A Method and System for Generating Dynamic Simulation Graphics of Light Pollution from Building Glass Curtain Walls

Abstract

This invention discloses a method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls, belonging to the field of dynamic light pollution simulation technology. This method and system, by integrating scene data from transportation hubs and sports venues, constructs a linked three-dimensional model and combines it with a dynamic pedestrian occlusion correction algorithm to improve the accuracy of light pollution simulation. Its dynamic simulation graphics can intuitively display the light pollution situation along the entire commuting route, and the dual-dimensional judgment can accurately assess the impact. The output optimization suggestions are highly targeted, reducing traffic risks and improving commuting comfort, providing a scientific basis for light pollution control during events, and effectively solving the problems of existing technologies such as individual simulation, large errors, and lack of full-chain evaluation.

Description

Technical Field

[0001] This invention relates to the field of dynamic simulation technology of light pollution, specifically to a method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls. Background Technology

[0002] Existing methods and systems for generating dynamic simulation graphics of light pollution from architectural glass curtain walls still have the following drawbacks in practical use:

[0003] During sporting events, urban transportation hubs (such as subway and bus stations) surrounding sports venues form a "event-commuting" linkage scenario with the sports venues. Existing dynamic simulation technology for light pollution from building glass curtain walls cannot simultaneously and accurately simulate the impact of reflected light from glass curtain walls on traffic safety within transportation hubs (such as driver visibility and pedestrian movement) and the visual comfort of spectators and athletes commuting from transportation hubs to sports venues in this linkage scenario. Specifically, it only simulates the transportation hub or sports venue scenario separately, without considering the dynamic relationship between pedestrian and vehicle traffic between the two; it does not consider the dynamic impact of changes in pedestrian density on the light reflection path, resulting in large errors in light pollution simulation; and it cannot output light pollution assessment results for the entire commuting route from "transportation hub to sports venue," making it difficult to support targeted control of light pollution during events. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls, so as to solve the above-mentioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls, comprising the following steps:

[0006] S1: Acquire basic data, which includes 3D modeling data of building structure, spatial layout data, dynamic correlation data, and environmental data; wherein, the 3D modeling data of building structure is obtained by scanning the building structure of the stadium and surrounding transportation hubs with LiDAR, the spatial layout data is obtained by measuring the relevant areas around the transportation hubs and stadiums with a total station, the dynamic correlation data is obtained from the sports event management system and the transportation hub monitoring system, and the environmental data is obtained by collecting light sensors;

[0007] S2: Process the basic data to obtain processed data, including the following sub-steps:

[0008] S21: Integrate the three-dimensional modeling data of the building structure with the spatial layout data to construct a three-dimensional scene model linking "transportation hub-sports stadium";

[0009] S22: Using the server timestamp as a reference, align the dynamic correlation data with the environmental data in time to generate a structured dataset;

[0010] S23: Based on the pedestrian density data in the structured dataset, the light reflection path is corrected using a pedestrian dynamic occlusion correction algorithm to obtain the corrected reflected light intensity data;

[0011] S24: Extract key nodes from the audience's commuting route, and generate motion light pollution intensity curves for each key node based on the corrected reflected light intensity data;

[0012] S3: The processed data is judged to obtain the judgment result, which includes the judgment result of traffic safety dimension and the judgment result of event commuting comfort dimension.

[0013] S4: Based on the judgment results, generate dynamic simulation graphics of light pollution from building glass curtain walls and optimization suggestions.

[0014] Further, in step S1, the three-dimensional modeling data of the building structure includes the location, material, tilt angle, and three-dimensional point cloud data of the glass curtain wall. The three-dimensional point cloud data is acquired by setting 12 evenly distributed scanning points within a 300-meter range of the stadium and surrounding transportation hubs, using a LiDAR device with an accuracy of ±2mm, and the acquisition time for each scanning point is no less than 10 minutes, with a point cloud data density ≥50 points / m². 2 The spatial layout data includes the length × width × height of the waiting area of ​​the transportation hub, the width of the passageway, the location of the entrance and exit, and the relevant data of the pedestrian passageway around the stadium. The relevant data is obtained by measuring with a total station.

[0015] Further, in step S1, the dynamically correlated data includes the schedule, the number of spectators exiting the station at different times, the commuting routes of athletes and staff, pedestrian density, and vehicle flow trajectories. The pedestrian density is collected by high-definition cameras deployed at the transportation hub and updated every 5 minutes. The vehicle flow trajectories are obtained through the transportation hub monitoring system. The environmental data includes the solar altitude angle, light intensity, atmospheric scattering coefficient, glass curtain wall reflectivity, and average pedestrian reflectivity. The acquisition accuracy of the solar altitude angle is ±0.1°, and the measurement range of light intensity is 0-2000W / m². 2 The accuracy is ±5W / ㎡.

[0016] Furthermore, in step S23, the specific process of the dynamic pedestrian occlusion correction algorithm is as follows:

[0017] The occlusion coefficient η is determined based on the real-time pedestrian density ρ_pedestrian. η = min(ρ_pedestrian) 人流 / 1.0,1.0), when ρ 人流When ≥1.0 people / m², η=1.0; when ρ 人流 When η = 0, η = 0;

[0018] The corrected reflected light intensity I is calculated based on the occlusion coefficient η. ' I ' =I 原 ×[(1-η)×ρ 幕墙 +η×ρ 人 ], where I 原 The initial reflected light intensity is not considered when pedestrian flow obstructs the view. 幕墙 ρ represents the reflectivity of the glass curtain wall in the corresponding area. 人 It is the average reflectance of the human flow.

[0019] Furthermore, in step S3, the process of obtaining the traffic safety dimension judgment result is as follows: set the reflected light brightness threshold L1 = 500 cd / m². 2 The system collects the reflected light intensity L in real time through a light sensor. When L > L1 and the illuminated area belongs to the vehicle traffic path or pedestrian passage, it is determined to trigger a "traffic risk warning".

[0020] Furthermore, in step S3, the process of obtaining the result of the event commuting comfort dimension judgment is as follows: setting the reflected light duration threshold T = 3 seconds and the brightness threshold L2 = 300 cd / m². 2 The light intensity fluctuation threshold ΔI = 20% / s is calculated by continuous sampling. The light intensity fluctuation value ΔI = |I(i+1)-I(i)| / I(i)×100% is calculated. When the light intensity of 6 consecutive sampling points of a certain node is greater than L2 and ΔI is greater than 20%, it is determined to be "comfort impact".

[0021] Furthermore, in step S4, the process of generating the dynamic simulation graphics is as follows: a three-dimensional visualization scene is constructed using the Unity3D engine, and a simulation image is generated every 30 seconds with the time axis as the horizontal axis. "High-risk congestion points" that meet the warning conditions of traffic safety dimension are highlighted in red in the image.

[0022] Further, in step S4, the process of generating the optimization suggestion is as follows: when a glass curtain wall area triggers ≥3 warnings within a preset time period, a "temporary shading film installation suggestion" is output, wherein the required shading film reflectance ρ in the "temporary shading film installation suggestion" is... 需 =ρ 原 -Δρ, where Δρ=(L 实测 -L 阈值 ) / L 入射 ×100%; When the light pollution risk level of a transportation hub is ≥3 during a certain period, output "suggestions for adjusting the frequency of shuttle bus departures".

[0023] The system for generating dynamic simulation graphics of light pollution from building glass curtain walls includes a data acquisition module, a data processing module, a data judgment module, and a result generation module.

[0024] The data acquisition module is used to perform the operation of step S1 to acquire the basic data;

[0025] The data processing module is connected to the data acquisition module and is used to receive the basic data and perform the operation of step S2 to process the basic data and obtain the processed data.

[0026] The data judgment module is connected to the data processing module and is used to receive the processed data and perform the operation of step S3 to judge the processed data and obtain the judgment result.

[0027] The result generation module is connected to the data judgment module and is used to receive the judgment result and perform the operation of step S4 to generate dynamic simulation graphics of light pollution from building glass curtain walls and optimization suggestions.

[0028] Furthermore, the data acquisition module includes a lidar scanning unit, a total station measurement unit, a dynamic data acquisition unit, and an environmental data acquisition unit; the lidar scanning unit is used to acquire 3D modeling data of the building structure, the total station measurement unit is used to acquire spatial layout data, the dynamic data acquisition unit is used to acquire dynamically correlated data, and the environmental data acquisition unit is used to acquire environmental data; the data processing module includes a model building unit, a data fusion unit, a light intensity correction unit, and a curve generation unit; the model building unit is used to build a linked 3D scene model, the data fusion unit is used to generate a structured dataset, the light intensity correction unit is used to correct reflected light intensity using a dynamic pedestrian occlusion correction algorithm, and the curve generation unit is used to generate a motion path light pollution intensity curve.

[0029] Compared with existing technologies, the method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls provided by this invention have the following advantages:

[0030] This method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls integrates scene data from transportation hubs and sports venues to construct a linked 3D model. Combined with a dynamic occlusion correction algorithm for pedestrian flow, it improves the accuracy of light pollution simulation. Its dynamic simulation graphics can intuitively display the light pollution situation along the entire commuting route, and the two-dimensional judgment can accurately assess the impact. The output optimization suggestions are highly targeted, which can reduce traffic risks and improve commuting comfort, providing a scientific basis for light pollution control during events and effectively solving the problems of existing technologies such as individual simulation, large errors, and lack of full-chain evaluation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0032] Figure 1 This is a schematic diagram of the data collection and flow of the "transportation hub-sports venue" linkage according to the present invention;

[0033] Figure 2 This is a logic diagram of the dynamic occlusion correction algorithm for pedestrian flow according to the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0035] Please see Figure 1 , 2 A method and system for generating dynamic simulation graphics of light pollution from building glass curtain walls, comprising the following steps:

[0036] S1: Acquire basic data, which includes 3D modeling data of building structure, spatial layout data, dynamic correlation data and environmental data; among them, 3D modeling data of building structure is obtained by scanning the building structure of stadium and surrounding transportation hubs with LiDAR, spatial layout data is obtained by measuring the relevant areas around transportation hubs and stadiums with total station, dynamic correlation data is obtained from sports event management system and transportation hub monitoring system, and environmental data is obtained by collecting light sensor.

[0037] S2: Process the basic data to obtain the processed data, including the following sub-steps:

[0038] S21: Integrate the 3D modeling data of the building structure with the spatial layout data to construct a 3D scene model linking "transportation hub-sports stadium";

[0039] S22: Based on the server timestamp, align the dynamically related data and environmental data in time to generate a structured dataset;

[0040] S23: Based on the pedestrian density data in the structured dataset, the light reflection path is corrected by the pedestrian dynamic occlusion correction algorithm to obtain the corrected reflected light intensity data;

[0041] S24: Extract key nodes from the audience's commuting route and generate motion light pollution intensity curves for each key node based on the corrected reflected light intensity data;

[0042] S3: Judge the processed data and obtain the judgment results, including the judgment results of traffic safety dimension and the judgment results of event commuting comfort dimension;

[0043] S4: Generate dynamic simulation graphics of light pollution from building glass curtain walls and optimization suggestions based on the judgment results.

[0044] In step S1, the 3D modeling data of the building structure includes the location, material, tilt angle, and 3D point cloud data of the glass curtain wall. The 3D point cloud data is acquired by setting 12 evenly distributed scanning points within a 300-meter range of the stadium and surrounding transportation hubs, using a LiDAR device with an accuracy of ±2mm, and the acquisition time for each scanning point is no less than 10 minutes, with a point cloud data density of ≥50 points / m². 2 The glass curtain wall area was scanned in detail, and its material parameters and tilt angle were recorded. The spatial layout data included the length × width × height of the waiting area of ​​the transportation hub, the width of the passageway, the location of the entrance and exit, and the relevant data of the pedestrian passage around the stadium. The relevant data were obtained by measuring with a total station and a CAD format floor plan was generated.

[0045] In step S1, the dynamically correlated data includes the schedule, the number of spectators exiting the station at different times, the commuting routes of athletes and staff, pedestrian density, and vehicle flow trajectories. Pedestrian density is collected by high-definition cameras deployed at the transportation hub and updated every 5 minutes. Vehicle flow trajectories are obtained through the transportation hub monitoring system, and pedestrian density and vehicle flow trajectories are extracted in real time using video analysis algorithms. The data is uploaded to the server via a 5G network every 5 minutes. Environmental data includes solar altitude angle, light intensity, atmospheric scattering coefficient, glass curtain wall reflectivity, and average reflectivity of pedestrians. The acquisition accuracy of the solar altitude angle is ±0.1°, and the measurement range of light intensity is 0-2000W / m². 2 The accuracy is ±5W / m 2 .

[0046] 3D scene construction: Point cloud processing software is used to denoise the LiDAR data, and discrete points with a distance ≥ 3 times the standard deviation are deleted. The point cloud data and CAD plan layout are overlaid using a seven-parameter coordinate transformation method to generate a 3D mesh model containing buildings, transportation facilities and passages.

[0047] Four-dimensional dataset generation: Based on the server timestamp, dynamic pedestrian flow data (with timestamp) and environmental data (collected every 10 seconds) are time-aligned, and interpolation is used to fill data gaps. For example, when illumination data is missing, it is filled with 90% of the average of the previous 5 minutes. Finally, a structured dataset D=(t, x, y, z, N pedestrian flow, I illumination, h solar altitude angle) is generated, where t is time and (x, y, z) are spatial coordinates.

[0048] In step S23, the specific process of the dynamic occlusion correction algorithm for pedestrian flow is as follows:

[0049] The occlusion coefficient η is determined based on the real-time pedestrian density ρ_pedestrian. η = min(ρ_pedestrian) 人流 / 1.0,1.0), when ρ 人流 When ≥1.0 people / m², η=1.0; when ρ 人流 When η = 0, η = 0;

[0050] The corrected reflected light intensity I is calculated based on the occlusion coefficient η. ' I ' =I 原 ×[(1-η)×ρ 幕墙 +η×ρ 人 ], where I 原 The initial reflected light intensity is not considered when pedestrian flow obstructs the view. 幕墙 ρ represents the reflectivity of the glass curtain wall in the corresponding area. 人 It is the average reflectance of the human flow.

[0051] In step S3, the process of obtaining the traffic safety dimension judgment result is as follows: set the reflected light brightness threshold L1 = 500 cd / m². 2 The system collects the reflected light intensity L in real time through a light sensor. When L > L1 and the illuminated area belongs to the vehicle traffic path or pedestrian passage, it is determined to trigger a "traffic risk warning".

[0052] In step S3, the process of obtaining the results of the event commuting comfort dimension judgment is as follows: set the reflected light duration threshold T = 3 seconds and the brightness threshold L2 = 300 cd / m². 2 The light intensity fluctuation threshold ΔI = 20% / s is calculated by continuous sampling. The light intensity fluctuation value ΔI = |I(i+1)-I(i)| / I(i)×100%, where ΔI is the percentage of light intensity fluctuation (unit: % / s), I(i) is the light intensity value of the i-th sample (unit: cd / m²), and I(i+1) is the light intensity value of the (i+1)-th sample (sampling interval 0.5 seconds). When the light intensity of 6 consecutive sampling points of a certain node is greater than L2 and ΔI is greater than 20%, it is determined to be "comfort impact".

[0053] In step S4, the dynamic simulation graphics are generated as follows: a three-dimensional visualization scene is constructed using the Unity3D engine, with the time axis as the horizontal axis, and a simulation image is generated every 30 seconds. "High-risk congestion points" are highlighted in red in the image. Among them, the areas that meet the warning conditions of the traffic safety dimension are "high-risk congestion points". The size of the marking changes dynamically with the risk level. The higher the risk level, the larger the marking size.

[0054] Light pollution display along visitor movement: Light pollution intensity color bands are superimposed on the three-dimensional path of visitor movement. Red: L>500cd / ㎡; Yellow: 300cd / ㎡<L≤500cd / ㎡; Green: L≤300cd / ㎡. The transparency of the color bands changes dynamically over time. The longer the light pollution lasts, the lower the transparency.

[0055] In step S4, the optimization suggestion generation process is as follows: when a glass curtain wall area triggers ≥3 warnings within a preset time period, a "Temporary Shielding Film Installation Suggestion" is output, and the required reflectivity ρ of the shading film in the "Temporary Shielding Film Installation Suggestion" is... 需 =ρ 原 -Δρ, where Δρ=(L 实测 -L 阈值 ) / L 入射 ×100%, of which:

[0056] ρ 需 The required reflectivity of the light-shielding film (unit: %)

[0057] ρ 原 The reflectivity of the original glass curtain wall (unit: %)

[0058] Δρ represents the reflectance that needs to be reduced (in %).

[0059] L 实测 The measured reflected light intensity (unit: cd / m²) 2 )

[0060] L 阈值 The light intensity threshold for the corresponding scene (e.g., L threshold = 500 cd / m² in a traffic scene). 2 )

[0061] L 入射 Incident light intensity (collected by a light sensor, unit: cd / m²) 2 When the light pollution risk level of a transportation hub is ≥3 during a certain period, a "suggestion for adjusting the frequency of shuttle bus departures" will be output.

[0062] The system for generating dynamic simulation graphics of light pollution from building glass curtain walls includes a data acquisition module, a data processing module, a data judgment module, and a result generation module.

[0063] The data acquisition module is used to perform the operation in step S1 and acquire basic data;

[0064] The data processing module, connected to the data acquisition module, is used to receive basic data and execute the operation in step S2 to process the basic data and obtain the processed data.

[0065] The data judgment module is connected to the data processing module. It is used to receive the processed data and perform the operation in step S3 to judge the processed data and obtain the judgment result.

[0066] The result generation module, connected to the data judgment module, is used to receive the judgment results and execute the operation of step S4 to generate dynamic simulation graphics of light pollution from building glass curtain walls and optimization suggestions.

[0067] The data acquisition module includes a lidar scanning unit, a total station measurement unit, a dynamic data acquisition unit, and an environmental data acquisition unit. The lidar scanning unit is used to acquire 3D modeling data of the building structure, the total station measurement unit is used to acquire spatial layout data, the dynamic data acquisition unit is used to acquire dynamically correlated data, and the environmental data acquisition unit is used to acquire environmental data. The data processing module includes a model building unit, a data fusion unit, a light intensity correction unit, and a curve generation unit. The model building unit is used to build a linked 3D scene model, the data fusion unit is used to generate a structured dataset, the light intensity correction unit is used to correct reflected light intensity using a dynamic occlusion correction algorithm for pedestrian flow, and the curve generation unit is used to generate light pollution intensity curves along pedestrian paths.

[0068] The movement route in the text is a dynamic route.

[0069] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for generating dynamic simulation graphics of light pollution from building glass curtain walls, characterized in that, Includes the following steps: S1: Acquire basic data, which includes 3D modeling data of building structure, spatial layout data, dynamic correlation data, and environmental data; wherein, the 3D modeling data of building structure is obtained by scanning the building structure of the stadium and surrounding transportation hubs with LiDAR, the spatial layout data is obtained by measuring the relevant areas around the transportation hubs and stadiums with a total station, the dynamic correlation data is obtained from the sports event management system and the transportation hub monitoring system, and the environmental data is obtained by collecting light sensors; S2: Process the basic data to obtain processed data, including the following sub-steps: S21: Integrate the three-dimensional modeling data of the building structure with the spatial layout data to construct a "transportation hub-sports stadium" linked three-dimensional scene model; S22: Using the server timestamp as a reference, align the dynamic correlation data with the environmental data in time to generate a structured dataset; S23: Based on the pedestrian density data in the structured dataset, the light reflection path is corrected using a pedestrian dynamic occlusion correction algorithm to obtain the corrected reflected light intensity data; S24: Extract key nodes from the audience's commuting route, and generate motion light pollution intensity curves for each key node based on the corrected reflected light intensity data; S3: The processed data is judged to obtain the judgment result, which includes the judgment result of traffic safety dimension and the judgment result of event commuting comfort dimension. S4: Based on the judgment results, generate dynamic simulation graphics of light pollution from building glass curtain walls and optimization suggestions; In step S1, the dynamically correlated data includes the competition schedule, the number of spectators exiting the station at different times, the commuting routes of athletes and staff, pedestrian density, and vehicle flow trajectories. The pedestrian density is collected by high-definition cameras deployed at the transportation hub and updated every 5 minutes. The vehicle flow trajectories are obtained through the transportation hub monitoring system. The environmental data includes the solar altitude angle, light intensity, atmospheric scattering coefficient, glass curtain wall reflectivity, and average pedestrian reflectivity. The solar altitude angle is collected with an accuracy of ±0.1°, and the light intensity is measured within the range of 0-2000 W / m². 2 The accuracy is ±5W / m 2 ; In step S3, the process of obtaining the result of the event commuting comfort dimension judgment is as follows: setting the reflected light duration threshold T = 3 seconds and the brightness threshold L2 = 300 cd / m². 2 The light intensity fluctuation threshold ΔI = 20% / s is calculated by continuous sampling. The light intensity fluctuation value ΔI = |I(i+1)-I(i)| / I(i)×100% is calculated. When the light intensity of 6 consecutive sampling points of a certain node is greater than L2 and ΔI is greater than 20%, it is determined to be "comfort impact".

2. The method for generating dynamic simulation graphics of light pollution from building glass curtain walls according to claim 1, characterized in that, In step S1, the 3D modeling data of the building structure includes the location, material, tilt angle, and 3D point cloud data of the glass curtain wall. The 3D point cloud data is acquired by setting 12 evenly distributed scanning points within a 300-meter range of the stadium and surrounding transportation hubs, using a LiDAR device with an accuracy of ±2mm, and the acquisition time for each scanning point is no less than 10 minutes, with a point cloud data density ≥50 points / m². 2 The spatial layout data includes the length × width × height of the waiting area of ​​the transportation hub, the width of the passageway, the location of the entrances and exits, and the relevant data of the pedestrian passages around the sports stadium. The relevant data is obtained by measuring with a total station.

3. The method for generating dynamic simulation graphics of light pollution from building glass curtain walls according to claim 1, characterized in that, In step S23, the specific process of the dynamic pedestrian occlusion correction algorithm is as follows: The occlusion coefficient η is determined based on the real-time pedestrian density ρ_pedestrian. η = min(ρ_pedestrian) 人流 / 1.0,1.0), when ρ 人流 When ≥1.0 people / m², η=1.0; when ρ 人流 When η = 0, η = 0; The corrected reflected light intensity I is calculated based on the occlusion coefficient η. ' I ' =I 原 ×[(1-η)×ρ 幕墙 +η×ρ 人 ], where I 原 The initial reflected light intensity is not considered when pedestrian flow obstructs the view. 幕墙 ρ represents the reflectivity of the glass curtain wall in the corresponding area. 人 It is the average reflectance of the human flow.

4. The method for generating dynamic simulation graphics of light pollution from building glass curtain walls according to claim 1, characterized in that, In step S3, the process of obtaining the traffic safety dimension judgment result is as follows: set the reflected light brightness threshold L1 = 500 cd / m 2 The system collects the reflected light intensity L in real time through a light sensor. When L > L1 and the illuminated area belongs to the vehicle traffic path or pedestrian passage, it is determined to trigger a "traffic risk warning".

5. The method for generating dynamic simulation graphics of light pollution from building glass curtain walls according to claim 1, characterized in that, In step S4, the process of generating the dynamic simulation graphics is as follows: a three-dimensional visualization scene is constructed using the Unity3D engine, and a simulation image is generated every 30 seconds with the time axis as the horizontal axis. "High-risk congestion points" that meet the warning conditions of traffic safety dimension are highlighted in red in the image.

6. The method for generating dynamic simulation graphics of light pollution from building glass curtain walls according to claim 1, characterized in that, In step S4, the process of generating the optimization suggestion is as follows: when a glass curtain wall area triggers ≥3 warnings within a preset time period, a "temporary shading film installation suggestion" is output, wherein the required shading film reflectance ρ in the "temporary shading film installation suggestion" is... 需 =ρ 原 -Δρ, where Δρ=(L 实测 -L 阈值 ) / L 入射 ×100%; When the light pollution risk level of a transportation hub is ≥3 during a certain period, output "Suggestions for adjusting the frequency of shuttle bus departures".

7. A dynamic simulation graphics generation system for light pollution from building glass curtain walls, characterized in that, It includes a data acquisition module, a data processing module, a data judgment module, and a result generation module; The data acquisition module is used to perform the operation of step S1 in claim 1 to acquire the basic data; The data processing module is connected to the data acquisition module and is used to receive the basic data and perform the operation of step S2 in claim 1 to process the basic data and obtain the processed data. The data judgment module is connected to the data processing module and is used to receive the processed data and perform the operation of step S3 in claim 1 to judge the processed data and obtain a judgment result. The result generation module is connected to the data judgment module and is used to receive the judgment result and perform the operation of step S4 in claim 1 to generate dynamic simulation graphics of light pollution of building glass curtain walls and optimization suggestions.

8. The dynamic simulation graphics generation system for light pollution from building glass curtain walls according to claim 7, characterized in that, The data acquisition module includes a lidar scanning unit, a total station measurement unit, a dynamic data acquisition unit, and an environmental data acquisition unit. The lidar scanning unit is used to acquire 3D modeling data of the building structure, the total station measurement unit is used to acquire spatial layout data, the dynamic data acquisition unit is used to acquire dynamically correlated data, and the environmental data acquisition unit is used to acquire environmental data. The data processing module includes a model building unit, a data fusion unit, a light intensity correction unit, and a curve generation unit. The model building unit is used to build a linked 3D scene model, the data fusion unit is used to generate a structured dataset, the light intensity correction unit is used to correct reflected light intensity using a dynamic pedestrian occlusion correction algorithm, and the curve generation unit is used to generate a light pollution intensity curve for pedestrian traffic.

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