper and lower layers. The upper and lower chord members and web members can have different cross-sectional dimensions.

[0088] The spatial truss beam structure has an upper chord formed by offsetting the upper layer (i.e., the second single-layer grid line) to both sides by a predetermined distance, while the lower chord is generated from the grid lines of the lower layer (i.e., the first single-layer grid line). Diagonal web members are set between the upper and lower chords. The spatial truss beam structure adopts an inverted triangular cross section. For the spatial truss beam structure, two different span directions can be selected. The cross-sectional dimensions of the members and the structural materials (type of steel) are defined by specific parameters.

[0089] In another preferred embodiment, obtaining a first random variable related to the outer boundary of the fixed seat and generating a roof plan profile curve based on the first random variable includes:

[0090] Generate an initial roof plan profile curve based on the first random variable;

[0091] Based on the area enclosed by the initial roof plan contour curve, the area of ​​the activity area, and the number of seats per unit, the number of fixed seats is calculated, and it is determined whether the number of fixed seats is greater than a preset fixed seat threshold.

[0092] If so, the initial roof plan profile curve is used as the generated roof plan profile curve;

[0093] If not, the first random variable is retrieved again to generate a new initial roof plan profile curve until the calculated number of fixed seats is greater than the fixed seat threshold.

[0094] In another preferred embodiment, the seating requirement includes the maximum number of seats for the target sporting event;

[0095] The process of generating a bowl-shaped seating area based on the outer boundary of the event venue and the audience seating requirements specifically includes:

[0096] Based on the outer boundary of the activity area, the maximum number of seats, and the preset first system parameters, a bowl-shaped space for the seating area is drawn using a drawing method; wherein, the first system parameters include the standard specifications within the seating area.

[0097] For example, the standard specifications within the seating area include at least one of the following: a C value (120 mm) for indicating the visible range, a row spacing of seats ≥ 0.8 m, a seat width ≥ 0.5 m, a maximum number of seats between two aisles ≤ 21, an aisle width ≥ 1.2 m, the number of fixed seats in a cross section, and the number of rows of fixed seats in each group.

[0098] Please see Figure 2 , Figure 2 This is a flowchart illustrating a preferred embodiment of a morphological classification method for a stadium model provided by the present invention. The morphological classification method for the stadium model includes:

[0099] S21, Obtain multiple stadium models generated according to the stadium model generation method described in any one of the above;

[0100] S22, extract design variables from each of the described gymnasium models;

[0101] S23, through self-organizing mapping, based on the design variables of each stadium model, cluster the stadium models to obtain several stadium morphology clusters; wherein, the stadium morphology cluster includes at least one stadium model.

[0102] Preferably, the step of extracting design variables from each of the stadium models specifically includes:

[0103] Extract several initial design variables from each of the described gymnasium models;

[0104] For each stadium model, the initial design variables are scaled, and the scaled initial design variables are used to form the design variables of the stadium model.

[0105] Specifically, the scaling process involves scaling from 0 to 1. It should be understood that the scaling process can eliminate the influence of different variables due to their different domains.

[0106] For example, the initial design variables include: the length of the fixed seating outer boundary in the X (minor axis of the activity area) direction, the asymmetry ratio of the fixed seating outer boundary in the X (minor axis of the activity area) direction, the length of the fixed seating outer boundary in the Y (major axis of the activity area) direction, the asymmetry ratio of the fixed seating outer boundary in the Y (major axis of the activity area) direction, the corner position of the fixed seating outer boundary in the four quadrants, the curve type of the fixed seating outer boundary, the structural height of the roof center point (for double-layer space truss beams and space truss beams), the clear height of the activity area center point, and the ridge line curve type of the structure in the X or Y direction (which can be a curve or a broken line).

[0107] It's important to note that before using self-organizing maps (SAMR), the topology type of the two-dimensional single-layer neural network (commonly grids or triangular networks) and the number of nodes need to be defined. The number of nodes determines the number of groups that objects can be grouped into. Typically, the number of groups is less than or equal to the number of nodes (because some nodes may not capture any objects during iteration, forming empty groups, which also indicates that the set number of groups is greater than the actual number of groups). Additionally, the number of iterations, the step size (learning rate) of each node in each iteration, and the relevant parameters regarding the range of influence of each node's movement on surrounding nodes also need to be defined. This embodiment uses a 10x10 triangular network, which can divide all generated stadium models into 100 (or slightly less than 100) categories. The generated stadium models will be clustered using SAMR. In practice, depending on the total number of generated stadium models and the size of the triangular network, users can also select a portion of the generated stadium models (obtained through sampling) for SAMR clustering to save computation time.

[0108] Understandably, a node can be considered the most representative object in its group, generating the most representative stadium model among all types of stadium models. These representative designs are distributed across an unfolded two-dimensional network, with similar, representative stadium models (node ​​designs) clustered together, while those that are not identical are spaced further apart. This allows designers to view all categories of stadium models using a single two-dimensional network, with each node design representing several stadium models within its respective category, thus showcasing the distribution of different types of stadium models in the design space.

[0109] Please see Figure 3 , Figure 3 This is a schematic diagram of a preferred embodiment of a terminal device provided by the present invention. The terminal device includes a processor 301, a memory 302, and a computer program stored in the memory 302 and configured to be executed by the processor 301. When the processor 301 executes the computer program, it implements the method described in any of the above embodiments.

[0110] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0111] The processor 301 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 301 can be any conventional processor. The processor 301 is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0112] The memory 302 mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory 302 can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), and a flash card, or it can be other volatile solid-state storage devices.

[0113] It should be noted that the aforementioned terminal devices may include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 3The structural diagram is merely an example of the terminal device described above and does not constitute a limitation on the terminal device described above. It may include more or fewer components than shown in the diagram, or combine certain components, or use different components.

[0114] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the methods described in any of the above embodiments.

[0115] This invention provides a method for generating stadium models, a method for classifying their forms, equipment, and a medium. The advantages are that it can quickly generate a large number of stadium models of various sizes and architectural forms that meet user input requirements, and can accurately and efficiently classify the large number of generated stadium models, thereby facilitating users to analyze and select stadium models generated from different design schemes.

[0116] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0117] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating a stadium model, characterized in that, include: Obtain the stadium requirement information input by the user; wherein, the stadium requirement information includes the activity venue requirements and the spectator seating requirements; The event venue is generated according to the event venue requirements, and a bowl-shaped space for the seating area is generated based on the outer boundary of the event venue and the audience seating requirements. Obtain a first random variable related to the outer boundary of the fixed seat, and generate a roof plan profile curve based on the first random variable; The roof plan outline curve is projected onto the bowl-shaped space, and the bowl-shaped space is cut by the audience seating boundary curve obtained by the projection to obtain the audience seating shape; The roof shape is determined based on the audience seating boundary curve and the second random variable related to the roof parameters. A stadium model was constructed based on the roof shape and the spectator seating shape. Specifically, generating the roof plan profile curve based on the first random variable includes: Based on the first random variable, determine several boundary curve control points, boundary curve types, and the symmetry of the fixed seats relative to the activity area. Determine the interpolation curve formed by several boundary curve control points; Based on the boundary curve type and the symmetry, the interpolated curve is modified to obtain the roof plan profile curve.

2. The method for generating a stadium model as described in claim 1, characterized in that, The second random variable includes the height parameters of several boundary curve control points, the ridgeline curve types in the X and Y directions, and the spatial net height of the center point of the activity area. The step of determining the roof shape based on the audience seating boundary curve and the obtained second random variable related to the roof parameters specifically includes: Set corresponding height parameters for each boundary curve control point on the audience seating boundary curve to generate the roof structure boundary curve; Based on the roof structure boundary curve and the space clear height, a first X-direction ridge curve and a first Y-direction ridge curve are generated; Based on the ridgeline curve type, the first X-direction ridge curve and the first Y-direction ridge curve are modified respectively to obtain the second X-direction ridge curve and the second Y-direction ridge curve. The roof shape is determined based on the roof structure boundary curve, the second X-direction ridge curve, and the second Y-direction ridge curve.

3. The method for generating a stadium model as described in claim 1, characterized in that, The stadium model constructed based on the roof shape and the spectator seating shape specifically includes: Based on the roof shape, set the horizontal projection dimension parameters on the structural grid to generate the first single-layer structural grid; Based on the first single-layer structure network and the preset structural parameters, a second single-layer structure network is generated superimposed on the first single-layer structure network to obtain a double-layer structure network. A stadium model is constructed based on the shape of the audience seating and the first single-layer structural net; or, a stadium model is constructed based on the shape of the audience seating and the double-layer structural net. The first single-layer structural net is used to construct a single-layer reticulated shell structure, and the double-layer structural net is used to construct a double-layer spatial grid structure or a spatial truss beam structure.

4. The method for generating a stadium model as described in claim 1, characterized in that, The step of obtaining a first random variable related to the outer boundary of the fixed seating area and generating a roof plan profile curve based on the first random variable includes: Generate an initial roof plan profile curve based on the first random variable; Based on the area enclosed by the initial roof plan contour curve, the area of ​​the activity area, and the number of seats per unit, the number of fixed seats is calculated, and it is determined whether the number of fixed seats is greater than a preset fixed seat threshold. If so, the initial roof plan profile curve is used as the generated roof plan profile curve; If not, the first random variable is retrieved again to generate a new initial roof plan profile curve until the calculated number of fixed seats is greater than the fixed seat threshold.

5. The method for generating a stadium model as described in claim 1, characterized in that, The seating requirements include the maximum number of seats for the target sporting event; The process of generating a bowl-shaped seating area based on the outer boundary of the event venue and the audience seating requirements specifically includes: Based on the outer boundary of the activity area, the maximum number of seats, and the preset first system parameters, a bowl-shaped space for the seating area is drawn using a drawing method; wherein, the first system parameters include the standard specifications within the seating area.

6. A morphological classification method for stadium models, characterized in that, include: Obtain multiple stadium models generated by the stadium model generation method according to any one of claims 1 to 5; Design variables were extracted from each of the described stadium models; By using self-organizing mapping, based on the design variables of each stadium model, the stadium models are clustered to obtain several stadium morphology clusters; wherein, each stadium morphology cluster includes at least one stadium model.

7. The morphological classification method for gymnasium models as described in claim 6, characterized in that, The extraction of design variables from each of the aforementioned stadium models specifically includes: Extract several initial design variables from each of the described gymnasium models; For each stadium model, the initial design variables are scaled, and the scaled initial design variables are used to form the design variables of the stadium model.

8. A terminal device, characterized in that, include: A memory and a processor, the memory being used to store a program, the processor executing the program stored in the memory, and when the program stored in the memory is executed, the processor being used to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1 to 7.

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