A method for architectural space design analysis based on VR and dwell time

Abstract

The present invention discloses a method for architectural space design analysis using VR and based on residence time, including the steps of setting up models and VR scenes, touring and data collection, data import and processing, lightweighting and exporting, importing pre-processed data, constructing an analysis grid, calculating the distance between trajectory points and grid points, calculating the quantitative value of the residence time of grid points, and visualizing the data. The present invention adopts a method of grid division based on the real architectural space, and completes the visualization of the data by projecting the coordinates of the trajectory points and their corresponding residence times onto the grid plane, thereby achieving a true mapping between the analysis data and the architectural space; there is no need to topologically process the architectural space, and the visualization results correspond one-to-one with the real architectural space, so the results are more intuitive. In addition, the accuracy of the spatial analysis can be adjusted by selecting the grid size according to the needs to meet the needs of different spatial scales and research contents, which has a wider applicability and a wider range of applications.

Description

Technical Field

[0001] The present invention relates to the technical field of architectural design analysis and evaluation, and in particular to a method for performing architectural space design analysis using VR and based on residence time. Background Art

[0002] In traditional architectural design, the spatial experience is primarily judged by the architect's personal experience, often diverging from the actual user experience of the built space. Architectural ergonomics aims to establish a correlation between human factors measurement data and the quality of architectural spaces. By collecting and analyzing human factors data on how people experience architectural spaces, it is possible to objectively and quantitatively assess the quality of architectural spaces.

[0003] Different functional layouts and streamlined organizations in architectural design will significantly affect the gathering and stay of people in different areas. Therefore, by analyzing the time people stay in different areas, we can effectively test whether the spatial design has achieved the expected effect, and thus realize the quantitative evaluation of the architectural space experience.

[0004] As virtual reality technology matures, VR scenarios can be created during the design phase, allowing users to experience unfinished spaces in advance. During this process, the system can record user movement paths and their corresponding timestamps to capture detailed behavioral data. However, in engineering design, there is currently a lack of effective data processing and visualization methods to transform this raw data into a more intuitive and understandable form, allowing designers to more easily utilize this information for design optimization.

[0005] Therefore, a spatial form research tool—the human factor spectrum—has emerged. This is a graphical analysis tool that describes the fundamental human factors attributes of a space based on the "space-time" relationship of human movement within it. Through the human factor spectrum, a series of human factor parameters can be linked to specific architectural spatial characteristics. For example, "A Graphical Design Method Based on Human Factors Data in Built Spaces" specifically uses domain segmentation, spatial topological relationship induction, basic time assignment, human factor spectrum creation, and infilling with measured human body dynamic measurement data to map the connection between human factor parameters and architectural space.

[0006] However, the application of human factors spectra in engineering design presents several challenges. First, spatial topology is difficult to implement. The basic framework of the human factors spectra involves dividing architectural space into domains and establishing spatial topological relationships between these domains. However, the method of domain division relies heavily on the designer's understanding of spatial characteristics and professional judgment, resulting in non-uniform results. Different spatial topological relationships can significantly impact subsequent data analysis, ultimately affecting design decisions and the overall spatial experience. Second, the results are not intuitive enough. The use of human factors spectra relies on abstract representation of architectural space through spatial topological relationships. Therefore, the results obtained from data analysis based on human factors spectra must be translated back into the actual architectural space based on spatial topological relationships before further understanding and design optimization can be implemented. This process lacks objective data and intuitive visualization, making both the process and results less intuitive. Third, the level of detail is insufficient. When using human factors spectra, architectural space is divided into several spatial domains, which constitute the smallest unit of analysis results. However, for architectural design, this level of detail can sometimes be insufficient to fully meet design requirements.

[0007] The present invention provides a method for architectural space design analysis using VR and based on residence time to solve the above problems. Summary of the Invention

[0008] The present invention provides a method for architectural space design analysis using VR and based on residence time, which realizes data visualization and makes the results more intuitive. In addition, the accuracy of spatial analysis can be adjusted to meet different needs.

[0009] The technical solution adopted by the present invention to solve the above technical problems is:

[0010] A method for performing architectural space design analysis based on dwell time using VR includes the following steps:

[0011] 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;

[0012] 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 records the original data of the tour through the VR device. The original data includes trajectory coordinate data and its corresponding timestamp data;

[0013] S3, data processing and export: Create original trajectory points based on trajectory coordinate data, use the time corresponding to the original trajectory points as the original stay duration of the original trajectory points, process the original trajectory points and original stay duration data and write them into the data table;

[0014] S4, data import: read all processed data in S3 through data analysis and visualization models, the total amount of data is N;

[0015] S5, build analysis grid: In the data analysis and visualization model, build an analysis plane according to the activity area and divide it into M grid cells. Extract the center point C of each grid cell. m , i.e., grid points;

[0016] S6, calculate the distance between the trajectory point and the grid point: establish a projection plane based on the actual spatial form of the building, project the trajectory point and the grid point vertically onto the projection plane, and calculate the projected trajectory point P n With C at all grid points m Distance D n,m ;

[0017] S7, calculate the quantitative value of the grid point residence time: set the weight, and calculate the influence value t of the residence time corresponding to each trajectory point on all grid points in turn n,m , and then calculate the quantized value of the grid point residence time t m , that is, the sum of the impact values of the stay time corresponding to the trajectory points of all experimenters on this grid point, and then calculate the quantitative value t of the stay time of all grid points according to the above operation m ;

[0018] S8, data visualization: Set a reasonable range for the color legend, and assign color to the corresponding grid surface based on the color value mapped by the quantized value of the grid point's dwell time to achieve data visualization.

[0019] Furthermore, in step S3, the total amount of original data is large and needs to be lightweighted. The specific operations are as follows:

[0020] S31, importing raw data: importing the raw data into the data processing model, creating raw trajectory points using the x, y, and z coordinate data of the raw trajectory points, converting the timestamp data corresponding to the raw trajectory points, and calculating the time difference as the original stay duration of the raw trajectory points;

[0021] S32, lightweighting of original data: lightweighting repeated original trajectory points at the same position, setting a threshold, grouping the original trajectory points according to the set threshold, and calculating the representative trajectory point P of each group of original trajectory points. n, group the original stay durations corresponding to the same group of original trajectory points in the same way, sum the time data, and obtain each representative trajectory point P n The corresponding total stay time t n ;

[0022] S33, lightweight data processing and export: according to the operation of S32, the original data of each experimenter is lightweight processed, and after the processing is completed, the lightweight data of all experimenters are written into the data table.

[0023] Furthermore, in step S6, for locations inside the building where there is a height difference, when establishing a projection plane, it is necessary to establish a projection plane with the height difference and project the trajectory points and grid points onto the corresponding projection plane.

[0024] Furthermore, in step S7, the distance D between the track point and the grid point is used. n,m The reciprocal of is the influence value of the weight calculation grid point t n,m , the calculation formula is

[0025]

[0026] Furthermore, in step S7, the quantized value of the grid point dwell time t m The calculation formula is

[0027]

[0028] The beneficial effects of the present invention are as follows:

[0029] The method uses a gridding method based on real architectural spaces to achieve realistic mapping. Data visualization is achieved by projecting trajectory point coordinates and their corresponding dwell times onto a grid plane. This eliminates the need for topological processing of the architectural space, and the visualization results correspond one-to-one with the real architectural space, making the results more intuitive. Furthermore, the accuracy of spatial analysis can be adjusted by selecting the desired grid size to accommodate different spatial scales and research content, providing broader applicability and a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the process steps of the present invention;

[0031] Figure 2 A schematic diagram of a single original trajectory point of the present invention;

[0032] Figure 3 Schematic diagram of original trajectory points and representative trajectory points of the present invention;

[0033] Figure 4 Schematic diagram of the analysis grid of the present invention;

[0034] Figure 5 Schematic diagram of trajectory point and grid point projection of the present invention;

[0035] Figure 6 This is a schematic diagram of the residence time visualization results of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] 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.

[0038] like Figures 1 to 6 As shown, a method for architectural space design analysis using VR and based on residence time includes three parts: data collection, data preprocessing, and data analysis and visualization. The data collection part includes the construction, tour and data collection of models and VR scenes. The VR scene is constructed and corresponding data is collected through the VR tour of the experimenter. The data preprocessing part includes the operations of importing, processing, lightweighting and exporting data in the data processing model, preprocessing the collected data so that the data meets the requirements of further processing and can be visualized. The data analysis and visualization part includes importing the preprocessed data into the data analysis and visualization model, constructing the analysis grid, calculating the distance between the trajectory point and the grid point, calculating the quantitative value of the grid point residence time and performing data visualization. The preprocessed data is calculated to obtain the corresponding data, and then the data is processed for visualization, which can intuitively perform quantitative evaluation of the architectural space quality.

[0039] The implementation principle of the present invention is to first build a VR tour scene based on a real architectural design plan, then conduct a VR tour through the experimenter to obtain the tour trajectory and stay time that match the real architectural space, and then map the above parameters to a grid plane constructed based on the architectural space, and use this mapping relationship to calculate the mutual influence between the person and the architectural space, and digitize this influence through a calculation formula. After obtaining all the data, the data is visualized and converted to form a visual picture, thus realizing an intuitive and visual quantitative evaluation of the architectural space. The quantitative analysis method of the present invention divides the architectural space according to appropriate specifications. Compared with the method of artificially dividing the domain in the prior art, it has the advantages of unified parameters, less external interference, and less influence of human factors. It will not cause large differences in the results due to personal influence, and the final calculation results are relatively unified and accurate; secondly, the coordinates of the trajectory points and their corresponding stay time are mapped to the grid plane and digitized through a calculation formula. After the data is visualized, the results can be seen intuitively without the need for manual translation, and the above calculation process ensures the accuracy and precision of the calculation. At the same time, combined with the standardization and consistency of the grid division, the results are finally intuitive and accurate.

[0040] like Figure 1 As shown, a specific implementation of a method for architectural space design analysis using VR and based on residence time is as follows.

[0041] Perform the data collection operation first.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Then proceed with the data preprocessing part.

[0046] S3, data processing and export: In the data processing model, original trajectory points are created based on the trajectory coordinate data. The time corresponding to the original trajectory point is used as the original stay duration of the original trajectory point. The original trajectory point and original stay duration data are processed and written into the data table.

[0047] Since the amount of original data is large and there is a large amount of duplicate data, the process of directly using the original data to process and obtain visualization results is cumbersome and time-consuming. Therefore, it is necessary to perform lightweight preprocessing on the original data to reduce the amount of calculated data and thus reduce the processing time. However, data lightweighting may lead to reduced data accuracy. Therefore, it is necessary to adopt appropriate lightweight operations to ensure that the processed data still meets the accuracy requirements. The specific data preprocessing operations are as follows.

[0048] like Figure 2 As shown, S31, raw data import: import the raw data into the data processing model, create the original trajectory point with the x, y, z coordinate data of the original trajectory point, convert the timestamp data corresponding to the original trajectory point, the unit is unified into milliseconds, and calculate the time difference between each frame and the previous frame, which is used as the original stay time of the original trajectory point;

[0049] like Figure 3 As shown in S32, raw data lightweighting: When the experimenter stays at a certain position, the system will keep recording, resulting in a large number of overlapping raw trajectory points. In order to reduce the computational complexity of subsequent data processing, the repeated raw trajectory points at the same position need to be lightweighted;

[0050] The specific operation is as follows: set a threshold value of 100 mm as the distance, group the original trajectory points according to the set threshold value according to the aggregation situation, and calculate the center point of each group of original trajectory points as the representative trajectory point P of the experimenter at this position. n , and group the original stay duration corresponding to the same group of original trajectory points in the same grouping way, and sum up this group of time data to obtain the experimenter at each representative trajectory point P n The total stay time t corresponding to the above n ;

[0051] After the above lightweight processing, the total amount of data is reduced to less than 1% of the total amount of original data, but the calculation accuracy is not affected; S33, lightweight data processing and export: According to the operation of S32, the original data of each experimenter is lightweight processed. After the processing is completed, the lightweight data of all experimenters, i.e., the trajectory point P n and the corresponding total stay time t n Write to the data table.

[0052] Finally, the data analysis and visualization operations are carried out.

[0053] S4, data import: read all the processed data in S3 through data analysis and visualization model, that is, the representative trajectory points P of all experimental personnel n and the corresponding total stay time t n , the total amount of data is N.

[0054] like Figure 4 As shown in S5, construct the analysis grid: in the data analysis and visualization model, establish an analysis plane map based on the activity area, and divide it into M unit grids according to the specifications of 0.5m*0.5m, and extract the center point C of each unit grid. m , that is, the grid points.

[0055] like Figure 5 As shown, S6, calculate the distance between the trajectory point and the grid point: establish a projection plane based on the actual structure of the building, and respectively represent the trajectory point P n and grid point C m Project vertically onto the projection plane and calculate the representative trajectory point P n and grid point C m The distance D between the projection points n,m , a single D n,m is a representative trajectory point P n With a grid point C m The distance of each representative trajectory point P needs to be calculated during this step. n With all grid points C m The corresponding distances D n,m , and each representative trajectory point P calculated above n All D n,m Divide into the same group.

[0056] Distance D n,m When calculating, the divided grid is used as the coordinate basis, and then the trajectory point P is represented n With grid point C m Connect the lines to represent the trajectory point P nThe length of the line connecting the x, y, and z coordinate data and the relative position of the grid is calculated. This length represents the trajectory point P. n With a grid point C m Distance D n,m , calculate a representative trajectory point P in turn according to the above method n With all grid points C m Distance D n,m .

[0057] There are a large number of locations with height differences in building structures. In the visualization results, such locations need to have obvious faults. Therefore, when establishing a projection plane, it is necessary to establish a projection plane with height differences. In addition, when constructing grid points and projection trajectory points, it is necessary to project both the grid points and trajectory points of the analysis grid onto a projection plane with correct height differences before performing calculations to ensure the accuracy of the calculation results.

[0058] S7, calculate the quantitative value of the grid point residence time: using the trajectory point P n With grid point C m Distance D n,m The reciprocal of is used as the weight, and the influence value t of the stay time corresponding to each trajectory point on all grid points is calculated in turn. n,m ;

[0059] Influence value of grid point t n,m The calculation formula is:

[0060]

[0061] People and building spaces influence each other. When people stay in a certain grid area, they are not only affected by the space corresponding to the current grid, but also by other surrounding spaces. Similarly, when people are in a certain space, they will occupy the surrounding space and thus affect the surrounding environment. Therefore, we cannot simply consider the trajectory point P in the current grid area. n and the corresponding total stay time t n ,We need to set weights, comprehensively consider the interactive relationship between people and building space, and obtain more accurate calculation results;

[0062] After calculating the influence value t of the grid point n,m Then, calculate the quantized value of the residence time of a single grid point t m , that is, the sum of the impact values of the stay time corresponding to the trajectory points of all experimenters on this grid point, and the quantitative value of the stay time at the grid point t m The calculation formula is:

[0063]

[0064] After calculating the impact of the stay time of each trajectory point on all grid points, n,m Then, we can get the influence value t of a certain grid point on one of the trajectory points. n,m , and then the influence value t of all trajectory points on this grid point n,m By superposition, we can get the sum of the impact values of all trajectory points on this grid point, that is, the quantitative value of the stay time t m , and then calculate each grid point according to the above operation to obtain the quantized value of the residence time of all grid points t m , at this time, the quantized value of the residence time of all grid points is t m These are all specific numerical values. After visual conversion of these numerical values, they become visualization result diagrams.

[0065] like Figure 6 As shown in S8, data visualization: quantize the dwell time of the grid point to t m Corresponding to the grid, reasonably set the reasonable range of the color legend, and quantify the grid point stay time t m The mapped color values are used to color the corresponding grid surfaces to achieve data visualization.

[0066] The present invention collects data and extracts trajectory points based on real building spaces. It then constructs an analysis plane corresponding to the scope, scale, and spatial relationships of the real building space. The trajectory points, dwell time, and analysis plane are mapped to form an associated description model. The statistical units of dwell time are divided into grid points. By statistically analyzing and calculating the dwell time at each grid point, the influence of the building space corresponding to that grid point on dwell time is determined. This influence is then expanded to all grid points within the entire building space. After visualization, the quality of the building space can be intuitively and quantitatively evaluated. This method allows for convenient and accurate quantification of building space quality, and provides intuitive, visual quantification, resulting in better results and greater applicability.

[0067] 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 architectural space design analysis using VR and based on dwell time, 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 and records the original data of the tour through the VR device. The original data includes trajectory coordinate data and its corresponding timestamp data; S3, data processing and export: Create original trajectory points based on trajectory coordinate data, use the time corresponding to the original trajectory points as the original stay duration of the original trajectory points, process the original trajectory points and original stay duration data and write them into the data table; S4, data import: read all processed data in S3 through data analysis and visualization models, the total amount of data is N; S5, build analysis grid: In the data analysis and visualization model, build an analysis plane based on the activity area and divide it into M grid cells, extract the center point of each grid cell , i.e., grid points; S6, calculate the distance between the trajectory point and the grid point: establish a projection plane based on the actual spatial form of the building, project the trajectory point and the grid point vertically onto the projection plane, and calculate the distance between the trajectory point and the grid point after projection. With all grid points distance ; S7, calculate the quantitative value of the grid point residence time: set the weight, and calculate the influence of the residence time corresponding to each trajectory point on all grid points in turn , and then calculate the quantized value of the grid point residence time , that is, the sum of the impact values of the stay time corresponding to the trajectory points of all experimenters on this grid point, and then calculate the quantitative value of the stay time of all grid points according to the operation ; S8, data visualization: Set a reasonable range for the color legend, and assign color to the corresponding grid surface based on the color value mapped by the quantized value of the grid point's dwell time to achieve data visualization.

2. The method for architectural space design analysis using VR and based on residence time according to claim 1, characterized in that: In step S3, the total amount of original data is large and needs to be lightweighted. The specific operations are as follows: S31, importing raw data: importing the raw data into the data processing model, creating raw trajectory points using the x, y, and z coordinate data of the raw trajectory points, converting the timestamp data corresponding to the raw trajectory points, and calculating the time difference as the original stay duration of the raw trajectory points; S32, lightweighting of original data: lightweighting repeated original trajectory points at the same position, setting a threshold, grouping the original trajectory points according to the set threshold, and calculating the representative trajectory point of each group of original trajectory points , group the original stay durations corresponding to the same group of original trajectory points in the same way, sum the time data, and obtain the representative trajectory point The corresponding total stay time ; S33, lightweight data processing and export: according to the operation of S32, the original data of each experimenter is lightweight processed, and after the processing is completed, the lightweight data of all experimenters are written into the data table.

3. The method for architectural space design analysis using VR and based on dwell time according to claim 2, characterized in that: In step S6, for locations inside the building where there is a height difference, when establishing a projection plane, it is necessary to establish a projection plane with the height difference and project the trajectory points and grid points onto the corresponding projection plane.

4. The method for architectural space design analysis using VR and based on dwell time according to claim 3, characterized in that: In step S7, the distance between the track point and the grid point The reciprocal of is the influence value of the weight calculation grid point , the calculation formula is 。 5. The method for architectural space design analysis using VR and based on residence time according to claim 1, characterized in that: In step S7, the grid point stay time quantized value The calculation formula is 。

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