A method and system for calculating the number of residents affected by drone flight noise.

Abstract

This invention relates to a method and system for calculating the number of residents affected by drone flight noise, belonging to the field of drone intelligent control technology. To address the problem of accurately calculating the number of residents affected by drone noise, this invention establishes a modified acoustic power calculation model based on the drone's flight state; calculates the sound pressure level of the drone after initial propagation attenuation; considers the influence of buildings on the sound wave propagation path to obtain the corrected sound pressure level of the drone; calculates the total sound pressure level of the drone at the receiving point; calculates the equivalent sound level of the drone at the receiving point based on the total sound pressure level of the drone at the receiving point; integrates these to obtain the time equivalent sound level; obtains the sound intensity based on the time equivalent sound level; and constructs an urban noise exposure risk index by comprehensively considering sound intensity, population density, and regional vulnerability; and considers the urban noise exposure risk index and the receiving point perception score in the disturbance population assessment model, weighting and summing the disturbance population at each receiving point to obtain the total number of residents affected by drone flight noise.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) intelligent control technology, specifically relating to a method and system for calculating the number of residents affected by UAV flight noise. Background Technology

[0002] In recent years, with the rapid development of drone technology and the continuous expansion of its commercial applications, the use of multi-drone collaborative flight in urban low-altitude airspace has become increasingly popular. Drones have shown great potential in logistics delivery, emergency rescue, and urban management, but they have also brought new environmental problems, among which noise pollution has become a significant hidden danger affecting the quality of life of urban residents.

[0003] Compared to traditional aircraft, drones exhibit unique and complex noise characteristics. Drones typically fly at lower altitudes, close to ground residents, and when multiple drones operate collaboratively, they exhibit distributed and dynamic characteristics, resulting in a wider distribution of noise sources. Furthermore, the complex urban built environment makes it difficult to accurately predict the propagation path and attenuation characteristics of drone noise. These characteristics make assessing the noise impact of drones a complex systems engineering project.

[0004] Accurate assessment of the noise impact of drones has significant practical implications and application value. It not only provides a scientific basis for drone operation and management, helping to formulate reasonable flight paths and schedules, but also offers decision support for urban planning and the delineation of drone application areas. Furthermore, accurate assessment results help control the impact of drone operations on residents' quality of life and provide crucial technical support for the development of relevant regulations and standards.

[0005] However, existing noise assessment methods have many limitations. These methods often use overly simplified static sound source models, ignoring the dynamic impact of flight conditions on sound energy radiation; they fail to fully consider urban environmental characteristics in propagation models, and their handling of building shielding and reflection effects is not precise enough; atmospheric absorption models lack in-depth consideration of frequency characteristics, making it difficult to accurately reflect propagation characteristics under different meteorological conditions; exposure assessment methods are too crude, failing to comprehensively consider factors such as population density and regional functional characteristics; and they also lack systematic modeling of the superposition effect of multi-source noise, making it difficult to cope with multi-UAV collaborative operation scenarios. Summary of the Invention

[0006] The problem to be solved by this invention is to accurately calculate the number of residents affected by drone noise, and proposes a method and system for calculating the number of residents affected by drone flight noise.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for calculating the number of residents affected by drone flight noise includes the following steps:

[0009] S1. Incorporate the real-time speed, acceleration, and attitude angle changes of the UAV into the modeling system to establish a UAV flight state-corrected acoustic power calculation model;

[0010] S2. Considering the flight state of the UAV and the energy attenuation of the sound wave during propagation, the UAV flight state modified sound power calculation model obtained in step S1 is modified to calculate the sound pressure level of the UAV after initial propagation attenuation.

[0011] S3. Considering the influence of buildings on the sound wave propagation path, construct a reflection enhancement term based on the incident angle, reflection area, and reflection coefficient of the wall reflection. Then, based on the reflection enhancement term and the blocking attenuation term, correct the initial propagation attenuation sound pressure level of the UAV obtained in step S2 to obtain the corrected sound pressure level of the UAV.

[0012] S4. Based on the corrected sound pressure level of the UAV obtained in step S3, construct the sound energy superposition model of the UAV, calculate the total sound pressure level of the UAV at the receiving point, and then calculate the equivalent sound level of the UAV at the receiving point based on the total sound pressure level of the UAV at the receiving point.

[0013] S5. Considering the difference between day and night in residents' perception of noise, the equivalent sound level of the drone at the receiving point obtained in step S4 is divided into the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, and then integrated to obtain the time equivalent sound level.

[0014] S6. Based on the time equivalent sound level obtained in step S5, the sound level intensity is obtained, and the urban noise exposure risk index is constructed by comprehensively considering the sound level intensity, population density and regional vulnerability.

[0015] S7. In the disturbance population assessment model, the urban noise exposure risk index obtained in step S6 is considered as a spatial weight, and the receiving point perception score corresponding to the impact of the time equivalent sound level obtained in step S5 is considered. The disturbance population at each receiving point is weighted and accumulated to obtain the total number of residents affected by the drone flight noise.

[0016] Furthermore, step S1 incorporates the real-time velocity, acceleration, and attitude angle changes of the UAV into the modeling system. By normalizing and weighting the dynamic variables, a modified acoustic power calculation model for the UAV's flight state is established, expressed as:

[0017]

[0018] in, Let be the sound power level of the i-th UAV at time t. This is the static reference sound power level, obtained from the manufacturer's data; Let be the flight speed of the i-th drone, obtained from the sensor; For reference flight speed, determined by industry standards; Let be the acceleration of the i-th drone, obtained from the sensor; The attitude angle change of the i-th UAV is obtained from the sensor; , , These are the weighting coefficients corresponding to flight speed, acceleration, and attitude angle, respectively, determined based on expert experience.

[0019] Furthermore, the specific implementation method of step S2 includes the following steps:

[0020] S2.1. Considering the flight status of the UAV, by recording the position of each UAV at each moment and the coordinates of the ground receiving point, the Euclidean distance is calculated, and the expression is obtained as follows:

[0021]

[0022] in, The spatial position of the i-th UAV at time t is obtained by the positioning system; The spatial location of receiving point j is obtained by the positioning system; Let be the distance between the i-th drone and the receiving point j;

[0023] S2.2. Considering the energy attenuation of sound waves during propagation, including free-space geometric diffusion, atmospheric absorption, and ground reflection absorption, the free-space geometric diffusion mechanism is due to the uniform propagation of sound waves on a sphere, with energy attenuating inversely with the square of the distance. Loss characterization was performed; atmospheric absorption mechanism is related to frequency, temperature, and humidity, and is a dissipation effect caused by the thermal motion of air molecules; ground reflection absorption mechanism is caused by the interaction between sound waves and the earth's surface;

[0024] The acoustic power calculation model for the UAV flight state obtained in step S1 is corrected based on the energy attenuation of sound waves during propagation, resulting in the initial propagation attenuation sound pressure level of the UAV, expressed as:

[0025]

[0026]

[0027] in, Let be the sound pressure level after initial propagation attenuation between the i-th UAV and the receiving point j; Let be the ground reflection absorption term between the i-th UAV and the receiving point j; Atmospheric absorption coefficient; The ground reflection absorption coefficient is determined by combining the ground type.

[0028] Furthermore, the specific implementation method of step S3 includes the following steps:

[0029] S3.1. Consider the impact of buildings on the sound wave propagation path, including obstruction and attenuation, and reflection enhancement;

[0030] S3.2. Determine the existence of occlusion using geometric ray tracing, and introduce an occlusion attenuation term. It is determined by experts based on their experience and the actual scenario;

[0031] S3.3. Based on the incident angle, reflection area, and reflection coefficient of the wall reflection, a reflection enhancement term is constructed, expressed as follows:

[0032]

[0033] in, For reflection enhancement, The wall reflectance coefficient is determined based on the wall material. The angle of incidence is obtained through geometric calculations combining the propagation path and the wall normal. The visible wall reflective area is determined by expert experience in combination with the actual scenario.

[0034] S3.4. Based on the initial propagation attenuation sound pressure level of the UAV obtained in step S2, which is corrected by the reflection enhancement term and the occlusion attenuation term, the corrected sound pressure level of the UAV is obtained, and the expression is:

[0035]

[0036] in, Let be the corrected sound pressure level between the i-th UAV and the receiving point j.

[0037] Furthermore, the specific implementation method of step S4 includes the following steps:

[0038] S4.1. Construct a superposition model of the UAV's acoustic energy, and calculate the expression for the total sound pressure level of the UAV at the receiving point:

[0039]

[0040] in, Let j be the total sound pressure level at receiving point j. The number of drones is determined by the drone monitoring system;

[0041] S4.2. Based on the total sound pressure level of the UAV at the receiving point, the equivalent sound level of the UAV at the receiving point is calculated using the energy equivalent integration method, and the expression is:

[0042]

[0043] in, Let J be the equivalent sound level of the UAV at receiving point j. The duration is determined based on the actual task requirements.

[0044] Furthermore, the specific implementation method of step S5 includes the following steps:

[0045] S5.1. Calculate the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, respectively, and obtain the expression:

[0046]

[0047]

[0048] in, The equivalent sound level of the drone at receiving point j during the daytime; The equivalent sound level of the drone at receiving point j at night. Daytime duration, The duration of nighttime will be determined based on actual needs;

[0049] S5.2. Combine the equivalent sound level of the daytime drone at receiving point j and the equivalent sound level of the nighttime drone at receiving point j obtained in step S5.1 to obtain the time equivalent sound level of receiving point j, expressed as:

[0050]

[0051] in, Let j be the time-equivalent sound level at receiving point j. The penalty weight is determined at night.

[0052] Furthermore, in step S6, taking into account sound level intensity, population density, and regional vulnerability, a multiplicative coupling method is used to construct the urban noise exposure risk index, expressed as:

[0053]

[0054] in, Let j be the urban noise exposure risk index corresponding to receiving point j. , These represent the minimum and maximum sound levels within the region; This represents the maximum population density, obtained from the management department. The population density of the area corresponding to receiving point j is obtained from the management department. The regional vulnerability factor corresponding to receiving point j is used to reflect the social attributes of the region and is determined in conjunction with urban GIS, land use, or social structure.

[0055] Furthermore, the specific implementation method of step S7 includes the following steps:

[0056] S7.1. Calculate the corresponding receiver perception score based on the impact of the time-equivalent sound level obtained in step S5. The expression is:

[0057]

[0058] in, The perception score for receiving point j; The parameters for sensing the rate of change are determined by expert experience; The sensitivity threshold is determined by expert experience.

[0059] S7.2. Based on the perception score obtained in step S7.1, simulate the subjective perception intensity of humans to different sound levels. Then, correlate the perception score with the population size at receiver point j. Multiplying the urban noise exposure risk index corresponding to receiver point j by the weighted disturbance population at that point, and summing the results over all receiver points, yields the number of residents affected by the drone's flight noise. The expression is as follows:

[0060]

[0061] in, The population covered by receiving point j is obtained from the management department. The number of receiving points is determined based on the regular grid division, building data, or the number of points of interest. This represents the total number of residents affected by the noise from drone flights.

[0062] A system for calculating the number of residents affected by drone flight noise includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of the method for calculating the number of residents affected by drone flight noise.

[0063] The beneficial effects of this invention are:

[0064] This invention discloses a method for calculating the number of residents affected by drone flight noise, addressing technical challenges such as accurate simulation of dynamic sound source characteristics, calculation of sound wave propagation paths in complex urban environments, and regionally differentiated exposure risk assessment. This method utilizes techniques such as a flight-state-corrected sound power model, a building obstruction and reflection model, a frequency estimation-optimized atmospheric absorption model, and the construction of an exposure risk index to achieve accurate calculation of the number of residents affected by drone noise.

[0065] This invention discloses a method for calculating the number of residents affected by drone flight noise. It integrates flight parameter-driven sound source modeling, 3D trajectory simulation, building obstruction and terrain impact correction, atmospheric absorption models, exposure risk identification, and path optimization feedback to form a complete physical modeling and decision support closed loop. Simultaneously, it introduces a frequency estimation method based on propeller parameters, improving the physical reliability and computational practicality of the atmospheric absorption model.

[0066] This invention discloses a method for calculating the number of residents affected by drone flight noise. It establishes a dynamic sound source model considering flight status, improves noise propagation calculations in urban environments, constructs a comprehensive exposure risk assessment system, and innovatively introduces time-equivalent sound level and perceived disturbance function to reflect the spatiotemporal differences in noise impact. These technological innovations give this method a solid physical foundation and high practical value, enabling it to provide scientific decision support for drone noise management. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a method for calculating the number of residents affected by drone flight noise, as described in this invention.

[0068] Figure 2 This is a bar chart showing the number of residents affected by noise at the nine receiving points calculated by the method of this invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0070] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0071] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 and attached Figure 2 Detailed explanation is as follows:

[0072] Example 1:

[0073] A method for calculating the number of residents affected by drone flight noise includes the following steps:

[0074] S1. Incorporate the real-time speed, acceleration, and attitude angle changes of the UAV into the modeling system to establish a UAV flight state-corrected acoustic power calculation model;

[0075] Furthermore, step S1 incorporates the real-time velocity, acceleration, and attitude angle changes of the UAV into the modeling system. By normalizing and weighting the dynamic variables, a modified acoustic power calculation model for the UAV's flight state is established, expressed as:

[0076]

[0077] in, Let be the sound power level of the i-th UAV at time t. This is the static reference sound power level, obtained from the manufacturer's data; Let be the flight speed of the i-th drone, obtained from the sensor; For reference flight speed, determined by industry standards; Let be the acceleration of the i-th drone, obtained from the sensor; The attitude angle change of the i-th UAV is obtained from the sensor; , , The weighting coefficients for flight speed, acceleration, and attitude angle are determined based on expert experience. In UAV noise modeling, sound source power is the most fundamental and crucial input parameter, directly determining the intensity of the sound field distribution. However, traditional methods typically use a fixed static sound power level, neglecting the dynamic influence of flight state on sound energy radiation, making it difficult to accurately reflect the real sound source behavior in complex flight missions. The UAV flight state-corrected sound power calculation model improves the physical accuracy and adaptability of the model.

[0078] Furthermore, , , It has the function of adjusting dimensions.

[0079] S2. Considering the flight state of the UAV and the energy attenuation of the sound wave during propagation, the UAV flight state modified sound power calculation model obtained in step S1 is modified to calculate the sound pressure level of the UAV after initial propagation attenuation.

[0080] Furthermore, the specific implementation method of step S2 includes the following steps:

[0081] S2.1. Considering the flight status of the UAV, by recording the position of each UAV at each moment and the coordinates of the ground receiving point, the Euclidean distance is calculated, and the expression is obtained as follows:

[0082]

[0083] in, The spatial position of the i-th UAV at time t is obtained by the positioning system; The spatial location of receiving point j is obtained by the positioning system; Let be the distance between the i-th drone and the receiving point j;

[0084] Furthermore, the propagation path and distance of sound waves play a fundamental role in calculating propagation attenuation, determining obstruction, and assessing reflection effects. By recording the position of each UAV at every moment and calculating the Euclidean distance between it and the ground receiving point, the propagation path length can be obtained.

[0085] S2.2. Considering the energy attenuation of sound waves during propagation, including free-space geometric diffusion, atmospheric absorption, and ground reflection absorption, the free-space geometric diffusion mechanism is due to the uniform propagation of sound waves on a sphere, with energy attenuating inversely with the square of the distance. Loss characterization was performed; atmospheric absorption mechanism is related to frequency, temperature, and humidity, and is a dissipation effect caused by the thermal motion of air molecules; ground reflection absorption mechanism is caused by the interaction between sound waves and the earth's surface;

[0086] The acoustic power calculation model for the UAV flight state obtained in step S1 is corrected based on the energy attenuation of sound waves during propagation, resulting in the initial propagation attenuation sound pressure level of the UAV, expressed as:

[0087]

[0088]

[0089] in, Let be the sound pressure level after initial propagation attenuation between the i-th UAV and the receiving point j; Let be the ground reflection absorption term between the i-th UAV and the receiving point j; Atmospheric absorption coefficient; The ground reflection absorption coefficient is determined by considering the ground type. These three factors are combined into a total propagation attenuation term along the propagation path, used to calculate the initial sound pressure level. The ground reflection term further considers the reflection coefficient of the ground material to differentiate modeling for different urban areas.

[0090] Furthermore, the atmospheric absorption coefficient in step S2.2 is calculated by first estimating the main radiation frequency of the UAV, which is determined by the number of propeller blades and the rotational speed. Then, the main radiation frequency of the UAV is substituted into the empirical atmospheric absorption model, and the atmospheric absorption coefficient related to the main radiation frequency of the UAV is calculated by combining the actual temperature and humidity. The expression is as follows:

[0091]

[0092]

[0093] in, Let be the estimated value of the main radiated frequency of the i-th UAV. The number of propeller blades for the i-th UAV is determined by the manufacturer's data; Let be the rotational speed of the i-th drone, obtained from the sensor; The actual temperature is obtained from the sensor; For reference temperature, determined by expert experience; Relative humidity, determined by a sensor; This is the frequency correction factor. This is the relative humidity correction factor. The values ​​are relative humidity square correction factors, all obtained from experiments or references.

[0094] Furthermore, atmospheric absorption is a significant frequency-dependent attenuation mechanism in sound wave propagation, and its intensity is influenced by the sound frequency, air temperature, and relative humidity. High-frequency sound waves are more easily absorbed by air molecules during propagation, leading to rapid energy attenuation. This invention first estimates the main radiation frequency of the UAV, which is determined by the number of propeller blades and the rotational speed, and then substitutes this frequency into an empirical atmospheric absorption model, combining it with actual temperature and humidity to calculate the frequency-dependent absorption coefficient. This method makes propagation modeling closer to real meteorological conditions, and is particularly suitable for scenarios where long-range noise propagation is prone to occur, such as high humidity and high temperature in summer.

[0095] S3. Considering the influence of buildings on the sound wave propagation path, construct a reflection enhancement term based on the incident angle, reflection area, and reflection coefficient of the wall reflection. Then, based on the reflection enhancement term and the blocking attenuation term, correct the initial propagation attenuation sound pressure level of the UAV obtained in step S2 to obtain the corrected sound pressure level of the UAV.

[0096] In complex urban environments, buildings significantly impact the propagation path of sound waves, potentially causing attenuation due to obstruction or enhanced reflection. When obstructed, sound waves are blocked by tall buildings in their propagation path, resulting in a sharp drop in sound pressure level; conversely, when there is no obstruction but a nearby wall is present, some sound waves are reflected, amplifying the sound energy at the receiving point.

[0097] Furthermore, the specific implementation method of step S3 includes the following steps:

[0098] S3.1. Consider the impact of buildings on the sound wave propagation path, including obstruction and attenuation, and reflection enhancement;

[0099] S3.2. Determine the existence of occlusion using geometric ray tracing, and introduce an occlusion attenuation term. It is determined by experts based on their experience and the actual scenario;

[0100] S3.3. Based on the incident angle, reflection area, and reflection coefficient of the wall reflection, a reflection enhancement term is constructed, expressed as follows:

[0101]

[0102] in, For reflection enhancement, The wall reflectance coefficient is determined based on the wall material. The angle of incidence is obtained through geometric calculations combining the propagation path and the wall normal. The visible wall reflective area is determined by expert experience in combination with the actual scenario.

[0103] S3.4. Based on the initial propagation attenuation sound pressure level of the UAV obtained in step S2, which is corrected by the reflection enhancement term and the occlusion attenuation term, the corrected sound pressure level of the UAV is obtained, and the expression is:

[0104]

[0105] in, Let be the corrected sound pressure level between the i-th UAV and the receiving point j.

[0106] S4. Based on the corrected sound pressure level of the UAV obtained in step S3, construct the sound energy superposition model of the UAV, calculate the total sound pressure level of the UAV at the receiving point, and then calculate the equivalent sound level of the UAV at the receiving point based on the total sound pressure level of the UAV at the receiving point.

[0107] In scenarios where multiple drones operate simultaneously in urban areas, the sound energy generated by multiple sound sources will superimpose at the same receiving point, thus requiring the construction of a sound energy superposition model. Since sound pressure level has dimensions of two units, it cannot be directly superimposed using algebraic addition. This invention first converts the sound pressure level of each sound source into linear sound energy, then sums them, and finally converts the total energy back to a total sound pressure level expressed in dB. This method ensures physical energy conservation and also reflects the interference enhancement effect when multiple sound sources emit sound simultaneously in space.

[0108] Furthermore, the specific implementation method of step S4 includes the following steps:

[0109] S4.1. Construct a superposition model of the UAV's acoustic energy, and calculate the expression for the total sound pressure level of the UAV at the receiving point:

[0110]

[0111] in, Let j be the total sound pressure level at receiving point j. The number of drones is determined by the drone monitoring system;

[0112] S4.2. Based on the total sound pressure level of the UAV at the receiving point, the equivalent sound level of the UAV at the receiving point is calculated using the energy equivalent integration method, and the expression is:

[0113]

[0114] in, Let J be the equivalent sound level of the UAV at receiving point j. The duration is determined based on the actual task requirements.

[0115] Since noise is a time-varying signal during flight, a representative intensity index needs to be obtained through time averaging. The equivalent sound level is the average sound energy intensity per unit time, used to describe the noise exposure level throughout the mission. This method calculates the average sound energy intensity per unit time based on the time-series sound pressure level at the receiving point using energy equivalent integration.

[0116] S5. Considering the difference between day and night in residents' perception of noise, the equivalent sound level of the drone at the receiving point obtained in step S4 is divided into the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, and then integrated to obtain the time equivalent sound level.

[0117] Furthermore, the specific implementation method of step S5 includes the following steps:

[0118] S5.1. Calculate the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, respectively, and obtain the expression:

[0119]

[0120]

[0121] in, The equivalent sound level of the drone at receiving point j during the daytime; The equivalent sound level of the drone at receiving point j at night. Daytime duration, The duration of nighttime will be determined based on actual needs;

[0122] S5.2. Combine the equivalent sound level of the daytime drone at receiving point j and the equivalent sound level of the nighttime drone at receiving point j obtained in step S5.1 to obtain the time equivalent sound level of receiving point j, expressed as:

[0123]

[0124] in, Let j be the time-equivalent sound level at receiving point j. The penalty weight is determined at night.

[0125] S6. Based on the time equivalent sound level obtained in step S5, the sound level intensity is obtained, and the urban noise exposure risk index is constructed by comprehensively considering the sound level intensity, population density and regional vulnerability.

[0126] Judging the degree of noise impact solely based on sound level or population size often fails to accurately reflect the true exposure risk in different areas. In real urban environments, the harm of noise disturbances depends not only on the intensity of the sound source but also on the combined modulation of population exposure density and regional functional vulnerability. For example, sensitive areas such as hospitals and schools may experience stronger disturbances even with lower exposure sound levels. Therefore, this method proposes an integrated urban noise exposure risk index that comprehensively considers three key dimensions: sound level intensity, population density, and regional vulnerability. We first normalize each dimension to ensure a uniform scale, and then construct a risk score for each receiving point using a multiplicative coupling method, serving as an important weighting factor for subsequent disturbance assessments.

[0127] Furthermore, in step S6, taking into account sound level intensity, population density, and regional vulnerability, a multiplicative coupling method is used to construct the urban noise exposure risk index, expressed as:

[0128]

[0129] in, Let j be the urban noise exposure risk index corresponding to receiving point j. , These represent the minimum and maximum sound levels within the region; This represents the maximum population density, obtained from the management department. The population density of the area corresponding to receiving point j is obtained from the management department. The regional vulnerability factor corresponding to receiving point j is used to reflect the social attributes of the region and is determined in conjunction with urban GIS, land use, or social structure.

[0130] S7. In the disturbance population assessment model, the urban noise exposure risk index obtained in step S6 is considered as a spatial weight, and the receiving point perception score corresponding to the impact of the time equivalent sound level obtained in step S5 is considered. The disturbance population at each receiving point is weighted and accumulated to obtain the total number of residents affected by the drone flight noise.

[0131] Traditional methods for assessing population disturbance typically rely on sound level thresholds to determine whether an area is "affected" or use perceived disturbance functions to estimate the degree of disturbance. While these methods take subjective perception into account, they do not adequately incorporate the heterogeneity of risk exposure in urban spaces.

[0132] Furthermore, the specific implementation method of step S7 includes the following steps:

[0133] S7.1. Calculate the corresponding receiver perception score based on the impact of the time-equivalent sound level obtained in step S5. The expression is:

[0134]

[0135] in, The perception score for receiving point j; The parameters for sensing the rate of change are determined by expert experience; The sensitivity threshold is determined by expert experience.

[0136] S7.2. Based on the perception score obtained in step S7.1, simulate the subjective perception intensity of humans to different sound levels. Then, correlate the perception score with the population size at receiver point j. Multiplying the urban noise exposure risk index corresponding to receiver point j by the weighted disturbance population at that point, and summing the results over all receiver points, yields the number of residents affected by the drone's flight noise. The expression is as follows:

[0137]

[0138] in, The population covered by receiving point j is obtained from the management department. The number of receiving points is determined based on the regular grid division, building data, or the number of points of interest. This represents the total number of residents affected by the noise from drone flights.

[0139] The specific implementation of this method in a certain area is as follows: Three UAVs are known to be flying, with corresponding UAV coordinates: U1:(75,75,50), U2:(225,225,50), U3:(150,150,50); corresponding to 9 ground receiving points, with coordinates as follows:

[0140] R1:(0,0,0),R2:(0,150,0),R3:(0,300,0),R4:(150,0,0),R5:(150,150 ,0),R6:(150,300,0),R7:(300,0,0),R8:(300,150,0),R9:(300,300,0);

[0141] Using the method in this embodiment, the number of residents affected by noise at the nine receiving points was calculated, as shown in Table 1 and... Figure 2 As shown, the total number of residents affected by the drone's flight noise was determined to be 30.

[0142] Table 1

[0143]

[0144] Example 2:

[0145] A system for calculating the number of residents affected by drone flight noise includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of the method for calculating the number of residents affected by drone flight noise as described in Embodiment 1.

[0146] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0147] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for calculating the number of residents affected by drone flight noise, characterized in that, Includes the following steps: S1. Incorporate the real-time speed, acceleration, and attitude angle changes of the UAV into the modeling system to establish a UAV flight state-corrected acoustic power calculation model; S2. Considering the flight state of the UAV and the energy attenuation of the sound wave during propagation, the UAV flight state modified sound power calculation model obtained in step S1 is modified to calculate the sound pressure level of the UAV after initial propagation attenuation. S3. Considering the influence of buildings on the sound wave propagation path, construct a reflection enhancement term based on the incident angle, reflection area, and reflection coefficient of the wall reflection. Then, based on the reflection enhancement term and the blocking attenuation term, correct the initial propagation attenuation sound pressure level of the UAV obtained in step S2 to obtain the corrected sound pressure level of the UAV. S4. Based on the corrected sound pressure level of the UAV obtained in step S3, construct the sound energy superposition model of the UAV, calculate the total sound pressure level of the UAV at the receiving point, and then calculate the equivalent sound level of the UAV at the receiving point based on the total sound pressure level of the UAV at the receiving point. S5. Considering the difference between day and night in residents' perception of noise, the equivalent sound level of the drone at the receiving point obtained in step S4 is divided into the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, and then integrated to obtain the time equivalent sound level. S6. Based on the time equivalent sound level obtained in step S5, the sound level intensity is obtained, and the urban noise exposure risk index is constructed by comprehensively considering the sound level intensity, population density and regional vulnerability. S7. In the disturbance population assessment model, the urban noise exposure risk index obtained in step S6 is considered as a spatial weight, and the receiving point perception score corresponding to the impact of the time equivalent sound level obtained in step S5 is considered. The disturbance population at each receiving point is weighted and accumulated to obtain the total number of residents affected by the drone flight noise.

2. The method for calculating the number of residents affected by drone flight noise according to claim 1, characterized in that, Step S1 introduces the real-time velocity, acceleration, and attitude angle changes of the UAV into the modeling system. By normalizing and weighting the dynamic variables, a modified acoustic power calculation model for the UAV's flight state is established, expressed as: ； in, Let be the sound power level of the i-th UAV at time t. This is the static reference sound power level, obtained from the manufacturer's data; Let be the flight speed of the i-th drone, obtained from the sensor; For reference flight speed, determined by industry standards; Let be the acceleration of the i-th drone, obtained from the sensor; The attitude angle change of the i-th UAV is obtained from the sensor; , , These are the weighting coefficients corresponding to flight speed, acceleration, and attitude angle, respectively, determined based on expert experience.

3. The method for calculating the number of residents affected by drone flight noise according to claim 2, characterized in that, The specific implementation method of step S2 includes the following steps: S2.

1. Considering the flight status of the UAV, by recording the position of each UAV at each moment and the coordinates of the ground receiving point, the Euclidean distance is calculated, and the expression is obtained as follows: ； in, The spatial position of the i-th UAV at time t is obtained by the positioning system; The spatial location of receiving point j is obtained by the positioning system; Let be the distance between the i-th drone and the receiving point j; S2.

2. Considering the energy attenuation of sound waves during propagation, including free-space geometric diffusion, atmospheric absorption, and ground reflection absorption, the free-space geometric diffusion mechanism is due to the uniform propagation of sound waves on a sphere, with energy attenuating inversely with the square of the distance. Loss characterization was performed; atmospheric absorption mechanism is related to frequency, temperature, and humidity, and is a dissipation effect caused by the thermal motion of air molecules; ground reflection absorption mechanism is caused by the interaction between sound waves and the earth's surface; The acoustic power calculation model for the UAV flight state obtained in step S1 is corrected based on the energy attenuation of sound waves during propagation, resulting in the initial propagation attenuation sound pressure level of the UAV, expressed as: ； ； in, Let be the sound pressure level after initial propagation attenuation between the i-th UAV and the receiving point j; Let be the ground reflection absorption term between the i-th UAV and the receiving point j; Atmospheric absorption coefficient; The ground reflection absorption coefficient is determined by combining the ground type.

4. The method for calculating the number of residents affected by drone flight noise according to claim 3, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Consider the impact of buildings on the sound wave propagation path, including obstruction and attenuation, and reflection enhancement; S3.

2. Determine the existence of occlusion using geometric ray tracing, and introduce an occlusion attenuation term. It is determined by experts based on their experience and the actual scenario; S3.

3. Based on the incident angle, reflection area, and reflection coefficient of the wall reflection, a reflection enhancement term is constructed, expressed as follows: ； in, For reflection enhancement, The wall reflectance coefficient is determined based on the wall material. The angle of incidence is obtained through geometric calculations combining the propagation path and the wall normal. The visible wall reflective area is determined by expert experience in combination with the actual scenario. S3.

4. Based on the initial propagation attenuation sound pressure level of the UAV obtained in step S2, which is corrected by the reflection enhancement term and the occlusion attenuation term, the corrected sound pressure level of the UAV is obtained, and the expression is: ； in, Let be the corrected sound pressure level between the i-th UAV and the receiving point j.

5. The method for calculating the number of residents affected by drone flight noise according to claim 4, characterized in that, The specific implementation method of step S4 includes the following steps: S4.

1. Construct a superposition model of the UAV's acoustic energy, and calculate the expression for the total sound pressure level of the UAV at the receiving point: ； in, Let j be the total sound pressure level at receiving point j. The number of drones is determined by the drone monitoring system; S4.

2. Based on the total sound pressure level of the UAV at the receiving point, the equivalent sound level of the UAV at the receiving point is calculated using the energy equivalent integration method, and the expression is: ； in, Let J be the equivalent sound level of the UAV at receiving point j. The duration is determined based on the actual task requirements.

6. The method for calculating the number of residents affected by drone flight noise according to claim 5, characterized in that, The specific implementation method of step S5 includes the following steps: S5.

1. Calculate the equivalent sound level of the drone during the day and the equivalent sound level of the drone at night, respectively, and obtain the expression: ； ； in, The equivalent sound level of the drone at receiving point j during the daytime; The equivalent sound level of the drone at receiving point j at night. Daytime duration, The duration of nighttime will be determined based on actual needs; S5.

2. Combine the equivalent sound level of the daytime drone at receiving point j and the equivalent sound level of the nighttime drone at receiving point j obtained in step S5.1 to obtain the time equivalent sound level of receiving point j, expressed as: ； in, Let j be the time-equivalent sound level at receiving point j. The penalty weight is determined at night.

7. The method for calculating the number of residents affected by drone flight noise according to claim 6, characterized in that, Step S6 comprehensively considers sound level intensity, population density, and regional vulnerability, and constructs the urban noise exposure risk index using a multiplicative coupling method, expressed as: ； in, Let j be the urban noise exposure risk index corresponding to receiving point j. , These represent the minimum and maximum sound levels within the region; This represents the maximum population density, obtained from the management department. The population density of the area corresponding to receiving point j is obtained from the management department. The regional vulnerability factor corresponding to receiving point j is used to reflect the social attributes of the region and is determined in conjunction with urban GIS, land use, or social structure.

8. The method for calculating the number of residents affected by drone flight noise according to claim 7, characterized in that, The specific implementation method of step S7 includes the following steps: S7.

1. Calculate the corresponding receiver perception score based on the impact of the time-equivalent sound level obtained in step S5. The expression is: ； in, The perception score for receiving point j; The parameters for sensing the rate of change are determined by expert experience; The sensitivity threshold is determined by expert experience. S7.

2. Based on the perception score obtained in step S7.1, simulate the subjective perception intensity of humans to different sound levels. Then, correlate the perception score with the population size at receiver point j. Multiplying the urban noise exposure risk index corresponding to receiver point j by the weighted disturbance population at that point, and summing the results over all receiver points, yields the number of residents affected by the drone's flight noise. The expression is as follows: ； in, The population covered by receiving point j is obtained from the management department. The number of receiving points is determined based on the regular grid division, building data, or the number of points of interest. This represents the total number of residents affected by the noise from drone flights.

9. A system for calculating the number of residents affected by drone flight noise, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed, implements the steps of a method for calculating the number of residents affected by drone flight noise as described in any one of claims 1-8.

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