A method for evaluating performance of city logo design

Abstract

The application provides a city identification design performance evaluation method, relates to the technical field of performance evaluation, and introduces a solid angle quantitative index to describe the performance of identification design, so as to make up for the problem that quantitative indexes are lacked in the evaluation of the emergency response capability of a city area, has important theoretical value and practical significance for improving the safety resilience of a smart city, and the application also considers various city elements and the behavior capability of people when calculating, so that dynamic perception, group statistics and complex scene correction are realized. The application not only provides quantitative indexes for the evaluation of the emergency response capability of a city, but also can be used for guiding the design of an identification system by a chief engineer.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology, specifically a method for evaluating the performance of urban signage design. Background Technology

[0002] Currently, performance evaluation methods for urban signage design primarily focus on the design elements of the signage itself (font, size, icons, etc.), lacking a systematic evaluation of the dynamic human-signage interaction in complex urban scenarios and failing to assess its usability and suitability during urban use. Traditional methods for solving wayfinding signage design problems mainly focus on the building operation and maintenance process, without incorporating performance evaluation of the signage system design scheme before building completion, which increases the time and financial investment required for subsequent signage modifications. Current signage performance research methods mainly rely on virtual scenario experiments, judging based on the experimenter's sensitivity to the signage (response accuracy, response time), which is overly dependent on subjective human factors and lacks objective judgment using quantitative data.

[0003] In the indicator system construction phase, the vast majority of urban area evacuation capacity assessment indicators were qualitative (as shown in the screenshot), lacking scientific quantitative indicators to describe evacuation capacity. Furthermore, the design parameters of urban elements (buildings, roads, signage, building occupancy, etc.) were ignored, leading to a significant decoupling between the assessment results and actual evacuation scenarios. In the weight determination and model building phase, the setting of influencing factors (weights) for each evaluation indicator almost entirely relied on expert back-to-back scoring, excessively depending on subjective judgment and lacking objective data support. In the model usage phase, traditional data collection methods such as on-site visits and questionnaires were required, which suffers from inherent defects such as poor data timeliness and low spatial coverage, while also consuming significant human, material, and time resources. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to propose a method for evaluating the performance of urban signage design, comprising:

[0005] Step 1: Construct a 3D city area model of the target city area. The 3D city area model includes buildings, roads, signs, evacuation start points, and evacuation end points. The signs include at least building signs and road signs. The evacuation start points are the evacuation exits of buildings, and the evacuation end points are pre-set safe locations. Based on the initial top view of the 3D city area model, obtain the target top view and acquire the number of people to be evacuated from each evacuation start point, i.e., the number of people N inside each building. i ;

[0006] Step 2: In the target top view, using a pathfinding algorithm, for each evacuation starting point, calculate the corresponding evacuation destination. The vector pointing from the evacuation starting point to its corresponding evacuation destination is used as the path, resulting in multiple paths. Then, based on the number of people N to be evacuated from each evacuation starting point... i Calculate the path weight p i ;

[0007] Step 3: Divide the target top view into grids according to the preset number and shape of grids to obtain the divided top view;

[0008] Step 4: In the divided top view, determine the locations of multiple sampling points based on the path, and calculate the attention coefficient k for each sampling point location. p ;

[0009] Step 5: Calculate the solid angle of the sampling point location, and correct the solid angle of the sampling point location to obtain the solid angle Ω of the sampling point location based on the field of view correction. m ;

[0010] Step 6: Based on the illumination conditions of the target city area, calculate the solid angle Ω of the sampling point location based on field-of-view correction. m After correction, the solid angle Ω' of the sampling point location after illumination correction is obtained. m ;

[0011] Step 7: Based on the scene complexity of the 3D city area model, adjust the solid angle Ω' of the sampling point location after lighting correction. m After correction, the solid angle Ω″ of the scene complexity correction at the sampling point location is obtained. m ;

[0012] Step 8: Based on the attention coefficient k p Solid angle Ω″ for scene complexity correction m The values ​​are then adjusted to obtain the evacuation capacity evaluation value Ω for the target urban area.

[0013] Optionally, in step 1, based on the initial top view of the 3D urban area model, the target top view is obtained, including:

[0014] Obtain the initial top view of the 3D urban area model, and mark the starting and ending points of the evacuation as points on the initial top view to obtain the target top view.

[0015] Optionally, in step 2, the number of people N to be evacuated from the evacuation starting point can be determined. i Calculate the path weight p i Specifically, this is achieved through the following formula:

[0016]

[0017] Where, p i Let represent the weight of the i-th path, and k be the total number of paths.

[0018] Optionally, step 4 specifically includes:

[0019] Step 4.1: In the divided top view, for each path, determine the grids that intersect with the path to obtain the grids that need to be of interest;

[0020] Step 4.2: Obtain the center position of the grid that needs to be focused on as the sampling point position, and obtain multiple sampling point positions corresponding to each path;

[0021] Step 4.3: For each sampling point location on each path, calculate the distance between the sampling point location and the evacuation starting point of that path, and calculate the distance between the sampling point location and the preset road intersection location in the divided top view. The distance with the largest value among these two distances is taken as the target distance r1 for the sampling point location. Then, based on the target distance r1, calculate the attention coefficient k for the sampling point location. p Specifically, this is achieved through the following formula:

[0022]

[0023] Where Δ1 and β are correction coefficients.

[0024] Optionally, step 5 specifically includes:

[0025] Step 5.1: Divide each marker in the 3D city area model into multiple grids of size dA. For each sampling point location, determine the visible markers at that location from all markers. Then, based on the national height statistical probability density distribution, calculate the solid angle dΩ of each grid among the visible markers. m0 Specifically, it is calculated using the following formula:

[0026]

[0027] Where r2 represents the straight-line distance from the eyeball position to the marked grid position, which is a function with height as the independent variable, and α2 is the probability corresponding to different heights;

[0028] Step 5.2: According to the direction of the vector of each path, for two adjacent sampling point positions, the first vector is obtained by pointing from the previous sampling point position to the next sampling point position, and then multiple first vectors are obtained. The direction of the first vector is the main view direction of the previous sampling point position, and then the main view direction of each sampling point is obtained.

[0029] Step 5.3: For each grid in each visible marker at the sampling point location, obtain the 3D coordinates of the grid and the human eye position at the sampling point location. Obtain a line segment of a preset length along the main viewing direction at the human eye position. Use this line segment as the diameter and construct a sphere based on the diameter. Obtain the chord length l of the line connecting the 3D coordinates of the grid and the human eye position to the sphere. Then, based on the chord length l and the diameter d, calculate the field of view correction coefficient k1 of the grid, specifically through the following formula:

[0030]

[0031] Step 5.4: For each grid, compare the grid's field of view correction factor k1 with the grid's solid angle dΩ. m0 Multiplying these yields the solid angle dΩ of the mesh, based on the view correction. m Specifically, this is achieved through the following formula:

[0032] dΩ m =k1dΩ m0 ;

[0033] solid angle dΩ based on view correction for all grids in all visible markers m Integrating, we obtain the solid angle Ω of the sampling point location based on the field of view correction. m .

[0034] Optionally, step 6 specifically includes:

[0035] Step 6.1: Under a clear sky according to CIE standards, obtain the average glare intensity DGP of the target city area during a preset time period. Then, the probability of a person seeing the sign is 1-DGP. Use the probability of a person seeing the sign as the illumination correction coefficient k2 for the solid angle.

[0036] Step 6.2: Combine the illumination correction coefficient k2 of the solid angle with the solid angle Ω based on the field of view correction. m Multiplying these yields the illumination-corrected solid angle Ω' at the sampling point location. m Specifically, this is achieved through the following formula:

[0037] Ω′ m =k2Ω m .

[0038] Optionally, step 7 specifically includes:

[0039] Step 7.1: For each sampling point location, calculate the ratio of all visible marker pixels within the field of view of the human eye location to the total number of pixels in the field of view, and use this ratio as the scene complexity correction coefficient k3 for the sampling point location;

[0040] Step 7.2: Combine the scene complexity correction coefficient k3 at the sampling point location with the solid angle Ω' after lighting correction.m Multiplying these yields the solid angle Ω″ of the scene complexity correction at the sampling point location. m Specifically, this is achieved through the following formula:

[0041] Ω″ m =k3Ω′ m .

[0042] Optionally, step 8 specifically includes:

[0043] Step 8.1: For each sampling point location, calculate the attention coefficient k. p Solid angle Ω″ with scene complexity correction m Multiplying these yields the final solid angle Ω″′ of the sampling point location. m Specifically, this is achieved through the following formula:

[0044] Ω″′ m =k p Ω″ m ;

[0045] Step 8.2: For each path, calculate the final solid angle Ω″′ corresponding to the positions of all sampling points along the path. m Adding them together gives the final solid angle of the path, which is achieved using the following formula:

[0046]

[0047] Where s represents the sum of the sampling point positions on the i-th path, Ω″′ i Let Ω″′ represent the final solid angle of the i-th path. mn This represents the final solid angle of the nth sampling point on the i-th path;

[0048] Step 8.3: For each path, assign the path weight p i The final solid angle Ω″′ of the path i Multiply to obtain the path-corrected solid angle p. i Ω″′ i ;

[0049] Step 8.4: Correct the solid angle p of all paths i Ω″′ i The values ​​are added together to obtain the evacuation capacity evaluation value Ω of the target urban area, which is achieved through the following formula:

[0050] Ω=∑p i Ω″′ i .

[0051] The beneficial effects of adopting the above technical solution are as follows:

[0052] Compared with existing technologies, this invention introduces the solid angle as a quantitative indicator to describe the performance of signage design, making up for the lack of quantitative indicators in the assessment of emergency response capabilities in urban areas. It has important theoretical value and practical significance for improving the safety resilience of smart cities. This invention also takes into account various urban elements and human behavior capabilities during the calculation, realizing dynamic perception, group statistics and complex scenario correction. This invention not only provides quantitative indicators for the assessment of urban emergency response capabilities, but can also be used to guide chief engineers in the design of signage systems. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for evaluating the performance of urban signage design in an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a top view of the target in an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the path in an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the mesh that needs to be considered in the embodiments of the present invention;

[0057] Figure 5 This is a schematic diagram of the human eye position and visible markings in an embodiment of the present invention. Detailed Implementation

[0058] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0059] To address the problems existing in the prior art, this invention provides a method for evaluating the performance of urban signage design, combining... Figure 1 This may include the following steps:

[0060] Step 1: Construct a 3D city area model of the target city area. The 3D city area model includes buildings, roads, signs, evacuation start points (location_ins) and evacuation end points (location_outs). The buildings are represented in 3D boundary form (Brep). The signs include at least building signs and road signs. The signs and roads are represented by 2D surfaces. The signs record the geometric range and location. The roads are used to describe the passable area. The evacuation start points are the evacuation exit locations of the buildings. The evacuation end points are the pre-set safe locations.

[0061] Based on the initial top view of the 3D urban area model, the target top view is obtained. Specifically, the initial top view of the 3D urban area model is obtained, and the starting and ending points of the evacuation are marked on the initial top view as points to obtain the target top view, such as... Figure 2 As shown, the number of people to be evacuated from each evacuation point, i.e., the number of people N inside the building, is obtained. i ;

[0062] Step 2: In the target top view, using a pathfinding algorithm, such as A*, DFS, BFS, etc., calculate the evacuation endpoint corresponding to each evacuation starting point. Specifically, select the evacuation endpoint closest to the evacuation starting point. It should be noted that if the distance between the evacuation starting point and multiple evacuation endpoints is equal, then randomly select one as the evacuation endpoint corresponding to the evacuation starting point.

[0063] Using the vectors pointing from the evacuation starting point to its corresponding evacuation endpoint as paths, multiple paths are obtained, such as... Figure 3 As shown, the number of people N to be evacuated from the evacuation starting point is then determined. i Calculate the path weight p i Specifically, this is achieved through the following formula:

[0064]

[0065] Where, p i Let represent the weight of the i-th path, and k be the total number of paths.

[0066] Step 3: Divide the target top view into grids according to the preset number and shape of grids to obtain the divided top view;

[0067] The number of grids is limited, and the number, size, and shape of the grids depend on the specific urban area. The grid division methods include structured quadrilateral grid division, Delaunay triangulation, and adaptive grid division.

[0068] Step 4: In the divided top view, determine the locations of multiple sampling points based on the path, and calculate the attention coefficient k for each sampling point location. p ;

[0069] Step 4.1: In the divided top view, for each path, determine the grids that intersect with the path to obtain the grids of interest, such as... Figure 4 ;

[0070] Step 4.2: Obtain the center position of the grid that needs to be focused on as the sampling point position, and obtain multiple sampling point positions corresponding to each path;

[0071] Step 4.3: For each sampling point location on each path, calculate the distance between the sampling point location and the evacuation starting point of that path, and calculate the distance between the sampling point location and the preset road intersection location in the divided top view. The distance with the largest value among these two distances is taken as the target distance r1 for the sampling point location. Then, based on the target distance r1, calculate the attention coefficient k for the sampling point location. p Specifically, this is achieved through the following formula:

[0072]

[0073] Where α1 and β are correction coefficients.

[0074] Step 5: Calculate the solid angle of the sampling point location, and correct the solid angle of the sampling point location to obtain the solid angle Ω of the sampling point location based on the field of view correction. m ;

[0075] Step 5.1: Divide each marker in the 3D city area model into multiple grids of size dA. For each sampling point location, determine the visible markers at that location from all markers. Then, based on the national height statistical probability density distribution, calculate the solid angle dΩ of each grid among the visible markers. m0 Specifically, it is calculated using the following formula:

[0076]

[0077] Where r2 represents the straight-line distance from the eyeball position to the marked grid position, which is a function with height as the independent variable, and α2 is the probability corresponding to different heights;

[0078] Step 5.2: According to the direction of the vector of each path, for two adjacent sampling point positions, the first vector is obtained by pointing from the previous sampling point position to the next sampling point position, and then multiple first vectors are obtained. The direction of the first vector is the main view direction of the previous sampling point position, and then the main view direction of each sampling point is obtained.

[0079] Step 5.3: For each grid within each visible marker at the sampling point location, obtain the grid's 3D coordinates and the human eye position at the sampling point location, and combine them... Figure 5 A line segment of a preset length is obtained at the human eye position along the main viewing direction. In this invention, the preset length is selected in unit 1. This line segment is used as the diameter, and a sphere is constructed based on the diameter to obtain a mesh (i.e., Figure 5The chord length *l* connecting the three-dimensional coordinates of the grid (representing the human eye position) to the sphere intersects the grid. The direction from the human eye position to the grid is the grid orientation. Based on the chord length *l* and the diameter *d*, the grid's field of view correction coefficient *k1* is calculated using the following formula:

[0080]

[0081] Step 5.4: For each grid, compare the grid's field of view correction factor k1 with the grid's solid angle dΩ. m0 Multiplying these yields the solid angle dΩ of the mesh, based on the view correction. m Specifically, this is achieved through the following formula:

[0082] dΩ m =k1dΩ m0 ;

[0083] solid angle dΩ based on view correction for all grids in all visible markers m Integrating, we obtain the solid angle Ω of the sampling point location based on the field of view correction. m .

[0084] Step 6: Based on the illumination conditions of the target city area, calculate the solid angle Ω of the sampling point location based on field-of-view correction. m After correction, the solid angle Ω' of the sampling point location after illumination correction is obtained. m ;

[0085] Step 6.1: Under a clear sky according to CIE standards, obtain the average glare intensity DGP of the target city area during a preset time period. In this invention, the average glare intensity DGP of the target scene from 8:30 to 17:30 is used. Then, the visibility probability of a person to the sign is 1-DGP. The visibility probability of a person to the sign is used as the illumination correction coefficient k2 of the solid angle.

[0086] Step 6.2: Combine the illumination correction coefficient k2 of the solid angle with the solid angle Ω based on the field of view correction. m Multiplying these yields the illumination-corrected solid angle Ω' at the sampling point location. m Specifically, this is achieved through the following formula:

[0087] Ω′ m =k2Ω m .

[0088] Step 7: Based on the scene complexity of the 3D city area model, adjust the solid angle Ω' of the sampling point location after lighting correction. m After correction, the solid angle Ω″ of the scene complexity correction at the sampling point location is obtained. m This more accurately reflects the visibility of signs in complex environments.

[0089] Step 7.1: For each sampling point location, calculate the ratio of all visible marker pixels within the field of view of the human eye location to the total number of pixels in the field of view, and use this ratio as the scene complexity correction coefficient k3 for the sampling point location;

[0090] Step 7.2: Combine the scene complexity correction coefficient k3 at the sampling point location with the solid angle Ω' after lighting correction. m Multiplying these yields the solid angle Ω″ of the scene complexity correction at the sampling point location. m Specifically, this is achieved through the following formula:

[0091] Ω″ m =k3Ω′ m .

[0092] Step 8: Based on the attention coefficient k p Solid angle Ω″ for scene complexity correction m The values ​​are then adjusted to obtain the evacuation capacity evaluation value Ω for the target urban area.

[0093] Step 8.1: For each sampling point location, calculate the attention coefficient k. p Solid angle Ω″ with scene complexity correction m Multiplying these yields the final solid angle Ω″ of the sampling point location. m Specifically, this is achieved through the following formula:

[0094] Ω″′ m =k p Ω″ m ;

[0095] Step 8.2: For each path, calculate the final solid angle Ω″′ corresponding to the positions of all sampling points along the path. m Adding them together gives the final solid angle of the path, which is achieved using the following formula:

[0096]

[0097] Where s represents the sum of the sampling point positions on the i-th path, Ω″′ i Let Ω″′ represent the final solid angle of the i-th path. mn This represents the final solid angle of the nth sampling point on the i-th path;

[0098] Step 8.3: For each path, assign the path weight p i The final solid angle Ω″′ of the path i Multiply to obtain the path-corrected solid angle p. i Ω″′ i ;

[0099] Step 8.4: Correct the solid angle p of all pathsi Ω″′ i The values ​​are added together to obtain the evacuation capacity evaluation value Ω of the target urban area, which is achieved through the following formula:

[0100] Ω=∑p i Ω″′ i .

[0101] Because guiding signage plays a significant role in the evacuation process during sudden accidents in cities or urban areas, this invention proposes a method for performance evaluation of urban signage design. This method not only provides quantitative indicators for assessing urban emergency response capabilities but can also guide chief engineers in designing signage systems. Invention:

[0102] 1. Considering the spatial information of urban areas (including the locations of buildings, roads, signs, and shelters, i.e., evacuation point locations) and the distribution of people, the focus is on evacuation routes in the event of a sudden accident. The optimal set of paths from building exits to shelters in the urban area is obtained through a pathfinding algorithm, and path weights are allocated according to the pedestrian flow of each building to ensure the spatial coupling between the evaluation model and the actual distribution of evacuation pressure.

[0103] 2. When calculating solid angles, the size of the solid angles will vary depending on the height of the person being calculated. The probability density distribution of height will be used to calculate the solid angles.

[0104] 3. Considering the human field of vision, pay attention to the human eye's recognition effect on different locations within the field of vision, and correct the field of vision for the solid angle.

[0105] 4. Considering the glare problem under sunlight, the solid angle under illumination is corrected to achieve an evaluation of the visibility of the signage under natural lighting conditions.

[0106] 5. Correct the scene complexity and describe the "visibility" and "identifiability" performance of the markers during the evacuation process.

[0107] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for evaluating the performance of urban signage design, characterized in that, include: Step 1: Construct a 3D urban area model of the target city area. The 3D urban area model includes buildings, roads, signs, evacuation start points and evacuation end points. The signs include at least building signs and road signs. The evacuation start points are the evacuation exits of the buildings. The evacuation end points are the pre-set safe locations. Based on the initial top view of the 3D urban area model, the target top view is obtained, and the number of people to be evacuated from each evacuation point, i.e., the number of people N inside the building, is obtained. i ; Step 2: In the target top view, using a pathfinding algorithm, for each evacuation starting point, calculate the corresponding evacuation destination. The vector pointing from the evacuation starting point to its corresponding evacuation destination is used as the path, resulting in multiple paths. Then, based on the number of people N to be evacuated from each evacuation starting point... i Calculate the weight of the path ; Step 3: Divide the target top view into grids according to the preset number and shape of grids to obtain the divided top view; Step 4: In the divided top view, determine the locations of multiple sampling points based on the path, and calculate the attention coefficient for each sampling point location. ; Step 5: Calculate the solid angle of the sampling point location, and correct the solid angle of the sampling point location to obtain the solid angle of the sampling point location based on the field of view correction. ; Step 5.1: Divide each marker in the 3D city area model into multiple grids of size dA. For each sampling point location, determine the visible markers at that location from all markers. Then, based on the national height statistical probability density distribution, calculate the solid angle of each grid among the visible markers. Specifically, it is calculated using the following formula: ； in, The straight-line distance from the eye position to the marked grid position is a function of height as the independent variable. These represent the probabilities corresponding to different heights. Step 5.2: According to the direction of the vector of each path, for two adjacent sampling point positions, the first vector is obtained by pointing from the previous sampling point position to the next sampling point position, and then multiple first vectors are obtained. The direction of the first vector is the main view direction of the previous sampling point position, and then the main view direction of each sampling point is obtained. Step 5.3: For each grid in each visible marker at the sampling point location, obtain the 3D coordinates of the grid and the human eye position at the sampling point location. Obtain a line segment of a preset length along the main viewing direction at the human eye position. Use this line segment as the diameter and construct a sphere based on the diameter. Obtain the chord length of the intersection between the line connecting the 3D coordinates of the grid and the human eye position and the sphere. l Furthermore, based on the chord length l and diameter d Calculate the field of view correction coefficient of the grid. Specifically, this is achieved through the following formula: ； Step 5.4: For each grid cell, adjust the field of view correction factor. solid angle with the grid Multiplying yields the solid angle of the mesh based on view correction. Specifically, this is achieved through the following formula: ； Solid angle based on view correction for all grids in all visible markers Integrating, we obtain the solid angle of the sampling point location based on the field of view correction. ; Step 6: Based on the lighting conditions of the target city area, determine the solid angle of the sampling point location based on field-of-view correction. After correction, the solid angle after illumination correction at the sampling point location is obtained. ; Step 7: Based on the scene complexity of the 3D city area model, adjust the solid angle of the sampling point location after lighting correction. After correction, the solid angle of the scene complexity correction at the sampling point location is obtained. ; Step 8: Based on attention coefficient Solid angles corrected for scene complexity The adjustments are then made, and the evacuation capacity evaluation value of the target urban area is calculated. .

2. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, Step 1, based on the initial top view of the 3D urban area model, obtains the target top view, including: Obtain the initial top view of the 3D urban area model, and mark the starting and ending points of the evacuation as points on the initial top view to obtain the target top view.

3. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, In step 2, the number of people N to be evacuated from the evacuation starting point is determined. i Calculate the weight of the path Specifically, this is achieved through the following formula: ； in, Indicates the first i The weight of each path, k This represents the total number of paths.

4. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, Step 4 specifically includes: Step 4.1: In the divided top view, for each path, determine the grids that intersect with the path to obtain the grids that need to be of interest; Step 4.2: Obtain the center position of the grid that needs to be focused on as the sampling point position, and obtain multiple sampling point positions corresponding to each path; Step 4.3: For each sampling point location on each path, calculate the distance between the sampling point location and the evacuation starting point of that path, and calculate the distance between the sampling point location and the preset road intersection location in the divided top view. The distance with the largest difference between the sampling point location and the evacuation starting point of that path, and between the sampling point location and the preset road intersection location in the divided top view, is taken as the target distance for the sampling point location. And then based on the target distance Calculate the attention coefficient of the sampling point location. Specifically, this is achieved through the following formula: ; in, and β This is a correction factor.

5. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, Step 6 specifically includes: Step 6.1: Under a clear sky according to CIE standards, obtain the average glare intensity (DGP) of the target city area during a preset time period. The probability of human visibility of the sign is then 1-DGP. This probability of human visibility of the sign is used as the illumination correction coefficient for the solid angle. ; Step 6.2: Adjust the lighting correction factor for the solid angle. , and solid angle based on field of view correction Multiplying these yields the illumination-corrected solid angle at the sampling point location. Specifically, this is achieved through the following formula: 。 6. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, Step 7 specifically includes: Step 7.1: For each sampling point location, calculate the ratio of all visible marker pixels within the field of view of the human eye to the total number of pixels in the field of view, and use this ratio as the scene complexity correction coefficient for the sampling point location. ; Step 7.2: Adjust the scene complexity coefficient at the sampling point location. solid angle after lighting correction Multiplying these yields the solid angle of the scene complexity correction at the sampling point location. Specifically, this is achieved through the following formula: 。 7. The method for evaluating the performance of urban signage design according to claim 1, characterized in that, Step 8 specifically includes: Step 8.1: For each sampling point location, calculate the attention coefficient. Solid angle with scene complexity correction Multiplying them together gives the final solid angle of the sampling point location. Specifically, this is achieved through the following formula: ； Step 8.2: For each path, calculate the final solid angle corresponding to the positions of all sampling points along the path. Adding them together gives the final solid angle of the path, which is achieved using the following formula: ; in, s Indicates the first i The sum of the sampling point locations on each path, Indicates the first i The final solid angle of each path, Indicates the first i On the path, the first n The final solid angle at each sampling point location; Step 8.3: For each path, assign path weights. The final solid angle of the path Multiply to obtain the solid angle after path correction. ; Step 8.4: Correct the solid angle of all paths Add them together to obtain the evacuation capacity evaluation value of the target urban area. Specifically, this is achieved through the following formula: 。

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