CN113032885B - Vision relation analysis method, device and computer storage medium - Google Patents

Abstract

The invention discloses a visual field relation analysis method, equipment and a computer storage medium, wherein the method comprises the following steps: generating reachable relation data and visible relation data of each grid in the building plan; calculating the average visible depth and the average observed depth of each grid according to the reachable relation data and the visible relation data based on the reachable space of the building plan, and completing the view relation analysis of each grid; the invention solves the problem that the analysis method for splitting the two space experience modes of motion and vision in the prior art cannot truly describe the motion perception experience of classical gardens in China and the space characteristics of 'seen' and 'seen' of scenes, and better reveals the space perception characteristics of different parts of classical gardens in China through the quantitative analysis method for 'seen' and 'seen' space experience.

Description

Viewshed relationship analysis method, device and computer storage medium

Technical Field

The present invention relates to the analysis of the relationship between classical gardens and visual fields, and in particular to a visual field relationship analysis method, device and computer storage medium.

Background Art

In general buildings, the relationship between visibility and accessibility is almost the same, that is, most places that can be seen can be walked directly to. However, there is a common phenomenon of separation and dislocation between accessibility and visibility in the space of Chinese classical gardens, that is, the places within sight cannot be directly reached, and often require a tortuous path to reach. The asymmetry of the relationship between accessibility and visibility brings about a unique spatial experience of "seeing" and "being seen" in classical gardens: some places are easy to walk to and have a good view, while other places are not easy to walk to but are easy to see. For this experience, most studies are only described through literary language or photos, and cannot be objectively described from a quantitative perspective.

Although the visual graph analysis (VGA) of the Space Syntax theory attempts to simulate the changes in visual relationships during movement by continuously describing the visible visual field, the current methods are basically powerless for the spatial system where the accessibility and visual relationships of classical gardens are significantly misaligned. Therefore, in the application, these two relationships have to be separated, and "movement and vision" are modeled and analyzed as two independent systems: one is the accessibility layer visual graph model, and the other is the visual graph model. It is difficult for a single visual analysis or accessibility analysis to truly reflect the visual relationship of the space: a simple visual analysis ignores the obstacles to movement caused by transparent walls (such as windows, etc.) or low obstacles and overestimates the visibility of the space. People cannot "fly" from one position to another position to see other invisible places through the above obstacles; a simple accessibility analysis regards the transparent boundary as the same as the physical boundary. Because the role of "sight first" is not considered, the analysis results inevitably underestimate the visibility of the space.

This method of artificially separating the two spatial experience modes of "movement and vision" is naturally unable to truly describe the movement perception experience and the spatial characteristics of "seeing" and "being seen" in Chinese classical gardens. Therefore, it has many limitations in practical applications and is difficult to deal with the analysis of complex spaces.

Summary of the invention

In view of this, the embodiments of the present application provide a visual field relationship analysis method, device and computer storage medium to solve the problem that the analysis method in the prior art that separates the two spatial experience modes of movement and vision cannot truly describe the motion perception experience of Chinese classical gardens and the spatial characteristics of "seeing" and "being seen" of the scenery.

The present application provides a method for analyzing visual relations, the method comprising:

Generate reachability relationship data and visual relationship data for each grid in the building plan;

Based on the accessible space of the building plan, the average visible depth and the average viewed depth of each grid are calculated according to the accessible relationship data and the visual relationship data, and the visual relationship analysis of each grid is completed.

In one embodiment, generating reachable relationship data and visible relationship data for each grid in the building plane includes:

Get building plans;

Drawing the building plan as a space syntax analysis base map, and dividing the space syntax analysis base map into a first number of grids of equal size;

Based on the spatial syntax analysis base map, the reachable attribute data of each grid is stored in the first data table in sequence according to the grid numbering sequence, and a reachable layer spatial relationship diagram data table is generated;

Based on the spatial syntax analysis base map, the visual attribute data of each grid is stored in the second data table in sequence according to the grid numbering sequence, and a visual layer spatial relationship diagram data table is generated.

In one embodiment, the reachable space based on the building plan, calculating the average visible depth and the average viewed depth of each grid according to the reachable relationship data and the visual relationship data, and completing the visual relationship analysis of each grid includes:

Based on the reachable layer spatial relationship diagram data table and the visible layer spatial relationship diagram data table, obtaining the reachable grid matrix and the visible grid matrix of each grid by the number of the grid;

Based on the reachable grid matrix and the visible grid matrix, calculating the average visible depth of each grid using a first preset method;

Based on the reachable grid matrix and the visible grid matrix, an average viewed depth of each grid is calculated using a second preset method.

In one embodiment, the calculating the average visible depth of each grid using a first preset method based on the reachable grid matrix and the visible grid matrix includes:

A starting grid is selected, and the number of visible grids corresponding to the starting grid is V ₀ ; wherein the starting grid is any grid in the space syntax analysis base map;

Starting from the starting grid, executing the first reachable topology, obtaining the total number of directly reachable grids of the first reachable topology and directly visible grids corresponding to the directly reachable grids, recorded as the number of newly added visible grids V ₁ of the first reachable topology;

Starting from any newly added visible grid of the last reachable topology, execute the i-th reachable topology, obtain the total number of directly reachable grids of the i-th reachable topology and the directly reachable grids corresponding to the directly reachable grids, and record it as the number of newly added visible grids V _i of the i-th reachable topology; wherein i is a positive integer;

Repeat the above operation until the number of visible grids of the starting grid reaches the total number of grids minus 1, and then stop the topological operation; wherein the total number of grids is the first number;

Based on the number of visible grids V ₀ corresponding to the starting grid, the current reachable topology depth i, the number of newly added visible grids V _i of the i-th reachable topology, the total number of reachable topology operations and the total number of grids, the average visible depth of the starting grid is calculated.

In one embodiment, the average visible depth of each grid is the average topological depth of other grids that can be reached or viewed by any starting grid in the space syntax analysis base map.

In one embodiment, the calculating the average viewed depth of each grid using a second preset method based on the reachable grid matrix and the visible grid matrix includes:

Taking the starting grid as the target, among the first number of grids, the grids of the directly visible starting grid are obtained, which are recorded as a visible grid area, and the number of grids in the visible grid area is D ₀ ;

Eliminate grids in the visible grid area from the first number of grids to obtain remaining grids;

Execute the first reachable topology in the remaining grids to obtain the number of grids in the remaining grids that can reach the visible grid area, which is recorded as the number of newly added reachable grids D ₁ of the first reachable topology;

Adding the newly added reachable grid obtained from the last reachable topology to the visible grid area to generate a new visible grid area;

Eliminate the new visible grid area from the remaining grids to obtain a new remaining grid;

Execute the i-th reachable topology in the new remaining grids, obtain the number of grids in the new remaining grids that can reach the new visible grid area, and record it as the number of newly added reachable grids D _i of the i-th reachable topology; wherein i is a positive integer;

Repeat the above operation until the number of the new remaining grids is reduced to zero, and then stop the topological operation;

The average visible depth of the starting grid is calculated based on the number of grids D ₀ in the visible grid area, the current reachable topology depth i, the number of newly added reachable grids D _i of the i-th reachable topology, the total number of reachable topology operations and the total number of grids.

In one embodiment, the average viewed depth of each grid is the average topological depth of any grid-reachable or visible starting point grid in the space syntax analysis base map.

In one embodiment, the method further includes:

Perform visualization operations based on the results of the viewshed relationship analysis for each raster.

In order to achieve the above-mentioned purpose, a computer storage medium is also provided, on which a program of the viewshed relationship analysis method is stored. When the program of the viewshed relationship analysis method is executed by a processor, the steps of any of the above-mentioned viewshed relationship analysis methods are implemented.

To achieve the above-mentioned purpose, a visual relationship analysis device is also provided, comprising a memory, a processor, and a program of a visual relationship analysis method stored in the memory and executable on the processor, wherein the processor implements any of the steps of the visual relationship analysis method described above when executing the program of the visual relationship analysis method.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

Generate the reachability relationship data and visual relationship data of each grid in the building plan; through the calculation of the software, generate accurate reachability relationship data and visual relationship data of each grid in the building plan, to ensure the correctness of the subsequent calculation of the average visual depth and average viewed depth of each grid;

Based on the accessible space of the building plan, the average visible depth and the average viewed depth of each grid are calculated according to the accessible relationship data and the visual relationship data, and the visual relationship analysis of each grid is completed; in the accessible space of the building area, that is, the space accessible to people (the range defined by the accessible layer), other spaces are excluded; the limitation here avoids increasing the spatial volume of the model (the number of grids in the plane), thereby avoiding the mathematical errors caused by the skewness of the data distribution; through the quantified average visible depth and average viewed depth, the relationship analysis of each grid is accurately performed, thereby revealing the spatial characteristics of the building.

The present application solves the problem that the analysis methods in the prior art that separate the two spatial experience modes of movement and vision cannot truly describe the motion perception experience of Chinese classical gardens and the spatial characteristics of "seeing" and "being seen" of the scenery. Through the quantitative analysis method of the spatial experience of "seeing" and "being seen", the spatial perception characteristics of different parts of Chinese classical gardens are better revealed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a first embodiment of a method for analyzing visual relations in the present application;

FIG. 2 is a schematic diagram of a specific flow chart of step S110 in the first embodiment of the view relation analysis method of the present application;

Figure 3 is an example of the Wangshiyuan plan and the corresponding accessible layer base map and visible layer base map;

FIG4 is a schematic diagram of a specific flow chart of step S120 in the first embodiment of the view relation analysis method of the present application;

FIG5 is a schematic diagram of a specific flow chart of step S122 of the view relation analysis method of the present application;

FIG6 is a schematic diagram of the measurement method of “seeing” (left picture) and “being seen” (right picture) in the visual field relationship analysis method of this application

FIG. 7 is a schematic diagram of a specific flow chart of step S123 of the view relation analysis method of the present application;

FIG8 is a flow chart of a second embodiment of the view relationship analysis method of the present application;

FIG9 is a schematic diagram comparing the analysis results of the prior art and the present method;

FIG10 is a schematic diagram of the hardware architecture of a visual relationship analysis device involved in an embodiment of the present application;

DETAILED DESCRIPTION

It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

The main solution of the embodiment of the present invention is: generating reachable relationship data and visual relationship data for each grid in the building plan; based on the reachable space of the building plan, calculating the average visual depth and the average viewed depth of each grid according to the reachable relationship data and the visual relationship data, and completing the visual field relationship analysis of each grid; the present invention solves the problem that the analysis method in the prior art that separates the two spatial experience modes of movement and vision cannot truly describe the motion perception experience of Chinese classical gardens and the spatial characteristics of "seeing" and "being seen" of the scenery, and better reveals the spatial perception characteristics of different parts of Chinese classical gardens through the quantitative analysis method of the spatial experience of "seeing" and "being seen".

In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

Referring to FIG. 1 , FIG. 1 is a first embodiment of the view relation analysis method of the present application, the method comprising:

Step S110: Generate reachable relationship data and visible relationship data for each grid in the building plan.

Specifically, the reachable relationship data and the visible relationship data of each grid in the building plan can be generated by a preset method, wherein the preset method can be the use of relevant software. In this embodiment, the spatial syntax analysis base map can be generated by CAD software or other methods; the generation of grids and the reachable relationship diagram data table and the visual relationship diagram data table utilize DepthmapX software.

Specifically, the reachable relationship data may be a reachable layer spatial relationship diagram data table; the visual relationship data may be a visible layer spatial relationship diagram data table; this is not limited to the above data tables, and may also include other reachable or visual relationship data.

Step S120: Based on the accessible space of the building plan, the average visible depth and the average viewed depth of each grid are calculated according to the accessible relationship data and the visible relationship data, and the visual relationship analysis of each grid is completed.

Specifically, in this embodiment, the accessible space of the building plan only includes the space that can be entered by people, that is, the range defined by the accessible layer, and other spaces are excluded; the limitation here can avoid increasing the value of the spatial quantity of the model (the number of grids in the plane), thereby reducing errors; because the calculation of the average topological depth is obviously affected by the value of the spatial quantity, it may cause statistical errors due to the skewness of the data distribution.

Specifically, analyzing the average visible depth and average viewed depth of each generated grid can better measure the “depth” and “shallowness” of the visual relationship.

Specifically, the Visibility Graph Analysis (VGA) method of Space Syntax theory is an important technical means for spatial research in the field of architecture, and has formed a series of computer software tools, such as DepthmapX, Isovist, DecodingSpaceToolbox, Syntax2D, etc. Taking the most influential space syntax software DepthmapX as an example, this method covers the analyzed building plane with a uniform grid of a certain density, and draws the isovists (i.e., the range of the view area from the 360-degree field of view of the point and the morphological composition properties of the view polygon formed by the wall occlusion) of each grid (center point); in addition to measuring the geometric morphological properties of the isovists, the degree of overlap of the isovists between grids and the topological depth change of the visual relationship are measured by indicators such as connectivity and mean depth. Affected by the space dividing objects such as walls in the building plane layout, the shape and size of the isovists vary with the different observation points in the building plane, reflecting the changes in vision experienced by people during movement. A series of continuous equal horizons form a continuous scene, reflecting scenes such as walking in a building. A large number of empirical studies at home and abroad have revealed that the topological connection pattern of the horizon of the building layout affects people's cognitive process and behavior, such as the organization of movement lines or the static space occupancy pattern.

In the above embodiment, the beneficial effect is as follows: by quantifying the average visible depth and the average viewed depth of each grid, the spatial experience of "seeing" and "being seen" is made more obvious, and the spatial experience characteristics and differences of "seeing" and "being seen" in complex architectural spaces (such as classical gardens) are quantitatively described, so as to better understand the spatial design methods of classical gardens, thereby better revealing the spatial perception characteristics of different parts of Chinese classical gardens.

Referring to FIG. 2 , FIG. 2 is a specific implementation step of step S110 in the first embodiment of the viewshed relationship analysis method of the present application, wherein the generation of reachable relationship data and visible relationship data of each grid in the building plane includes:

Step S111: Obtain a building plan.

Specifically, an architectural floor plan, also known as a floor plan, is a drawing that uses a horizontal projection method and corresponding legends to show the walls, doors, windows, stairs, floors, internal functional layout and other construction conditions of a new building or structure.

It should be noted that the application mainly uses the garden plan (jpg or vector mapping, please ensure that the plane has a scale or drawing unit) as the research object, but it is not limited to the garden plan, and is applicable to complex architectural spaces that combine accessible space with visible space. In this application, the Master of Nets Garden of Suzhou Garden is taken as an example to illustrate a specific embodiment, but this method is not limited to the Master of Nets Garden.

Step S112: drawing the building plan as a space syntax analysis base map, and dividing the space syntax analysis base map into a first number of grids of equal size.

Specifically, install CAD software and DepthmapX (the latest version of DepthmapX is v0.8) for Windows system; import the jpg picture or vector mapping map of the Wangshiyuan plan in CAD drawing software such as Autocad, and then draw the spatial syntax VGA analysis base map; be sure to distinguish between two types of boundaries in the plane during drawing: visible boundaries and reachable boundaries. The visible boundary refers to an opaque entity such as a wall that blocks the line of sight above the eye level; the reachable boundary refers to a boundary that blocks the movement of the human body. In addition to the former, it also includes some low-height (below the eye level) or transparent boundaries, such as railings, greenery, water surface, floor-to-ceiling glass, etc. (Figure 3, the left figure in Figure 3 is the Wangshiyuan plan, the figure is the foot-level reachable layer VGA base map (drawn according to the architectural plane boundary conditions of the human foot level (Knee-level)), and the right figure is the foot-level visible layer VGA (drawn according to the boundary conditions of the human eye level (Eye-level))). Refer to the instructions in Chapter 4 of the Ministry of Housing and Urban-Rural Development's 13th Five-Year Plan textbook "Space Syntax Tutorial", draw the visible boundary and the accessible boundary on two layers (such as "Visualboundary" and "Access boundary") respectively, and ensure that the outer boundaries of each layer are closed, and then save the drawn graphics in dxf file format.

Step S113: Based on the spatial syntax analysis base map, the reachable attribute data of each grid is stored in the first data table in sequence according to the grid numbering sequence, and a reachable layer spatial relationship diagram data table is generated.

Specifically, import the dxf file stored previously into DepthmapX. Make sure that the accessible layers and visible graph layers in Drawing Layers are both turned on. Referring to the instructions in Chapter 4, 4.2.2 of the Spatial Syntax Tutorial, set the VGA analysis grid size to 0.6 meters, fill the range of the accessible space, and then generate the spatial relationship diagram of the accessible layer. Then, the spatial relationship diagram of the accessible layer is output as a CSV file. The menu path of DepthmapX is: Map→Export→VisibilityGraph Connections as CSV… The output CSV file stores the information of other grids directly connected to each grid in the form of grid ID numbers. For easy distinction, this file can be named "AccessLinks.csv".

Specifically, the reachable attribute data may include a reachable grid matrix, a connection relationship, an average depth, etc., which are not limited here.

Step S114: Based on the spatial syntax analysis base map, the visual attribute data of each grid is stored in the second data table in sequence according to the grid numbering sequence, and a visual layer spatial relationship diagram data table is generated.

Specifically, import the dxf file stored previously into DepthmapX. Make sure that both the reachable layer and the visible layer in Drawing Layers are turned on. Refer to the instructions in Chapter 4, 4.2.2 of the Spatial Syntax Tutorial to set the VGA analysis grid size to 0.6 meters to fill the range of the reachable space. Refer to the instructions in Chapter 4, 4.3.2 of the Spatial Syntax Tutorial to switch to Drawing Layers as the current working layer, close the reachable layer of the imported dxf file, and make sure that only the visible layer is turned on. The spatial relationship diagram generated is the relationship diagram of the visible layer. Subsequently, the spatial relationship diagram of the visible layer is exported as a CSV file. The menu path of DepthmapX is shown above. For easy distinction, the data table of the visible layer can be named "VisibilityLinks.csv".

Specifically, the visual attribute data may include a visual grid matrix, a connection relationship, an average depth, etc., which are not limited here.

In the above embodiment, the beneficial effect is: through the above steps, the spatial relationship diagram data table of the reachable layer and the spatial relationship diagram data table of the visible layer are correctly generated, thereby ensuring the correctness of the subsequent calculation of the average visible depth and the average viewed depth of the grid, thereby ensuring that the spatial characteristics of the building garden are correctly analyzed.

Referring to FIG. 4 , FIG. 4 is a specific implementation step of step S120 in the first embodiment of the viewshed relationship analysis method of the present application, wherein the reachable space based on the building plan, the average visible depth and the average viewed depth of each grid are calculated according to the reachable relationship data and the visual relationship data, and the viewshed relationship analysis of each grid is completed, including:

Step S121: Based on the reachable layer spatial relationship diagram data table and the visible layer spatial relationship diagram data table, obtain the reachable grid matrix and the visible grid matrix of each grid through the grid numbering.

Specifically, in the process of generating a reachable layer spatial relationship diagram data table and generating a visible layer relationship diagram data table, if the position of the grid does not change, the grid numbers in the two layers are exactly the same, and the space at the same position obtains visible attribute data and reachable attribute data.

Step S122: Based on the reachable grid matrix and the visible grid matrix, calculate the average visible depth of each grid using a first preset method.

Specifically, in this embodiment, the average visible depth of each grid is calculated by C++ language, and the calculation result is stored in a txt file, but it is not limited to the above language and the above file storage method, and can be dynamically adjusted according to needs.

Step S123: Based on the reachable grid matrix and the visible grid matrix, calculate the average viewed depth of each grid using a second preset method.

Specifically, it has been explained in step S122 and will not be repeated here.

In the above embodiment, the beneficial effect is that the average visible depth and the average viewed depth of each grid are correctly calculated by the first preset method and the second preset method, thereby ensuring that the spatial information of each grid can be accurately analyzed.

Referring to FIG. 5 , FIG. 5 is a specific implementation step of step S122 in the viewshed relationship analysis method of the present application, wherein the average visible depth of each grid is calculated using a first preset method based on the reachable grid matrix and the visible grid matrix, including:

Step S1221: Select a starting grid, and the number of visible grids corresponding to the starting grid is V ₀ ; wherein the starting grid is any grid in the space syntax analysis base map.

Step S1222: Starting from the starting grid, execute the first reachable topology, obtain the total number of directly reachable grids of the first reachable topology and directly visible grids corresponding to the directly reachable grids, and record it as the number of newly added visible grids V ₁ of the first reachable topology.

Step S1223: Starting from any newly added visible grid of the last reachable topology, execute the i-th reachable topology, obtain the total number of directly reachable grids of the i-th reachable topology and the directly reachable grids corresponding to the directly reachable grids, and record it as the number of newly added visible grids V _i of the i-th reachable topology; wherein i is a positive integer.

Step S1224: Repeat the above operation until the number of visible grids of the starting grid reaches the total number of grids minus 1, and then stop the topological operation; wherein the total number of grids is the first number.

Step S1225: Calculate the average visual depth of the starting grid based on the number of visible grids V ₀ corresponding to the starting grid, the current reachable topology depth i, the number of newly added visible grids V _i of the i-th reachable topology, the total number of reachable topology operations and the total number of grids.

Specifically, the topological depth algorithm (average visible depth) of "view analysis" is as follows:

The calculation idea of the above code is:

Make a reachable topology for the current visible grid set of the starting grid. Each topology will add new visible grids until the number of visible grids of the starting grid reaches (total number of grids - 1).

Assume that the current reachable topology depth is i, and the number of newly added visible grids of the i-th reachable topology is V _i , the total number of reachable topology is n, the number of visible grids at the starting grid is V ₀ , and the total number of grids is C. Then the calculation formula for the average visible depth is:

The specific steps of the above algorithm code are:

1) Starting from the starting grid, make a reachable topology. The total number of directly reachable grids of the first reachable topology and the grids directly visible to these reachable grids is recorded as the number of newly added visible grids V ₁ of the first topology;

2) Starting from the visible grids of the first topology, make a second reachable topology. The directly reachable grid data of this topology and the total number of directly visible grids of these reachable grids are subtracted from the number of visible grids V ₁ of the previous reachable topology to obtain the number of newly added visible grids V ₂ of the second topology;

3) Starting from the visible grids of the second topology, make the third reachable topology. The directly reachable grid data of this topology and the total number of directly visible grids of these reachable grids are subtracted from the number of visible grids V ₂ of the previous reachable topology to obtain the number of newly added visible grids V ₃ of the second topology.

4) And so on, until the number of visible grids of the starting grid reaches the total number of grids in the map, the topology stops;

5) According to formula 1, calculate the average visual topological depth of the starting grid.

In the above embodiment, the beneficial effect is: ensuring the correct calculation of the average visible depth of each grid, so as to better quantify the spatial experience of "seeing".

In one of the embodiments, the average visible depth of each grid is the average topological depth of other grids that can be reached or viewed by any starting grid in the space syntax analysis base map.

Specifically, referring to the left figure of FIG6 , a schematic diagram of a method for measuring the average visual topological depth of a point at position J is shown. Taking the left figure of FIG6 as an example, the average topological depth of “seeing” at point J is:

Depth 1 (visible): 1080 grids;

Depth 2 (reachable + visible): 2234 grids;

Depth 3 (reachable + visible): 835 grids;

Depth 4 (reachable + visible): 298 grids;

Depth 5 (reachable + visible): 139 grids;

Depth 6 (reachable + visible): 679 grids;

Depth 7 (reachable + visible): 1438 grids;

Depth 8 (reachable + visible): 611 grids;

Depth 9 (reachable + visible): 76 grids;

Depth 10 (reachable + visible): 194 grids;

Depth 11 (reachable + visible): 349 grids;

Depth 12 (reachable + visible): 96 grids;

Depth 13 (reachable + visible): 51 grids;

Average topological depth = 37246 (total depth) / 8080 (total number of grids) = 4.610.

Each topological search of visual relationships is based on the newly added reachable spatial position in the previous search until all grids are exhausted. In this way, the average topological depth of other grids "viewed" from position J can be obtained.

Referring to FIG. 7 , FIG. 7 is a specific implementation step of step S123 of the viewshed relationship analysis method of the present application, wherein the average viewed depth of each grid is calculated using a second preset method based on the reachable grid matrix and the visible grid matrix, including:

Step S1231: Taking the starting grid as the target, among the first number of grids, obtain the grids of the directly visible starting grid, record them as visible grid area, and the number of grids in the visible grid area is D ₀ .

Step S1232: Eliminate grids in the visible grid area from the first number of grids to obtain remaining grids.

Step S1233: Execute the first reachable topology in the remaining grids to obtain the number of grids in the remaining grids that can reach the visible grid area, which is recorded as the newly added reachable grid number D ₁ of the first reachable topology.

Step S1234: adding the newly added reachable grid obtained from the last reachable topology to the visible grid area to generate a new visible grid area.

Step S1235: Eliminate the new visible grid area from the remaining grids to obtain a new remaining grid.

Step S1236: Execute the i-th reachable topology in the new remaining grids to obtain the number of grids in the new remaining grids that can reach the new visible grid area, recorded as the number of newly added reachable grids D _i of the i-th reachable topology; wherein i is a positive integer.

Step S1237: Repeat the above operation until the number of new remaining grids is reduced to zero, and then stop the topological operation.

Step S1238: Calculate the average visible depth of the starting grid based on the number of grids D ₀ in the visible grid area, the current reachable topology depth i, the number of newly added reachable grids D _i of the i-th reachable topology, the total number of reachable topology operations and the total number of grids.

The calculation idea of the above code is:

Find the grid that can directly see the starting grid from the graph, record it as the current visible grid area, find out the number of grids that can reach the current visible grid area through a reachable topology from the remaining grids each time, add the searched reachable grids to the visible grid area, and remove them from the remaining grids; continue to search for grids that can be reached through a reachable topology from the remaining grids until all grids in the map are searched.

Assume that the current reachable topology depth is i, and the number of reachable grids of the i-th reachable topology is D _i , the total number of reachable topology is n, the number of grids that can directly see the starting grid in the map is D ₀ , and the total number of grids is C, then the calculation formula for the average visible depth is:

The specific steps of the above algorithm code are:

1) Find the grid from the map that can directly see the starting grid, and the corresponding grid number is recorded as D ₀ ;

2) Find the grid from the map that can directly see the starting grid, and record it as the current visible grid area;

3) After removing the visible grids, search for the number of grids that are reachable through the first reachable topology in the remaining grids, and record it as the number of newly added reachable grids of the first topology as D ₁ ;

4) Add D ₁ to the visible grid area, and remove the last reachable grid D ₁ from the remaining grids, and then search the number of grids reachable by the first reachable topology in the remaining grids, and record it as the number of reachable grids of the second topology as D ₂ ;

5) Add D ₂ to the visible grid area, and remove the last reachable grid D ₂ from the remaining grids, and then search the number of grids that are reachable through the first reachable topology in the remaining grids, and record it as the number of reachable grids of the second topology as D ₃ ;

6) And so on, until the number of remaining grids is reduced to zero, and the topology stops;

7) According to formula 2, calculate the average viewed topological depth of the starting point grid.

In the above embodiment, the beneficial effect is: ensuring the correct calculation of the average viewed depth of each grid, thereby better quantifying the spatial experience of “being viewed”.

In one of the embodiments, the average visible depth of each grid is the average topological depth of any grid reachable or visible starting point grid in the space syntax analysis base map.

Specifically, referring to the right figure of FIG6 , which is a schematic diagram of a method for measuring the average viewed topological depth of point J at position, as shown in the right figure of FIG6 , the average topological depth of point J at position “being viewed”.

Depth 1 (visible): 1080 grids;

Depth 2 (reachable): 1598 grids;

Depth 3 (reachable): 879 grids;

Depth 4 (reachable): 1930 grids;

Depth 5 (reachable): 1243 grids;

Depth 6 (reachable): 929 grids;

Depth 7 (reachable): 326 grids;

Depth 8 (reachable): 95 grids;

Average topological depth = 29464 (total depth) / 8080 (total number of grids) = 3.647.

As can be seen from the right figure of Figure 6, for the calculation of the "viewed" indicator, only the connections at the first topological depth are visible relationship connections, and all connections at other topological depths are reachable relationship connections.

Referring to FIG. 8 , FIG. 8 is a second embodiment of the view relation analysis method of the present application, the method further includes:

Step S210: Obtain the reachable relationship data and visible relationship data of each grid in the building plan through a preset method.

Step S220: Based on the accessible space of the building plan, the average visible depth and the average viewed depth of each grid are calculated according to the accessible relationship data and the visible relationship data, and the visual relationship analysis of each grid is completed.

Step S230: Perform visualization operations based on the viewshed relationship analysis results of each grid.

Compared with the first embodiment, the second embodiment includes step S230, and the other steps have been described in the first embodiment and will not be repeated here.

Specifically, the txt file of the calculation results can be imported into the VGA analysis file of DepthmapX and visualized. For the specific implementation steps, please refer to the introduction of Chapter 4, Section 4.5.3 of the "Spatial Syntax Tutorial", which will not be repeated here. The average depth of the accessible layer of the Wangshiyuan space, the average depth of the visible layer, and the average depth of "seeing" and "being seen" are compared in Figure 9; as shown in Figure 9, it is a comparison of the old and new methods of visible graph analysis (VGA). The two pictures on the left are the average depths of the two systems of the accessible layer and the visible layer of Wangshiyuan analyzed by the VGA method of the prior art of spatial syntax; the two pictures on the right are the spatial depth analysis of "seeing" and "being seen" in this embodiment; it should be noted that the darker the grid color in Figure 9, the smaller the average topological depth.

In the above embodiment, there is a beneficial effect that after performing the visualization operation, it can be easily seen that the method can better reveal the spatial perception characteristics of different parts of the garden.

The present application also provides a computer storage medium, on which a program of a viewshed relationship analysis method is stored. When the program of the viewshed relationship analysis method is executed by a processor, the steps of any of the above-mentioned viewshed relationship analysis methods are implemented.

The present application also provides a visual relationship analysis device, comprising a memory, a processor, and a program of a visual relationship analysis method stored in the memory and executable on the processor, wherein the processor implements any of the steps of the visual relationship analysis method described above when executing the program of the visual relationship analysis method.

The present application relates to a visual relationship analysis device 010 including: as shown in FIG10 , at least one processor 012 and a memory 011 .

The processor 012 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 012 or the instruction in the form of software. The above processor 012 can be a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 011, and the processor 012 reads the information in the memory 011 and completes the steps of the above method in combination with its hardware.

It can be understood that the memory 011 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct RAM bus random access memory (DRRAM). The memory 011 of the system and method described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (6)

1. A method of view relationship analysis, the method comprising:

obtaining a building plan;

drawing the building plan into a space syntactic analysis base map, and dividing the space syntactic analysis base map into a first number of grids with equal size;

based on the space syntactic analysis base map, storing the reachable attribute data of each grid into a first data table in turn according to the serial number sequence of the grids, and generating a reachable layer space relation graphic data table;

based on the space syntax analysis base map, sequentially storing the visual attribute data of each grid into a second data table according to the serial number sequence of the grids, and generating a visual layer space relation graphic data table;

Acquiring an reachable grid matrix and a visible grid matrix of each grid through the serial number of the grid based on the reachable layer space relation graphic data table and the visible layer space relation graphic data table;

calculating the average visible depth of each grid by using a first preset method based on the reachable grid matrix and the visible grid matrix;

calculating the average perceived depth of each grid by using a second preset method based on the reachable grid matrix and the visible grid matrix;

completing analysis of the view relationship of each grid;

the method for calculating the average visual depth of each grid by using a first preset method based on the reachable grid matrix and the visual grid matrix comprises the following steps:

s1221: selecting a starting point grid, wherein the number of visible grids corresponding to the starting point grid isV ₀ The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the starting point grid is any grid in the space syntactic analysis base graph;

s1222: starting from the starting point grid, executing a first reachable topology, obtaining the total number of the directly reachable grids of the first reachable topology and the directly reachable grids corresponding to the directly reachable grids, and recording the total number as the newly added number of the directly reachable grids of the first reachable topology V ₁ ；

S1223: starting from any newly added visual grid of the last reachable topology, executing the firstiA secondary reachable topology, obtaining the firstiThe total number of directly reachable grids of the secondary reachable topology and the directly reachable grids corresponding to the directly reachable grids is recorded as the firstiNew incremental visual grid number for sub-reachable topologyV _i； wherein ,iis a positive integer;

s1224: repeating steps S1221 to S1223 until the number of visible grids of the starting grid reaches the total number of grids minus 1, and stopping the topology operation; wherein the total grid number is the first number;

s1225: based on the number of visible grids corresponding to the starting gridV ₀ Current reachable topology depthiSaid first stepiNew incremental visual grid number for sub-reachable topologyV _i The total times of the reachable topology and the total grid quantity are carried out, and the average visible depth of the starting point grids is calculated;

the calculating the average depth of view of each grid by using a second preset method based on the reachable grid matrix and the visible grid matrix comprises the following steps:

s1231: the first number of grids is used as targets, grids of the direct visual starting point grids are obtained and marked as visual grid areas, and the number of grids of the visual grid areas is D ₀ ；

S1232: removing grids of the visible grid area from the first number of grids to obtain remaining grids;

s1233: executing a first reachable topology in the remaining grids, obtaining the number of grids in the remaining grids, which can reach the visible grid area, and recording the number of grids as a new reachable grid number of the first reachable topologyD ₁ ；

S1234: adding the newly added reachable grid obtained by the last reachable topology into the visible grid area to generate a new visible grid area;

s1235: removing the new visible grid area from the remaining grids to obtain new remaining grids;

s1236: executing the first in the new remaining gridiA sub-reachable topology, obtaining the number of grids in the new remaining grids, which can reach the new visible grid area, which is recorded as the firstiNewly added number of reachable grids of sub-reachable topologyD _i； wherein ,iis a positive integer;

s1237: repeating steps S1231 to S1236 until the new remaining number of grids is reduced to zero, and stopping the topology operation;

s1238: grid number based on the visual grid areaD ₀ Current reachable topology depthiSaid first stepiNewly added number of reachable grids of sub-reachable topology D _i And carrying out the total times of the reachable topology and the total grid number, and calculating the average observed depth of the starting point grid.

2. The visual field relation analysis method of claim 1, wherein the average visual depth of each grid is an average topological depth at which any one starting grid in the space syntax analysis base map is reachable or at which other grids are visible.

3. The visual field relation analysis method of claim 1, wherein the average perceived depth of each grid is the average topological depth of any one of the grid reachable or visible starting point grids in the spatial syntactic analysis base.

4. The visual field relationship analysis method of claim 1, wherein the method further comprises:

based on the analysis result of the view relationship of each grid, a visualization operation is performed.

5. A computer storage medium, wherein a program of a visual field relation analysis method is stored on the computer storage medium, and the program of the visual field relation analysis method realizes the steps of the visual field relation analysis method according to any one of claims 1 to 4 when being executed by a processor.

6. A visual field relation analysis device comprising a memory, a processor and a program of visual field relation analysis methods stored on the memory and executable on the processor, the processor implementing the steps of the visual field relation analysis method of any one of claims 1 to 4 when executing the program of visual field relation analysis method.

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