Thermal exposure risk calculation method based on urban travel people flow and built environment dynamics

Abstract

The invention relates to a thermal exposure risk calculation method based on urban trip people flow and built environment dynamics, and the method comprises the steps: obtaining the average radiation temperature calculation input data of an urban environment, and calculating the average radiation temperature of the urban environment according to the average radiation temperature calculation input data; calculating general thermal climate index distribution of the urban environment based on the average radiation temperature, and determining a thermal exposure time period based on the general thermal climate index distribution so as to obtain thermal exposure duration according to the thermal exposure time period; and acquiring the hourly travel pedestrian flow of the urban environment, counting the accumulated travel pedestrian flow in the thermal exposure time period based on the hourly travel pedestrian flow and the thermal exposure time period, and calculating the thermal exposure risk according to the accumulated travel pedestrian flow. Therefore, the problems that in the related technology, due to dependence on static population data, the people flow dynamic state of urban day and night travel with high temporal-spatial resolution is difficult to reflect, and a thermal risk calculation result based on the static population data cannot meet the requirements of urban space environment exposure refined analysis and early warning are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and in particular relates to a heat exposure risk calculation method based on urban travel flow and built environment dynamics. BACKGROUND

[0002] At present, cities are rich in resources and population, and with the frequent occurrence of urban heat islands and extreme heat wave events, the heat exposure risk of urban residents traveling is aggravated. Accurate built environment assessment is extremely important for reducing the heat exposure of walking and cycling flow under extreme high temperature invasion and reducing the heat risk in the travel process.

[0003] In related technologies, heat exposure data mostly use statistical yearbooks or open global static public population density data such as LandScan and WorldPop. These data rely on population surveys in residential areas, and take years as the time statistical unit and communities or streets as the spatial statistical unit.

[0004] However, in related technologies, due to the reliance on static population data, it is difficult to reflect the high spatio-temporal resolution of urban day-night travel flow dynamics, so that the heat risk calculation result based on static population data ignores the spatio-temporal synchronization of travel flow and its potential harmful space, and cannot meet the needs of fine analysis and early warning of urban space environment exposure, which needs to be improved. SUMMARY

[0005] The present application provides a heat exposure risk calculation method based on urban travel flow and built environment dynamics to solve the problem that in related technologies, due to the reliance on static population data, it is difficult to reflect the high spatio-temporal resolution of urban day-night travel flow dynamics, so that the heat risk calculation result based on static population data cannot meet the needs of fine analysis and early warning of urban space environment exposure.

[0006] The first aspect embodiment of the present application provides a heat exposure risk calculation method based on urban travel flow and built environment dynamics, comprising the following steps: obtaining average radiation temperature calculation input data of a city environment, and calculating the average radiation temperature of the city environment according to the average radiation temperature calculation input data; based on the average radiation temperature, calculating the general heat climate index distribution of the city environment, and determining the heat exposure period based on the general heat climate index distribution, to obtain the heat exposure time length according to the heat exposure period; obtaining the hourly travel flow of the city environment, and based on the hourly travel flow and the heat exposure period, counting the cumulative travel flow in the heat exposure period, and calculating the heat exposure risk according to the cumulative travel flow.

[0007] Through the aforementioned technical means, the embodiments of this application can incorporate urban dynamic pedestrian flow and built-up environment thermal exposure data. By calculating the average radiation temperature, the distribution of the general thermal climate index, and the thermal exposure period, combined with hourly pedestrian flow statistics, a quantitative assessment of thermal exposure risk can be achieved. This helps to accurately understand the true spatiotemporal distribution of pedestrian exposure risk during extreme weather, promote the precise monitoring and calculation of urban spatial thermal risk during extreme heat waves, and thus accurately assess residents' thermal exposure, identify high-risk urban locations, support sustainable urban construction, and promote social health equity.

[0008] Optionally, in one embodiment of this application, the step of obtaining the input data for calculating the average radiation temperature of the urban environment includes: obtaining land cover data, building digital surface models, vegetation canopy digital surface models, digital elevation models, and hourly meteorological driving data, and generating reclassified land cover data, sky view factor, building wall height and wall orientation data based on the land cover data, the building digital surface models, and the vegetation canopy digital surface models to obtain the input data for calculating the average radiation temperature of the urban environment.

[0009] Through the above-mentioned technical means, the embodiments of this application can automatically generate key parameters such as land cover data and sky view factor based on multi-source geographic information data to support the calculation of mean radiation temperature, improve the completeness of environmental parameter extraction and simulation accuracy, thereby laying a reliable foundation for subsequent high spatiotemporal resolution urban thermal environment simulation and enhancing the accuracy and stability of overall thermal risk assessment.

[0010] Optionally, in one embodiment of this application, the formula for calculating the heat exposure period is: , in, I For indicator functions, t For local time, It is a general thermal climate index.

[0011] Through the above-mentioned technical means, the embodiments of this application can use the general thermal climate index threshold as the standard, and by judging the heat exposure period hour by hour, adapt to the thermal environment changes of different spaces in the city, realize the accurate screening and statistics of heat exposure periods, and provide a clear time frame for subsequent cumulative travel flow statistics and risk intensity calculation.

[0012] Optionally, in one embodiment of this application, the hourly pedestrian flow calculation formula is: , in, i For grid numbering, t For local time, For a moment t population density grid i population density difference value of adjacent time period.

[0013] Through the above technical means, the embodiment of the present application can estimate the trip passenger flow of the grid by the population number change in the adjacent time period grid, and the calculation method is simple, easy to implement and understand. The method can track the dynamic change of urban passenger flow in real time based on calling the user mobile terminal to obtain dynamic population density, is suitable for high-frequency data processing, and has good timeliness. It is helpful to accurately understand the spatio-temporal distribution of trip passenger flow during extreme heat waves, and promote the accurate monitoring of urban space heat exposure risk.

[0014] Optionally, in an embodiment of the present application, after calculating the heat exposure risk according to the cumulative trip passenger flow, it further includes: normalizing the heat exposure risk in the calculation area to output a relative heat exposure risk intensity distribution map.

[0015] Through the above technical means, the embodiment of the present application can eliminate the influence of regional differences by normalization, present the risk distribution in a spatial visualization form, make the heat exposure risk have cross-regional comparability, optimize resource allocation and intervention strategy, and promote the fine management of urban built environment.

[0016] The second aspect embodiment of the present application provides a heat exposure risk calculation device based on urban trip passenger flow and built environment dynamics, including: an acquisition module for acquiring average radiation temperature calculation input data of a city environment and calculating the average radiation temperature of the city environment according to the average radiation temperature calculation input data; a determination module for calculating the general heat climate index distribution of the city environment based on the average radiation temperature, and determining the heat exposure period based on the general heat climate index distribution, so as to obtain the heat exposure duration according to the heat exposure period; a calculation module for acquiring the hourly trip passenger flow of the city environment, and based on the hourly trip passenger flow and the heat exposure period, counting the cumulative trip passenger flow in the heat exposure period, and calculating the heat exposure risk according to the cumulative trip passenger flow.

[0017] Through the above technical means, the embodiment of the present application can introduce the urban dynamic passenger flow and built environment heat exposure data, calculate the average radiation temperature, the general heat climate index distribution and the heat exposure period, and realize the quantitative evaluation of the heat exposure risk by combining the hourly trip passenger flow statistics, so as to help accurately understand the real spatio-temporal distribution of trip passenger flow exposure risk during extreme weather, promote the accurate monitoring and calculation of urban space heat risk during extreme heat waves, and then accurately evaluate the heat exposure of residents and identify the high-risk points of the city, so as to support sustainable urban construction and promote social health and fairness.

[0018] Optionally, in one embodiment of this application, the acquisition module includes: an acquisition data unit, used to acquire land cover data, building digital surface model, vegetation canopy digital surface model, digital elevation model and hourly meteorological driving data, and generate reclassified land cover data, sky view factor, building wall height and wall orientation data based on the land cover data, the building digital surface model and the vegetation canopy digital surface model, so as to obtain the average radiation temperature calculation input data of the urban environment.

[0019] Through the above-mentioned technical means, the embodiments of this application can automatically generate key parameters such as land cover data and sky view factor based on multi-source geographic information data to support the calculation of mean radiation temperature, improve the completeness of environmental parameter extraction and simulation accuracy, thereby laying a reliable foundation for subsequent high spatiotemporal resolution urban thermal environment simulation and enhancing the accuracy and stability of overall thermal risk assessment.

[0020] Optionally, in one embodiment of this application, the formula for calculating the heat exposure period is: , in, I For indicator functions, t For local time, It is a general thermal climate index.

[0021] Through the above-mentioned technical means, the embodiments of this application can use the general thermal climate index threshold as the standard, and by judging the heat exposure period hour by hour, adapt to the thermal environment changes of different spaces in the city, realize the accurate screening and statistics of heat exposure periods, and provide a clear time frame for subsequent cumulative travel flow statistics and risk intensity calculation.

[0022] Optionally, in one embodiment of this application, the hourly pedestrian flow calculation formula is: , in, i For grid numbering, t For local time, For a moment t population density For grid i The difference in population density between adjacent time periods.

[0023] Through the above-mentioned technical means, the embodiments of this application can estimate the travel flow of a grid by using the population change within adjacent time periods. The calculation method is simple, easy to implement and understand. By calling the user's mobile terminal to obtain dynamic population density, the dynamic changes of urban population flow can be tracked in real time. It is suitable for high-frequency data processing, has good timeliness, and helps to accurately understand the spatiotemporal distribution of travel flow during extreme heat waves, thus promoting the accurate monitoring of urban spatial heat exposure risks.

[0024] Optionally, in one embodiment of this application, it further includes: a processing module, used to normalize the heat exposure risk within the calculation area after calculating the heat exposure risk based on the cumulative passenger flow, so as to output a relative heat exposure risk intensity distribution map.

[0025] Through the above-mentioned technical means, the embodiments of this application can eliminate the impact of regional differences through normalization, present the risk distribution in a spatially visualized form, make the heat exposure risk comparable across regions, optimize resource allocation and intervention strategies, and promote the refined governance of the urban built environment.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the thermal exposure risk calculation method based on urban pedestrian flow and built environment dynamics as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for calculating thermal exposure risk based on urban pedestrian flow and the dynamics of the built environment.

[0029] This application's embodiments can incorporate dynamic urban pedestrian flow and built-up environment thermal exposure data. By calculating average radiation temperature, general thermal climate index distribution, and thermal exposure periods, combined with hourly pedestrian flow statistics, a quantitative assessment of thermal exposure risk can be achieved. This helps to accurately understand the true spatiotemporal distribution of pedestrian exposure risk during extreme weather, promotes precise monitoring and calculation of urban spatial thermal risk during extreme heat waves, and thus accurately assesses residents' thermal exposure, identifies high-risk urban locations, supports sustainable urban development, and promotes social health equity. This solves the problems in related technologies where reliance on static population data makes it difficult to reflect the dynamics of urban daytime and nighttime travel with high spatiotemporal resolution, resulting in thermal risk calculations based on static population data failing to meet the needs of refined analysis and early warning of urban spatial environmental exposure.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics, according to an embodiment of this application. Figure 2 A schematic diagram of the Tmrt (Mean Radiant Temperature) distribution at 5m resolution during a summer afternoon at 2 PM, according to an embodiment of this application; Figure 3 This is a schematic diagram of the UTCI (Universal Thermal Climate Index) distribution at 5m resolution during a summer afternoon at 2 pm, according to an embodiment of this application. Figure 4 This is a schematic diagram showing the distribution of meteorological stations within the range used to drive the calculation of mean radiative temperature and urban sub-meteorological stations used for Kriging interpolation, according to an embodiment of this application. Figure 5 This is a schematic diagram of the distribution of heat exposure duration at 200m resolution on a typical day during a summer heat wave, provided according to an embodiment of this application. Figure 6 This is a simplified diagram illustrating the calculation logic for hourly spatial changes in pedestrian traffic according to an embodiment of this application, and the corresponding changes in the number of spatial locations of user mobile terminals and the spatial changes in pedestrian traffic. Figure 7This is a schematic diagram of the dynamic relative thermal risk intensity distribution at a resolution of 200m, provided according to an embodiment of this application. Figure 8 This is a visualization diagram of a high spatiotemporal resolution thermal risk calculation framework based on "time-space-human behavior" provided according to an embodiment of this application; Figure 9 This is a flowchart illustrating the data processing and overall calculation of a "time-space-human behavior" thermal risk assessment calculation framework provided according to an embodiment of this application; Figure 10 This is a schematic diagram of a thermal exposure risk calculation device based on urban pedestrian flow and built environment dynamics, according to an embodiment of this application. Figure 11 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Calculation device for thermal exposure risk based on urban pedestrian flow and built environment dynamics; 100-Acquisition module, 200-Determination module, 300-Calculation module; 1101-Memory, 1102-Processor, 1103-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following describes, with reference to the accompanying drawings, an embodiment of the present application's method for calculating thermal exposure risk based on dynamic urban pedestrian flow and built environment. Addressing the issues raised in the background section regarding the reliance on static population data, which makes it difficult to reflect the dynamic urban pedestrian flow during day and night with high spatiotemporal resolution, the thermal risk calculation results based on static population data cannot meet the needs of refined analysis and early warning of urban spatial environmental exposure. This application provides a method for calculating thermal exposure risk based on dynamic urban pedestrian flow and built environment. This method incorporates dynamic urban pedestrian flow and built environment thermal exposure data. By calculating the average radiation temperature, the distribution of the general thermal climate index, and the thermal exposure period, combined with hourly pedestrian flow statistics, a quantitative assessment of thermal exposure risk is achieved. This helps to accurately understand the true spatiotemporal distribution of pedestrian exposure risk during extreme weather, promotes precise monitoring and calculation of urban spatial thermal risk during extreme heat waves, and thus accurately assesses residents' thermal exposure, identifies high-risk urban locations, supports sustainable urban construction, and promotes social health equity. This solves the problem in related technologies that rely on static population data, making it difficult to reflect the dynamic flow of people traveling in cities at high spatiotemporal resolution during day and night, and making the thermal risk calculation results based on static population data unable to meet the needs of refined analysis and early warning of urban spatial environment exposure.

[0035] Specifically, Figure 1 This is a flowchart illustrating a method for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics, as provided in an embodiment of this application.

[0036] like Figure 1 As shown, the method for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics includes the following steps: In step S101, the average radiation temperature of the urban environment is calculated by obtaining the input data for calculating the average radiation temperature of the urban environment.

[0037] It is understood that the input data for calculating the average radiation temperature in the embodiments of this application may include, but is not limited to, building digital surface models, vegetation canopy digital surface models, digital elevation models, hourly meteorological driving data, reclassified land cover data, sky view factor, building wall height and wall orientation data; average radiation temperature can be understood as an important physical indicator for measuring the thermal exposure of urban outdoor spaces. It reflects the thermal stress of people outdoors by calculating the average temperature of the radiation effect of the surrounding surfaces of the urban environment on the human body.

[0038] In practical implementation, this application embodiment can use the SOLWEIG (Solar and LongWave Environmental Irradiance Geometry model) plugin of the UMEP (Urban Multi-scale Environmental Predictor) tool in QGIS (Quantum Geographic Information System) to simulate the average radiation temperature of the urban environment at a height of 1.1m above the ground surface. The input data for calculating the average radiation temperature includes digital surface models of buildings, digital surface models of vegetation canopies, hourly meteorological driving data, digital elevation models, and reclassified land cover data, sky view factor, and wall height and orientation data obtained through the UMEP tool in QGIS. The meteorological driving data uses measured data from suburban meteorological stations to provide direct and diffuse radiation. It is recommended that the output raster data resolution be 1-10m, the tree radiation transmittance be taken as 6%, and only tree canopies above 2m are considered.

[0039] Furthermore, the mean radiant temperature was calculated using the six-axis radiation method to simulate the three-dimensional radiative flux density of the surrounding built environment. Long-wave radiation from the surrounding walls and sky was calculated by taking into account the wall and air temperatures, and short-wave radiation from the sky was calculated by considering the shading from surrounding buildings and vegetation. Finally, the mean radiant temperature was calculated. Figure 2 As shown.

[0040] The embodiments of this application can calculate the average radiation temperature by combining multi-source data acquisition with physical models, making the average radiation temperature results more consistent with the actual thermal environment characteristics of the city, improving the simulation accuracy, and providing reliable core parameters for the accurate calculation of the general thermal climate index.

[0041] Optionally, in one embodiment of this application, the input data for calculating the average radiation temperature of the urban environment includes: acquiring land cover data, building digital surface models, vegetation canopy digital surface models, digital elevation models, and hourly meteorological driving data, and generating reclassified land cover data, sky view factor, building wall height and wall orientation data based on the land cover data, building digital surface models, and vegetation canopy digital surface models to obtain the input data for calculating the average radiation temperature of the urban environment.

[0042] It is understood that, in the embodiments of this application, the building digital surface model can be the elevation information of urban surface elements, mainly including building roofs, which can be used to calculate the long-wave radiation from building walls and the shading of short-wave radiation from the sky; the vegetation canopy digital surface model can be a model describing the height of the top of the vegetation canopy, which can be used to quantify the shading of direct solar radiation by vegetation and the impact of long-wave radiation on the environment; the digital elevation model can be a base characterizing exposed natural terrain undulations, which can be used to distinguish terrain shadows and correct the height of other surface elements; the hourly meteorological driving data can be, but is not limited to, air temperature, relative humidity, and wind speed at a height of 10m above the ground; the reclassified land cover data can be a dataset characterizing urban land cover types generated through spatial classification technology; the sky view factor can be a parameter measuring the proportion of the visible sky at a certain point; and the building wall height and wall orientation data can be used to influence the sunshine duration and the amount of solar radiation received by the wall, thereby affecting the thermal radiation intensity of the urban environment.

[0043] For example, embodiments of this application can generate reclassified land cover data, sky view factor, building wall height and wall orientation data based on the acquired land cover data, building digital surface model, vegetation canopy digital surface model, digital elevation model and hourly meteorological driving data.

[0044] Specifically, land cover data is input into the UMEP reclassification processor to obtain reclassified land cover raster data with corresponding codes; building digital surface models are processed to obtain building wall height and wall orientation raster data, and a minimum wall height value is set, such as 3m, to filter out non-building walls; building and vegetation digital surface models are preprocessed to obtain sky view factor data that considers vegetation canopy distribution and transmittance, with a recommended value of 1.3-6% for vegetation radiation transmittance.

[0045] Furthermore, the formula for calculating the average radiant temperature is: , Where σ is the Boltzmann constant, ε p R represents the emissivity of the human body, and R represents the radiation received by the human body.

[0046] It is understood that the formula for calculating the average radiation temperature in the embodiments of this application is a standardized formula derived from the blackbody radiation law; the Boltzmann constant is a fundamental thermodynamic constant that characterizes the relationship between blackbody radiation energy and temperature; the emissivity of the human body is the proportion of energy emitted by the human body as a radiator; and the radiation received by the human body is the total radiation flux density received by the human body in the urban environment.

[0047] For example, embodiments of this application can use the six-axis radiation method to simulate the three-dimensional radiation flux density of the surrounding built environment. By calculating the wall temperature and air temperature of the surrounding environment, the long-wave radiation from the surrounding walls and sky is further calculated, and the short-wave radiation from the sky is calculated by taking into account the shading of surrounding buildings and vegetation. Finally, the average radiation temperature is calculated. , Where σ is the Boltzmann constant, with a value of 5. . 67·10 -8 W·m -2 · K -4 ; ε p R is the emissivity of the human body, with a standard value of 0.97; R is the radiation received by the human body, which can be estimated as: , in, Ki It consists of shortwave radiation components from six directions (north, south, west, east, up, and down). Li It is long-wave radiation. Fi It is the angular coefficient between a person and their surrounding environment. ξk It is the absorption coefficient of shortwave radiation, with a standard value of 0.7. The embodiments of this application can calculate the average radiation temperature using a physical formula based on human radiation balance. By introducing parameters such as the Boltzmann constant and human emissivity, it directly reflects the actual radiation exposure of the human body, ensuring the scientific validity of the calculation results.

[0048] The embodiments of this application can automatically generate key parameters such as reclassified land cover data, sky view factor, building wall height and wall orientation data based on multi-source geographic information data, in order to calculate the average radiation temperature, thereby improving the completeness of environmental parameter extraction and simulation accuracy, thus laying a reliable foundation for subsequent high spatiotemporal resolution urban thermal environment simulation and enhancing the accuracy and stability of overall thermal risk assessment.

[0049] In step S102, the distribution of the general thermal climate index of the urban environment is calculated based on the mean radiation temperature, and the heat exposure period is determined based on the distribution of the general thermal climate index, so as to obtain the heat exposure duration according to the heat exposure period.

[0050] It is understood that in the embodiments of this application, the general thermal climate index can be a thermal comfort index that comprehensively considers four factors: average radiation temperature, air temperature, relative humidity, and wind speed, and its value can directly correspond to the human body's heat stress level; the heat exposure period can be a continuous or intermittent time period during which the general thermal climate index reaches or exceeds the preset heat stress threshold; and the heat exposure duration can be the total cumulative number of hours of the heat exposure period.

[0051] In practical implementation, this application embodiment can input air temperature, relative humidity, average radiant temperature, and wind speed at a height of 10m above the ground to calculate a general thermal climate index: , in, It is a general thermal climate index. For air temperature, The mean radiation temperature. The wind speed at a height of 10m above the ground. For relative humidity, calculate the distribution of the general thermal climate index as follows: Figure 3 As shown. The calculation of the General Thermal Climate Index is implemented using the Python version of the original Fortran code published by the official General Thermal Climate Index website.

[0052] like Figure 4 As shown, measured data of temperature, humidity and wind speed are obtained by using Kriging interpolation in QGIS using measured data from urban sub-meteorological stations. The air temperature (°C), relative humidity (%) and near-surface wind speed (m / s) of the local climate zone can be calculated by using near-surface meteorological stations or mesoscale meteorological models such as WRF (Weather Research and Forecasting Model).

[0053] Based on the hourly changes of the general thermal climate index for each grid, and according to the temperature thresholds of the general thermal climate index classification, the exposure periods for different grids are further calculated and the corresponding exposure durations are obtained.

[0054] The embodiments of this application can comprehensively consider the actual thermal influence factors perceived by the human body, such as radiation, humidity, and wind speed, to determine the heat exposure period and duration. They also take into account the influence of built environment factors, clothing thermal resistance, and human physiological functions, so as to truly reflect the human thermal comfort state of the local urban environment and accurately match the actual thermal environment characteristics of different regions.

[0055] Optionally, in one embodiment of this application, the formula for calculating the heat exposure period is: , in, I For indicator functions, t For local time, It is a general thermal climate index.

[0056] It is understood that, in the embodiments of this application, the indicator function is a function that maps elements in a set to two values ​​(usually 0 and 1) to explicitly indicate whether the element satisfies a specific condition or belongs to a specific subset. The general thermal climate index is a widely used outdoor thermal comfort index with thermal stress grading. This index corresponds numerical values ​​to thermal stress intensity levels and can accurately reflect the human thermal comfort state in a local urban environment.

[0057] For example, embodiments of this application can, based on the hourly changes of the general thermal climate index for each grid, and according to the temperature thresholds of the general thermal climate index classification, further calculate the exposure periods for different grids and obtain the corresponding exposure durations, such as... Figure 5 As shown, the summer temperature threshold is 38°C, which is considered very strong heat stress, and the period when the General Climate Index is greater than 38°C is taken as the heat exposure period.

[0058] , , in, I For indicator functions, t For local time, This is a general thermal climate index. Based on the hourly distribution of the general thermal climate index and the corresponding thresholds, the exposure period to the environment can be further calculated, and the travel flow during the exposure period can be regarded as a population with potential heat risk.

[0059] This application embodiment can use a general thermal climate index threshold as a standard, and by judging the heat exposure period hour by hour, it can adapt to the thermal environment changes in different urban spaces, realize the accurate screening and statistics of heat exposure periods, and provide a clear time frame for subsequent cumulative travel flow statistics and risk intensity calculation.

[0060] In step S103, hourly pedestrian traffic in the urban environment is obtained, and based on the hourly pedestrian traffic and the heat exposure period, the cumulative pedestrian traffic during the heat exposure period is calculated, and the heat exposure risk is calculated based on the cumulative pedestrian traffic.

[0061] It is understood that, in the embodiments of this application, hourly passenger flow is the sum of the number of people traveling in different areas of the city per hour; cumulative passenger flow can be the sum of hourly passenger flow in a certain area during the heat exposure period, reflecting the scale of the population exposed to the heat environment during that period; heat exposure risk can be a quantitative indicator that combines cumulative passenger flow and heat exposure period.

[0062] In actual implementation, the embodiments of this application can perform hourly pedestrian flow calculation in the urban environment. Based on the outdoor pedestrian flow calculation method under the background of tidal travel, the relative difference in the hourly population distribution in two adjacent time periods is used as the hourly pedestrian flow of each grid, thereby obtaining the hourly outdoor pedestrian flow distribution.

[0063] Specifically, urban daily travel often exhibits a certain "tidal" pattern, forming diurnal tidal travel flows. Under this pattern, based on dynamic population data, it is assumed that each grid is considered a departure or destination point, and the inflow and outflow of people within each grid is unidirectional. All people crossing the grid boundary are considered to have travel behavior. Based on this assumption, it can be concluded that changes in the population within a grid between adjacent time periods represent the travel flow generated within that grid. The arrival or departure passenger flow of each grid is estimated by the difference in hourly population within the grid between adjacent time periods, such as... Figure 6 As shown, the computational grid resolution is consistent with the resolution of the weather station interpolation.

[0064] Optionally, in one embodiment of this application, the formula for calculating hourly pedestrian flow is: , in, i For grid numbering, t For local time, For a moment t population density For grid i The difference in population density between adjacent time periods.

[0065] In actual implementation, this embodiment of the application can estimate the incoming or outgoing pedestrian flow of each grid by the difference between the hourly population counts of adjacent time periods within the grid. The hourly pedestrian flow calculation formula is as follows: , in, i For grid numbering, t For local time, For a moment t population density For grid i The population density difference between adjacent time periods and the hourly relative population distribution come from dynamic population distribution databases (such as urban population geographic big data from dynamic population distribution databases like Baidu Maps Insight Platform). The traffic data is the cumulative value within each hour, that is, the number of users' mobile terminals calling API (Application Programming Interface) within the region within one hour.

[0066] The embodiments of this application can estimate the travel flow of a grid by using the population change within adjacent time periods. The calculation method is simple, easy to implement and understand. By calling the user's mobile terminal to obtain dynamic population density, the dynamic changes of urban population flow can be tracked in real time. It is suitable for high-frequency data processing, has good timeliness, and helps to accurately understand the spatiotemporal distribution of travel flow during extreme heat waves, thus promoting the precise monitoring of urban spatial heat exposure risks.

[0067] Furthermore, the cumulative passenger flow during the exposure period is calculated. After obtaining the hourly passenger flow and exposure period within each grid, the cumulative passenger flow during the exposure period within the grid is calculated. The travel heat exposure risk within a grid is determined by the cumulative passenger flow during the exposure period; the higher the passenger flow, the higher the heat risk. Figure 7 As shown in the figure, urban spaces with high heat exposure risk and surrounding key public infrastructure are marked. Large office parks, large medical institutions, high-speed rail stations, and commercial outlets are among the key urban public facilities with higher risk. Therefore, the cumulative pedestrian traffic during the exposure period is used to quantify the relative risk of different land uses. , in, This represents the heat exposure risk value. For grid i The difference in population density between adjacent time periods.

[0068] This application embodiment can overlay dynamic hourly pedestrian traffic data with precisely identified hot exposure periods in time and space, accurately matching hot exposure periods with pedestrian traffic, and quantifying hot exposure risk by accumulating pedestrian traffic, thereby improving the spatial accuracy and scientific nature of risk assessment, and providing accurate decision-making basis for urban thermal risk prevention and control under the influence of dynamic pedestrian traffic.

[0069] Optionally, in one embodiment of this application, after calculating the heat exposure risk based on the cumulative passenger flow, the method further includes: normalizing the heat exposure risk within the calculation area to output a relative heat exposure risk intensity distribution map.

[0070] It is understood that the normalization process in the embodiments of this application can be understood as transforming the heat exposure risk values ​​of different regions into the same scale, eliminating the magnitude impact caused by objective differences such as regional area and basic population flow, and facilitating cross-regional comparison of heat exposure risk intensity.

[0071] In practical implementation, this embodiment can normalize the cumulative pedestrian traffic during the exposure period within the calculation area. The cumulative pedestrian traffic during the exposure period for each grid is divided by the maximum risk value of the land use samples within the calculation area to obtain the dynamic relative thermal exposure risk for each grid, and a relative thermal exposure risk intensity distribution map is generated. The formula for calculating relative thermal exposure risk is as follows: , in, This represents the cumulative pedestrian traffic during the exposure period for each grid within the example area. and These represent the maximum and minimum cumulative pedestrian traffic during the exposure period across all grids within the calculation area. Grid i The relative heat risk intensity is calculated by normalizing the cumulative passenger flow during the heat exposure period by subtracting the minimum value and dividing by the difference between the maximum and minimum values ​​within the calculation area.

[0072] The embodiments of this application can eliminate the impact of regional differences through normalization, present the risk distribution in a spatially visualized form, make heat exposure risks comparable across regions, optimize resource allocation and intervention strategies, and promote the refined governance of the urban built environment.

[0073] Specifically, it can be combined with Figure 8 and Figure 9 As shown, a specific embodiment is used to elaborate in detail on the working principle of the thermal exposure risk calculation method based on urban pedestrian flow and built environment dynamics in this application.

[0074] Figure 8 This is a visualization diagram of a high spatiotemporal resolution thermal risk calculation framework based on "time-space-human behavior" provided according to an embodiment of this application.

[0075] like Figure 8 As shown, the embodiments of this application can derive the spatial distribution of thermal risk by integrating three core elements: hourly environmental simulation (time), spatial distribution mapping (space), and dynamic population movement (human behavior), thereby achieving a more accurate and dynamic assessment of urban thermal risk.

[0076] Specifically, in hazard assessment, hourly UTCI distribution data is used as a basis and compared with UTCI thresholds to calculate the exposure duration for each spatial unit, ultimately outputting the hourly hazard spatial distribution. In exposure assessment, hourly dynamic big data on pedestrian traffic is introduced to calculate pedestrian density. By screening the active population during the hot exposure period, hourly pedestrian traffic in hot-risk areas is obtained. Simultaneously, vulnerability factors are considered, along with the proportion of vulnerable groups (the elderly and children) in the pedestrian traffic, to further process the hourly pedestrian traffic in risk areas: by overlaying the hazard and exposure analysis results and comprehensively considering vulnerability factors, the cumulative pedestrian traffic in risk areas is calculated, ultimately outputting a quantified hot-risk level in the form of a visualized hot-risk distribution map. This completes high spatiotemporal resolution hot-risk calculation based on "time-space-human behavior".

[0077] Figure 9 This is a flowchart illustrating the data processing and overall calculation of a "time-space-human behavior" thermal risk assessment calculation framework provided according to an embodiment of this application.

[0078] like Figure 9 As shown, embodiments of this application may include the following steps: Step S901: Input data preprocessing for mean radiation temperature calculation.

[0079] In this embodiment, the average radiation temperature calculation input data preprocessing can be performed. The data preprocessing requires the use of UMEP and mainly includes the following three steps (in no particular order): a. Inputting land cover data into the UMEP reclassification processor and preprocessing to obtain reclassified land cover raster data with corresponding codes; b. Preprocessing the building DSM to obtain building wall height and wall orientation raster data, and setting a minimum wall height (3m) to filter non-building walls; c. Preprocessing the building and vegetation DSM to obtain sky view factor data considering vegetation canopy distribution and transmittance. The recommended value for vegetation radiation transmittance is 1.3-6%.

[0080] Step S902: Calculation of hourly average radiation temperature.

[0081] This embodiment of the application can perform hourly average radiation temperature calculations using the SOLWEIG module of the UMEP tool in QGIS, simulating the average radiation temperature at a height of 1.1m above the ground surface. Required input data includes digital surface models of buildings, digital surface models of vegetation canopies, hourly meteorological driving data, digital elevation models, and reclassified land cover data, sky view factor, and wall height and orientation data obtained through preprocessing using the UMEP tool in QGIS. Meteorological driving data uses measured data from suburban weather stations to provide direct and diffuse radiation. It is recommended that the output raster data resolution be 1-10m, the calculation assumes a tree radiation transmittance of 6%, and only considers tree canopies taller than 2m.

[0082] The mean radiation temperature calculation mechanism uses the six-axis radiation method to simulate the three-dimensional radiation flux density of the surrounding built environment. By calculating the wall temperature and air temperature of the surrounding environment, it further calculates the long-wave radiation from the surrounding walls and sky, and takes into account the shading of surrounding buildings and vegetation to calculate the short-wave radiation from the sky, and finally calculates the mean radiation temperature.

[0083] , in, σ It is the Boltzmann constant (5 . 67·10 -8 W·m -2 · K -4 ), ε p It is the emissivity of the human body (the standard value is 0.97). R The radiation received by the human body can be estimated as follows: , in, Ki It consists of shortwave radiation components from six directions (north, south, west, east, up, and down). Li It is long-wave radiation. Fi It is the angular coefficient between a person and their surrounding environment. ξk It is the absorption coefficient of shortwave radiation, with a standard value of 0.7.

[0084] Step S903: Calculate hourly temperature, humidity and wind speed.

[0085] In this embodiment of the application, Kriging interpolation can be used to calculate the air temperature (°C), relative humidity (%), and wind speed (m / s) at a height of 10m above the ground within the grid, based on the measured values ​​of the sub-meteorological stations.

[0086] Step S904: Hourly UTCI (Universal Thermal Climate Index) calculation.

[0087] In this embodiment of the application, the UTCI can be calculated by inputting air temperature (°C), relative humidity (%), average radiation temperature (°C), and wind speed (m / s) at a height of 10m above the ground.

[0088] , The code used to compute UTCI is derived from the Python version of the original Fortran code published by the UTCI official website.

[0089] Step S905: Calculation of the exposure period in the built environment.

[0090] In this embodiment, based on the hourly changes of UTCI for each grid and according to the temperature threshold of UTCI classification, the exposure period for different grids can be further calculated and the corresponding exposure duration can be obtained. Figure 5 The summer temperature threshold was set at 38°C (very high heat stress), and the exposure period was defined as the time when the UTCI was greater than 38°C.

[0091] , , in, t Local time.

[0092] Step S906: Calculate hourly pedestrian flow.

[0093] In this embodiment, urban daily travel often exhibits a certain "tidal" pattern, forming a diurnal tidal travel flow. Under this pattern, based on dynamic population data, it is assumed that each grid is considered a departure point or destination, and the inflow and outflow of people within each grid during each time period is unidirectional. All people crossing the grid boundary are considered to have travel behavior. Based on this assumption, it can be concluded that changes in the population within a grid during adjacent time periods represent the generation of travel flow within that grid. The arrival or departure passenger flow of each grid is estimated by the difference in hourly population within the grid between adjacent time periods. Δpop The calculation grid resolution is consistent with the resolution of the weather station interpolation.

[0094] , The hourly relative population distribution data comes from dynamic population distribution databases (such as urban population geographic big data from dynamic population distribution databases like Baidu Maps Insight Platform), and the pedestrian flow data is the cumulative value within each hour, that is, the number of users' mobile terminals calling the API within the region within one hour. pop ).

[0095] Step S907: Calculation of cumulative passenger flow during the exposure period.

[0096] In this embodiment, the cumulative passenger flow during the exposure period can be calculated. The risk of heat exposure in a local city is determined by the cumulative passenger flow during the exposure period; the higher the passenger flow, the higher the heat risk. Therefore, the calculation uses the cumulative passenger flow during the exposure period to quantify the relative risk of different land uses.

[0097] .

[0098] Step S908: Calculation of dynamic relative heat exposure risk.

[0099] In this embodiment, the cumulative pedestrian traffic during the exposure period of each grid is subtracted from the minimum risk of land use samples within the calculation range, and then divided by the difference between the maximum and minimum risk of land use samples within the calculation range to obtain the dynamic relative thermal exposure risk of each grid.

[0100] , in, This represents the cumulative pedestrian traffic during the exposure period for each grid within the example area. and These represent the maximum and minimum values ​​of the cumulative passenger flow during the exposure period across all grids within the calculation range.

[0101] The method for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics proposed in this application can incorporate urban dynamic pedestrian flow and built environment thermal exposure data. By calculating the average radiation temperature, the distribution of the general thermal climate index, and the thermal exposure period, combined with hourly pedestrian flow statistics, a quantitative assessment of thermal exposure risk can be achieved. This helps to accurately understand the true spatiotemporal distribution of pedestrian exposure risk during extreme weather, promotes the precise monitoring and calculation of urban spatial thermal risk during extreme heat waves, and thus accurately assesses residents' thermal exposure, identifies high-risk urban locations, supports sustainable urban construction, and promotes social health equity. This solves the problem in related technologies where reliance on static population data makes it difficult to reflect the dynamics of urban daytime and nighttime pedestrian flow with high spatiotemporal resolution, resulting in thermal risk calculations based on static population data failing to meet the needs of refined analysis and early warning of urban spatial environmental exposure.

[0102] Secondly, referring to the accompanying drawings, an apparatus for calculating thermal exposure risk based on urban pedestrian flow and built environment dynamics is proposed according to an embodiment of this application.

[0103] Figure 10 This is a schematic diagram of the structure of the thermal exposure risk calculation device based on urban pedestrian flow and built environment dynamics according to an embodiment of this application.

[0104] like Figure 10As shown, the thermal exposure risk calculation device 10 based on urban pedestrian flow and built environment dynamics includes: The acquisition module 100 is used to acquire the average radiation temperature calculation input data of the urban environment, and calculate the average radiation temperature of the urban environment based on the average radiation temperature calculation input data.

[0105] The determination module 200 is used to calculate the distribution of the general thermal climate index of the urban environment based on the mean radiation temperature, and to determine the heat exposure period based on the distribution of the general thermal climate index, so as to obtain the heat exposure duration according to the heat exposure period.

[0106] The calculation module 300 is used to obtain hourly pedestrian traffic in the urban environment, and based on the hourly pedestrian traffic and the hot exposure period, to calculate the cumulative pedestrian traffic during the hot exposure period, and to calculate the hot exposure risk based on the cumulative pedestrian traffic.

[0107] Optionally, in one embodiment of this application, the acquisition module 100 includes: an acquisition data unit.

[0108] The data acquisition unit is used to acquire land cover data, building digital surface models, vegetation canopy digital surface models, digital elevation models, and hourly meteorological driving data. Based on the land cover data, building digital surface models, and vegetation canopy digital surface models, it generates reclassified land cover data, sky view factor, building wall height, and wall orientation data to obtain the input data for calculating the average radiation temperature of the urban environment.

[0109] Optionally, in one embodiment of this application, the formula for calculating the heat exposure period is: , in, I For indicator functions, t For local time, It is a general thermal climate index.

[0110] Optionally, in one embodiment of this application, the formula for calculating hourly pedestrian flow is: , in, i For grid numbering, t For local time, For a moment t population density For grid i The difference in population density between adjacent time periods.

[0111] Optionally, in one embodiment of this application, the thermal exposure risk calculation device 10 based on urban pedestrian flow and built environment dynamics further includes a processing module.

[0112] The processing module is used to normalize the heat exposure risk within the calculation area after calculating the heat exposure risk based on the cumulative passenger flow, so as to output a relative heat exposure risk intensity distribution map.

[0113] It should be noted that the foregoing explanation of the embodiment of the thermal exposure risk calculation method based on urban pedestrian flow and built environment dynamics also applies to the thermal exposure risk calculation device based on urban pedestrian flow and built environment dynamics in this embodiment, and will not be repeated here.

[0114] The thermal exposure risk calculation device based on urban pedestrian flow and built environment dynamics proposed in this application can incorporate urban dynamic pedestrian flow and built environment thermal exposure data. By calculating the average radiation temperature, the distribution of the general thermal climate index, and the thermal exposure period, combined with hourly pedestrian flow statistics, it can achieve a quantitative assessment of thermal exposure risk. This helps to accurately understand the true spatiotemporal distribution of pedestrian exposure risk during extreme weather, promotes the precise monitoring and calculation of urban spatial thermal risk during extreme heat waves, and thus accurately assesses residents' thermal exposure, identifies high-risk urban locations, supports sustainable urban construction, and promotes social health equity. This solves the problem in related technologies where reliance on static population data makes it difficult to reflect the dynamics of urban daytime and nighttime pedestrian flow with high spatiotemporal resolution, resulting in thermal risk calculations based on static population data failing to meet the needs of refined analysis and early warning of urban spatial environmental exposure.

[0115] Figure 11 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 1101, the processor 1102, and the computer program stored on the memory 1101 and executable on the processor 1102.

[0116] When the processor 1102 executes the program, it implements the thermal exposure risk calculation method based on urban pedestrian flow and built environment dynamics provided in the above embodiments.

[0117] Furthermore, electronic devices also include: Communication interface 1103 is used for communication between memory 1101 and processor 1102.

[0118] The memory 1101 is used to store computer programs that can run on the processor 1102.

[0119] The memory 1101 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage.

[0120] If the memory 1101, processor 1102, and communication interface 1103 are implemented independently, then the communication interface 1103, memory 1101, and processor 1102 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0121] Optionally, in a specific implementation, if the memory 1101, processor 1102, and communication interface 1103 are integrated on a single chip, then the memory 1101, processor 1102, and communication interface 1103 can communicate with each other through an internal interface.

[0122] The processor 1102 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0123] This application also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating thermal exposure risk based on urban pedestrian flow and the dynamics of the built environment.

[0124] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described method for calculating thermal exposure risk based on urban pedestrian flow and the dynamics of the built environment.

[0125] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0126] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0127] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0128] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0129] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0130] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0131] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0132] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calculating heat exposure risk based on urban trip flows and built environment dynamics, characterized in that, The method comprises the following steps: obtaining average radiant temperature calculation input data of an urban environment, and calculating an average radiant temperature of the urban environment according to the average radiant temperature calculation input data; based on the average radiant temperature, calculating a general thermal climate index distribution of the urban environment, and determining a heat exposure period based on the general thermal climate index distribution, so as to obtain a heat exposure duration according to the heat exposure period; obtaining hourly travel person flow of the urban environment, and based on the hourly travel person flow and the heat exposure period, counting cumulative travel person flow in the heat exposure period, and calculating heat exposure risk according to the cumulative travel person flow.

2. The method of claim 1, wherein, The obtaining of the average radiant temperature calculation input data of the urban environment comprises: obtaining land cover data, building digital surface model, vegetation crown digital surface model, digital elevation model and hourly meteorological driving data, and generating reclassified land cover data, sky view factor, building wall height and wall orientation data based on the land cover data, the building digital surface model and the vegetation crown digital surface model, so as to obtain the average radiant temperature calculation input data of the urban environment.

3. The method of claim 1, wherein, The calculation formula of the heat exposure period is: ， wherein, I is an indicator function, t is local time, is the universal thermal climate index.

4. The method of claim 1, wherein, The calculation formula of the hourly travel person flow is: ， wherein, i is a grid number, t is local time, is a time of day t is a population density, is a grid i is a population density difference value for an adjacent time period.

5. The method of claim 1, wherein, After the heat exposure risk is calculated according to the cumulative travel person flow, the method further comprises: normalizing the heat exposure risk in the calculation area to output a relative heat exposure risk intensity distribution map.

6. An urban trip-based and built environment dynamic-based heat exposure risk calculation device, characterized by, The method comprises: an obtaining module, configured to obtain average radiant temperature calculation input data of an urban environment, and calculate an average radiant temperature of the urban environment according to the average radiant temperature calculation input data; a determining module, configured to, based on the average radiant temperature, calculate a general thermal climate index distribution of the urban environment, and determine a heat exposure period based on the general thermal climate index distribution, so as to obtain a heat exposure duration according to the heat exposure period; a calculating module, configured to obtain hourly travel person flow of the urban environment, and based on the hourly travel person flow and the heat exposure period, count cumulative travel person flow in the heat exposure period, and calculate heat exposure risk according to the cumulative travel person flow.

7. The apparatus of claim 6, wherein, The obtaining module comprises: a data obtaining unit, configured to obtain land cover data, building digital surface model, vegetation crown digital surface model, digital elevation model and hourly meteorological driving data, and generate reclassified land cover data, sky view factor, building wall height and wall orientation data based on the land cover data, the building digital surface model and the vegetation crown digital surface model, so as to obtain average radiant temperature calculation input data of the urban environment.

8. An electronic device, comprising: The method comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the heat exposure risk calculation method based on urban travel person flow and built environment dynamics according to any one of claims 1-5.

9. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the heat exposure risk calculation method based on urban travel person flow and built environment dynamics according to any one of claims 1-5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed for implementing the urban travel flow and built environment dynamics based heat exposure risk calculation method according to any one of claims 1-5.

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