A method and system for assessing noise exposure considering unmanned aerial vehicles

Abstract

The application discloses a kind of considering the noise exposure evaluation method and system of unmanned aerial vehicle, the method includes: obtaining city environment data and unmanned aerial vehicle related data, the sound source modeling of unmanned aerial vehicle noise is carried out and the propagation of unmanned aerial vehicle noise in city environment is simulated, generate unmanned aerial vehicle noise map;With present situation city noise map, the city noise map of unmanned aerial vehicle noise map and present situation city noise map is obtained by merging calculation;Identify crowd distribution in different time periods and map to geographical space, form crowd distribution map in different time periods, combine multi-source data to carry out feature identification and analysis to crowd, obtain crowd distribution map with crowd feature information;According to city noise map and crowd distribution map, calculate regional noise pollution total amount and per capita noise pollution index, and calculate the noise exposure level of different crowds.The application perfects the noise exposure evaluation method, improves the space-time accuracy of unmanned aerial vehicle noise exposure evaluation.

Description

Technical Field

[0001] This invention relates to the field of drone noise assessment technology, and in particular to a method and system for assessing the noise exposure of drones. Background Technology

[0002] With the rapid development of drone technology, drones are increasingly being used in logistics, urban surveying, and environmental monitoring. However, the widespread use of drones has also brought new problems, especially the increasingly prominent impact of drone noise on the urban environment and the disturbance it causes to residents' lives, which urgently needs attention and solutions. Traditional noise assessment methods mainly consider the propagation and simulation of traffic noise, but this method ignores the propagation characteristics of drone noise in complex urban environments and its differentiated impact on different population groups in different areas, resulting in insufficient accuracy and timeliness of noise exposure assessments.

[0003] To address the aforementioned issues, existing technologies have proposed several solutions. For example, one proposed method for urban low-altitude UAV path planning considers safety risks and noise impacts, constructing a multi-objective path planning model based on these factors. However, this approach lacks a comprehensive analysis of population distribution and activity characteristics, resulting in assessment results that fail to accurately reflect the actual noise exposure of the population. Another example is a proposed risk map design method for small urban air traffic UAVs, which incorporates population density and public noise acceptance to create a risk map. However, it insufficiently considers population segmentation and the cumulative effects of existing urban noise environments, making it difficult to accurately assess the impact of UAV noise on the overall urban noise environment.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for assessing noise exposure of unmanned aerial vehicles (UAVs), aiming to solve the problem that existing technologies neglect the propagation characteristics of UAV noise in complex urban environments and its differentiated impact on people in different areas, resulting in insufficient accuracy and timeliness of noise exposure assessment.

[0006] To achieve the above objectives, the present invention provides a noise exposure assessment method considering unmanned aerial vehicles (UAVs), the method comprising the following steps:

[0007] Acquire urban environmental data and drone-related data, wherein the urban environmental data includes urban built environment data and atmospheric environment data;

[0008] Based on the urban environmental data and the drone-related data, the source of drone noise is modeled and the propagation of drone noise in the urban environment is simulated to generate a drone noise map.

[0009] By combining the existing urban noise map and based on the noise superposition pattern, the drone noise map and the existing urban noise map are merged to calculate an urban noise map that includes drone noise.

[0010] By aggregating data from multiple sources, the distribution of people in different time periods is identified, and the distribution of people in different time periods is mapped onto geospatial data to form a population distribution map for different time periods;

[0011] Based on the population distribution maps at different time periods, the characteristics of the population are identified and analyzed by combining multi-source data. Based on the behavioral preferences of different groups, population profiles are constructed from different dimensions to obtain population distribution maps with population characteristic information.

[0012] Based on the urban noise map and the population distribution map with population characteristic information, the total regional noise pollution and the per capita noise pollution index are calculated, and the noise exposure level of different population groups is calculated.

[0013] Optionally, the noise exposure assessment method considering unmanned aerial vehicles (UAVs) includes urban built environment data such as: topographic elevation, land use, vegetation cover, building vectors, road network, points of interest, and street view images.

[0014] The atmospheric environmental data includes: wind speed and direction at the location of the flight, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity.

[0015] The drone-related data includes: flight trajectory, flight time, and drone model.

[0016] Optionally, in the noise exposure assessment method considering unmanned aerial vehicles, the sound source modeling specifically includes:

[0017] Based on the UAV-related data, the UAV's three-dimensional spatial trajectory, speed, pitch angle, and propeller speed are obtained. A UAV flight dynamics model is constructed and superimposed onto a three-dimensional urban real-scene model built based on urban built environment data and atmospheric environment data to achieve real-time and accurate positioning of the UAV in the urban environment.

[0018] Optionally, the noise exposure assessment method considering drones, wherein the simulation of propagation in an urban environment specifically includes:

[0019] The drone noise propagation simulation uses noise mapping technology to simulate the propagation of drone noise in urban environments. It imports urban built environment data, then imports drone sound source data, and imports the A-weighted sound pressure level, frequency distribution, emission direction, and intensity distribution of the sound source based on actual measurement results. The drone is treated as a point source model, a specific propagation model is selected, and atmospheric environmental parameters are set according to the actual urban conditions. The drone noise map is obtained through simulation.

[0020] Optionally, in the aforementioned method for assessing noise exposure to drones, the multi-source data includes: mobile phone signaling data, social network data, point-of-interest data, and transportation data.

[0021] The system aggregates multi-source data, identifies population distribution across different time periods, and maps this distribution onto a geographic space to create population distribution maps for different time periods. Specifically, this includes:

[0022] By using programming techniques to automatically aggregate mobile phone signaling data, social network data, point of interest data, and transportation data from geographic big data, we can identify the distribution of people at different times and obtain population distribution data.

[0023] The population distribution data is filtered, cleaned, and integrated to obtain the target population distribution data;

[0024] The target population distribution data is mapped onto a geographic space using geographic information system software, forming population distribution maps for different time periods.

[0025] Optionally, the noise exposure assessment method considering drones, wherein the step of using the crowd distribution map based on different time periods, combined with multi-source data to perform feature identification and analysis of the crowd, and constructing crowd profiles from different dimensions according to the behavioral preferences of different groups to obtain a crowd distribution map with crowd feature information, specifically includes:

[0026] Based on the population distribution data of different regions at different times, we identify and analyze the characteristics of the population to obtain population characteristic data, and classify and label the population according to age, income level and education level.

[0027] Using geographic information system software, spatiotemporal frequency analysis is performed on different groups of people based on anonymized location service data provided by operators. The behavioral preferences of different groups are obtained, including time preferences, spatial preferences, facility preferences and landscape preferences. The temporal trends and periodic changes of different groups of people's activities are identified. Combined with spatiotemporal distribution and profile data of the population, a population distribution map containing population structure characteristics is formed.

[0028] Optionally, in the aforementioned method for assessing noise exposure to drones, the total regional noise pollution is used to assess the cumulative effect of excessive noise on the exposed population. The calculation of the total noise pollution considers the number of exposed people in the region, the noise level in the area exceeding the noise standard, and the environmental noise limit.

[0029] Optionally, in the aforementioned method for assessing noise exposure to drones, the per capita noise pollution index is used to measure the average impact of excessive noise on an individual.

[0030] When calculating the per capita pollution level in a small area, noise points in each area need to be weighted by population density.

[0031] Furthermore, to achieve the above objectives, the present invention also provides a noise exposure assessment system considering unmanned aerial vehicles (UAVs), wherein the noise exposure assessment system considering UAVs includes:

[0032] The data collection module is used to acquire urban environmental data and drone-related data, including urban built environment data and atmospheric environment data.

[0033] The noise simulation module is used to model the source of drone noise and simulate the propagation of drone noise in the urban environment based on the urban environmental data and the drone-related data, and generate a drone noise map.

[0034] The noise map overlay module is used to combine the existing urban noise map and the existing urban noise map based on the noise overlay pattern to calculate an urban noise map that includes the drone noise.

[0035] The population distribution statistics module is used to collect multi-source data, identify the population distribution in different time periods, and map the population distribution to geospatial space to form population distribution maps for different time periods.

[0036] The crowd profiling module is used to identify and analyze the characteristics of the crowd based on the crowd distribution map at different time periods and combined with multi-source data. Based on the behavioral preferences of different groups, it constructs crowd profiles from different dimensions to obtain a crowd distribution map with crowd characteristic information.

[0037] The noise exposure calculation module is used to calculate the total regional noise pollution and the per capita noise pollution index based on the urban noise map and the population distribution map with population characteristic information, and to calculate the noise exposure level of different population groups.

[0038] In this invention, urban environmental data and drone-related data are acquired, including urban built environment data and atmospheric environment data. Based on the urban environmental data and the drone-related data, drone noise is modeled as a source and its propagation in the urban environment is simulated to generate a drone noise map. Combining the existing urban noise map with the existing urban noise map based on the superposition rules of noise, the drone noise map and the existing urban noise map are merged to obtain an urban noise map containing drone noise. Multi-source data is collected to identify the distribution of people at different times and map the distribution of people to geographic space to form a population distribution map for different times. Based on the population distribution maps for different times, multi-source data is used to identify and analyze the characteristics of the population. Based on the behavioral preferences of different groups, population profiles are constructed from different dimensions to obtain a population distribution map with population characteristic information. Based on the urban noise map and the population distribution map with population characteristic information, the total regional noise pollution and the per capita noise pollution index are calculated, and the noise exposure level of different groups is calculated. This invention provides a dynamic and accurate method for assessing the noise exposure levels of different population groups, including drone noise, through drone noise simulation and multi-source data-driven population analysis. It improves the noise exposure assessment method, enhances the spatiotemporal accuracy of drone noise exposure assessment, and can accurately evaluate the noise exposure of different population groups in urban environments, thereby improving the accuracy and timeliness of noise exposure assessment. Attached Figure Description

[0039] Figure 1 This is a flowchart of a preferred embodiment of the noise exposure assessment method for unmanned aerial vehicles (UAVs) of the present invention;

[0040] Figure 2 This is a flowchart illustrating the overall process of noise exposure assessment in a preferred embodiment of the noise exposure assessment method for unmanned aerial vehicles (UAVs) of the present invention.

[0041] Figure 3 This is a structural diagram of a preferred embodiment of the noise exposure assessment system for unmanned aerial vehicles (UAVs) of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0043] This invention provides a noise exposure assessment method considering unmanned aerial vehicles (UAVs), aiming to address the lack of UAV noise data in existing noise exposure assessments. First, it acquires urban built environment, meteorological data, and UAV (type, flight trajectory) related data to construct a UAV flight dynamics model. This model is then overlaid onto a 3D urban real-world model built based on the urban built environment and meteorological data, enabling real-time and accurate UAV positioning within the urban environment—i.e., sound source modeling. Next, based on the propagation, attenuation, and superposition laws of noise, noise mapping technology is used to simulate UAV noise, overlaid with the existing urban noise map to obtain an urban noise map including UAV noise. Subsequently, multi-source data is used to identify population distribution and form a population distribution map. Then, population characteristics are segmented based on data feature labels to create population profiles. Finally, two key indicators—total regional noise pollution and per capita noise pollution index—are introduced to quantify the noise exposure of different population groups. This invention, through UAV noise simulation and multi-source data-driven population analysis, provides a dynamic and accurate method to assess the noise exposure level including UAV noise for different population groups, improving noise exposure assessment methods and enhancing the spatiotemporal accuracy of UAV noise exposure assessment, thus providing a scientific basis for urban planning and related policy formulation.

[0044] The preferred embodiment of the present invention describes a noise exposure assessment method for unmanned aerial vehicles (UAVs), such as... Figure 1 and Figure 2 As shown, the noise exposure assessment method considering drones includes the following steps:

[0045] Step S10: Obtain urban environmental data and drone-related data, wherein the urban environmental data includes urban built environment data and atmospheric environment data.

[0046] Specifically, the urban environment is complex and has a significant impact on the propagation and exposure of drone noise. Urban built environment data and atmospheric environment data are essential data for simulating drone noise. The urban built environment data includes: terrain elevation, land use, vegetation cover, building vectors, road network, points of interest, and street view images.

[0047] The atmospheric environmental data includes: wind speed and direction at the location of the flight, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity.

[0048] In addition, the drone-related data includes: flight trajectory, flight time, and drone model.

[0049] Automated image processing software is used to preprocess urban environmental data and drone-related data, including correction, classification and analysis. Data from different sources is integrated and overlaid and analyzed using geographic information system software.

[0050] The above data is used to construct the dataset needed to simulate drone noise.

[0051] Step S20: Based on the urban environment data and the drone-related data, perform sound source modeling for drone noise and simulate the propagation of drone noise in the urban environment to generate a drone noise map.

[0052] Specifically, based on UAV-related data, the three-dimensional spatial trajectory of the UAV is obtained, as well as flight parameters such as speed, pitch angle, and propeller speed of the UAVs. A UAV flight dynamics model is constructed and superimposed onto a three-dimensional urban scene model built based on urban built environment and atmospheric environment data, so as to realize real-time and accurate positioning of UAVs in the urban environment, i.e., sound source modeling.

[0053] The drone noise propagation simulation uses noise mapping technology to simulate the propagation of drone noise in urban environments. First, urban built environment data is imported, followed by drone sound source data. Based on actual measurement results, the A-weighted sound pressure level (dB(A), frequency distribution, emission direction, and intensity distribution) of the sound source are imported. The drone is treated as a point source model and a specific propagation model is selected (e.g., the propagation model in ISO 9613-2-1996 "Calculation method for outdoor sound propagation attenuation"). Atmospheric environmental parameters are set according to the actual urban conditions, and a drone noise map (i.e., noise distribution results) is obtained through simulation.

[0054] Step S30: Combining the existing urban noise map with the noise superposition pattern, the drone noise map and the existing urban noise map are merged to calculate an urban noise map that includes drone noise.

[0055] Specifically, by combining the existing urban noise map with the noise superposition pattern, the drone noise map in step S20 is merged with the existing urban noise map to obtain an urban noise map that includes drone noise.

[0056] For example, using relevant noise simulation software, the noise of the drone can be defined as a point source, the noise source can be defined and the prediction area can be set, and the noise superposition simulation can be performed based on the built environment buildings and sound barriers and the noise superposition formula. The noise distribution map can be viewed through the visualization function.

[0057] Step S40: Collect multi-source data, identify the distribution of people in different time periods, and map the distribution of people to geospatial space to form a distribution map of people in different time periods.

[0058] Specifically, accurately describing the noise exposure levels of different population groups in urban areas requires a clear understanding of the population distribution within those areas. This invention uses programming technology to automatically aggregate multi-source data, including mobile signaling data, location-based social network data, point-of-interest data, and traffic data (traffic flow data, public transportation usage data, etc.), carrying high-precision timestamps and geographic coordinates. This dynamically reflects the spatiotemporal distribution characteristics of the population, identifying population distribution at different times through multi-source data. The population distribution data is then filtered, cleaned, and integrated (e.g., daily data is divided into 24 time periods by hour). Using geographic information system (GIS) software, the population distribution data is mapped onto a geographic space, forming population distribution maps for different time periods.

[0059] Step S50: Based on the population distribution maps of different time periods, combine multi-source data to perform feature identification and analysis on the population, and construct population profiles from different dimensions according to the behavioral preferences of different populations to obtain population distribution maps with population feature information (such as occupation, age, etc.).

[0060] Specifically, based on the acquisition of population distribution data for different regions at different times, this invention further identifies and analyzes population characteristics. It constructs population profiles from different dimensions, such as age, income level, and education level, using geographic big data, mobile signaling data, and social network data.

[0061] For example, based on the above data, the characteristics of the population are quantitatively interpreted, and the age is divided into seven age groups: under 17 years old, 18-24 years old, 25-44 years old, 45-64 years old, 65-74 years old, 75-84 years old, and over 85 years old. According to the regional income standard, the income level is divided into low income (annual family income is below the local minimum living standard), lower-middle income (annual family income is between the minimum living standard and the middle income level), middle income (annual family income is within the middle income level, usually referring to an annual family income between 40,000 and 120,000 yuan), upper-middle income (annual family income is above the middle income level but below the high income level), and high income group (annual family income is above the high income level, usually referring to an annual family income exceeding 120,000 yuan). Based on the classification system of the International Organization for Standardization and UNESCO, the education level of the population is divided into preschool education, primary education, secondary education (junior high school, senior high school, vocational high school), higher education (undergraduate, master's degree, doctoral degree), adult education, and continuing education (night school, correspondence education, online courses), and the data is cleaned and labeled. Furthermore, using Geographic Information System (GIS) software and anonymized location service data provided by telecom operators, spatiotemporal frequency analysis was conducted on different population groups to derive their behavioral preferences (including time preferences, spatial preferences, facility preferences, and landscape preferences), identifying the temporal trends and periodic changes in their activities. Finally, by combining the spatiotemporal distribution and profiling data of the population, a population distribution map with characteristic information (such as occupation and age) was generated.

[0062] For example, by using geographic information system (GIS) software to perform frequency analysis on spatiotemporal behavioral data of people, we can identify their resting points. We can set spatial thresholds (100 meters) and temporal thresholds (10 minutes). When a person's trajectory point moves less than 100 meters within 10 minutes, these trajectory points are clustered into the same resting point, and the geometric center of the resting point is used as its representative location. This reveals the behavioral preferences of different groups, encompassing temporal preferences, spatial preferences, facility preferences, and landscape preferences, and analyzes the population composition of different urban areas.

[0063] Step S60: Based on the urban noise map and the population distribution map with population characteristic information, calculate the total regional noise pollution and the per capita noise pollution index, and calculate the noise exposure level of different population groups.

[0064] Specifically, this invention introduces two key indicators that comprehensively reflect the cumulative effect of noise exposure and the spatiotemporal distribution of noise pollution: Total Noise Exposure Metric of Pollution (TNEMIP) and Average Noise Exposure Metric of Pollution per capita (ANEMIP). These indicators quantify the impact of traffic noise on exposed populations. The Total Noise Exposure Metric of Pollution (TNEMIP) aims to assess the cumulative effect of excessive noise on exposed populations, while the Average Noise Exposure Metric of Pollution per capita measures the average impact of excessive noise on individuals. Combining the urban noise map simulated in step S30 and the population distribution map containing population structure characteristics obtained in step S50, and based on the environmental noise limits for the five types of acoustic environmental functional zones specified in the acoustic environmental quality standards, the Total Noise Exposure Metric of Pollution and the Average Noise Exposure Metric of Pollution per capita are calculated, and the noise exposure levels of different population groups are also calculated.

[0065] When calculating the per capita pollution level in a small area, the noise points in each area need to be weighted by population density. Therefore, in application, the formulas for the total noise pollution and the per capita noise pollution index need to be rewritten.

[0066] The low-altitude economy is developing rapidly, and drones are being used more and more widely in multiple fields and scenarios. The noise exposure assessment for drone noise has improved the noise exposure assessment method and can accurately quantify the impact of drone noise on different groups of people. This provides a scientific basis for urban planning and related policy formulation, helps to achieve effective prevention and control of drone noise, and promotes the sustainable development of the low-altitude economy.

[0067] By integrating multi-source data such as mobile signaling data, social network data, point of interest data, and transportation data to describe crowd activity, the crowd activity analysis becomes more comprehensive and in-depth, improving the accuracy of noise exposure assessment. At the same time, the real-time updated data makes the crowd activity analysis more dynamic, which is conducive to conducting long-term noise exposure assessments.

[0068] This invention integrates and processes multi-source data, including mobile signaling data, social network data, point-of-interest data, and transportation data, to describe the spatiotemporal distribution of crowd activity. This method overcomes the limitations of crowd activity analysis based on a single data source, improves the accuracy and precision of crowd activity data, and makes noise exposure assessment more scientific and accurate.

[0069] This invention analyzes multi-source data to create a multi-source data population profile, enabling refined segmentation of noise-exposed populations. This allows for a more accurate understanding of the degree of noise exposure to different populations caused by drone noise, making noise exposure assessment more practically meaningful.

[0070] This invention establishes a complete automated process for simulating and overlaying urban drone noise by integrating and preprocessing drone data and urban environmental data. Furthermore, it integrates multi-source data to analyze urban population distribution and profiling, enabling the calculation of drone-related noise exposure. This achieves high-precision spatiotemporal analysis of noise exposure levels for different population groups, allowing the assessment results to more accurately reflect the noise exposure characteristics of various populations in different regions and time periods. This innovative achievement provides strong data support for urban planning and promotes research on urban drone noise exposure.

[0071] This invention provides a dynamic and accurate method for assessing drone noise exposure levels based on drone noise simulation and multi-source data-driven crowd activity analysis. This invention improves noise exposure assessment methods, enhances the spatiotemporal accuracy of drone noise exposure assessment, provides a scientific basis for urban planning and related policy formulation, and strengthens the accuracy and timeliness of planning and policy responses.

[0072] This invention specifically addresses the issue of drones as a noise source, systematically assessing drone noise exposure levels to overcome the limitations of existing noise exposure assessment technologies in low-altitude applications. It also provides noise exposure assessments tailored to different population groups. This invention can more accurately reveal the noise exposure levels and dynamic changes associated with drone noise, offering targeted data support for urban planning and policy formulation.

[0073] This invention constructs a complete process for assessing drone noise exposure levels based on drone noise and the spatiotemporal distribution of people. This process forms a comprehensive multi-source data-driven drone noise exposure level assessment technology framework through multi-source data processing, noise simulation, and noise exposure calculation.

[0074] Furthermore, such as Figure 3 As shown, based on the above-described method for assessing noise exposure to unmanned aerial vehicles (UAVs), this invention also provides a noise exposure assessment system considering UAVs, wherein the noise exposure assessment system considering UAVs includes:

[0075] Data collection module 51 is used to acquire urban environmental data and drone-related data, wherein the urban environmental data includes urban built environment data and atmospheric environment data;

[0076] The noise simulation module 52 is used to model the source of drone noise and simulate the propagation of drone noise in the urban environment based on the urban environment data and the drone-related data, and generate a drone noise map.

[0077] The noise map overlay module 53 is used to combine the existing urban noise map and the existing urban noise map based on the noise overlay pattern to calculate an urban noise map that includes the drone noise.

[0078] The population distribution statistics module 54 is used to collect multi-source data, identify the population distribution in different time periods, and map the population distribution to geospatial space to form a population distribution map for different time periods.

[0079] The crowd profile building module 55 is used to identify and analyze the characteristics of the crowd based on the crowd distribution map at different time periods and combined with multi-source data. Based on the behavioral preferences of different groups, it builds crowd profiles from different dimensions to obtain a crowd distribution map with crowd characteristic information.

[0080] The noise exposure calculation module 56 is used to calculate the total regional noise pollution and the per capita noise pollution index based on the urban noise map and the population distribution map with population characteristic information, and to calculate the noise exposure level of different population groups.

[0081] In summary, this invention provides a method and system for assessing noise exposure of unmanned aerial vehicles (UAVs). The method includes: acquiring urban environmental data and UAV-related data, wherein the urban environmental data includes urban built environment data and atmospheric environment data; modeling the source of UAV noise and simulating the propagation of UAV noise in the urban environment based on the urban environmental data and the UAV-related data, generating a UAV noise map; combining the existing urban noise map with the existing urban noise map based on the superposition law of noise to obtain an urban noise map containing UAV noise; aggregating multi-source data to identify the distribution of people in different time periods and mapping the distribution of people to geographic space to form a population distribution map for different time periods; based on the population distribution maps for different time periods, combining multi-source data to perform feature identification and analysis of the population, constructing population profiles from different dimensions according to the behavioral preferences of different population groups, and obtaining a population distribution map with population feature information; and calculating the total regional noise pollution and per capita noise pollution index based on the urban noise map and the population distribution map with population feature information, and calculating the noise exposure level of different population groups. This invention improves the noise exposure assessment method, enhances the spatiotemporal accuracy of UAV noise exposure assessment, and provides a scientific basis for urban planning and related policy formulation.

[0082] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0083] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for assessing noise exposure considering unmanned aerial vehicles (UAVs), characterized in that, The noise exposure assessment method considering drones includes: Acquire urban environmental data and drone-related data, wherein the urban environmental data includes urban built environment data and atmospheric environment data; Based on the urban environmental data and the drone-related data, the source of drone noise is modeled and the propagation of drone noise in the urban environment is simulated to generate a drone noise map. By combining the existing urban noise map and based on the noise superposition pattern, the drone noise map and the existing urban noise map are merged to calculate an urban noise map that includes drone noise. By aggregating data from multiple sources, the distribution of people in different time periods is identified, and the distribution of people in different time periods is mapped onto geospatial data to form a population distribution map for different time periods; Based on the population distribution maps at different time periods, the characteristics of the population are identified and analyzed by combining multi-source data. Based on the behavioral preferences of different groups, population profiles are constructed from different dimensions to obtain population distribution maps with population characteristic information. Based on the urban noise map and the population distribution map with population characteristic information, the total regional noise pollution and the per capita noise pollution index are calculated, and the noise exposure level of different population groups is calculated.

2. The noise exposure assessment method considering unmanned aerial vehicles according to claim 1, characterized in that, The urban built environment data includes: topographic elevation, land use, vegetation cover, building vectors, road network, points of interest, and street view images; The atmospheric environmental data includes: wind speed and direction at the location of the flight, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity. The drone-related data includes: flight trajectory, flight time, and drone model.

3. The noise exposure assessment method considering unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The sound source modeling specifically includes: Based on the UAV-related data, the UAV's three-dimensional spatial trajectory, speed, pitch angle, and propeller speed are obtained. A UAV flight dynamics model is constructed and superimposed onto a three-dimensional urban real-scene model built based on urban built environment data and atmospheric environment data to achieve real-time and accurate positioning of the UAV in the urban environment.

4. The noise exposure assessment method considering unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The simulation of propagation in the urban environment specifically includes: The drone noise propagation simulation uses noise mapping technology to simulate the propagation of drone noise in urban environments. It imports urban built environment data, then imports drone sound source data, and imports the A-weighted sound pressure level, frequency distribution, emission direction, and intensity distribution of the sound source based on actual measurement results. The drone is treated as a point source model, a specific propagation model is selected, and atmospheric environmental parameters are set according to the actual urban conditions. The drone noise map is obtained through simulation.

5. The noise exposure assessment method considering unmanned aerial vehicles according to claim 1, characterized in that, The multi-source data includes: mobile signaling data, social network data, point-of-interest data, and transportation data; The system aggregates multi-source data, identifies population distribution across different time periods, and maps this distribution onto a geographic space to create population distribution maps for different time periods. Specifically, this includes: By using programming techniques to automatically aggregate mobile phone signaling data, social network data, point of interest data, and transportation data from geographic big data, we can identify the distribution of people at different times and obtain population distribution data. The population distribution data is filtered, cleaned, and integrated to obtain the target population distribution data; The target population distribution data is mapped onto a geographic space using geographic information system software, forming population distribution maps for different time periods.

6. The noise exposure assessment method considering unmanned aerial vehicles according to claim 1, characterized in that, The aforementioned population distribution maps based on different time periods, combined with multi-source data, perform feature identification and analysis on the population, and construct population profiles from different dimensions according to the behavioral preferences of different groups, thereby obtaining population distribution maps with population feature information, specifically including: Based on the population distribution data of different regions at different times, we identify and analyze the characteristics of the population to obtain population characteristic data, and classify and label the population according to age, income level and education level. Using geographic information system software, spatiotemporal frequency analysis is performed on different groups of people based on anonymized location service data provided by operators. The behavioral preferences of different groups are obtained, including time preferences, spatial preferences, facility preferences and landscape preferences. The temporal trends and periodic changes of different groups of people's activities are identified. Combined with spatiotemporal distribution and profile data of the population, a population distribution map containing population structure characteristics is formed.

7. The noise exposure assessment method considering unmanned aerial vehicles according to claim 1, characterized in that, The total noise pollution in the area is used to assess the cumulative effect of excessive noise on the exposed population. The calculation of the total noise pollution takes into account the number of exposed people in the area, the noise level in the area with excessive noise, and the environmental noise limit.

8. The noise exposure assessment method considering unmanned aerial vehicles according to claim 1, characterized in that, The per capita noise pollution index is used to measure the average impact of excessive noise on individuals. When calculating the per capita pollution level in a small area, noise points in each area need to be weighted by population density.

9. A noise exposure assessment system considering unmanned aerial vehicles (UAVs), characterized in that, The noise exposure assessment system considering unmanned aerial vehicles includes: The data collection module is used to acquire urban environmental data and drone-related data, including urban built environment data and atmospheric environment data. The noise simulation module is used to model the source of drone noise and simulate the propagation of drone noise in the urban environment based on the urban environmental data and the drone-related data, and generate a drone noise map. The noise map overlay module is used to combine the existing urban noise map and the existing urban noise map based on the noise overlay pattern to calculate an urban noise map that includes the drone noise. The population distribution statistics module is used to collect multi-source data, identify the population distribution in different time periods, and map the population distribution to geospatial space to form population distribution maps for different time periods. The crowd profiling module is used to identify and analyze the characteristics of the crowd based on the crowd distribution map at different time periods and combined with multi-source data. Based on the behavioral preferences of different groups, it constructs crowd profiles from different dimensions to obtain a crowd distribution map with crowd characteristic information. The noise exposure calculation module is used to calculate the total regional noise pollution and the per capita noise pollution index based on the urban noise map and the population distribution map with population characteristic information, and to calculate the noise exposure level of different population groups.

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