Urban public space optimization method based on crowd behavior simulation

Abstract

The invention discloses an urban public space optimization method based on crowd behavior simulation. The method comprises the following steps: acquiring urban wind field parameters, urban air pollutant concentration data and geometrical characteristics of buildings; calculating a turbulence diffusion coefficient by adopting a computational fluid dynamics simulation method; acquiring real-time meteorological data according to the real-time meteorological data API to calculate atmospheric stability; determining concentration distribution of pollutants at different time and spatial positions by adopting a pollutant Lagrange particle tracking method; constructing a crowd behavior pattern classification model, and determining crowd behavior patterns in different public spaces; determining an optimal green belt and ventilation corridor configuration and an optimal public space ventilation optimization scheme; and the influence of different configurations on the ventilation effect and pollution inhibition is evaluated. According to the method, a more accurate and more efficient optimization scheme is provided for urban public space design by integrating multi-dimensional factors such as meteorology, building distribution, crowd behaviors and air flow, and the living quality of citizens and the comfort level of the public space are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of smart city planning, and particularly to an urban public space optimization method based on crowd behavior simulation. Background Art

[0002] With the acceleration of the global urbanization process, the urban population density is increasing day by day, and the urban air quality problem is also becoming increasingly serious. High population density and busy traffic activities have led to the accumulation of air pollutants, which has caused great negative impacts on human health and the environment. At present, most of the urban air quality optimization schemes focus on simple measures such as increasing greenery and improving ventilation. Although these methods can alleviate the air pollution problem to a certain extent, they often ignore the complexity of air flow and pollutant diffusion, and lack accurate prediction and control of the actual environmental dynamics. For example, traditional green belt designs and ventilation corridor layouts are designed only based on experience, lacking comprehensive analysis of wind fields, air pollutant concentrations, building distributions, and crowd activities. Therefore, the best air flow effect and pollutant control effect cannot be achieved in complex urban environments. In addition, many existing ventilation optimization schemes usually adopt static calculation models, ignoring dynamic factors such as meteorological conditions, wind speed and direction, and real-time pollutant concentration changes. In most cases, the ventilation model only considers the wind field characteristics of a local area, but does not consider the influence of population density on air flow. The presence and movement of people will affect the air flow path and the pollutant diffusion process. Especially in high-density areas, the effects of air flow and pollutant diffusion are closely related to the crowd activity pattern, which is often ignored by traditional air flow models. In addition, traditional air flow and pollutant diffusion models mostly rely on static data, lack adaptability to dynamic meteorological environments, and are difficult to provide effective air quality management strategies in rapidly changing urban environments. Current urban space designs usually ignore the interaction between crowd behavior patterns and air flow, resulting in poor air flow or pollutant accumulation, affecting the comfort of public spaces and air quality. Especially in busy areas of large cities, there is usually a lack of comprehensive air flow and crowd behavior analysis models, and the layout of public spaces cannot be scientifically optimized, resulting in low air flow efficiency and ineffective pollutant diffusion. Therefore, the current urban air quality optimization technology faces multiple challenges, including lack of real-time response to dynamic meteorological data, ignoring the influence of population density and crowd behavior patterns on air flow, limitations of ventilation and pollution control schemes, etc. These problems make the existing technology less than satisfactory in dealing with urban air pollution and optimizing public space designs, and there is an urgent need for a new, more comprehensive, and more accurate technical method to address the complex problems of urban air quality and public space optimization. Summary of the Invention

[0003] In view of the problems existing in the above-mentioned prior art, the present invention provides an optimization method for urban public spaces based on crowd behavior simulation, which mainly includes:

[0004] Obtain urban wind field parameters and urban air pollutant concentration data through the monitoring network of meteorological stations, and use unmanned aerial vehicle lidar technology to obtain the geometric characteristics of buildings;

[0005] According to the historical meteorological data of urban monitoring points, predict the turbulent kinetic energy of the monitoring points, calculate the turbulent dissipation rate, use the computational fluid dynamics simulation method to obtain the three-dimensional velocity field of air flow within the city, and calculate the turbulent diffusion coefficient of air flow in different urban areas;

[0006] Obtain real-time meteorological data according to the real-time meteorological data API, calculate the atmospheric stability, and determine the stability classification result based on the preset stability classification standard;

[0007] Use the large eddy simulation method to determine the change trend of air flow under different weather conditions, and use the Lagrangian particle tracking method for pollutants to simulate the diffusion process of pollutants in the air, and determine the concentration distribution of pollutants at different times and spatial positions;

[0008] Obtain the public space layout data and the real-time crowd density data within the urban area, construct a crowd behavior pattern classification model, determine the crowd behavior patterns in different public spaces, and predict the crowd density in each public space within a preset future time period;

[0009] Determine the optimal green belt and ventilation corridor configuration according to the pollutant concentration, crowd density, air velocity and green space wind direction data, and combine the turbulent diffusion and pollutant concentration gradient to determine the optimal public space ventilation optimization plan;

[0010] According to the optimized green belt and ventilation corridor layout, building distribution and wind field parameters, combine the air flow simulation and pollutant diffusion trajectory to evaluate the impact of different configurations on ventilation effect and pollution suppression, and optimize the public space ventilation path, green belt distribution and building spacing configuration.

[0011] Further, the step of obtaining urban wind field parameters and urban air pollutant concentration data through the monitoring network of meteorological stations, and using unmanned aerial vehicle lidar technology to obtain the geometric characteristics of buildings includes:

[0012] According to the monitoring network of meteorological stations, wind speed sensors, humidity sensors and temperature sensors are used to obtain urban wind field parameters, record the timestamp of each data, and upload it to the urban air monitoring database. The wind field parameters include wind direction, wind speed, humidity and air temperature; according to the arranged fixed and mobile PM2.5, NO2, and CO sensors, urban air pollutant concentration data are obtained, the monitoring point location and monitoring period are recorded, and the sliding window method is used to process the monitoring data in different time periods to obtain the pollutant concentration change curves per hour, per day and per month; by comparing with the wind field parameters, the space-time interpolation method is used to fill in the missing pollutant concentration data to obtain a pollutant concentration distribution map with space-time continuity; the unmanned aerial vehicle lidar technology is used to scan the urban building structure to obtain the geometric features of the buildings, including the height, density, shape and ground features of the buildings.

[0013] Furthermore, based on the historical meteorological data of urban monitoring points, the turbulent kinetic energy of the monitoring points is predicted, the turbulent dissipation rate is calculated, and the computational fluid dynamics simulation method is used to obtain the three-dimensional velocity field of air flow within the city, and the turbulent diffusion coefficient of air flow in different urban areas is calculated, including:

[0014] According to the air temperature and air pressure, the Sutherland formula is used to determine the air viscosity; based on the historical meteorological data of urban monitoring points and the corresponding turbulent kinetic energy, a recurrent neural network is used for model training to construct a turbulent kinetic energy prediction model. The meteorological data includes wind field parameters and air pressure; according to the real-time monitored meteorological data, the turbulent kinetic energy prediction model is used to determine the turbulent kinetic energy of the monitoring points; based on the turbulent kinetic energy, the standard turbulent dissipation rate formula is used to calculate the turbulent dissipation rate ε, where k is the turbulent kinetic energy, l is the characteristic length, which is the size of the building, and C μ is a model constant obtained by fitting historical data; based on the turbulent model parameters, the computational fluid dynamics simulation method is used to simulate the obtained wind field parameters, air pollutant concentration data and geometric features of the buildings to obtain the three-dimensional velocity field of air flow within the entire city; according to the air viscosity, turbulent kinetic energy and turbulent dissipation rate of different urban areas, the standard k-ε turbulent model is used to calculate the turbulent diffusion effect to obtain the turbulent diffusion coefficient of air flow in different urban areas; according to the urban pollution source emission data, the emission rate of each pollution source is obtained, combined with the pollutant diffusion coefficient and meteorological data, and the Reynolds-averaged Navier-Stokes equations are used to simulate the change of pollutant concentration to determine the pollutant concentration distribution in the city.

[0015] Furthermore, the real-time meteorological data is obtained according to the real-time meteorological data API, the atmospheric stability is calculated, and based on the preset stability classification standard, the stability classification result is determined, including:

[0016] According to the real-time meteorological data API, obtain real-time meteorological data, and obtain meteorological data at different time points by setting the request time interval; use the radar system interface to extract meteorological data at different altitudes in the atmosphere by setting the monitoring altitude range; calculate the atmospheric stability using the Monin-Obukhov length formula based on the ground temperature, wind speed, and atmospheric boundary layer height data; determine the stability classification result, including stable, neutral, and unstable, based on the preset stability classification criteria according to the atmospheric stability calculation result.

[0017] Furthermore, the large eddy simulation method is used to determine the change trend of air flow under different weather conditions, and the Lagrangian particle tracking method for pollutants is used to simulate the diffusion process of pollutants in the air to determine the concentration distribution of pollutants at different time and space positions, including:

[0018] According to the wind field parameters, historical meteorological data, real-time meteorological data, and atmospheric stability, use the large eddy simulation method to calculate the response of air flow under different weather conditions to obtain the change trend of air flow under different weather conditions; according to the coordinates of the pollution source, wind speed, pollutant diffusion coefficient, large eddy simulation results, and real-time meteorological data, use the Lagrangian particle tracking method for pollutants to simulate the diffusion and movement process of pollutants in the air to obtain the flow trajectory of pollutants; combine the fluid dynamics simulation and large eddy simulation results to generate the diffusion path and concentration change diagram of pollutants to determine the concentration distribution of pollutants at different time and space positions.

[0019] Furthermore, obtain the public space layout data and real-time crowd density data within the urban area, construct a crowd behavior pattern classification model, determine the crowd behavior patterns in different public spaces, and predict the crowd density in each public space within a preset future time period, including:

[0020] According to the urban planning database, through GIS data analysis methods, obtain the location coordinates and spatial geometric shapes of urban green spaces, parks, squares, and open streets, and use GIS software for data processing. Divide the public spaces in the city by functional areas, identify the locations and boundaries of different public spaces, and determine the public space layout data, including the locations, areas, shapes, and mutual relationships of each public space; according to the video surveillance system installed in the city, obtain real-time video surveillance data, determine the real-time pedestrian flow density data within the urban area, and use the convolutional neural network algorithm to identify pedestrian trajectories, residence times, and walking speeds; combine the pedestrian flow density data with the public space layout data to obtain the differences in pedestrian flow distribution among different public space types; according to pedestrian trajectories, residence times, and walking speeds, use the decision tree algorithm for model training to construct a crowd behavior pattern classification model, and determine the crowd behavior patterns within different public spaces. The crowd behavior patterns include, but are not limited to, aggregation, retention, and flow patterns; according to historical pedestrian flow data, crowd behavior models, and meteorological data, use the long short-term memory network algorithm for model training to construct a crowd flow trend prediction model to predict the pedestrian flow density of each public space within a preset future time period.

[0021] Furthermore, based on the pollutant concentration, pedestrian flow density, air velocity, and green space wind direction data, determine the optimal configuration of green belts and ventilation corridors, and combine turbulent diffusion with the pollutant concentration gradient to determine the optimal public space ventilation optimization plan, including:

[0022] Based on the pollutant concentration, pedestrian flow density, and air velocity data, use the non-dominated sorting genetic algorithm to construct an optimization model, set multiple objective functions, including minimizing pollutant concentration, maximizing air flow efficiency, and optimizing the public space layout, and use the non-dominated sorting method to evaluate the advantages and disadvantages of each candidate plan to obtain the optimal public space layout plan, including minimizing pollutant concentration and the best configuration of air flow; based on the green space distribution, wind direction changes, and air quality index data, use the grid search method. By setting the grid range, gradually adjust the positions and widths of the green belts and ventilation corridors, and use the method of computational fluid dynamics simulation after each adjustment to determine the air flow, pollutant concentration distribution, and air quality index, evaluate each layout plan, and judge its effects in terms of air flow and pollution diffusion to obtain the optimal configuration of green belts and ventilation corridors; based on the turbulent diffusion coefficient and the pollutant concentration gradient, use the convection-diffusion equation to determine the diffusion rate J of pollutants, where K t is the turbulent diffusion coefficient, is the pollutant concentration gradient; based on the building spacing, wind speed, and air pollution diffusion rate data, a multi-objective linear programming model is used to set multiple optimization objectives, including minimizing pollutant concentration and maximizing air flow efficiency, and constraint conditions are established, including building spacing, wind speed range, and vent location. The linear programming method is used to optimize the ventilation design of the public space and determine the ventilation optimization plan for the public space; according to the ventilation effects of different ventilation optimization plans for the public space, the optimal ventilation optimization plan for the public space is determined, including the best position of the vent, the optimization of the ventilation path, and the optimal configuration of air flow.

[0023] Further, based on the optimized layout of the green belt and ventilation corridor, building distribution, and wind field parameters, combined with air flow simulation and pollutant diffusion trajectories, the impacts of different configurations on ventilation effects and pollution suppression are evaluated, and the ventilation path, green belt distribution, and building spacing configuration of the public space are optimized, including:

[0024] According to the layout of the green belt and ventilation corridor adjusted using the optimal green belt and ventilation corridor configuration, geometric feature data of the building distribution and green belt are obtained. Combining wind field parameters and air pollutant concentration data, the velocity field of air flow, pollutant diffusion trajectories, and air quality index are determined using the method of computational fluid dynamics simulation, and the changes in air flow and pollutant concentration under different green belt and ventilation corridor configurations are determined; according to the changes in air flow and pollutant concentration under different green belt and ventilation corridor configurations, the air flow optimization effects and pollutant diffusion suppression effects under different green belt configurations are evaluated, and the green belt and ventilation corridor designs are optimized; according to the adjusted ventilation optimization plan for the public space and the crowd density data, the air flow effects and crowd activity patterns in the public space are evaluated, and the spatial configuration and layout of the public space are optimized; according to the evaluation results of the green belt and ventilation corridor layout and the evaluation results of the ventilation optimization plan for the public space, the optimal configuration of the public space layout is determined, including the ventilation path, green belt layout, vent location, green space distribution, and building spacing in each public space area.

[0025] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0026] The present invention provides an optimization method for urban public spaces based on crowd behavior simulation. By obtaining wind field parameters, air pollutant concentrations, and building geometric features in real time, the present invention comprehensively analyzes the characteristics of air flow, predicts the air flow trends under different weather conditions, and combines historical meteorological data to predict the turbulent kinetic energy and turbulent diffusion coefficient, simulating the dynamic diffusion process of pollutants. The present invention calculates the atmospheric stability using real-time meteorological data, combines the large eddy simulation method to dynamically predict the air flow trends, and simulates the pollutant diffusion process through the Lagrangian particle tracking method to track the diffusion of pollutants in real time. By combining crowd behavior pattern data, the present invention dynamically adjusts the layout of public spaces to ensure good air flow and pollutant diffusion effects in areas with high population flow densities. With the calculation of atmospheric stability and the simulation of pollutant diffusion, the present invention can dynamically evaluate the pollutant concentration distribution, optimize the configuration of green belts and ventilation corridors, and achieve efficient dilution and diffusion of pollutants. By combining population flow density data and behavior pattern classification, the present invention optimizes the ventilation paths and spatial layouts of public spaces, improving the air quality while enhancing the comfort of crowd activities. By comprehensively considering multi-dimensional factors such as meteorology, building distribution, crowd behavior, and air flow, the present invention provides a more accurate and efficient optimization scheme for urban public space design, promotes the sustainable development of the urban environment, and significantly improves the quality of life of citizens and the comfort of public spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of an optimization method for urban public spaces based on crowd behavior simulation according to the present invention;

[0028] Figure 2 is a schematic diagram of an optimization method for urban public spaces based on crowd behavior simulation according to the present invention;

[0029] Figure 3 is another schematic diagram of an optimization method for urban public spaces based on crowd behavior simulation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] As Figures 1 - 3 , an optimization method for urban public spaces based on crowd behavior simulation in this embodiment may specifically include:

[0032] Step S101, obtaining urban wind field parameters and urban air pollutant concentration data through the monitoring network of meteorological stations, and obtaining the geometric features of buildings using unmanned aerial vehicle lidar technology.

[0033] According to the monitoring network of meteorological stations, wind speed sensors, humidity sensors and temperature sensors are used to obtain urban wind field parameters, record the timestamp of each data, and upload it to the urban air monitoring database. The wind field parameters include wind direction, wind speed, humidity and air temperature. According to the arranged fixed and mobile PM2.5, NO2, and CO sensors, urban air pollutant concentration data are obtained, the monitoring point locations and monitoring periods are recorded, and the sliding window method is used to process the monitoring data in different time periods to obtain the pollutant concentration change curves per hour, per day, and per month. By comparing with the wind field parameters, the space-time interpolation method is used to fill in the missing pollutant concentration data to obtain a pollutant concentration distribution map with space-time continuity. The unmanned aerial vehicle lidar technology is used to scan the urban building structure to obtain the geometric characteristics of the buildings, including the height, density, shape and ground characteristics of the buildings.

[0034] Exemplarily, a set of meteorological sensors and pollutant monitoring devices are arranged in the central area of a certain city. For example, wind speed sensors, humidity sensors and temperature sensors are installed and the monitoring starts at 8:00 am on November 1, 2020. Through these sensors, data including wind direction, wind speed, humidity and air temperature are obtained in real time. If during this period, the wind speed is 5 m / s, the wind direction is north, the humidity is 60%, and the air temperature is 18 °C. At the same time, PM2.5, NO2 and CO sensors are installed at multiple monitoring points in the city. Sensors are arranged in the southern, eastern and western parts of the urban area respectively, and these sensors record the concentration of air pollutants once an hour. The PM2.5 sensor at the southern monitoring point measured the PM2.5 concentration of 35 micrograms per cubic meter, the NO2 concentration of 25 micrograms per cubic meter, and the CO concentration of 0.3 milligrams per cubic meter at 8:00 am on November 1, 2020. These data will be recorded according to the timestamp during the monitoring process and uploaded to the urban air monitoring database. The sliding window method is used to process the hourly data into daily and monthly pollutant concentration change curves. For example, through the sliding window method, the PM2.5 concentration change curve for the whole day of November 1, 2020 at this monitoring point is obtained, showing that the PM2.5 concentration generally remains between 30 and 40 micrograms per cubic meter throughout the day. However, due to data missing at some monitoring points and the inability to provide complete pollutant concentration data, to solve this problem, through the space-time interpolation method, the missing data are interpolated according to the wind field parameters of the surrounding monitoring points, such as wind speed and wind direction, so as to generate a complete pollutant concentration distribution map. In addition, the building structure of this city is scanned by the unmanned aerial vehicle lidar technology to obtain the geometric characteristics of each building. For example, the height of a certain building is 120 meters, the density is 30%, the building shape is a multi-story structure, and the ground characteristics around this building are an urban square with a smooth surface, which is suitable for data collection.

[0035] Step S102: Predict the turbulent kinetic energy of the monitoring point based on the historical meteorological data of the urban monitoring point, calculate the turbulent dissipation rate, use the computational fluid dynamics simulation method to obtain the three-dimensional velocity field of the air flow within the city, and calculate the turbulent diffusion coefficient of the air flow in different urban areas.

[0036] Determine the air viscosity using the Sutherland formula based on the air temperature and pressure. Based on the historical meteorological data of the urban monitoring point and the corresponding turbulent kinetic energy, use a recurrent neural network for model training to construct a turbulent kinetic energy prediction model. The meteorological data includes wind field parameters and air pressure. Based on the real-time monitored meteorological data, use the turbulent kinetic energy prediction model to determine the turbulent kinetic energy of the monitoring point. Based on the turbulent kinetic energy, use the standard turbulent dissipation rate formula to calculate the turbulent dissipation rate ε, where k is the turbulent kinetic energy, l is the characteristic length, which is the size of the building, and C μ is a model constant obtained by fitting historical data. Based on the turbulent model parameters, use the computational fluid dynamics simulation method to simulate the obtained wind field parameters, air pollutant concentration data, and geometric characteristics of the buildings to obtain the three-dimensional velocity field of the air flow within the entire city. According to the air viscosity, turbulent kinetic energy, and turbulent dissipation rate of different urban areas, use the standard k-ε turbulence model to calculate the turbulent diffusion effect and obtain the turbulent diffusion coefficient of the air flow in different urban areas. According to the urban pollution source emission data, obtain the emission rate of each pollution source, combine the pollutant diffusion coefficient and meteorological data, and use the Reynolds-averaged Navier-Stokes equations to simulate the change of pollutant concentration to determine the pollutant concentration distribution within the city.

[0037] Exemplarily, in an air quality monitoring system of a certain city, the meteorological station determines the air viscosity by recording the air temperature and pressure data in real time. Suppose on November 2, 2020, the air temperature is 20 °C and the air pressure is 101325 Pa, and the Sutherland formula is used to calculate the air viscosity. According to this formula, the air viscosity is approximately 1.81×10^-5 Pa·s. In the historical meteorological data of this city, the turbulent kinetic energy k of the monitoring point is recorded as 0.5 m2 / s2. Combining the wind field parameters of this monitoring point, such as the wind speed of 5.2 m / s and the wind direction of southeast, and the air pressure data, a recurrent neural network is used to model the turbulent kinetic energy to construct a turbulent kinetic energy prediction model. After training, the turbulent kinetic energy prediction model can predict the turbulent kinetic energy based on real-time meteorological data. In a new time period, the turbulent kinetic energy prediction model predicts that the turbulent kinetic energy of this monitoring point is 0.6 m2 / s2. Based on this turbulent kinetic energy and the known building size, such as there is a 50-meter-high building near the monitoring point, use the standard turbulent dissipation rate formula to calculate the turbulent dissipation rate ε, where k is the turbulent kinetic energy, l is the characteristic length, usually the size of the building, which is 50 meters, and Cμ is a model constant, taken as 0.09, obtained by fitting historical data. By substituting data, the turbulent dissipation rate ε is calculated to be 0.015 W / kg. Using these turbulent model parameters, computational fluid dynamics simulations are carried out. In the simulations, the obtained wind field parameters, pollutant concentrations such as the PM2.5 concentration of 35 μg / m3, and the geometric characteristics of buildings are input. The turbulent diffusion coefficients of each urban area are calculated using the standard k-ε turbulent model. In the city center, the turbulent diffusion coefficient is 0.12 m2 / s, while in the suburbs, the diffusion coefficient is 0.08 m2 / s. By combining the pollutant emission data of the city, such as the NO2 emission rate of 10 g / s in an industrial area, the Reynolds-averaged Navier-Stokes equations are used to simulate the change of pollutant concentration. The simulation results show that around the industrial area, the concentration of PM2.5 is relatively high, about 45 μg / m3, while in the suburban area of the city, the concentration is relatively low, only 25 μg / m3.

[0038] Step S103: Obtain real-time meteorological data according to the real-time meteorological data API, calculate the atmospheric stability, and determine the stability classification result based on the preset stability classification standard.

[0039] According to the real-time meteorological data API, real-time meteorological data are obtained, and meteorological data at different time points are obtained by setting the request time interval. The radar system interface is called, and meteorological data at different heights of the atmosphere are extracted by setting the monitoring height range. According to the ground temperature, wind speed, and atmospheric boundary layer height data, the Monin-Obukhov length formula is used to calculate the atmospheric stability. Based on the calculation result of the atmospheric stability, the stability classification result is determined according to the preset stability classification standard, including stable, neutral, and unstable.

[0040] Exemplarily, on November 3, 2020, meteorological data of a certain city was obtained using a real-time meteorological data API. The request time interval was set to once per hour through the API, and data such as the temperature, wind speed, and wind direction of the city were recorded. At 9 am, the real-time data showed that the temperature was 22°C, the wind speed was 3.5 m / s, the wind direction was southwest, and the air pressure was 101320 Pa. Next, the radar system interface was called, the monitoring altitude range was set from 0 to 500 meters, and meteorological data at different altitudes in the atmosphere was extracted. At an altitude of 100 meters, the temperature was 19°C and the wind speed was 4.1 m / s. At an altitude of 300 meters, the temperature dropped to 16°C and the wind speed was 5.2 m / s. Based on the ground temperature of 22°C, wind speed of 3.5 m / s, and atmospheric boundary layer height of 1000 meters, the Monin-Obukhov length formula was used to calculate the atmospheric stability. According to these input values, the calculated Monin-Obukhov length was 50 meters, which indicated that the atmosphere was in a relatively unstable state. Because according to the Monin-Obukhov length classification, when its value is small, when the Monin-Obukhov length > 200 meters, the atmosphere is usually in a stable state. In a stable situation, the vertical mixing of the air flow is weak, the air is relatively calm, the vertical diffusion of heat and pollutants is restricted, and it is usually characterized by low convective activity. When 100 meters < Monin-Obukhov length < 200 meters, the atmospheric stability is neutral. In this case, the vertical mixing ability of the atmosphere is moderate, the diffusion of heat and pollutants is relatively balanced, not particularly strong, nor completely restricted. When the Monin-Obukhov length < 100 meters, the atmosphere is usually in an unstable state. The vertical mixing of the atmosphere is strong, the vertical diffusion of heat and pollutants is obvious, convection is likely to occur, and the air flow is intense. In this case, the pollutants diffuse quickly, usually characterized by strong convective activity. The current Monin-Obukhov length is 50 meters, indicating that the atmosphere is relatively unstable and prone to convection. Therefore, the stability state of the atmosphere in this city is unstable. This unstable state usually means that the vertical mixing of the air flow is strong, which is suitable for the diffusion of pollutants, but may also lead to drastic changes in local meteorological conditions. Under this condition, the concentration of pollutants may be affected by changes in wind speed and temperature, the diffusion effect is strong, and the air quality may change rapidly.

[0041] Step S104, use the large eddy simulation method to determine the change trend of air flow under different weather conditions, and use the Lagrangian particle tracking method for pollutants to simulate the diffusion process of pollutants in the air, and determine the concentration distribution of pollutants at different times and spatial positions.

[0042] According to the wind field parameters, historical meteorological data, real-time meteorological data, and atmospheric stability, the large eddy simulation method is used to calculate the response of air flow under different weather conditions, and the changing trend of air flow under different weather conditions is obtained. According to the coordinates of the pollution source, wind speed, pollutant diffusion coefficient, large eddy simulation results, and real-time meteorological data, the Lagrangian particle tracking method for pollutants is used to simulate the diffusion and movement process of pollutants in the air, and the flow trajectory of pollutants is obtained. Combining the fluid dynamics simulation and the large eddy simulation results, the diffusion path and concentration change map of pollutants are generated, and the concentration distribution of pollutants at different time and space positions is determined.

[0043] Exemplarily, in an air quality monitoring system of a certain city, meteorological data is collected regularly. On November 4, 2020, real-time wind field parameters and historical meteorological data of this city were obtained. At 8:00 am, the real-time data showed that the wind speed in this city was 4 m / s, the wind direction was southeast, the temperature was 20 °C, and the air pressure was 101,300 Pa. Based on these data and combined with historical meteorological data, the atmospheric stability was calculated, and the Monin-Obukhov length was obtained as 40 meters, indicating that the atmosphere was in an unstable state and prone to vertical convection. Based on the atmospheric stability and wind field parameters, the large eddy simulation method was used to simulate the air flow in this city. Under such unstable weather conditions, the simulation results showed that the air flow presented relatively intense turbulent activities, and the wind speed would increase with the height. Specifically, at a height of 100 meters from the ground, the wind speed increased from 4 m / s to 5.5 m / s, showing strong air flow fluctuations in the vertical direction. The pollutant diffusion model was applied to the pollution sources in this city. Assuming that there was a pollution source in an industrial area in the north of the city, with an emission rate of 10 g / s and mainly emitting NO2, using the pollutant diffusion coefficient of 0.1 m2 / s and real-time meteorological data, the diffusion process of pollutants in the air was simulated by the Lagrangian particle tracking method of pollutants. According to the large eddy simulation results, the diffusion of pollutants was affected by the wind field. The flow trajectory showed that the pollutants quickly diffused in the area with a larger wind speed and moved southeast. As time passed, the concentration of pollutants gradually diffused from the source area to the surrounding areas. During real-time monitoring, it was identified that the pollutants had started to diffuse to the south of the city at 10:00, and the concentration value reached 30 μg / m3, while the concentration near the source was 120 μg / m3. Through simulation, the diffusion path and concentration change diagram of pollutants were obtained, showing the changes in pollutant concentration at different times and different spatial positions. For example, at 11:00, the pollutants had spread to an area about 2 kilometers away from the pollution source, and the concentration reached 50 μg / m3 in this area. Combining the results of fluid dynamics simulation and large eddy simulation, it was analyzed that in the central area of the city, due to the dense distribution of buildings and the influence of wind speed, the diffusion of pollutants was relatively complex, and the strong fluctuations in air flow made the diffusion path of pollutants show a certain curved shape, while in the open area, the diffusion of pollutants was relatively uniform. Based on the results of fluid dynamics simulation and large eddy simulation, the diffusion path and concentration change diagram of pollutants were drawn, showing the pollutant concentration distribution at different times and spatial positions.

[0044] Step S105, obtain the public space layout data and the real-time crowd density data within the urban area, construct a crowd behavior pattern classification model, determine the crowd behavior patterns in different public spaces, and predict the crowd density in each public space within a preset future time period.

[0045] According to the urban planning database, through GIS data analysis methods, obtain the location coordinates and spatial geometric shapes of urban green spaces, parks, squares, and open streets, and use GIS software for data processing. Divide the public spaces in the city by functional areas, identify the locations and boundaries of different public spaces, and determine the public space layout data, including the locations, areas, shapes, and mutual relationships of each public space. According to the video surveillance system installed in the city, obtain real-time video surveillance data, determine the real-time pedestrian flow density data within the urban area, and use the convolutional neural network algorithm to identify pedestrian trajectories, stay times, and walking speeds. Combine the pedestrian flow density data with the public space layout data to obtain the differences in pedestrian flow distribution among different public space types. According to the pedestrian trajectories, stay times, and walking speeds, use the decision tree algorithm for model training to construct a crowd behavior pattern classification model, and determine the crowd behavior patterns within different public spaces. The crowd behavior patterns include, but are not limited to, aggregation, retention, and flow patterns. According to the historical pedestrian flow data, crowd behavior models, and meteorological data, use the long short-term memory network algorithm for model training to construct a crowd flow trend prediction model and predict the pedestrian flow density in each public space within a preset future time period.

[0046] For example, on November 5, 2020, the public space layout data of a certain city was obtained using the urban planning database and GIS software. Through GIS analysis, the location coordinates and spatial geometry of green spaces, parks, squares and open streets in the city were extracted. Specifically, in the city center, there is a park with an area of 15,000 square meters surrounded by open streets and squares. Through GIS data processing, these public spaces were divided according to function, and the location, area and shape of different public spaces were determined. At the same time, the relationship between them was analyzed. For example, the park is connected to the surrounding open streets to form a public area with strong mobility, while the nearby square is a more static area mainly used for leisure and social activities. At the same time, the video surveillance system installed in the city collects crowd density data in real time. Through the analysis of surveillance videos, the convolutional neural network algorithm was used to identify pedestrian trajectories, dwell time and walking speed. At 10:00 am on November 5, 2020, the surveillance data showed that there were about 500 people in the park area in the city center, with an average dwell time of 20 minutes and a pedestrian walking speed of about 1.2m / s. At the same time, there were about 300 people in the square area, staying for 30 minutes and walking at a speed of 1.0m / s. Based on these data, the differences in the distribution of crowds in different types of public spaces were analyzed, and it was found that the crowds in the park area were more dynamic, while the crowds in the square area were more concentrated and stayed longer. Combining pedestrian trajectories, stay time and walking speed, the decision tree algorithm was used for training to construct a crowd behavior pattern classification model, which can identify crowd behavior patterns in different public spaces. For the park area, the model identified more flow patterns, that is, the crowd moved quickly in the space, while in the square area, the retention pattern and aggregation pattern were identified, indicating that the crowd was more inclined to stay and gather. Combining historical crowd flow data, crowd behavior models and meteorological data, such as the temperature of the day was 23℃ and the wind speed was 2m / s, the long short-term memory network algorithm was used for model training, and a crowd flow trend prediction model was constructed. Through this model, it is predicted that in the next 4 hours, that is, by 2:00 pm on November 5, 2020, the crowd density in the city center park will increase to 700 people, while the density in the square will increase to 450 people. The model also shows that due to the warm weather and lower wind speed, the crowd will mainly concentrate in the open area of the park, while the crowd flow in the square will be smaller and mainly remain in a stagnant state.

[0047] Step S106, determine the optimal configuration of green belts and ventilation corridors based on pollutant concentration, crowd density, air velocity and green space wind direction data, and determine the optimal public space ventilation optimization plan by combining turbulent diffusion and pollutant concentration gradient.

[0048] Based on pollutant concentration, population density, and air velocity data, a non-dominated sorting genetic algorithm is used to construct an optimization model. Multiple objective functions are set, including minimizing pollutant concentration, maximizing air flow efficiency, and optimizing the public space layout. The non-dominated sorting method is used to evaluate the pros and cons of each candidate solution, and the optimal public space layout solution is obtained, including minimizing pollutant concentration and the best configuration of air flow. According to green space distribution, wind direction changes, and air quality index data, a grid search method is adopted. By setting the grid range, the positions and widths of green belts and ventilation corridors are gradually adjusted. After each adjustment, the method of computational fluid dynamics simulation is used to determine air flow, pollutant concentration distribution, and air quality index, evaluate each layout solution, and judge its effect on air flow and pollution diffusion, and obtain the optimal configuration of green belts and ventilation corridors. According to the turbulent diffusion coefficient and pollutant concentration gradient, the convection-diffusion equation is used to determine the diffusion rate J of pollutants, where K t is the turbulent diffusion coefficient, is the pollutant concentration gradient. Based on building spacing, wind speed, and air pollution diffusion rate data, a multi-objective linear programming model is used to set multiple optimization objectives, including minimizing pollutant concentration and maximizing air flow efficiency, and constraint conditions are established, including building spacing, wind speed range, and ventilation opening position. The linear programming method is used to optimize the ventilation design of the public space, and the ventilation optimization plan for the public space is determined. According to the ventilation effects of different ventilation optimization plans for the public space, the optimal ventilation optimization plan for the public space is determined, including the best position of the ventilation opening, the optimization of the ventilation path, and the optimal configuration of air flow.

[0049] Exemplarily, in an air quality optimization case of a central park in a certain city, the PM2.5 concentration in the park is obtained as 55 μg / m3, the NO2 concentration is 45 μg / m3, the wind speed is 3.5 m / s, the wind direction is southeast, the pedestrian flow density is 200 people, the maximum pedestrian flow density is 400 people, and the temperature is 22°C through the monitoring system. The goal is to use the non-dominated sorting genetic algorithm to construct an optimization model, aiming to minimize pollutant concentrations, maximize air flow efficiency, and optimize the public space layout. The genetic algorithm is used to optimize the layout plan of the park, and the goal is to find a layout plan that can effectively reduce the PM2.5 and NO2 concentrations and improve the air flow efficiency. According to different candidate plans, the genetic algorithm evaluates the advantages and disadvantages of each plan. The optimized plan is to adjust the green belt, building spacing, ventilation opening position, and green space distribution, and finally obtain the best configuration. In this plan, the green belt in the center of the park is set to 5000 square meters, and the ventilation openings are set at the north and south ends of the park to maximize the use of the natural wind direction to enhance air flow and reduce pollutant accumulation. Based on the green space distribution, wind direction change, and air quality index data, the grid search method is used to further optimize the layout of the green belt and ventilation corridors. By adjusting the width and position of the ventilation corridors and using fluid dynamics simulation to evaluate the effect after each adjustment, an optimized configuration of the green belt and ventilation corridors is obtained. In the simulation, the adjusted air flow path shows that the wind speed reaches 4.2 m / s inside the park, the pollutant concentration decreases by 15% at the south end of the park, and the air quality index increases by 10%. On this basis, the convection-diffusion equation is used to calculate the diffusion rate J of pollutants. If the turbulent diffusion coefficient is 0.12 m2 / s and the pollutant concentration gradient is 0.5 μg / m3 / m, the diffusion rate of pollutants is It shows that the diffusion of pollutants is effectively controlled in the optimized configuration. Through the multi-objective linear programming model, the building spacing, wind speed, and ventilation opening position are optimized. During the optimization process, multiple constraint conditions are set, such as the building spacing is not less than 10 meters, the wind speed range is maintained at 2.5 - 5 m / s, and the layout of the ventilation openings should avoid hindering air circulation as much as possible. Through linear programming optimization, the optimal public space ventilation plan is found, including the position of the ventilation openings, the optimization of the ventilation path, and the best configuration of air flow.

[0050] Step S107, according to the optimized layout of the green belt and ventilation corridors, building distribution, and wind field parameters, combined with air flow simulation and pollutant diffusion trajectories, evaluate the impact of different configurations on ventilation effects and pollution suppression, and optimize the ventilation path, green belt distribution, and building spacing configuration of the public space.

[0051] According to the adjusted layout of green belts and ventilation corridors using the optimal configuration of green belts and ventilation corridors, obtain the geometric feature data of building distribution and green belts. Combine the wind field parameters and air pollutant concentration data, and use the method of computational fluid dynamics simulation to determine the velocity field of air flow, the diffusion trajectory of pollutants, and the air quality index, and determine the changes in air flow and pollutant concentration under different configurations of green belts and ventilation corridors. According to the changes in air flow and pollutant concentration under different configurations of green belts and ventilation corridors, evaluate the air flow optimization effect and the suppression effect of pollutant diffusion under different green belt configurations, and optimize the design of green belts and ventilation corridors. According to the adjusted public space ventilation optimization plan and crowd density data, evaluate the air flow effect and crowd activity patterns in public spaces, and optimize the spatial configuration and layout of public spaces. According to the evaluation results of the green belt and ventilation corridor layout and the evaluation results of the public space ventilation optimization plan, determine the optimal configuration of the public space layout, including the ventilation paths of each public space area, the green belt layout, the ventilation hole positions, the green space distribution, and the building spacing.

[0052] Exemplarily, in an air flow and pollutant control optimization project in a central park of a certain city, a new design plan was obtained by optimizing the layout of green belts and ventilation corridors. This optimization plan aims to achieve the best air flow and pollutant control based on multiple factors such as wind field parameters, air pollutant concentrations, building distributions, and pedestrian flow densities. If the width of the new green belt layout is set to 30 meters and the length is 500 meters, it is arranged around the main flow path of the park. In addition, the ventilation corridors are designed as two areas with a width of 50 meters and a depth of 200 meters, located at the east and west ends of the park respectively, to guide and accelerate air flow. According to this layout and combined with wind field data, the wind speed near the ventilation corridors was measured to reach 4 m / s, and the wind speed in the central area of the park was 3.5 m / s, with the wind direction being southeast. By using the fluid dynamics simulation method, the air flow velocity field and the diffusion trajectory of pollutants under different green belt and ventilation corridor configurations were analyzed. The simulation results show that the optimized layout plan effectively reduces the PM2.5 concentration in the southern area of the park by approximately 20%. Specifically, the PM2.5 concentration in the southern area decreases from 55 μg / m3 before optimization to 42 μg / m3. At the same time, the air quality index of the entire park increases by 15% and reaches a relatively healthy level. The diffusion path of pollutants also shows a more uniform distribution, and through the guidance of the ventilation corridors, the accumulation of pollutants is significantly inhibited. Based on this optimized configuration, the air flow and pollutant concentration changes under different green belt and ventilation corridor layouts were evaluated. After optimization, the green belts not only effectively increase air mobility but also, through the blocking effect of vegetation on air flow, effectively reduce the accumulation of high-concentration pollutants. Through further simulation, it was identified that this configuration can reduce the pollutant concentration in the park by 25% and maintain relatively stable air flow, ensuring the effect of fresh air. According to the ventilation optimization plan for public spaces and the pedestrian flow density data, the air flow effect and crowd activity patterns in the park were further evaluated. If the maximum pedestrian flow density in the park is 400 people, under the optimized layout, the crowd activity pattern is mainly a flowing pattern, that is, pedestrians mainly move along the paths of the green belts and ventilation corridors. Due to the smooth air flow, there are no longer overly concentrated congestion phenomena in crowd activities, improving the comfort of public spaces. Combining the evaluation results of the green belt and ventilation corridor layouts, the optimal configuration of the public space layout was determined. In this configuration, the green belts of the park are evenly distributed on both sides of the main flow path, with appropriate width and density. Ventilation holes are set in the middle and at the ends of the ventilation corridors to ensure unobstructed air flow. The building spacing is set to 20 meters to ensure unobstructed air flow and not affect crowd activities. This optimal plan not only significantly improves the air quality of the park but also optimizes the mobility and comfort of the crowd, achieving the dual optimization goals of air flow and public space design.

[0053] The above description is only a preferred embodiment of the present application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the present application that have similar functions.

Claims

1. An optimization method for urban public spaces based on crowd behavior simulation, characterized in that, The method includes: Obtaining urban wind field parameters and urban air pollutant concentration data through the monitoring network of meteorological stations, and using unmanned aerial vehicle lidar technology to obtain the geometric features of buildings; Predicting the turbulent kinetic energy at the monitoring points according to the historical meteorological data of urban monitoring points, calculating the turbulent dissipation rate, using the computational fluid dynamics simulation method to obtain the three-dimensional velocity field of air flow within the urban area, and calculating the turbulent diffusion coefficient of air flow in different urban areas; Obtaining real-time meteorological data according to the real-time meteorological data API, calculating the atmospheric stability, and determining the stability classification result based on the preset stability classification standard; Using the large eddy simulation method to determine the change trend of air flow under different weather conditions, and using the Lagrangian particle tracking method for pollutants to simulate the diffusion process of pollutants in the air, and determining the concentration distribution of pollutants at different time and space positions; Obtaining the public space layout data and the real-time pedestrian flow density data within the urban area, constructing a crowd behavior pattern classification model, determining the crowd behavior patterns in different public spaces, and predicting the pedestrian flow density in each public space within a preset future time period; Determining the optimal green belt and ventilation corridor configuration according to the pollutant concentration, pedestrian flow density, air velocity and green space wind direction data, and combining the turbulent diffusion and pollutant concentration gradient to determine the optimal public space ventilation optimization plan; According to the optimized green belt and ventilation corridor layout, building distribution and wind field parameters, combining the air flow simulation and pollutant diffusion trajectory, evaluating the impact of different configurations on the ventilation effect and pollution suppression, and optimizing the public space ventilation path, green belt distribution and building spacing configuration.

2. The method according to claim 1, wherein The obtaining urban wind field parameters and urban air pollutant concentration data through the monitoring network of meteorological stations, and using unmanned aerial vehicle lidar technology to obtain the geometric features of buildings includes: According to the monitoring network of meteorological stations, using wind speed sensors, humidity sensors and temperature sensors to obtain urban wind field parameters, recording the timestamp of each data, and uploading it to the urban air monitoring database. The wind field parameters include wind direction, wind speed, humidity and air temperature; According to the arranged fixed and mobile PM2.5, NO2, and CO sensors, obtaining the urban air pollutant concentration data, recording the monitoring point location and monitoring period, and using the sliding window method to process the monitoring data in segments to obtain the pollutant concentration change curves per hour, per day and per month; By comparing with the wind field parameters, using the spatio-temporal interpolation method to fill in the missing pollutant concentration data to obtain a pollutant concentration distribution map with spatio-temporal continuity; Using unmanned aerial vehicle lidar technology to scan the urban building structure to obtain the geometric features of buildings, including the height, density, shape and ground features of buildings.

3. The method according to claim 1, wherein The predicting the turbulent kinetic energy at the monitoring points according to the historical meteorological data of urban monitoring points, calculating the turbulent dissipation rate, using the computational fluid dynamics simulation method to obtain the three-dimensional velocity field of air flow within the urban area, and calculating the turbulent diffusion coefficient of air flow in different urban areas includes: According to the air temperature and air pressure, use the Sutherland formula to determine the air viscosity; according to the historical meteorological data of urban monitoring points and the corresponding turbulent kinetic energy, use a recurrent neural network for model training to construct a turbulent kinetic energy prediction model, where the meteorological data includes wind field parameters and air pressure; according to the real-time monitored meteorological data, use the turbulent kinetic energy prediction model to determine the turbulent kinetic energy of the monitoring point; based on the turbulent kinetic energy, use the standard turbulent dissipation rate formula to calculate the turbulent dissipation rate ε, where k is the turbulent kinetic energy, l is the characteristic length, which is the size of the building, and C μ is a model constant obtained by fitting historical data; based on the turbulent model parameters, use the computational fluid dynamics simulation method to simulate the obtained wind field parameters, air pollutant concentration data, and geometric characteristics of the building to obtain the three-dimensional velocity field of air flow within the entire city; according to the air viscosity, turbulent kinetic energy, and turbulent dissipation rate in different urban areas, use the standard k-ε turbulence model to calculate the turbulent diffusion effect to obtain the turbulent diffusion coefficient of air flow in different urban areas; according to the urban pollution source emission data, obtain the emission rate of each pollution source, combine the pollutant diffusion coefficient and meteorological data, and use the Reynolds-averaged Navier-Stokes equations to simulate the change of pollutant concentration to determine the pollutant concentration distribution within the city.

4. The method according to claim 1, wherein Obtaining real-time meteorological data according to the real-time meteorological data API, calculating the atmospheric stability, and determining the stability classification result based on a preset stability classification standard, including: Obtaining real-time meteorological data according to the real-time meteorological data API, and obtaining meteorological data at different time points by setting the request time interval; using the radar system interface to extract meteorological data at different heights in the atmosphere by setting the monitoring height range; calculating the atmospheric stability according to the ground temperature, wind speed, and atmospheric boundary layer height data using the Monin-Obukhov length formula; determining the stability classification result based on the preset stability classification standard according to the atmospheric stability calculation result, including stable, neutral, and unstable.

5. The method according to claim 1, wherein Using the large-eddy simulation method to determine the change trend of air flow under different weather conditions, and using the Lagrangian particle tracking method for pollutants to simulate the diffusion process of pollutants in the air, and determining the concentration distribution of pollutants at different time and space positions, including: Calculating the response of air flow under different weather conditions using the large-eddy simulation method according to the wind field parameters, historical meteorological data, real-time meteorological data, and atmospheric stability, and obtaining the change trend of air flow under different weather conditions; simulating the diffusion and movement process of pollutants in the air using the Lagrangian particle tracking method for pollutants according to the coordinates of the pollution source, wind speed, pollutant diffusion coefficient, large-eddy simulation results, and real-time meteorological data, and obtaining the flow trajectory of pollutants; combining the fluid dynamics simulation and the large-eddy simulation results to generate the diffusion path and concentration change map of pollutants, and determining the concentration distribution of pollutants at different time and space positions.

6. The method according to claim 1, wherein Obtaining the public space layout data and the real-time population density data within the urban area, constructing a crowd behavior pattern classification model, determining the crowd behavior patterns in different public spaces, and predicting the population density in each public space within a preset future time period, including: Based on the urban planning database, obtain the location coordinates and spatial geometric shapes of urban green spaces, parks, squares, and open streets through GIS data analysis methods, and use GIS software for data processing. Divide the public spaces in the city by functional areas, identify the locations and boundaries of different public spaces, and determine the public space layout data, including the locations, areas, shapes, and mutual relationships of each public space; According to the video surveillance system installed in the city, obtain real-time video surveillance data, determine the real-time pedestrian flow density data within the urban area, and use the convolutional neural network algorithm to identify pedestrian trajectories, residence times, and walking speeds; Combine the pedestrian flow density data with the layout data of public spaces to obtain the differences in pedestrian flow distribution of different public space types; According to pedestrian trajectories, residence times, and walking speeds, use the decision tree algorithm for model training to construct a crowd behavior pattern classification model, and determine the crowd behavior patterns within different public spaces. The crowd behavior patterns include, but are not limited to, aggregation, retention, and flow patterns; According to historical pedestrian flow data, crowd behavior models, and meteorological data, use the long short-term memory network algorithm for model training to construct a crowd flow trend prediction model to predict the pedestrian flow density of each public space within a preset future time period.

7. The method according to claim 1, wherein Determine the optimal green belt and ventilation corridor configurations based on pollutant concentration, pedestrian flow density, air velocity, and green belt wind direction data, and combine turbulent diffusion with the pollutant concentration gradient to determine the optimal public space ventilation optimization plan, including: According to the pollutant concentration, population density, and air velocity data, a non-dominated sorting genetic algorithm is used to construct an optimization model, and multiple objective functions are set, including minimizing pollutant concentration, maximizing air flow efficiency, and optimizing the public space layout. The non-dominated sorting method is used to evaluate the advantages and disadvantages of each candidate solution to obtain the optimal public space layout solution, including minimizing pollutant concentration and the best configuration of air flow. According to the green space distribution, wind direction change, and air quality index data, a grid search method is adopted. By setting the grid range, the position and width of the green belt and ventilation corridor are gradually adjusted. After each adjustment, the method of computational fluid dynamics simulation is used to determine the air flow, pollutant concentration distribution, and air quality index, evaluate each layout solution, and judge its effect in terms of air flow and pollution diffusion to obtain the optimal configuration of the green belt and ventilation corridor. According to the turbulent diffusion coefficient and pollutant concentration gradient, the convection-diffusion equation is used to determine the diffusion rate J of pollutants, where K t is the turbulent diffusion coefficient, and is the pollutant concentration gradient. Based on the building spacing, wind speed, and air pollution diffusion rate data, a multi-objective linear programming model is used to set multiple optimization objectives, including minimizing pollutant concentration and maximizing air flow efficiency, and constraint conditions are established, including building spacing, wind speed range, and ventilation opening position. The linear programming method is used to optimize the ventilation design of the public space to determine the ventilation optimization plan for the public space. According to the ventilation effects of different ventilation optimization plans for the public space, the optimal ventilation optimization plan for the public space is determined, including the best position of the ventilation opening, the optimization of the ventilation path, and the optimal configuration of air flow.

8. The method according to claim 1, wherein Based on the optimized green belt and ventilation corridor layouts, building distributions, and wind field parameters, combine air flow simulation with pollutant diffusion trajectories to evaluate the impacts of different configurations on ventilation effects and pollution suppression, and optimize the ventilation paths, green belt distributions, and building spacing configurations of public spaces, including: According to the green belt and ventilation corridor layouts adjusted using the optimal green belt and ventilation corridor configurations, obtain the geometric feature data of building distributions and green belts, and combine wind field parameters and air pollutant concentration data. Use the method of computational fluid dynamics simulation to determine the velocity field of air flow, the diffusion trajectories of pollutants, and the air quality index, and determine the changes in air flow and pollutant concentration under different green belt and ventilation corridor configurations; According to the changes in air flow and pollutant concentration under different green belt and ventilation corridor configurations, evaluate the air flow optimization effects and the suppression effects of pollutant diffusion under different green belt configurations, and optimize the design of green belts and ventilation corridors; According to the adjusted public space ventilation optimization plan and pedestrian flow density data, evaluate the air flow effects and crowd activity patterns in public spaces, and optimize the spatial configurations and layouts of public spaces; According to the evaluation results of the green belt and ventilation corridor layouts and the evaluation results of the public space ventilation optimization plan, determine the optimal configuration of the public space layout, including the ventilation paths, green belt layouts, ventilation hole positions, green space distributions, and building spacings in each public space area.

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