Noise exposure evaluation method and system considering unmanned aerial vehicle

Abstract

The invention discloses a noise exposure evaluation method and system considering an unmanned aerial vehicle, and the method comprises the steps: obtaining urban environment data and unmanned aerial vehicle related data, carrying out the sound source modeling of the noise of the unmanned aerial vehicle, simulating the propagation of the noise of the unmanned aerial vehicle in an urban environment, and generating an unmanned aerial vehicle noise map; combining and calculating the unmanned aerial vehicle noise map and the current city noise map in combination with the current city noise map to obtain a city noise map; identifying crowd distribution conditions in different time periods and mapping the crowd distribution conditions to geographic space to form crowd distribution maps in different time periods, and performing feature identification and analysis on crowds in combination with multi-source data to obtain a crowd distribution map with crowd feature information; and according to the urban noise map and the crowd distribution map, calculating the total amount of regional noise pollution and the per capita noise pollution index, and calculating the noise exposure levels of different crowds. According to the method, the noise exposure evaluation method is perfected, and the space-time accuracy of unmanned aerial vehicle noise exposure evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV noise assessment, and particularly to a noise exposure assessment method and system considering UAVs. Background Art

[0002] In the context of the rapid development of UAV technology, UAVs are increasingly widely used in fields such as logistics transportation, urban mapping, and environmental monitoring. However, the widespread use of UAVs has also brought new problems. In particular, the impact of UAV noise on the urban environment and the interference caused to residents' lives have become increasingly prominent, and urgent attention and solutions are needed. Traditional noise assessment methods mainly consider the propagation and simulation of traffic noise, but this method ignores the propagation characteristics of UAV noise in complex urban environments and its differential impact on populations in different regions, resulting in insufficient accuracy and timeliness of noise exposure assessment.

[0003] In response to the above problems, some solutions have been proposed in related prior arts. For example, a method for path planning of low-altitude UAVs in cities considering safety risks and noise impacts is proposed, and a multi-objective path planning model based on safety risks and noise impacts is constructed. However, this solution lacks a comprehensive analysis of population distribution and activity characteristics, resulting in the evaluation results being unable to accurately reflect the actual noise exposure of the population. For example, a method for designing a risk map for the operation of light and small urban air traffic UAVs is proposed, which introduces population density and public noise acceptance to draw a risk map, but insufficient consideration is given to aspects such as population division and the superposition effect of the existing urban noise environment, making it difficult to accurately evaluate the impact of UAV noise on the overall urban noise environment.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the Invention

[0005] The main object of the present invention is to provide a noise exposure assessment method and system considering UAVs, aiming to solve the problem in the prior art that the propagation characteristics of UAV noise in complex urban environments and its differential impact on populations in different regions are ignored, resulting in insufficient accuracy and timeliness of noise exposure assessment.

[0006] To achieve the above object, the present invention provides a noise exposure assessment method considering UAVs, and the noise exposure assessment method considering UAVs includes the following steps: Obtain urban environmental data and UAV-related data, where the urban environmental data includes urban built environment data and atmospheric environmental data; According to the urban environmental data and the UAV-related data, perform sound source modeling on UAV noise and simulate the propagation of UAV noise in the urban environment to generate a UAV noise map; Combined with the current urban noise map, based on the superposition law of noise, the drone noise map and the current urban noise map are merged and calculated to obtain an urban noise map including drone noise; Collect multi-source data, identify the population distribution in different time periods, and map the population distribution to the geographical space to form a population distribution map for different time periods; Based on the population distribution maps for different time periods, combined with multi-source data, conduct feature recognition and analysis of the population. According to the behavior preferences of different populations, construct population portraits from different dimensions to obtain a population distribution map with population characteristic information; According to the urban noise map and the population distribution map with population characteristic information, calculate the total regional noise pollution amount and the per capita noise pollution index, and calculate the noise exposure levels of different populations.

[0007] Optionally, for the above-mentioned noise exposure assessment method considering drones, the urban built environment data includes: terrain elevation, land use, vegetation cover, building vectors, road networks, points of interest, and street view images; The atmospheric environment data includes: wind speed and direction, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity during flight at the assessment location; The drone-related data includes: flight trajectory, flight time, and drone model.

[0008] Optionally, for the above-mentioned noise exposure assessment method considering drones, the sound source modeling specifically includes: Based on the drone-related data, obtain the three-dimensional space trajectory of the drone, as well as the drone speed, pitch angle, and propeller rotation speed, construct a drone flight dynamics model, and superimpose the drone flight dynamics model onto the urban real-scene three-dimensional model established based on the urban built environment data and the atmospheric environment data to achieve real-time and accurate positioning of the drone in the urban environment.

[0009] Optionally, for the above-mentioned noise exposure assessment method considering drones, the simulation of the propagation in the urban environment specifically includes: The simulation of drone noise propagation uses noise map technology to simulate the propagation of drone noise in the urban environment. Import the urban built environment data, then import the drone sound source data. According to the actual measurement results, import the A-weighted sound pressure level, frequency distribution, emission direction, and intensity distribution of the sound source. Treat the drone as a point source model, select a specific propagation model, set the atmospheric environment parameters according to the actual situation of the city, and obtain the drone noise map through simulation.

[0010] Optionally, in the noise exposure assessment method considering drones, the multi-source data includes: mobile phone signaling data, social network data, point of interest data, and transportation travel data; Collect the multi-source data, identify the population distribution in different time periods, and map the population distribution to the geographical space to form a population distribution map for different time periods, specifically including: Automatically collect mobile phone signaling data, social network data, point of interest data, and transportation travel data in the geographical big data through programming technology, identify the population distribution in different time periods, and obtain population distribution data; Screen, clean, and integrate the population distribution data to obtain target population distribution data; Map the target population distribution data to the geographical space through geographical information system software mapping to form a population distribution map for different time periods.

[0011] Optionally, in the noise exposure assessment method considering drones, based on the population distribution map for different time periods, combine multi-source data to identify and analyze the characteristics of the population, and construct population portraits from different dimensions according to the behavior preferences of different populations to obtain a population distribution map with population characteristic information, specifically including: According to the obtained population distribution data of different regions in different time periods, identify and analyze the characteristics of the population to obtain population characteristic data, and divide and label the population according to age, income level, and education level; Use geographical information system software to conduct spatio-temporal frequency analysis on different populations based on the desensitized positioning service data provided by the operator to obtain the behavior preferences of different populations, where the behavior preferences include: time preference, space preference, facility preference, and landscape preference, and identify the time trends and periodic changes of different population activities, and combine the population spatio-temporal distribution and portrait data to form a population distribution map including population structure characteristics.

[0012] Optionally, in the noise exposure assessment method considering drones, the total regional noise pollution is used to evaluate the cumulative effect of excessive noise on the exposed population, and the calculation of the total noise pollution considers the number of exposed people in the region, the noise value of the noise-exceeding area, and the environmental noise limit.

[0013] Optionally, in the noise exposure assessment method considering drones, the per capita noise pollution index is used to measure the average impact degree of excessive noise on individuals; When calculating the per capita pollution amount of a small area, it is necessary to perform weighted calculation on the noise points in each region with the population density as the weight.

[0014] In addition, to achieve the above object, the present invention further provides a noise exposure assessment system considering drones, wherein the noise exposure assessment system considering drones includes: A data collection module for acquiring urban environmental data and drone-related data, where the urban environmental data includes urban built environment data and atmospheric environmental data; A noise simulation module for performing sound source modeling on drone noise and simulating the propagation of drone noise in the urban environment according to the urban environmental data and the drone-related data, and generating a drone noise map; A noise map overlay module for combining the current urban noise map and, based on the superposition law of noise, merging and calculating the drone noise map and the current urban noise map to obtain an urban noise map including drone noise; A population distribution statistics module for aggregating multi-source data, identifying the population distribution in different time periods, and mapping the population distribution to the geographical space to form a population distribution map in different time periods; A population portrait construction module for performing feature recognition and analysis on the population based on the population distribution maps in different time periods, combining multi-source data, and constructing population portraits from different dimensions according to the behavior preferences of different populations to obtain a population distribution map with population characteristic information; A noise exposure calculation module for calculating the total regional noise pollution and the per capita noise pollution index according to the urban noise map and the population distribution map with population characteristic information, and calculating the noise exposure levels of different populations.

[0015] In the present invention, urban environmental data and UAV-related data are obtained. The urban environmental data includes urban built environment data and atmospheric environmental data. Based on the urban environmental data and the UAV-related data, a sound source model of UAV noise is established and the propagation of UAV noise in the urban environment is simulated to generate a UAV noise map. Combining with the current urban noise map, based on the superposition law of noise, the UAV noise map and the current urban noise map are merged and calculated to obtain an urban noise map including UAV noise. A multi-source data set is used to identify the population distribution in different time periods, and the population distribution is mapped onto the geographical space to form a population distribution map for different time periods. Based on the population distribution maps for different time periods, multi-source data is combined to identify and analyze the characteristics of the population. According to the behavior preferences of different populations, population portraits are constructed from different dimensions to obtain a population distribution map with population characteristic information. According to the urban noise map and the population distribution map with population characteristic information, the total regional noise pollution amount and the per capita noise pollution index are calculated, and the noise exposure levels of different populations are calculated. The present invention provides a dynamic and accurate method for evaluating the noise exposure levels of different populations including UAV noise through UAV noise simulation and multi-source data-driven population analysis, improves the noise exposure evaluation method, enhances the spatio-temporal accuracy of UAV noise exposure evaluation, can accurately evaluate the noise exposure of UAV noise to different populations in the urban environment, and improves the accuracy and timeliness of noise exposure evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a preferred embodiment of the noise exposure evaluation method considering UAVs in the present invention; Figure 2 is an overall flowchart for realizing noise exposure evaluation in a preferred embodiment of the noise exposure evaluation method considering UAVs in the present invention; Figure 3 is a structural diagram of a preferred embodiment of the noise exposure evaluation system considering UAVs in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] The present invention provides a method for evaluating noise exposure considering unmanned aerial vehicles (UAVs), aiming to make up for the lack of UAV noise in existing noise exposure evaluations. First, obtain urban built environment data, meteorological data, and data related to UAVs (type, flight trajectory), construct a UAV flight dynamics model, and superimpose it on a three-dimensional urban real-scene model established based on the urban built environment and meteorological data to achieve real-time and accurate positioning of UAVs in the urban environment, that is, sound source modeling. Then, based on the laws of noise propagation, attenuation, and superposition, use noise mapping technology to simulate UAV noise, and superimpose the current urban noise map to obtain an urban noise map containing UAV noise. Subsequently, identify the population distribution through multi-source data and form a population distribution map. Then, divide the population characteristics according to data feature tags to create a population portrait. Finally, introduce two key indicators, the total regional noise pollution and the per capita noise pollution index, to quantify the noise exposure of different populations. The present invention provides a dynamic and accurate method to evaluate the noise exposure level including UAV noise for different populations through UAV noise simulation and multi-source data-driven population analysis, improves the noise exposure evaluation method, enhances the spatio-temporal accuracy of UAV noise exposure evaluation, and provides a scientific basis for urban planning and related policy formulation.

[0019] The method for evaluating noise exposure considering UAVs according to a preferred embodiment of the present invention, as Figure 1 and Figure 2 shown, the method for evaluating noise exposure considering UAVs includes the following steps: Step S10: Obtain urban environmental data and UAV-related data, where the urban environmental data includes urban built environment data and atmospheric environmental data.

[0020] Specifically, the urban environment is complex and has an important impact on UAV noise propagation and noise exposure. Urban built environment data and atmospheric environmental data are essential data for simulating UAV noise; the urban built environment data includes: terrain elevation, land use, vegetation cover, building vectors, road networks, points of interest, and street view images.

[0021] The atmospheric environmental data includes: wind speed and wind direction, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity during flight at the evaluation location.

[0022] In addition, the UAV-related data includes: flight trajectory, flight time, and UAV model.

[0023] Use automated image processing software to preprocess the urban environmental data and UAV-related data, including correction, classification, and analysis, integrate data from different sources, and perform data overlay and analysis through geographic information system software.

[0024] The above data constructs a data set required to simulate the noise of drones.

[0025] Step S20: Based on the urban environmental data and the drone-related data, perform sound source modeling on the drone noise and simulate the propagation of the drone noise in the urban environment to generate a drone noise map.

[0026] Specifically, based on the drone-related data, obtain the three-dimensional space trajectory of the drone, as well as flight parameters such as the speed, pitch angle, and propeller speed of the drones, construct a drone flight dynamics model, and superimpose it on the three-dimensional real-scene model of the city established according to the urban built environment and atmospheric environment data to achieve real-time and accurate positioning of the drone in the urban environment, that is, sound source modeling.

[0027] The simulation of the propagation of drone noise uses noise map technology to simulate the propagation of drone noise in the urban environment. First, import the urban built environment data, then import the drone sound source data, import the A-weighted sound pressure level dB(A), frequency distribution, emission direction, and intensity distribution of the sound source according to the actual measurement results, treat the drone as a point sound source model, select a specific propagation model (for example, select the propagation model in ISO9613-2-1996 "Calculation Method for Outdoor Sound Propagation Attenuation"), set the atmospheric environment parameters according to the actual situation of the city, and obtain the drone noise map (i.e., the noise distribution result) through simulation.

[0028] Step S30: Combine the current urban noise map, and based on the superposition law of noise, merge and calculate the drone noise map and the current urban noise map to obtain an urban noise map containing drone noise.

[0029] Specifically, combine the current urban noise map, and based on the superposition law of noise, merge and calculate the drone noise map in step S20 and the current urban noise map to obtain an urban noise map containing drone noise.

[0030] For example, use relevant noise simulation software, define the drone noise as a point sound source, define the noise source and set the prediction area, and perform noise superposition simulation according to the built environment buildings and sound barriers and the noise superposition formula, and view the noise distribution map through the visualization function.

[0031] Step S40: Aggregate multi-source data, identify the population distribution situation at different times, and map the population distribution situation onto the geographical space to form a population distribution map at different times.

[0032] Specifically, to accurately describe the noise exposure levels of different populations in urban areas, it is necessary to clarify the population distribution in urban areas. The present invention automatically aggregates multi-source data through programming techniques, including mobile phone signaling data, social network data (Location-Based Social Network), point-of-interest data, and traffic data (traffic flow data, public transportation usage data, etc.), which carry high-precision timestamps and geographical coordinates and can dynamically reflect the spatio-temporal distribution characteristics of the population. The population distribution in different time periods is identified through multi-source data. Subsequently, the population distribution data is screened, cleaned, and integrated (for example, the daily data is sorted into 24 time periods by hour), and through mapping with geographic information system software, the population distribution data is mapped onto the geographical space to form a population distribution map for different time periods.

[0033] Step S50: Based on the population distribution maps for different time periods, combined with multi-source data, conduct feature recognition and analysis on the population. According to the behavior preferences of different populations, construct population portraits from different dimensions to obtain a population distribution map with population characteristic information (such as occupation, age, etc.).

[0034] Specifically, on the basis of obtaining the population distribution data for different regions and different time periods, further conduct feature recognition and analysis on the population. The present invention constructs population portraits from different dimensions through geospatial big data, mobile phone signaling data, social network data, etc., such as age, income level, and education level.

[0035] For example: Based on the above data, the population characteristics are quantitatively explained. 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. The income level is divided into low income (household annual income is lower than the local minimum living security standard), medium - low income (household annual income is between the minimum living security standard and the medium - income level), medium income (household annual income is within the medium - income level range, usually referring to household annual income between 40,000 yuan and 120,000 yuan), medium - high income (household annual income is higher than the medium - income level but lower than the high - income level), and high - income group (household annual income is higher than the high - income level, usually referring to household annual income exceeding 120,000 yuan) according to the regional income standard. 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) based on the classification systems of the International Organization for Standardization and UNESCO. And the data is cleaned and labeled. Then, using geographic information system software, spatio - temporal frequency analysis is carried out on different populations based on the desensitized location service data provided by the operator to obtain the behavior preferences of different populations (including: time preference, space preference, facility preference, landscape preference), and the time trends and periodic changes of the activities of different populations are identified. Finally, a population distribution map with population characteristic information (such as occupation, age, etc.) is formed by combining the spatio - temporal distribution of the population and the portrait data.

[0036] For example, by using geographic information system software to conduct frequency analysis on the spatio - temporal behavior data of the population, the stopping points of the population are identified. A spatial threshold (100 meters) and a time threshold (10 minutes) are set. When the moving distance of a person's trajectory points is less than 100 meters within 10 minutes, these trajectory points are clustered into the same stopping point, and the geometric center of the stopping point is used as the representative position of the stopping point. This is used to reveal the behavior preferences of different populations, covering time preference, space preference, facility preference, and landscape preference, and to analyze the population composition in different regions of the city.

[0037] Step S60: According to the urban noise map and the population distribution map with population characteristic information, calculate the total regional noise pollution amount and the per - capita noise pollution index, and calculate the noise exposure levels of different populations.

[0038] Specifically, the present invention introduces two key indicators, namely the Total Noise Exposure Metric of Pollution (TNEMIP) and the Average Noise Exposure Metric of Pollution per capita (ANEMIP), which can comprehensively reflect the cumulative effect of noise exposure and the spatio-temporal distribution of noise pollution, so as to quantify the impact of traffic noise on the exposed population. The total regional noise pollution (TNEMIP) aims to evaluate the cumulative effect of excessive noise on the exposed population, while the average noise pollution index per capita is used to measure the average impact degree 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, according to the environmental noise limit regulations for five types of ambient noise functional areas in the ambient noise quality standard, the total regional noise pollution and the average noise pollution index per capita are calculated, and the noise exposure levels of different populations are calculated.

[0039] When calculating the per capita pollution amount of a small area, the noise points in each area need to be weighted by the population density. Therefore, in the application, the formulas of the total noise pollution and the average noise pollution index per capita need to be rewritten.

[0040] Currently, the low-altitude economy is developing rapidly, and drones are increasingly widely used in multiple fields and scenarios. The noise exposure assessment for drone noise improves the noise exposure assessment method and can accurately quantify the impact of drone noise on different populations, providing a scientific basis for urban planning and the formulation of relevant policies, helping to effectively prevent and control drone noise, and promoting the sustainable development of the low-altitude economy.

[0041] By integrating multi-source data such as mobile phone signaling data, social network data, point of interest data, and traffic travel data to describe population activities, the analysis of population activities becomes more comprehensive and in-depth, improving the accuracy of noise exposure assessment. At the same time, the real-time updated data makes the analysis of population activities more dynamic, which is conducive to carrying out long-term assessments of noise exposure.

[0042] The present invention realizes the integrated processing of multi-source data such as mobile phone signaling data, social network data, point of interest data, and traffic travel data, and describes the spatio-temporal distribution data of population activities. This method improves the limitations of population activity analysis based on a single data source, enhances the accuracy and precision of population activity data, and makes the noise exposure assessment more scientific and accurate.

[0043] The present invention analyzes multi-source data to construct a portrait of the population based on multi-source data, realizing the refined classification of the population exposed to noise, and further more accurately revealing the noise exposure levels of drones to different populations, making the noise exposure assessment more practically significant.

[0044] The present invention has formed a complete automated process for simulating the noise of drones in the city and overlaying the noise map by integrating and preprocessing drone data and urban environmental data, and further analyzing the urban population distribution and population portrait by integrating multi-source data to calculate the noise exposure of the drone population. It has achieved high-precision spatio-temporal analysis of the noise exposure levels of different populations, making the evaluation results more accurately reflect the noise exposure characteristics of different populations in different regions and at different times. This innovative achievement provides strong data support for urban planning and promotes the research on urban drone noise exposure.

[0045] Based on the simulation of drone noise and the analysis of population activities driven by multi-source data, the present invention provides a dynamic and accurate method for evaluating the drone noise exposure level. The present invention improves the noise exposure assessment method, enhances the spatio-temporal accuracy of the drone noise exposure assessment, provides a scientific basis for urban planning and the formulation of relevant policies, and enhances the accuracy and timeliness of planning and policy responses.

[0046] Starting specifically from the perspective of drones as noise sources, the present invention systematically evaluates the drone noise exposure level, makes up for the deficiencies of existing noise exposure assessment technologies in low-altitude application scenarios, and conducts noise exposure assessments for different populations. The present invention can more accurately reveal the noise exposure level and its dynamic changes of drone noise, providing targeted data support for urban planning and the formulation of relevant policies.

[0047] The present invention constructs a complete process for evaluating the drone noise exposure level based on the drone noise and the spatio-temporal distribution of the population. This process forms a comprehensive multi-source data-driven technical framework for evaluating the drone noise exposure level through multi-source data processing, noise simulation, and noise exposure calculation.

[0048] Furthermore, as Figure 3 shown, based on the above-mentioned method for evaluating the drone noise exposure considering the above considerations, the present invention also correspondingly provides a system for evaluating the drone noise exposure considering the drone, wherein the system for evaluating the drone noise exposure considering the drone includes: A data collection module 51 for obtaining urban environmental data and drone-related data, where the urban environmental data includes urban built environment data and atmospheric environmental data; A noise simulation module 52, configured to perform sound source modeling on the UAV noise and simulate the propagation of the UAV noise in the urban environment according to the urban environment data and the UAV-related data, and generate a UAV noise map; A noise map overlay module 53, configured to combine the current urban noise map, and based on the superposition law of noise, merge and calculate the UAV noise map and the current urban noise map to obtain an urban noise map including UAV noise; A crowd distribution statistics module 54, configured to aggregate multi-source data, identify the crowd distribution in different time periods, and map the crowd distribution to the geographical space to form a crowd distribution map in different time periods; A crowd portrait construction module 55, configured to perform feature recognition and analysis on the crowd based on the crowd distribution maps in different time periods, and combine multi-source data. According to the behavior preferences of different crowds, construct crowd portraits from different dimensions to obtain a crowd distribution map with crowd feature information; A noise exposure calculation module 56, configured to calculate the total regional noise pollution amount and the per capita noise pollution index according to the urban noise map and the crowd distribution map with crowd feature information, and calculate the noise exposure levels of different crowds.

[0049] In summary, the present invention provides a noise exposure assessment method and system considering UAVs. The method includes: obtaining urban environment data and UAV-related data, where the urban environment data includes urban built environment data and atmospheric environment data; performing sound source modeling on the UAV noise and simulating the propagation of the UAV noise in the urban environment according to the urban environment data and the UAV-related data, and generating a UAV noise map; combining the current urban noise map, and based on the superposition law of noise, merging and calculating the UAV noise map and the current urban noise map to obtain an urban noise map including UAV noise; aggregating multi-source data, identifying the crowd distribution in different time periods, and mapping the crowd distribution to the geographical space to form a crowd distribution map in different time periods; performing feature recognition and analysis on the crowd based on the crowd distribution maps in different time periods, and combining multi-source data. According to the behavior preferences of different crowds, construct crowd portraits from different dimensions to obtain a crowd distribution map with crowd feature information; calculating the total regional noise pollution amount and the per capita noise pollution index according to the urban noise map and the crowd distribution map with crowd feature information, and calculating the noise exposure levels of different crowds. The present invention improves the noise exposure assessment method, enhances the spatio-temporal accuracy of UAV noise exposure assessment, and provides a scientific basis for urban planning and the formulation of relevant policies.

[0050] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in such a process, method, article or terminal. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or terminal including that element.

[0051] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. An assessment method for noise exposure considering drones, characterized in that, The method for evaluating noise exposure considering drones includes: Obtain urban environmental data and drone-related data, where the urban environmental data includes urban built environment data and atmospheric environmental data; Based on the urban environmental data and the drone-related data, conduct sound source modeling for drone noise and simulate the propagation of drone noise in the urban environment to generate a drone noise map; Combined with the current urban noise map, based on the superposition law of noise, merge and calculate the drone noise map and the current urban noise map to obtain an urban noise map containing drone noise; Collect multi-source data, identify the population distribution in different time periods, and map the population distribution to the geographical space to form a population distribution map for different time periods; Based on the population distribution maps for different time periods, combine multi-source data to identify and analyze the characteristics of the population, and construct population portraits from different dimensions according to the behavior preferences of different populations to obtain a population distribution map with population characteristic information; According to 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 levels of different populations.

2. The method for evaluating noise exposure considering a drone according to claim 1, wherein The urban built environment data includes: terrain elevation, land use, vegetation cover, building vectors, road networks, points of interest, and street view images; The atmospheric environmental data includes: wind speed and direction, wind speed gradient, temperature gradient, atmospheric attenuation, absolute temperature, and absolute humidity during flight at the evaluation location; The drone-related data includes: flight trajectory, flight time, and drone model.

3. The method for evaluating noise exposure considering a drone according to claim 1, characterized in that, The sound source modeling specifically includes: Based on the drone-related data, obtain the three-dimensional space trajectory of the drone, as well as the drone speed, pitch angle, and propeller rotation speed, construct a drone flight dynamics model, and superimpose the drone flight dynamics model onto the urban real-scene three-dimensional model established according to the urban built environment data and the atmospheric environmental data to achieve real-time and accurate positioning of the drone in the urban environment.

4. The method for evaluating noise exposure considering an unmanned aerial vehicle according to claim 1, characterized in that, The simulation of the propagation in the urban environment specifically includes: The drone noise propagation simulation uses noise map technology to simulate the propagation of drone noise in the urban environment. Import the urban built environment data, then import the drone sound source data. According to the actual measurement results, import the A-weighted sound pressure level, frequency distribution, emission direction, and intensity distribution of the sound source. Treat the drone as a point source model, select a specific propagation model, set the atmospheric environmental parameters according to the actual situation of the city, and obtain the drone noise map through simulation.

5. The method for evaluating the noise exposure considering an unmanned aerial vehicle according to claim 1, characterized in that, The multi-source data includes: mobile phone signaling data, social network data, point of interest data, and traffic travel data; The collection of multi-source data, identification of the population distribution in different time periods, and mapping of the population distribution to the geographical space to form a population distribution map for different time periods specifically includes: Automatically collect mobile phone signaling data, social network data, point of interest data, and traffic travel data in geographical big data through programming technology, identify the population distribution in different time periods, and obtain population distribution data; Screen and clean the population distribution data and integrate them to obtain the target population distribution data; Map the target population distribution data onto the geographical space through the mapping of geographical information system software to form population distribution maps for different time periods.

6. The method for evaluating noise exposure considering an unmanned aerial vehicle according to claim 1, characterized in that Based on the population distribution maps for different time periods, combine multi-source data to identify and analyze the characteristics of the population, and construct population portraits from different dimensions according to the behavior preferences of different populations to obtain population distribution maps with population characteristic information, specifically including: According to the population distribution data of each region at different time periods, identify and analyze the characteristics of the population to obtain population characteristic data, and divide and label the population according to age, income level, and education level; Use geographical information system software to conduct spatio-temporal frequency analysis on different populations based on the desensitized positioning service data provided by operators to obtain the behavior preferences of different populations. The behavior preferences include: time preference, space preference, facility preference, and landscape preference, and identify the time trends and periodic changes of the activities of different populations, and combine the population spatio-temporal distribution and portrait data to form a population distribution map including population structure characteristics.

7. The method for evaluating the noise exposure considering an unmanned aerial vehicle according to claim 1, wherein The total regional noise pollution is used to evaluate 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 region, the noise value of the noise-exceeding area, and the environmental noise limit.

8. The method for evaluating noise exposure considering an unmanned aerial vehicle according to claim 1, wherein The per capita noise pollution index is used to measure the average impact degree of excessive noise on individuals; When calculating the per capita pollution amount of a small area, it is necessary to perform weighted calculation on the noise points in each region with the population density as the weight.

9. A noise exposure assessment system considering drones, characterized in that, The noise exposure assessment system considering drones includes: A data collection module for obtaining urban environmental data and drone-related data. The urban environmental data includes urban built environment data and atmospheric environmental data; A noise simulation module for performing sound source modeling on drone noise and simulating the propagation of drone noise in the urban environment according to the urban environmental data and the drone-related data to generate a drone noise map; A noise map overlay module for combining the current urban noise map and, based on the superposition law of noise, merging and calculating the drone noise map and the current urban noise map to obtain an urban noise map including drone noise; A population distribution statistics module for aggregating multi-source data, identifying the population distribution situation at different time periods, and mapping the population distribution situation onto the geographical space to form population distribution maps for different time periods; A population portrait construction module for, based on the population distribution maps for different time periods, combining multi-source data to identify and analyze the characteristics of the population, and constructing population portraits from different dimensions according to the behavior preferences of different populations to obtain population distribution maps with population characteristic information; A noise exposure calculation module for calculating the total regional noise pollution and the per capita noise pollution index according to the urban noise map and the population distribution map with population characteristic information, and calculating the noise exposure levels of different populations.

Images