BIM-based gymnasium stand sight line rise difference optimization method and system

Abstract

The invention discloses a BIM-based gymnasium stand sight line rise difference optimization method and system, and relates to the technical field of gymnasium construction, and the method comprises the steps: 1, obtaining a pre-established stand model; analyzing the model to obtain a competition field sideline, a stand position of any model area and a step point coordinate L1 of each row of steps in the model area; calculating eye point coordinates L2 when the person sits by matching a preset human body model / human body simulation parameter with the step point coordinates L1, and generating a sight line by taking the eye position of each person as a starting point and a viewpoint as an ending point; the viewpoints are intersection points of the sight lines and the competition field side lines; calculating the vertical distance between the sight of the next row of audiences and the eyes of the previous row of audiences to obtain a corresponding sight rise difference C; marking steps with the sight line rising difference C smaller than a preset lower limit threshold value, and generating a to-be-optimized area; and step 2, optimizing parameters of the grandstand. The method has the effect that related workers can conveniently and efficiently design the stand with better visual sense of audiences in the back row.

Description

Technical Field

[0001] This application relates to the technical field of stadium construction, and particularly relates to a method and system for optimizing the sight elevation difference of stadium stands based on BIM. Background Art

[0002] In the design of sports buildings, it is required that the stands have good visual quality. The main requirement for good visual quality is unobstructed sight lines. Among them, the main factor is the sight elevation difference (C value). The C value refers to the vertical distance between the sight lines of the rear-row audience and the eyes of the front-row audience. The larger the C value, the better the visual quality.

[0003] In many previous stand designs, the height of each row of steps in the stand is the same, and the C value of each row gradually decreases from bottom to top, often resulting in poor visual quality for the audience in the rear of the stand. If the C value of each row of seats is considered, the seats affect each other, and the work difficulty of relevant designers is great. Therefore, a new technical solution is proposed. Summary of the Invention

[0004] In order to enable relevant staff to efficiently design stands with better vision for the rear-row audience, this application provides a method and system for optimizing the sight elevation difference of stadium stands based on BIM.

[0005] In the first aspect, this application provides a method for optimizing the sight elevation difference of stadium stands based on BIM, adopting the following technical solution:

[0006] A method for optimizing the sight elevation difference of stadium stands based on BIM includes:

[0007] Step 1: Obtain original data, which includes:

[0008] Obtain a pre-established stand model, and divide the stand model into regions to obtain multiple model regions;

[0009] Analyze the model to obtain the sidelines of the stadium, the stand positions of any model region, and the step point coordinates L1 of each row of steps in the model region;

[0010] Calculate the eye point coordinates L2 of a person in a sitting position in combination with the step point coordinates L1 with a preset human model / human simulation parameter, and generate sight lines starting from the eye positions of each person and ending at the viewpoints; among them, the viewpoint is the intersection point of the sight line and the sidelines of the stadium;

[0011] Calculate the vertical distance between the sight lines of the rear-row audience and the eyes of the front-row audience to obtain the corresponding sight elevation difference C;

[0012] Mark the steps with the sight elevation difference C less than the preset lower limit threshold to generate an area to be optimized;

[0013] Step 2: Optimize the stand parameters, which includes:

[0014] Set the standard line-of-sight elevation difference C1, define i as the number of seat rows in the stands, and define h as the eye height when sitting;

[0015] Call the data of the area to be optimized, obtain the step point coordinates L1 of the i-th and i+1-th rows, and substitute the standard elevation difference C1 and eye height h to analyze the new coordinates of the (i + 1)-th row based on the process of calculating the line-of-sight elevation difference C in Step 1;

[0016] If the stands in the area to be optimized are completed with adjustment, then calculate the overall moving height;

[0017] Adjust and substitute the standard elevation difference C1, generate multiple different adjustment plans, and select the plan with the overall moving height closest to 0 as the optimized plan.

[0018] Optionally, the process of substituting the standard elevation difference C1 and eye height h to analyze the new coordinates of the (i + 1)-th row based on the calculation of the line-of-sight elevation difference C in Step 1 includes:

[0019] The step position coordinates of the i-th row + eye height h + standard elevation difference C1 to determine the intersection point of the line of sight vertically upward from the i-th row and the (i + 1)-th row;

[0020] Generate the line of sight of the eyes of the (i + 1)-th row based on the intersection point and the sidelines of the stadium, and calculate the new Z-axis parameter of the step point coordinates of the (i + 1)-th row based on the eye height h;

[0021] Calculate the height of the (i + 1)-th row of steps based on the new Z-axis parameter and the step point coordinates of the i-th row;

[0022] Update the model of the area to be optimized based on the height of the (i + 1)-th row of steps;

[0023] Calculate the new Z-axis parameter of the steps in a higher row until i + 1 is greater than or equal to the total number of rows of steps in the area to be optimized.

[0024] Optionally, Step 1, obtaining the original data, further includes:

[0025] Obtain the product list of preselected stand seat manufacturers and the corresponding product description information;

[0026] Obtain the seat board height parameter H based on the product list and product description information;

[0027] Determine whether the planned seat height h1 corresponding to any seat height parameter H is equal to the eye height h. If so, mark the corresponding seat as the first-fit seat; if not, let h - (h1 - H) to obtain the new eye height h, and mark the corresponding seats as the second-fit seat, the third-fit seat,... the nth-fit seat in ascending order of the difference h1 - H, where n is the total number of non-first-fit seats in the product list.

[0028] Optionally, it further includes:

[0029] Obtain the customization instructions for non-first-fit seats from the stadium seat manufacturer;

[0030] If the customization instructions include the legs of the stadium seat, send a customization feasibility inquiry to the stadium seat manufacturer with the planned seat height h1, and receive the feedback to determine whether to update the corresponding non-first-fit seat to the first-fit seat.

[0031] Optionally, it further includes:

[0032] Call the optimization scheme and obtain the corresponding overall moving height h2;

[0033] If h2 ≠ 0, calculate the fine-tuning parameter d, d = h2 / m, where m is the total number of steps in the area to be optimized;

[0034] If the stadium seats matching the optimization scheme have customization instructions and include the legs of the stadium seat, update the seat height parameter H of the corresponding seats based on d and generate customization data.

[0035] Optionally, in step one, obtain the original data, which includes:

[0036] Establish a data connection with the work system of the local physical examination unit and obtain the historical height data of local adults;

[0037] Update the eye height h based on the historical height data.

[0038] Optionally, in step one, obtain the original data, which includes:

[0039] Obtain the sports events planned for the stadium model currently matched;

[0040] Search for images of historical same sports events based on the sports events, perform image recognition and analysis to obtain the event activity range, and update the stadium sidelines.

[0041] In the second aspect, the present application provides a BIM-based stadium stand sight elevation difference optimization system, adopting the following technical solutions:

[0042] A BIM-based optimization system for the sight elevation difference of stadium stands includes a memory and a processor. The memory stores a computer program that can be loaded and executed by the processor, such as the above-mentioned BIM-based method for optimizing the sight elevation difference of stadium stands.

[0043] In summary, the present application includes the following beneficial technical effects: It can use BIM modeling technology in cooperation with artificial intelligence to automatically complete the locking of the stands in the area to be adjusted, analyze and generate multiple adjustment plans, and give recommended data, which can facilitate relevant staff to efficiently design stands with better vision for the rear-row audience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic diagram of the effect during the implementation process of the method of the present application;

[0045] Figure 2 is a schematic diagram of the effect when a human body model is placed in the stand model of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following will further elaborate on the present application Figure 1-2 in further detail.

[0047] The embodiment of the present application discloses a BIM-based method for optimizing the sight elevation difference of stadium stands.

[0048] Referring to Figure 1 and Figure 2 , the BIM-based method for optimizing the sight elevation difference of stadium stands includes:

[0049] Step 1: Obtain the original data, which includes:

[0050] S11. Obtain the pre-established stand model, and divide the stand model into regions to obtain multiple model regions.

[0051] The above-mentioned stand model refers to the stands in the stadium, which can be created by using Revit software to create a parametric stand BIM model. The stand model needs to be modeled separately by region (such as: circumferentially divided into multiple regions), and a parametric step family is used to automatically adjust the geometric dimensions of the seats.

[0052] S12. Parse the model to obtain the sidelines of the competition field, the stand positions of any model region, and the step point coordinates L1 of each row of steps in the model region.

[0053] Among them, an example of model parsing: Use Dynamo programming to read the sidelines of the competition field and the corresponding 2 steps on the stands in 1 region of the Revit model.

[0054] S13. Calculate the eye point coordinates L2 of a person in a sitting position using the preset human model / human simulation parameters in combination with the step point coordinates L1, and generate a line of sight starting from the eye position of each person and ending at the viewing point; where the viewing point is the intersection of the line of sight and the sideline of the stadium.

[0055] To reduce the size of the model, parameters are preferably put in instead of the human model. Both the human model and the human simulation parameters are used to determine the eye height of the audience, so as to generate a line of sight for analysis.

[0056] It can be understood that the coordinates in this application refer to three-dimensional coordinates (X, Y, Z).

[0057] S14. Calculate the vertical distance between the line of sight of the audience in the row behind and the eyes of the audience in the row in front, and obtain the corresponding line of sight elevation difference C.

[0058] S15. Mark the steps where the line of sight elevation difference C is less than the preset lower threshold (e.g., 0.06 m) to generate an area to be optimized. The model corresponding to the area to be optimized can be colored.

[0059] Step 2. Optimize the stand parameters, which includes:

[0060] S21. Set the standard line of sight elevation difference C1, define i as the number of seat rows in the stand, and define h as the eye height of a person in a sitting position.

[0061] Among them, C1 can be between 0.06 - 0.12 m, initialize i = 1, and h can be 1.2 m.

[0062] S22. Call the data of the area to be optimized to obtain the step point coordinates L1 of the i-th and (i + 1)-th rows, and substitute the standard elevation difference C1 and the eye height h to analyze the new coordinates of the (i + 1)-th row based on the process of calculating the line of sight elevation difference C in Step 1.

[0063] S23. If the adjustment of the stand in the area to be optimized is completed, calculate the overall movement height.

[0064] S24. Adjust and substitute the standard elevation difference C1 to generate multiple different adjustment plans, and select the plan with the overall movement height closest to 0 as the optimization plan.

[0065] According to the above settings, this method can use BIM modeling technology in combination with artificial intelligence to automatically complete the locking of the area to be optimized in the stand, analyze and generate multiple adjustment plans, and give recommended data, which can facilitate relevant staff to efficiently design a stand with better vision for the audience in the back row.

[0066] The analysis of the new coordinates of the (i + 1)-th row by substituting the standard elevation difference C1 and the eye height h based on the process of calculating the line of sight elevation difference C in Step 1 in S22 above includes:

[0067] The step position coordinates of the i-th row + the height h of the glasses + the standard elevation difference C1 are used to determine the intersection point of the line of sight vertically upward from the i-th row and the line of sight of the (i + 1)-th row;

[0068] Based on the intersection point and the sidelines of the stadium, generate the line of sight of the eyes in the (i + 1)-th row (i.e., two points determine a straight line), and calculate the new Z-axis parameter of the step point coordinates in the (i + 1)-th row based on the eye height h. That is, after the line of sight is determined, the Z-axis parameter of the starting position of the line of sight - h = the new Z-axis parameter of the step point coordinates in the (i + 1)-th row;

[0069] Calculate the height of the steps in the (i + 1)-th row according to the new Z-axis parameter and the step point coordinates in the i-th row, that is, the Z-axis parameter - the Z-axis parameter of the steps in the i-th row;

[0070] Update the model of the area to be optimized based on the height of the steps in the (i + 1)-th row. In the previous step, the height of the steps in the (i + 1)-th row was adjusted, and the positions of other steps in the model were correspondingly changed, that is, the Z-axis parameters of all steps above were adjusted;

[0071] Calculate the new Z-axis parameters of the steps in a higher row until i + 1 is greater than or equal to the total number of rows of steps in the area to be optimized.

[0072] According to the above content, it can be seen that this method can analyze the appropriate Z-axis parameters of the steps at a higher level by using the standard elevation difference C1, the eye height h, and the point position of the previous step, so as to guide the model adjustment.

[0073] In an embodiment of the present application, step one, obtaining the original data, further includes:

[0074] Obtain the product list of the preselected grandstand seat manufacturers and the corresponding product description information. This step can be to access the online mall of the manufacturer, perform OCR recognition on the display content in the online mall, and extract it; it can also be actively uploaded by the manufacturer. The product description information should include the product structure description, whether customization is accepted, etc.

[0075] Based on the product list and the product description information, obtain the seat board height parameter H of the seat;

[0076] Judge whether any seat board height parameter H is equal to the planned seat board height h1 corresponding to the eye height h. If so, mark the corresponding seat as the first-fit seat; if not, let h - (h1 - H) to obtain the new eye height h, because people sit on the seat board, so if the height of the seat board changes, the eye height will also change accordingly.

[0077] Mark the corresponding seats as the second-fit seats, the third-fit seats... the n-th fit seats according to the difference value of h1 - H from small to large, where n is the total number of non-first-fit seats in the product list.

[0078] According to the above content, on the one hand, this method can help the staff to choose the seat, and on the other hand, because the eye height h is not fixed but can be adjusted according to the seat, the aforementioned analysis and calculation process is more accurate.

[0079] Furthermore, the method further comprises:

[0080] Obtain the customizable instructions of the grandstand seat manufacturer for non-first fit seats;

[0081] If the customizable description includes the legs of the grandstand seats, a customization feasibility inquiry is sent to the grandstand seat manufacturer with the planned seat height h1. The inquiry can be sent via SMS or the corresponding App message;

[0082] Receive feedback and determine whether to update the corresponding non-first fitness seat to a first fitness seat, that is, to accept customization. Even if the seat height ≠ h1, the seat is re-marked as a first fitness seat.

[0083] According to the above settings, the method can be linked with manufacturers during application, and the data generated by analysis for reference by staff is more flexible and accurate.

[0084] Furthermore, the method further comprises:

[0085] Call the optimization scheme and obtain its corresponding overall moving height h2;

[0086] If h2≠0, then calculate the fine-tuning parameter d, d=h2 / m, where m is the total number of rows of steps in the area to be optimized, that is, d is equivalent to uniformly lowering the position of each row of steps.

[0087] If the grandstand seats matched by the optimization scheme have customizable instructions and include the legs of the grandstand seats, the seat height parameter H of the corresponding seat is updated based on d and customized data is generated.

[0088] According to the above settings, when the overall moving height of the stands cannot be 0, this method will further analyze based on the aforementioned linkage seat manufacturers and provide customized suggestions to reduce the cost, time waste and many approval difficulties caused by the overall structural adjustment of the sports hall.

[0089] In another embodiment of the method, step 1, obtaining original data, includes:

[0090] Establish data connection with the working system of the local physical examination unit (i.e., the physical examination center where the gymnasium is located) and obtain historical height data of local adults;

[0091] Update the eye height h based on historical height data. For example: get the height data of 1,000 adults aged 25-35, with half of them being male and half being female. Summarize the data and sort them by height to generate a data table. Delete 50 data points before and after the data table, and then calculate the average height. If the original eye height h corresponds to a height of 1.7m, and now it is 1.65, then subtract h by 0.025m.

[0092] According to the above settings, this method can also link with physical examination centers and other units to analyze the eye height that is more suitable for the local area, so that the design of the stands can be relatively more reasonable.

[0093] Further, step 1, obtaining original data, includes:

[0094] Get the sports events planned for the stadium that match the current stand model;

[0095] Based on the sport, search for historical images of the same sport, perform image recognition and analysis, obtain the scope of the event, and update the sidelines of the field.

[0096] Example: If the matching sport is badminton, then videos and images of badminton matches in the past year are searched on the Internet, and then athlete recognition and badminton recognition are performed, trajectory tracking is performed, image positioning relative to the stadium, and data such as the length of the stadium are searched. The activity range is calculated by proportional conversion, and the area with the highest overlap rate of activity ranges corresponding to multiple data is taken, and the distance relative to the corresponding stadium sideline or the ratio relative to the corresponding stadium length is calculated, and the distance is substituted into the current stadium for calculation to obtain the estimated activity range, and the outer boundary of the activity range is taken as the new stadium sideline.

[0097] According to the above settings, the method can also adjust the sidelines of the playing field used in the above analysis by analyzing the actual situation of the sports event to optimize the design of the stands.

[0098] The embodiment of the present application also discloses a BIM-based gymnasium stand sight height difference optimization system.

[0099] A BIM-based stadium stand sight height difference optimization system includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the above-mentioned BIM-based stadium stand sight height difference optimization method.

[0100] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A BIM-based method for optimizing the height difference of stadium stands, characterized in that: include: Step 1: Obtaining original data, which includes: Obtain a pre-established stand model, and divide the stand model into regions to obtain multiple model regions; Analyze the model to obtain the sidelines of the field, the location of the stands in any model area, and the step point coordinates L1 of each row of steps in the model area; The eye point coordinates L2 of the person in the sitting position are calculated by using the preset human body model / human body simulation parameters in combination with the step point coordinates L1, and the sight line is generated with the eye position of each person as the starting point and the viewpoint as the end point; wherein the viewpoint is the intersection of the sight line and the sideline of the court; Calculate the vertical distance between the eyes of the audience in the back row and the eyes of the audience in the front row to obtain the corresponding sight elevation difference C; Mark the steps where the sight elevation difference C is less than the preset lower threshold, and generate the area to be optimized; Step 2: Optimization of stand parameters, including: Set the standard sight elevation difference C1, define i as the number of seats in the stands, and define h as the eye height of a person in a sitting position; Call the data of the area to be optimized to obtain the step point coordinates L1 of the i and i+1 rows, substitute the standard elevation difference C1 and the eye height h to analyze the new coordinates of the i+1 row based on the process of calculating the sight elevation difference C in step 1; If the stands in the area to be optimized are adjusted, the overall moving height is calculated; The standard height difference C1 is substituted into the adjustment, and multiple different adjustment schemes are generated, and the scheme with the overall moving height closest to 0 is selected as the optimized scheme.

2. The BIM-based stadium stand sight elevation difference optimization method according to claim 1 is characterized in that: Substituting the standard elevation difference C1 and the eye height h into the process of calculating the sight elevation difference C based on step 1 to analyze the new coordinates of the i+1 row includes: The coordinates of the step position of the i-th row + the height of the glasses h + the standard height difference C1 determine the intersection of the i-th row vertically upward and the i+1-th row line of sight; Generate the sight line of the eyes in the i+1th row according to the intersection point and the sideline of the playing field, and calculate the new Z-axis parameters of the step point coordinates in the i+1th row based on the eye height h; Calculate the height of the i+1th row of steps based on the new Z-axis parameters and the coordinates of the step points in the i-th row; Update the model of the area to be optimized based on the height of the i+1th row of steps; Calculate the new Z-axis parameters of the higher row of steps until i+1 is greater than or equal to the total number of rows of steps in the area to be optimized.

3. The BIM-based stadium stand sight elevation difference optimization method according to claim 1 is characterized in that: Step 1: Obtaining original data, which also includes: Obtain a product list of pre-selected grandstand seat manufacturers and corresponding product description information; Based on the product list and product description information, the seat height parameter H of the seat is obtained; Determine whether any seat height parameter H is equal to the planned seat height h1 corresponding to the eye height h. If so, mark the corresponding seat as a first-fit seat. If not, let h-(h1-H) get the new eye height h, and mark the corresponding seat from small to large according to the difference between h1-H as a second-fit seat, a third-fit seat...the nth-fit seat, where n is the total number of non-first-fit seats in the product list.

4. The BIM-based stadium stand sight elevation difference optimization method according to claim 3 is characterized in that: Also includes: Obtain the customizable instructions of the stand seat manufacturer for non-first fit seats; If the customizable description includes the legs of the grandstand seats, a customization feasibility inquiry is sent to the grandstand seat manufacturer with the planned seat height h1, and feedback is received to determine whether to update the corresponding non-first fitness seat to a first fitness seat.

5. The BIM-based stadium stand sight elevation difference optimization method according to claim 4 is characterized in that: Also includes: Call the optimization scheme and obtain its corresponding overall moving height h2; If h2≠0, calculate the fine-tuning parameter d, d=h2 / m, where m is the total number of steps in the area to be optimized; If the grandstand seats matched by the optimization scheme have customizable instructions and include the legs of the grandstand seats, the seat height parameter H of the corresponding seat is updated based on d and customized data is generated.

6. The BIM-based stadium stand sight elevation difference optimization method according to claim 1 is characterized in that: Step 1: Obtaining original data, which includes: Establish data connection with the working system of local physical examination units and obtain historical height data of local adults; Update the eye height h based on historical height data.

7. The BIM-based stadium stand sight elevation difference optimization method according to claim 6 is characterized in that: Step 1: Obtaining original data, which includes: Get the sports events planned for the stadium that match the current stand model; Based on the sport, search for historical images of the same sport, perform image recognition and analysis, obtain the scope of the event, and update the sidelines of the field.

8. A BIM-based stadium stand sight height difference optimization system, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the BIM-based stadium stand sight elevation difference optimization method as described in any one of claims 1-7.

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