Method for carrying out building space design analysis by adopting VR and based on residence time

Abstract

The invention discloses a method for carrying out building space design analysis by adopting VR and based on residence time. Comprising the operation steps of building and touring a model and a VR scene, collecting data, importing and processing the data, lightening and exporting the data, importing preprocessed data, constructing and analyzing a grid, calculating the distance between a track point and a grid point, calculating a quantized value of retention time of the grid point, and visualizing the data. According to the method, the mode of grid division according to the real building space is adopted, the track point coordinates and the corresponding residence time are projected to the grid plane, data visualization is completed, and real mapping of analysis data and the building space is achieved; topology processing does not need to be carried out on the building space, and the visualization results are in one-to-one correspondence with the real building space, so that the results are more visual. In addition, the accuracy of spatial analysis can be adjusted by selecting the grid size according to requirements so as to adapt to the requirements of different spatial scales and research contents, and the method has wider applicability and wider application range.

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 based on residence time by using VR. Background Art

[0002] In the traditional architectural design process, the judgment of spatial experience mainly relies on the personal experience of the architect, and the result is often different from the actual experience of the users of the built space. Architectural ergonomics aims to establish the correlation between human factor measurement data and the quality of architectural space. By collecting and analyzing the human factor data of people experiencing space in the building, the quality of architectural space can be objectively and quantitatively evaluated.

[0003] Different functional layouts and streamline 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, it is possible to effectively test whether the space design has achieved the expected effect, thereby achieving a quantitative evaluation of the architectural space experience.

[0004] As virtual reality technology matures, VR scenes can be built during the design phase, allowing people to experience unbuilt spaces in advance through VR devices. During this process, the system can record the user's movement path and its corresponding timestamp to obtain detailed behavioral data. However, in engineering design, there is currently a lack of effective data processing and visualization methods to convert these raw data into a more intuitive and easy-to-understand form, allowing designers to more conveniently use this information for design optimization.

[0005] Therefore, a spatial morphology research tool - human factor spectrum has emerged. The human factor spectrum is a graphical analysis tool that describes the basic human factor attributes of space based on the "space-time" relationship of people traveling in space. Through the human factor spectrum, a series of human factor parameters can be linked to specific architectural space characteristics. For example, "A Graphic Design Method Based on Human Factor Data of Built Space" specifically uses domain segmentation, spatial topological relationship induction, basic duration assignment, human factor spectrum drawing, and actual human body dynamic measurement data filling to draw the connection between human factor parameters and architectural space.

[0006] However, there are many problems when the human factor spectrum is applied in the field of engineering design. First, spatial topology is not easy to operate; the basic framework of the human factor spectrum involves the division of architectural space into fields and the establishment of spatial topological relationships between these fields. However, the way of field division depends largely on the designer's understanding and professional judgment of spatial characteristics, so the result may not be unique, and different spatial topological relationships will have a significant impact on subsequent data analysis, which may in turn affect the final design decision and understanding of spatial experience. Second, the result is not intuitive enough; the basis of using the human factor spectrum 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 translated back into the actual architectural space by the designer based on the spatial topological relationship before further understanding and design optimization can be carried out. The entire operation process is not objective data, nor is it an intuitive visualization result, which makes the process and results not intuitive enough. Third, the degree of refinement is insufficient; when using the human factor spectrum, the architectural space will be divided into several spatial fields, which constitute the smallest unit of the analysis results. However, for architectural design, the degree of refinement of this division may sometimes be insufficient and it is difficult to fully meet the design requirements.

[0007] The present invention provides a method for performing architectural space design analysis based on residence time using VR 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 space analysis can be adjusted to meet different needs.

[0009] The technical solution adopted by the present invention to solve the above technical problems is: A method for architectural space design analysis using VR and based on dwell time, comprising the following steps: S1, build models and VR scenes: build a 3D model of the building, and 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 VR tour, define the VR navigation range, configure the data recording program, and then the experimenter conducts VR tour and records the original data of the tour through the VR device. The original data includes trajectory coordinate data and the corresponding timestamp data; S3, data processing and export: create original trajectory points according to 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 the 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 map based on the activity area and divide it into M grid cells. Extract the center point C of each grid cell. m , i.e., grid points; S6, calculate the distance between the trajectory point and the grid point: establish a projection plane according to the actual spatial form of the building, vertically project the trajectory point and the grid point onto the projection plane, and calculate the projected trajectory point P n With all grid points C m Distance D n,m ; 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 ; S8, data visualization: set a reasonable range of the color legend, and color the corresponding grid surface with the color value mapped by the quantized value of the grid point dwell time to achieve data visualization.

[0010] Furthermore, in step S3, the total amount of original data is large and needs to be lightweight processed. The specific operations are as follows: S31, importing original data: importing the original data into the data processing model, creating original trajectory points with the x, y, z coordinate data of the original trajectory points, converting the timestamp data corresponding to the original trajectory points, and calculating the time difference as the original stay time of the original 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 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 ; S33, lightweight data processing and export: lightweight processing is performed on the original data of each experimenter according to the operation of S32, and after the processing is completed, the lightweight data of all experimenters are written into the data table.

[0011] 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 the grid points onto the corresponding projection plane.

[0012] Further, in step S7, the distance D between the trajectory point and the grid point is n,m The inverse of is the influence value of the weight calculation grid point t n,m , the calculation formula is

[0013] Furthermore, in step S7, the grid point stay time quantized value t m The calculation formula is

[0014] The beneficial effects of the present invention are as follows: The method of grid division based on the real building space is adopted to achieve real mapping, and the data visualization is achieved by projecting the coordinates of the trajectory points and their corresponding residence time onto the grid plane; there is no need to topologically process the building space, and the visualization results correspond to the real building space one by one, so the results are more intuitive. In addition, the accuracy of 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 wider applicability and a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the process steps of the present invention; Figure 2 A schematic diagram of a single original trajectory point of the present invention; Figure 3 Schematic diagram of original trajectory points and representative trajectory points of the present invention; Figure 4 It is a schematic diagram of the analysis grid of the present invention; Figure 5 It is a schematic diagram of trajectory point and grid point projection of the present invention; Figure 6 This is a schematic diagram of the residence time visualization results of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] 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 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 a limitation on the present invention.

[0018] 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 operations of models and VR scenes. The VR scene is constructed and corresponding data is collected through VR tour of experimenters. The data preprocessing part includes the operations of data import, processing, lightweight and export 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 an analysis grid, calculating the distance between the trajectory point and the grid point, calculating the quantitative value of the residence time of the grid point and performing data visualization operations, calculating the preprocessed data to obtain the corresponding data, and then processing the data to achieve visualization, so as to intuitively perform quantitative evaluation on the quality of the architectural space.

[0019] 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 an experimenter, obtain a tour trajectory and a length of stay that match the real architectural space, and 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 personnel and the architectural space, and digitize this influence through a calculation formula, and after obtaining all the data, the data is visualized and converted, thereby forming a visualization picture, and realizing an intuitive visualization 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 field in the prior art, it has the advantages of unified parameters, less external interference, and less influence of human factors, and will not cause a large difference in the results due to personal influence. The final calculation result is relatively unified and accurate; secondly, the coordinates of the trajectory point and its corresponding stay time are mapped to the grid plane and digitized through a calculation formula, and then the data is visualized and converted to be able to intuitively see the results, without the need for manual translation, and the above calculation process ensures the precision and accuracy of the calculation, and at the same time, combined with the standardization and consistency of the grid division, the results are finally intuitive and precise.

[0020] 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.

[0021] Perform the data collection operation first.

[0022] S1, build models and VR scenes: construct a 3D model of the building, and then perform lightweight processing on the 3D model to ensure the smoothness of the picture in the VR scene, improve rendering efficiency and ensure visual quality. The judgment standard is "visibility". The specific operation is to remove all parts of the model that are not visible in the final VR experience in the modeling software, and for objects with smooth surfaces, convert them into multiple continuous planes, such as converting a cylinder into a polygonal column; Build a VR scene, import the processed 3D model into the modeling software, adjust the material parameters of the model 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 realism and immersion of the scene, and finally build the required VR scene.

[0023] S2, conduct VR tour: set the starting point of the VR tour, define the VR navigation range according to the expected tourable area in the VR scene, configure the data recording program, and then the experimenter conducts 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 the corresponding timestamp data.

[0024] Then proceed with the data preprocessing part.

[0025] S3, data processing and export: in the data processing model, original trajectory points are created according to the trajectory coordinate data, the time corresponding to the original trajectory points is used as the original stay duration of the original trajectory points, the original trajectory points and the original stay duration data are processed and written into the data table.

[0026] 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 for processing to 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.

[0027] like Figure 2As shown, S31, raw data import: import the raw data into the data processing model, create the raw trajectory point with the x, y, z coordinate data of the raw trajectory point, convert the timestamp data corresponding to the raw 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 raw trajectory point; like Figure 3 As shown, S32, lightweight original data: when the experimenter stays at a certain position, the system will keep recording, resulting in a large number of overlapping original trajectory points. In order to reduce the amount of calculation for subsequent data processing, it is necessary to lightweight the repeated original trajectory points at the same position; 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 time corresponding to the same group of original trajectory points in the same 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 n ; 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.

[0028] Finally, the data analysis and visualization operations are carried out.

[0029] S4, data import: read all the processed data in S3 through the data analysis and visualization model, that is, the representative trajectory points P of all experimenters n and the corresponding total stay time t n , the total amount of data is N.

[0030] like Figure 4 As shown in S5, construct the analysis grid: in the data analysis and visualization model, establish the analysis plane according to the activity area, and divide it into M unit grids according to the specification of 0.5m*0.5m, and extract the center point C of each unit grid. m , that is, the grid points.

[0031] 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 Cm 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 When calculating this step, it is necessary to calculate each representative trajectory point P 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 Into the same group.

[0032] Distance D n,m When calculating, the grid that has been divided 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 n The length of the line connecting the x, y, z coordinate data and the relative position of the grid is the length representing the trajectory point P. n With a grid point C m The distance D n,m , calculate a representative trajectory point P in turn according to the above method n With all grid points C m The distance D n,m .

[0033] There are a large number of locations with height differences in the building structure. 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 a height difference. When constructing grid points and projection trajectory points, it is necessary to project the grid points and trajectory points of the analysis grid onto the projection plane with the correct height difference changes before calculation to ensure the accuracy of the calculation results.

[0034] S7, calculate the quantitative value of the grid point residence time: using the trajectory point P n With grid point C m The distance D n,m The inverse 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 ; The influence value of the grid point t n,m The calculation formula is: 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 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 , it is necessary to set weights, comprehensively consider the interactive relationship between people and building space, and obtain more accurate calculation results; After calculating the influence value t of the grid point n,m Then, calculate the quantized value t of the residence time of a single grid point 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 of the grid point t m The calculation formula is: After calculating the impact of the stay time corresponding to each trajectory point on all grid points, n,m After that, 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 quantized 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 t of all grid points m , at this time, the quantized value of the residence time of all grid points is t m They are all specific numerical values. After the numerical values ​​are converted visually, they become the visualization result diagram.

[0035] 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 value assigns color to the corresponding grid surface to achieve data visualization.

[0036] The present invention collects data and extracts trajectory points based on real building space, and constructs an analysis plane corresponding to the scope, scale, and spatial relationship of the real building space, maps the trajectory points, the dwell time, and the analysis plane to form an associated description model, and divides the statistical unit of the dwell time into grid points. By statistics and calculations of the dwell time on the grid points, the influence of the building space corresponding to the grid points on the dwell time is obtained, and then expanded to the influence values ​​of all grid points in the entire building space. After visualization operation, the quality of the building space can be intuitively quantitatively evaluated. According to this method, the quality of the building space can be conveniently and accurately quantified, and it is an intuitive and visualized quantification, with better specific effects and better applicability.

[0037] It is obvious 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 present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for architectural space design analysis using VR and based on residence time, characterized in that: The following steps are involved: S1, build models and VR scenes: build a 3D model of the building, and 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 VR tour, define the VR navigation range, configure the data recording program, and then the experimenter conducts VR tour and records the original data of the tour through the VR device. The original data includes trajectory coordinate data and the corresponding timestamp data; S3, data processing and export: create original trajectory points according to 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 the 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 map based on the activity area and divide it into M grid cells. Extract the center point C of each grid cell. m , i.e., grid points; S6, calculate the distance between the trajectory point and the grid point: establish a projection plane according to the actual spatial form of the building, vertically project the trajectory point and the grid point onto the projection plane, and calculate the projected trajectory point P n With all grid points C m Distance D n,m ; 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 ; S8, data visualization: set a reasonable range of the color legend, and color the corresponding grid surface with the color value mapped by the quantized value of the grid point 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 original data: importing the original data into the data processing model, creating original trajectory points with the x, y, z coordinate data of the original trajectory points, converting the timestamp data corresponding to the original trajectory points, and calculating the time difference as the original stay time of the original 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 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 ; S33, lightweight data processing and export: lightweight processing is performed on the original data of each experimenter according to the operation of S32, 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 residence time according to claim 2 is characterized by: 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 the grid points onto the corresponding projection plane.

4. 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 distance D between the track point and the grid point is n,m The inverse of is the influence value of the weight calculation grid point t n,m , 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 t m The calculation formula is

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