A method for quantitative analysis of architectural space design using VR to collect walking trajectories

Abstract

This invention discloses a method for quantitatively analyzing architectural space design using VR to collect walking trajectories. By dividing the space into grids and counting the number of passes based on the number of intersections between the walking trajectories and the grid outlines, the walking trajectories are mapped in the real architectural space to achieve a quantitative evaluation of the architectural space. The invention uses a grid division method based on the real architectural space to achieve a true mapping of the grid plane and the architectural space. The walking trajectories are determined by connecting the trajectories and intersecting them with the grid. The superimposed intersection data is used as the number of passes, and this is used to determine the degree of preference for walking trajectories within the architectural space. Finally, the corresponding preferences are mapped using colors, and the results are visualized, ultimately achieving a quantitative evaluation of the architectural space.

Description

Technical Field

[0001] The present invention relates to the technical field of architectural space design, and in particular to a method for quantitatively analyzing architectural space design by collecting walking trajectories using VR. Background Art

[0002] Different functional layouts and streamlined organizations in architectural design significantly affect how people gather and stay in different areas, and also have a significant impact on their movement paths. Therefore, by analyzing the movement paths of people in different areas, it is possible to effectively verify whether the spatial layout design has achieved the desired effect, thereby achieving a quantitative assessment of the architectural space experience. With the gradual maturity of virtual reality technology, VR scenes can be built during the design phase, allowing people to experience unfinished spaces in advance through VR equipment. Using VR technology to record movement paths and their corresponding timestamps to quantitatively evaluate spatial layout is a feasible means.

[0003] For example, "A Design Method Based on Human Factors Data of Built Space" uses VR technology to analyze architectural space design, and draws the relationship between human factors parameters and architectural space through domain segmentation, spatial topological relationship summary, basic duration assignment, human factor spectrum drawing, and filling in actual human body dynamic measurement data, thereby realizing the evaluation of architectural space.

[0004] However, there are many technical problems when implementing the above technical means. First, the basic framework of the human factor spectrum involves dividing the architectural space into areas and establishing the spatial topological relationship between these areas. However, the specific method of area division depends on the designer's understanding of the spatial characteristics and professional judgment. Different people will adopt different division methods, resulting in non-unique and large differences in the results. In addition, different spatial topological relationships will have a significant impact on subsequent data analysis, and thus affect the final design results. Second, the basis of the above technical means is to abstractly express the architectural space through spatial topological relationships. Therefore, the results obtained after data analysis based on the human factor spectrum need to be further understood by the designer after being re-translated back into the actual architectural space based on the spatial topological relationship. This process is not objective data, nor is it an intuitive visualization result, and the result is not intuitive enough. Third, the architectural space will be divided into several spatial areas. These areas are the smallest units of analysis results, and their refinement usually cannot meet the design requirements. The present invention provides a method for quantitative analysis of architectural space design using VR to collect walking trajectories to solve the above problems. Summary of the Invention

[0005] The present invention provides a method for quantitatively analyzing architectural space design by using VR to collect walking trajectories. By dividing the space into grids and counting the number of passes based on the number of intersections between the walking trajectories and the grid contour lines, the walking trajectories are mapped in the real architectural space to achieve quantitative evaluation of the architectural space.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] A method for quantitatively analyzing architectural space design by using VR to collect walking trajectories includes the following steps:

[0008] S1, setting up the model and VR scene: Build a 3D model of the building, then perform lightweight processing on the 3D model to ensure rendering efficiency and visual quality; import the processed 3D model, and then adjust the parameters, visual elements and interior layout to form the required VR scene;

[0009] S2, conduct VR tour: set the starting point of the VR tour, define the VR navigation range, configure the data recording program, and then the experimenter conducts the VR tour, and uses the VR device to record the trajectory coordinate data and the corresponding timestamp data during the tour;

[0010] S3, raw data import: import the raw data into the data processing model, and create the original trajectory points with the x, y, and z coordinate data of the original trajectory points;

[0011] S4, lightweight original data: set the distance threshold, group the original trajectory points according to the set distance threshold, and calculate the representative trajectory point P of each group of original trajectory points n Coordinate data of

[0012] S5, draw the walking trajectory: connect the representative trajectory points P in chronological order n Draw a walking trajectory L n , this walking trajectory L n For the walking trajectory of one experimenter, draw the walking trajectories of all experimenters according to the above operation, and then write the walking trajectory data of all experimenters into the 3D model;

[0013] S6, data import: read the walking trajectory L of all experimenters n , the total amount of data is N;

[0014] S7, constructing an analysis grid: in the data analysis and visualization model, an analysis plane is established according to the activity area, and the plane is divided into M grid surfaces, and the contour lines of the grid surfaces are extracted;

[0015] S8, calculate the number of intersections between the walking trajectory and the grid contour line: calculate the walking trajectory L of a single experimenter nThe total number of intersections C with the grid contour line of each grid surface passed through n,m ;

[0016] S9, calculate the number of times the walking trajectory passes on the grid surface: calculate the walking trajectory L of all experimenters one by one n The total number of intersections C with the grid contour line of each grid surface passed through n,m Count the number of passes on each grid surface and get the total number of passes on each grid surface P m ;

[0017] S10, data visualization: the total number of passes P for each grid surface m Corresponding to the grid surface, set a reasonable threshold value for the color legend, and calculate the total number of grid surface passes P m The mapped color values are used to color the corresponding grid surfaces to achieve data visualization.

[0018] Furthermore, in step S4, the specific operation of lightweighting the original data is: setting the distance threshold to 100 mm, dividing the original trajectory points whose mutual distance is not greater than the distance threshold into the same group, and calculating the center point of each group of original trajectory points as the representative trajectory point P of the experimenter at this position n ; Calculate the representative trajectory point P n When the average coordinates of each group of original trajectory points are taken as the representative trajectory point P of the group of original trajectory points n 's coordinates.

[0019] Furthermore, in step S8, the total number of intersections C n,m When calculating, if the total number of intersections C n,m is an even number, this walking trajectory L n The number of passes P on this grid surface n,m The calculation formula is:

[0020]

[0021] If the total number of intersection points C n,m is an odd number, this walking trajectory L n The number of passes P on this grid surface n,m The calculation formula is:

[0022]

[0023] Furthermore, in step S9, the total number of passes of the grid surface P m The calculation formula is:

[0024]

[0025] The beneficial effects of the present invention are as follows:

[0026] The method of gridding based on real architectural space is used to achieve a true mapping between the grid plane and the architectural space. The walking trajectory is determined by connecting the trajectories and intersecting them with the grid. The superimposed intersection data is used as the number of passing trajectories. This is used to determine the walking trajectory preference within the architectural space. Finally, the corresponding preference degree is mapped with color, and the results are visualized, ultimately achieving a quantitative evaluation of the architectural space.

[0027] There's no need to topologically process the architectural space, and the visualization results correspond exactly to the actual architectural space, making them more intuitive. Furthermore, the level of detail in spatial analysis can be adjusted by selecting the desired grid size to accommodate different spatial scales and research needs, offering wider applicability and a wider scope of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the analysis process of the present invention;

[0029] Figure 2 Schematic diagram of a single walking trajectory of the present invention;

[0030] Figure 3 This is a schematic diagram of representative trajectory point confirmation of the present invention;

[0031] Figure 4 Schematic diagram of all walking trajectories of the present invention;

[0032] Figure 5 A schematic diagram of the intersection of the analysis plane grid and the walking trajectory of the present invention;

[0033] Figure 6 A schematic diagram of the intersection of the walking trajectory and the grid of the present invention;

[0034] Figure 7 This is a schematic diagram of the visualization of the traffic preference of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0037] like Figures 1 to 7 As shown in the figure, a method for quantitatively analyzing architectural space design by collecting walking trajectories using VR is proposed. First, three-dimensional modeling of the building and construction of the VR scene are carried out. The experimenter conducts a VR tour through the VR equipment, and the trajectory coordinates of the experimenter and the corresponding timestamp data are recorded by the VR equipment. After processing the data, scattered trajectory points are formed, and the trajectory points are connected into walking trajectories according to the timestamp data. At the same time, a plane analysis diagram is established based on the activity area and divided into grids. The walking trajectories are mapped to the corresponding grids of the plane analysis diagram, and the intersections of the walking trajectories and the grids are calculated and counted. The intersections of the walking trajectories of all the experimenters are superimposed to obtain the total number of intersections of the corresponding grids. The data of the total number of intersections are used to assign colors to the corresponding grids, so as to realize the visualization of spatial traffic preferences and intuitively perform quantitative evaluation of the architectural space quality.

[0038] The quantitative analysis method of the present invention divides the building space into grids according to appropriate specifications. Compared with the existing method of dividing the area by considering it, it has the advantages of unified parameters, less external interference, less influence of human factors, and flexible adjustment according to needs. It will not cause large differences in the results due to the influence of human factors, so that the final calculation results are relatively unified and accurate; secondly, the walking trajectory is directly mapped to the grid plane and accurate data is obtained through calculation. After the data is visualized, the results can be intuitively seen. The process no longer requires tedious manual translation operations, and does not rely on personal ability or experience. The accuracy of the analysis results will not be affected by personal factors, thus ensuring the precision and accuracy of the calculation. At the same time, combined with the standardization and consistency of the grid division, the results are finally intuitive and precise.

[0039] A method for quantitatively analyzing architectural space design by using VR to collect walking trajectories is described below.

[0040] S1, Setting up the model and VR scene: Build a 3D model of the building, then perform lightweight processing on the 3D model to ensure smoothness in the VR scene, improve rendering efficiency, and ensure visual quality. "Visibility" is used as the judgment standard. Specifically, the operation adopted is to remove all parts of the model that are not visible in the final VR experience in the modeling software. For objects with smooth surfaces, they are converted into multiple continuous planes, such as converting a cylinder into a polygonal column.

[0041] Build a VR scene, import the processed 3D model into the modeling software, adjust the model's material parameters according to the architectural design plan, and arrange elements such as lighting, tables and chairs, furniture, and plant decorations. At the same time, set the ambient lighting parameters to optimize the scene's realism and immersion, and finally build the required VR scene.

[0042] S2, conduct VR tour: set the starting point of the VR tour, define the VR navigation range according to the expected navigable area in the VR scene, configure the data recording program, and then have the experimenter conduct the VR tour. While conducting the tour, the original data of the experimenter's tour is recorded frame by frame through the VR device. After the tour, the data is saved and exported through the VR device. The original data includes trajectory coordinate data and corresponding timestamp data.

[0043] like Figure 2 As shown, S3, raw data import: import the raw data into the data processing model, and create raw trajectory points with the x, y, z coordinate data of the raw trajectory points;

[0044] like Figure 3 As shown in S4, original data lightweighting: When the experimenter stays at a certain location, the VR device will still record a large number of original trajectory points. In order to calculate the accuracy of the result, it is necessary to perform lightweight processing on the repeated original trajectory points at the same location. The specific operation of lightweight processing is: set the distance threshold to 100mm, divide the original trajectory points whose mutual distance is not greater than the distance threshold into the same group, and calculate the center point of each group of original trajectory points as the representative trajectory point P of the experimenter at this location. n ; Calculate the representative trajectory point P n When the average coordinates of each group of original trajectory points are taken as the representative trajectory point P of the group of original trajectory points n 's coordinates.

[0045] After lightweighting the original data, not only is the data volume smaller and the calculation faster, but duplicate data is also filtered out, making the calculation results of the walking trajectory more accurate.

[0046] like Figure 4 As shown, S5, draw the walking trajectory: connect the representative trajectory points P in chronological order n Draw a walking trajectory L n, this walking trajectory L n For the walking trajectory of one experimenter, draw the walking trajectories of all experimenters according to the above operation, and then write the walking trajectory data of all experimenters into the 3D model;

[0047] S6, data import: read the walking trajectory L of all experimenters n , the total amount of data is N.

[0048] like Figure 5 As shown, S7, constructing an analysis grid: in the data analysis and visualization model, an analysis plane is established according to the activity area, and it is divided into M grid surfaces, and the contour lines of the grid surfaces are extracted.

[0049] The purpose of establishing an analysis plane is to project the walking trajectories into the activity area of the building. When the walking trajectories are directly projected into the building space, effective calculations cannot be performed due to the lack of effective statistical standards. After the analysis plane is established, the walking trajectories are placed on the analysis plane and the grid on the analysis plane is used to count and calculate the walking trajectories, thereby achieving quantitative statistics of the walking trajectories.

[0050] like Figure 6 As shown in S8, calculate the number of intersections between the walking trajectory and the grid contour line: calculate the walking trajectory L of a single experimenter n The total number of intersections C with the grid contour line of each grid surface passed through n,m .

[0051] Total number of intersections C n,m When calculating, if the total number of intersections C n,m is an even number, this walking trajectory L n The number of passes P on this grid surface n,m The calculation formula is:

[0052]

[0053] If the total number of intersection points C n,m is an odd number, this walking trajectory L n The number of passes P on this grid surface n,m The calculation formula is:

[0054]

[0055] By counting walking trajectories L n The total number of intersections C with the grid contour line of each grid surface passed through n,m By determining the number of times a walking trajectory passes through the grid, it is possible to associate the walking trajectory that cannot be quantified with the real building space, perform calculations and statistics, and visualize the corresponding data.

[0056] S9, calculate the number of times the walking trajectory passes on the grid surface: calculate the walking trajectory L of all experimenters one by one n The number of passes P on each grid surface passed n,m , and sum up the number of passes on each grid surface to get the total number of passes on each grid surface P m ;

[0057] The total number of passes on the grid surface P m The calculation formula is:

[0058]

[0059] After the operations of S8 and S9, the walking trajectory of the experimenter can be quantified and calculated, and the intersection of the walking trajectory and the grid is still based on the association with the real building space. Ultimately, the statistics and calculations of the intersection still correspond to the real building space, ensuring the continuity of the conversion operation and the accuracy of the calculation results.

[0060] like Figure 7 As shown in S10, data visualization: the total number of passes P for each grid surface m Corresponding to the grid surface, set the reasonable range of the color legend, and the total number of grid surface passes P m The mapped color values are used to color the corresponding grid surfaces to achieve data visualization.

[0061] The present invention collects data and extracts trajectory points based on real architectural spaces. It then constructs an analysis plane corresponding to the scope, scale, and spatial relationships of the real architectural space. The pedestrian trajectory analysis plane is mapped to form an associated description model. The intersection of the walking trajectory and the grid is used as a statistical parameter. By statistically analyzing and calculating all intersections on the grid, the influence of the architectural space corresponding to this grid point on traffic preferences is determined. This is then expanded to the influence of all grid points within the entire architectural space on traffic preferences, thereby obtaining the traffic preferences of the real architectural space. Furthermore, the above data can be visualized, allowing for an intuitive quantitative evaluation of the architectural space quality. This method allows for convenient and accurate quantification of architectural space quality, and provides intuitive, visual quantification, resulting in better results and greater applicability.

[0062] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced herein, and any reference signs in the claims should not be construed as limiting the claims to which they relate.

Claims

1. A method for quantitatively analyzing architectural space design using VR to collect walking trajectories, characterized by: The following steps are involved: S1, setting up the model and VR scene: Build a 3D model of the building, then perform lightweight processing on the 3D model to ensure rendering efficiency and visual quality; import the processed 3D model, and then adjust the parameters, visual elements and interior layout to form the required VR scene; S2, conduct VR tour: set the starting point of the VR tour, define the VR navigation range, configure the data recording program, and then the experimenter conducts the VR tour. While the tour is going on, the original data of the experimenter's tour is recorded frame by frame through the VR device. The original data includes trajectory coordinate data and its corresponding timestamp data; S3, raw data import: import the raw data into the data processing model, and create the original trajectory points with the x, y, and z coordinate data of the original trajectory points; S4, lightweight original data: set the distance threshold, group the original trajectory points according to the set distance threshold, and calculate the representative trajectory point of each group of original trajectory points Coordinate data of S5, draw walking trajectory: connect the representative trajectory points in chronological order Draw a walking track , this walking trajectory For the walking trajectory of one experimenter, draw the walking trajectories of all experimenters according to the operation, and then write the walking trajectory data of all experimenters into the three-dimensional model; S6, data import: read the walking trajectories of all experimenters , the total amount of data is N; S7, constructing an analysis grid: in the data analysis and visualization model, an analysis plane is established according to the activity area, and the plane is divided into M grid surfaces, and the contour lines of the grid surfaces are extracted; S8, calculate the number of intersections between walking trajectories and grid contour lines: calculate the walking trajectories of individual experimenters The total number of intersections with the grid outline of each grid face passed through ; S9, calculate the number of times the walking trajectory passes through the grid surface: calculate the walking trajectories of all experimenters one by one The total number of intersections with the grid outline of each grid face passed through Count the number of passes on each grid surface and get the total number of passes on each grid surface ; S10, data visualization: the total number of passes for each grid surface Corresponding to the grid surface, set a reasonable threshold for the color legend, and use the total number of passes on the grid surface The mapped color values are used to color the corresponding grid surfaces to achieve data visualization.

2. The method for quantitatively analyzing architectural space design by using VR to collect walking trajectories according to claim 1 is characterized in that: In step S4, the specific operation of lightweighting the original data is: set the distance threshold to 100mm, divide the original trajectory points whose mutual distance is not greater than the distance threshold into the same group, and calculate the center point of each group of original trajectory points as the representative trajectory point of the experimenter at this position ; Calculate representative trajectory points When , the average coordinate of each group of original trajectory points is the representative trajectory point of the group of original trajectory points 's coordinates.

3. The method for quantitatively analyzing architectural space design using VR to collect walking trajectories according to claim 1 is characterized by: In step S8, the total number of intersections When calculating, if the total number of intersections is an even number, this walking trajectory Number of passes on this grid surface The calculation formula is: ； If the total number of intersections is an odd number, this walking trajectory Number of passes on this grid surface The calculation formula is: 。 4. The method for quantitatively analyzing architectural space design using VR to collect walking trajectories according to claim 3 is characterized by: In step S9, the total number of passes of the grid surface The calculation formula is: 。

Images