Unmanned aerial vehicle air pollution detection method and system

Abstract

The invention discloses an unmanned aerial vehicle air pollution detection method and system, relates to the technical field of unmanned aerial vehicles, and solves the technical problems that it is difficult to carry out key monitoring on a pollution area according to the characteristics of different grids in a supervision area, it is difficult to dynamically evaluate and adjust the task load of an unmanned aerial vehicle, and the reliability of the whole system is affected. The unmanned aerial vehicle adjusts an air pollution monitoring strategy according to a real-time analysis result of the edge node; adjacent grids are combined into a calculation task area, so that unmanned aerial vehicle resources are utilized more efficiently, repeated monitoring is avoided, and the overall working efficiency is improved; the data of the adjacent grids are analyzed in a centralized manner, so that the pollution condition in the region can be comprehensively evaluated, and the accuracy of the pollution index is improved; unmanned aerial vehicle detection is dynamically adjusted, and a flight path and a grid monitoring strategy are optimized; the task load of the unmanned aerial vehicle is dynamically evaluated and adjusted, faults or failures caused by overload of a single unmanned aerial vehicle are prevented, and the reliability of the whole system is improved.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicles (UAVs), and in particular to an air pollution detection method and system for UAVs. Background Art

[0002] With the acceleration of urbanization and the increase of industrial activities, the problem of air pollution has become increasingly serious, which has a profound impact on human health, ecological environment and social economy. According to the World Health Organization (WHO), air pollution has become one of the major health risks in the world, causing millions of premature deaths. Therefore, timely and accurate monitoring of air quality has become an important task. Traditional air quality monitoring methods mainly rely on fixed monitoring stations, which are usually unevenly distributed, difficult to cover all areas, and limited by equipment maintenance, data transmission and other issues. The rapid development of drone technology has provided a new solution for air pollution detection. Drones have the advantages of high flexibility, low cost, and rapid deployment. They can cover a wide area in a short time and obtain high-resolution data.

[0003] Most of them only plan the flight routes of drones. It is difficult to focus on monitoring polluted areas according to the characteristics of different grids in the regulatory area, and it is difficult to dynamically evaluate and adjust the mission load of drones. This can easily lead to malfunction or failure of a single drone due to overload, affecting the reliability of the entire system. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a method and system for detecting air pollution from unmanned aerial vehicles, which are used to solve the technical problems that it is difficult to focus on monitoring polluted areas according to the characteristics of different grids in the supervision area, and it is difficult to dynamically evaluate and adjust the mission load of the unmanned aerial vehicle, thereby affecting the reliability of the entire system.

[0005] To solve the above problems, the first aspect of the present invention provides a method and system for detecting air pollution of a drone, comprising:

[0006] Divide the management area into several grids, model the flight environment for each grid, and obtain the environmental data of each grid, including terrain, obstacles, buildings and flight restricted area data;

[0007] According to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are assigned to each grid for monitoring;

[0008] According to the topography of the management area, several adjacent grids are grouped into a computing task area. Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area.

[0009] The edge computing unit updates the grid detection task priority according to the real-time grid pollution index, obtains the task execution data of the drone in the corresponding computing task area, and the status data of the corresponding drone, dynamically evaluates the task load of each drone, and determines the unloading ratio of the drone task according to the task priority;

[0010] Through the collaborative optimization algorithm within the drone cluster, the comprehensive target cost of all drones in the management area is minimized and the tasks unloaded by the drones are allocated.

[0011] As a further solution of the present invention: according to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are allocated to each grid for monitoring, including the following steps:

[0012] Based on the terrain, obstacles, buildings and flight restricted area data of the grid, the flight detection area of ​​each grid is calculated by subtracting the obstacle area and flight restricted area from the total area of ​​the grid;

[0013] The factory area in the building area of ​​each grid is counted, and the grid detection requirement is calculated using the following formula:

[0014]

[0015] Among them, Es is the grid detection demand of the corresponding grid, Sa is the flight detection area of ​​the corresponding grid, μa is the mean of the flight detection areas of all grids, SE is the factory area of ​​the corresponding grid, and μE is the mean of the factory area of ​​all grids;

[0016] Assign one drone to each grid for monitoring. For grids with a detection requirement Es greater than 1.2 and less than 1.5, add one drone for monitoring. For grids with a detection requirement Es greater than or equal to 1.5, add 2-3 drones for monitoring.

[0017] As a further solution of the present invention: according to the topography of the management area, a number of adjacent grids are combined into a computing task area, including the following steps:

[0018] According to the topography of the management area, the grid complexity is calculated by the following formula:

[0019]

[0020] Wherein, Ed is the grid complexity of the corresponding grid, g is the number of buildings with a height greater than 50 meters in the corresponding grid, μg is the mean number of buildings with a height greater than 50 meters in all grids, Sr is the obstacle area of ​​the corresponding grid, μr is the mean obstacle area of ​​all grids, and Es is the grid detection requirement of the corresponding grid;

[0021] The grid complexities of adjacent grids are added together, and the areas where the sum of the grid complexities of adjacent grids is less than 3 are assigned to the same computing task area, dividing the computing task areas of the management area.

[0022] As a further solution of the present invention: each computing task area is assigned a drone equipped with an edge computing unit, and the edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area, including the following steps:

[0023] Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit calculates the pollution index of each pollutant based on the air detection data of the drone in the grid of the task area, including: nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5: Faq = C / Cmax, where C is the maximum value of the pollutant concentration measured by the drone in the grid, and Cmax is the maximum allowable concentration in the corresponding standard of the pollutant in the management area;

[0024] The grid pollution index is evaluated by the following formula:

[0025] F0=max(FaqSO2,FaqNO2,FaqVOCs,FaqPM2.5)

[0026]

[0027] Among them, F is the grid pollution index, F0 is the maximum value of the pollution index of the pollutant, FaqSO2, FaqNO2, FaqVOCs and FaqPM2.5 are the pollution indices of nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5 pollutants respectively.

[0028] As a further solution of the present invention: the edge computing unit updates the grid detection task priority according to the grid pollution index evaluated in real time, including the following steps:

[0029] The edge computing unit calculates the grid detection task priority coefficient based on the real-time evaluated grid pollution index and the grid complexity of the corresponding grid;

[0030]

[0031] According to the grid detection task priority coefficient G, if the grid detection task priority coefficient G is greater than 1 and less than or equal to 2, the corresponding grid priority is determined to be the second-level priority; if the grid detection task priority coefficient G is greater than 2, the corresponding grid priority is determined to be the first-level priority; if the grid detection task priority coefficient G is less than or equal to 1, the corresponding grid priority is determined to be the third-level priority, and the grid detection task priority is updated.

[0032] As a further solution of the present invention: by acquiring the task execution data of the UAV corresponding to the calculation task area and the status data of the corresponding UAV, the task load of each UAV is dynamically evaluated, including the following steps:

[0033] By obtaining the mission execution data of the UAV in the corresponding calculation mission area, including: mission completion ratio, route and location data, the mission load of the UAV is calculated using the following formula:

[0034]

[0035] Among them, Xa is the mission load of the UAV, fa is the mission completion ratio of the UAV, La is the total mission route length of the UAV, Lmax is the maximum value of the total mission route length of the UAV in the mission area, La1 is the completed mission route length of the UAV, Lb is the shortest distance to the obstacle area, and L0 is the maximum inner diameter of the grid where the UAV is located;

[0036] Calculate the status data of the corresponding drone in the mission area, including: network status, remaining power and communication status data. Calculate the state load of the drone using the following formula:

[0037]

[0038] Among them, Xb is the state load degree of the UAV, Tp is the mean value of the signal propagation delay time of the UAV, Tmax is the maximum value of the signal propagation delay time of the UAV in the calculation mission area, Pa is the remaining power of the UAV, and P0 is the total power of the UAV;

[0039] According to the real-time statistics of the mission load and state load of the UAV, the sum of the mission load and state load of the UAV is calculated, and the mission load of each UAV is dynamically evaluated;

[0040] If the sum of the drone's mission load and state load is greater than 5, the drone's mission load is overloaded. If the sum of the drone's mission load and state load is greater than 3 and less than or equal to 5, the drone's mission load is balanced. If the sum of the drone's mission load and state load is less than or equal to 3, the drone's mission load is insufficient.

[0041] As a further solution of the present invention: determining the unloading ratio of the UAV task according to the task priority includes the following steps:

[0042] Unload 5% of the remaining navigation tasks of the drones with overloaded drone mission load in the grid priority level 1 in the calculation mission area;

[0043] In the grid with a secondary priority level, the remaining navigation tasks of the drones with overloaded mission loads are unloaded by 10%;

[0044] In the three-level priority grid, the remaining navigation tasks of the drones with overloaded drone missions are unloaded by 15%.

[0045] As a further solution of the present invention: by using a collaborative optimization algorithm within a drone cluster, the comprehensive target cost of all drones in the management area is minimized, and the tasks unloaded by the drones are allocated, including the following steps:

[0046] The target costs of all drones in the management area are divided into the target cost of mission importance, the target cost of mission execution time, the target cost of energy consumption and the target cost of communication delay;

[0047] The task execution time target cost CoT is calculated by the following formula:

[0048]

[0049] Where n is the number of UAVs participating in the task in the management area, i∈(1,2,…,n), Di is the optimized remaining flight distance of the ith UAV, Vi is the average flight speed of the ith UAV, and Ttask is the fixed time required for each UAV to perform a specific detection task;

[0050] The energy consumption target cost CoE is calculated by the following formula:

[0051]

[0052] Among them, Pf i is the power consumption of the i-th UAV during flight, Tf i is the time required for the i-th UAV to fly, and Et i is the energy consumption of the i-th UAV for data detection;

[0053] The communication delay target cost CoD is calculated by the following formula:

[0054] Communication delay is usually related to the packet size, network bandwidth and transmission distance. It can be expressed by the following formula:

[0055]

[0056] Where m is the number of communication links for communication in the management area, j∈(1,2,…,m), Lj is the estimated size of the data packet transmitted on the j-th link, in bits, Bj is the bandwidth of the j-th link, in bits / second, and Tpj is the mean signal propagation delay time of the drone;

[0057] The comprehensive target cost Co of all drones is:

[0058] Co=w1*CoT+w2*CoE+w3*CoD

[0059] The comprehensive target cost of the UAV is the objective function, and the constraints are set as follows: the energy consumption of each UAV cannot exceed 80% of its maximum power, and all UAV tasks are completed within the specified time threshold;

[0060] The objective function is minimized under given constraints through the PSO particle swarm algorithm until the number of particle iterations reaches a preset threshold or the objective function change is less than the set threshold, then the particle iteration is stopped;

[0061] Get the comprehensive target cost of all UAVs and the minimum, and the optimized remaining route distance Di of the i-th UAV;

[0062] Calculate the optimized remaining route distance of the i-th UAV minus Di and the actual remaining route distance of the UAV, and based on the difference, prioritize the unloaded tasks of the UAV to the UAV with the largest difference result, and the task allocation amount of the UAV shall not exceed 10% of the optimized remaining route distance.

[0063] As another aspect of the present invention: a drone air pollution detection system, which adopts a drone air pollution detection method as described above to realize drone air pollution detection in a management area.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention processes data at the edge node and only needs to upload necessary information to the central server, reducing the demand for network bandwidth, especially in areas with poor network conditions. The drone adjusts the air pollution monitoring strategy based on the real-time analysis results of the edge node; it combines adjacent grids into computing task areas to more efficiently utilize drone resources, avoid duplicate monitoring, and improve overall work efficiency. By centrally analyzing the data of adjacent grids, it is convenient to comprehensively assess the pollution situation in the area, thereby improving the accuracy of the pollution index. The edge computing unit dynamically adjusts drone detection, optimizes flight routes and grid monitoring strategies to ensure coverage of all key areas.

[0066] By evaluating the pollution index and task load in real time, the system can flexibly adjust the task priority of the drone to cope with the changing environmental conditions and monitoring needs. According to the status and load of the drone, the tasks are reasonably allocated to ensure that each drone works efficiently within its capabilities and avoid excessive load or idle resources. By giving priority to grids with higher pollution indexes, potential environmental problems can be responded to more quickly and the overall monitoring efficiency can be improved. The task load of the drone is dynamically evaluated and adjusted to prevent failures or failures caused by overloading of a single drone, thereby improving the reliability of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0069] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] See also Figure 1 The first aspect of the present invention provides a method and system for detecting air pollution of a drone, including:

[0071] Divide the management area into several grids, model the flight environment for each grid, and obtain the environmental data of each grid, including terrain, obstacles, buildings and flight restricted area data;

[0072] According to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are assigned to each grid for monitoring;

[0073] According to the topography of the management area, several adjacent grids are grouped into a computing task area. Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area.

[0074] The edge computing unit updates the grid detection task priority according to the real-time grid pollution index, obtains the task execution data of the drone in the corresponding computing task area, and the status data of the corresponding drone, dynamically evaluates the task load of each drone, and determines the unloading ratio of the drone task according to the task priority;

[0075] Through the collaborative optimization algorithm within the drone cluster, the comprehensive target cost of all drones in the management area is minimized and the tasks unloaded by the drones are allocated.

[0076] Specifically, in this embodiment, the management area is divided into several grids, the flight environment model is performed for each grid, and the environmental data of each grid is obtained, and the environmental data includes: terrain, obstacles, buildings and flight restricted area data; according to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are allocated to each grid for monitoring;

[0077] By dividing the area into grids, refined management of different areas is achieved, making it easier to identify and monitor the environmental conditions in specific areas. Obtaining detailed environmental data, such as terrain, obstacles, buildings, and flight restrictions, makes it easier to reasonably allocate drones according to the detection needs of each grid, improve resource utilization efficiency, ensure that key areas are fully monitored, and avoid resource waste.

[0078] Monitoring different environmental characteristics can improve the accuracy and reliability of monitoring data, which will help to promptly detect and respond to air pollution problems. If environmental conditions within a grid change, for example, new buildings are built or new pollution sources appear, it will be easy to quickly adjust monitoring strategies and drone deployment, improving the ability to respond to emergencies.

[0079] According to the topography of the management area, several adjacent grids are grouped into a computing task area. Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area.

[0080] The edge computing unit can process and analyze data in real time on the drone, reducing data transmission delays and improving the response speed to changes in air quality. By processing data at the edge node, only the necessary information needs to be uploaded to the central server, reducing the demand for network bandwidth, especially in areas with poor network conditions.

[0081] The drone adjusts the air pollution monitoring strategy based on the real-time analysis results of the edge node, for example, the drone grid allocation or monitoring key areas. Adjacent grids are combined into computing task areas to more efficiently utilize drone resources, avoid repeated monitoring, and improve overall work efficiency.

[0082] By centrally analyzing the data of adjacent grids, it is convenient to comprehensively assess the pollution situation in the area, thereby improving the accuracy of the pollution index. The edge computing unit dynamically adjusts drone detection, optimizes flight routes and grid monitoring strategies to ensure coverage of all key areas. Edge computing enables multiple drones to work together to support large-scale environmental monitoring projects and improve overall monitoring capabilities.

[0083] The edge computing unit updates the grid detection task priority according to the real-time grid pollution index, obtains the task execution data of the drone in the corresponding computing task area, and the status data of the corresponding drone, dynamically evaluates the task load of each drone, and determines the unloading ratio of the drone task according to the task priority;

[0084] By evaluating the pollution index and task load in real time, the system can flexibly adjust the task priority of the drones to cope with changing environmental conditions and monitoring needs. According to the status and load of the drones, tasks are reasonably allocated to ensure that each drone works efficiently within its capabilities and avoids excessive load or idle resources. By giving priority to grids with higher pollution indexes, potential environmental problems can be responded to more quickly, improving overall monitoring efficiency.

[0085] Dynamically evaluate and adjust the mission load of drones to prevent malfunctions or failures caused by overload of a single drone, and improve the reliability of the entire system; multiple drones work together based on real-time data, and achieve more efficient joint monitoring through reasonable unloading and reallocation of tasks.

[0086] Through the collaborative optimization algorithm within the drone cluster, the comprehensive target cost of all drones in the management area is minimized, and the tasks unloaded by the drones are allocated;

[0087] The collaborative optimization algorithm can efficiently allocate tasks in the drone cluster, ensuring that the workload of each drone is reasonable, thereby improving overall resource utilization. By minimizing the comprehensive target cost, it is convenient to effectively reduce energy consumption and flight time, thereby reducing operating costs and improving economic benefits. The drone cluster can dynamically adjust task allocation according to real-time data and environmental changes, and improve the system's ability to adapt to changes. By reasonably allocating tasks, it ensures that key areas are fully monitored, thereby improving the accuracy and reliability of data. The drones in the cluster can achieve information sharing and collaboration through collaborative optimization algorithms, improving overall combat capabilities and efficiency.

[0088] In one embodiment of the present invention, according to the environmental data of each grid, the grid detection requirement of each grid is determined, and one or more drones are allocated to each grid for monitoring, including the following steps:

[0089] Based on the terrain, obstacles, buildings and flight restricted area data of the grid, the flight detection area of ​​each grid is calculated by subtracting the obstacle area and flight restricted area from the total area of ​​the grid;

[0090] The factory area in the building area of ​​each grid is counted, and the grid detection requirement is calculated using the following formula:

[0091]

[0092] Among them, Es is the grid detection demand of the corresponding grid, Sa is the flight detection area of ​​the corresponding grid, μa is the mean of the flight detection areas of all grids, SE is the factory area of ​​the corresponding grid, and μE is the mean of the factory area of ​​all grids;

[0093] Assign one drone to each grid for monitoring. For grids with a detection requirement Es greater than 1.2 and less than 1.5, add one drone for monitoring. For grids with a detection requirement Es greater than or equal to 1.5, add 2-3 drones for monitoring.

[0094] In one embodiment of the present invention, a plurality of adjacent grids are grouped into a computing task area according to the topography of the management area, including the following steps:

[0095] According to the topography of the management area, the grid complexity is calculated by the following formula:

[0096]

[0097] Wherein, Ed is the grid complexity of the corresponding grid, g is the number of buildings with a height greater than 50 meters in the corresponding grid, μg is the mean number of buildings with a height greater than 50 meters in all grids, Sr is the obstacle area of ​​the corresponding grid, μr is the mean obstacle area of ​​all grids, and Es is the grid detection requirement of the corresponding grid;

[0098] The grid complexities of adjacent grids are added together, and the areas where the sum of the grid complexities of adjacent grids is less than 3 are assigned to the same computing task area, dividing the computing task areas of the management area.

[0099] In one embodiment of the present invention, each computing task area is assigned a drone equipped with an edge computing unit, and the edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area, including the following steps:

[0100] Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit calculates the pollution index of each pollutant based on the air detection data of the drone in the grid of the task area, including: nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5: Faq = C / Cmax, where C is the maximum value of the pollutant concentration measured by the drone in the grid, and Cmax is the maximum allowable concentration in the corresponding standard of the pollutant in the management area;

[0101] The grid pollution index is evaluated by the following formula:

[0102] F0=max(FaqSO2,FaqNO2,FaqVOCs,FaqPM2.5)

[0103]

[0104] Among them, F is the grid pollution index, F0 is the maximum value of the pollution index of the pollutant, FaqSO2, FaqNO2, FaqVOCs and FaqPM2.5 are the pollution indices of nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5 pollutants respectively.

[0105] In one embodiment of the present invention, the edge computing unit updates the grid detection task priority according to the grid pollution index evaluated in real time, including the following steps:

[0106] The edge computing unit calculates the grid detection task priority coefficient based on the real-time evaluated grid pollution index and the grid complexity of the corresponding grid;

[0107]

[0108] According to the grid detection task priority coefficient G, if the grid detection task priority coefficient G is greater than 1 and less than or equal to 2, the corresponding grid priority is determined to be the second-level priority; if the grid detection task priority coefficient G is greater than 2, the corresponding grid priority is determined to be the first-level priority; if the grid detection task priority coefficient G is less than or equal to 1, the corresponding grid priority is determined to be the third-level priority, and the grid detection task priority is updated.

[0109] In one embodiment of the present invention, by acquiring the task execution data of the drone corresponding to the computing task area and the status data of the corresponding drone, the task load of each drone is dynamically evaluated, including the following steps:

[0110] By obtaining the mission execution data of the UAV in the corresponding calculation mission area, including: mission completion ratio, route and location data, the mission load of the UAV is calculated using the following formula:

[0111]

[0112] Among them, Xa is the mission load of the UAV, fa is the mission completion ratio of the UAV, La is the total mission route length of the UAV, Lmax is the maximum value of the total mission route length of the UAV in the mission area, La1 is the completed mission route length of the UAV, Lb is the shortest distance to the obstacle area, and L0 is the maximum inner diameter of the grid where the UAV is located;

[0113] Calculate the status data of the corresponding drone in the mission area, including: network status, remaining power and communication status data. Calculate the state load of the drone using the following formula:

[0114]

[0115] Among them, Xb is the state load degree of the UAV, Tp is the mean value of the signal propagation delay time of the UAV, Tmax is the maximum value of the signal propagation delay time of the UAV in the calculation mission area, Pa is the remaining power of the UAV, and P0 is the total power of the UAV;

[0116] According to the real-time statistics of the mission load and state load of the UAV, the sum of the mission load and state load of the UAV is calculated, and the mission load of each UAV is dynamically evaluated;

[0117] If the sum of the drone's mission load and state load is greater than 5, the drone's mission load is overloaded. If the sum of the drone's mission load and state load is greater than 3 and less than or equal to 5, the drone's mission load is balanced. If the sum of the drone's mission load and state load is less than or equal to 3, the drone's mission load is insufficient.

[0118] In one embodiment of the present invention, determining the unloading ratio of the UAV task according to the task priority includes the following steps:

[0119] Unload 5% of the remaining navigation tasks of the drones with overloaded drone mission load in the grid priority level 1 in the calculation mission area;

[0120] In the grid with a secondary priority level, the remaining navigation tasks of the drones with overloaded mission loads are unloaded by 10%;

[0121] In the three-level priority grid, the remaining navigation tasks of the drones with overloaded drone missions are unloaded by 15%.

[0122] In one embodiment of the present invention, the coordinated optimization algorithm within the drone cluster is used to minimize the comprehensive target cost of all drones in the management area and allocate the tasks unloaded by the drones, including the following steps:

[0123] The target costs of all drones in the management area are divided into the target cost of mission importance, the target cost of mission execution time, the target cost of energy consumption and the target cost of communication delay;

[0124] The task execution time target cost CoT is calculated by the following formula:

[0125]

[0126] Where n is the number of UAVs participating in the task in the management area, i∈(1,2,…,n), Di is the optimized remaining flight distance of the ith UAV, Vi is the average flight speed of the ith UAV, and Ttask is the fixed time required for each UAV to perform a specific detection task;

[0127] The energy consumption target cost CoE is calculated by the following formula:

[0128]

[0129] Among them, Pf i is the power consumption of the i-th UAV during flight, Tf i is the time required for the i-th UAV to fly, and Et i is the energy consumption of the i-th UAV for data detection;

[0130] The communication delay target cost CoD is calculated by the following formula:

[0131] Communication delay is usually related to the packet size, network bandwidth and transmission distance. It can be expressed by the following formula:

[0132]

[0133] Where m is the number of communication links for communication in the management area, j∈(1,2,…,m), Lj is the estimated size of the data packet transmitted on the j-th link, in bits, Bj is the bandwidth of the j-th link, in bits / second, and Tpj is the mean signal propagation delay time of the drone;

[0134] The comprehensive target cost Co of all drones is:

[0135] Co=w1*CoT+w2*CoE+w3*CoD

[0136] The comprehensive target cost of the UAV is the objective function, and the constraints are set as follows: the energy consumption of each UAV cannot exceed 80% of its maximum power, and all UAV tasks are completed within the specified time threshold;

[0137] The objective function is minimized under given constraints through the PSO particle swarm algorithm until the number of particle iterations reaches a preset threshold or the objective function change is less than the set threshold, then the particle iteration is stopped;

[0138] Get the comprehensive target cost of all UAVs and the minimum, and the optimized remaining route distance Di of the i-th UAV;

[0139] Calculate the optimized remaining route distance of the ith UAV minus Di and the actual remaining route distance of the UAV, and based on the difference, prioritize the unloaded tasks of the UAV to the UAV with the largest difference result, and the task allocation amount of the UAV shall not exceed 10% of the optimized remaining route distance.

[0140] As an embodiment of another aspect of the present invention, a drone air pollution detection system is provided, which adopts a drone air pollution detection method as described above to realize drone air pollution detection in a management area.

[0141] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for detecting air pollution from a drone, characterized in that: include: Divide the management area into several grids, model the flight environment for each grid, and obtain the environmental data of each grid, including terrain, obstacles, buildings and flight restricted area data; According to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are assigned to each grid for monitoring; According to the topography of the management area, several adjacent grids are grouped into a computing task area. Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area. The edge computing unit updates the grid detection task priority according to the real-time grid pollution index, obtains the task execution data of the drone in the corresponding computing task area, and the status data of the corresponding drone, dynamically evaluates the task load of each drone, and determines the unloading ratio of the drone task according to the task priority; Through the collaborative optimization algorithm within the drone cluster, the comprehensive target cost of all drones in the management area is minimized and the tasks unloaded by the drones are allocated.

2. The method for detecting air pollution from a drone according to claim 1, characterized in that: According to the environmental data of the grid, the grid detection requirement of each grid is determined, and one or more drones are allocated to each grid for monitoring, including the following steps: Based on the terrain, obstacles, buildings and flight restricted area data of the grid, the flight detection area of ​​each grid is calculated by subtracting the obstacle area and flight restricted area from the total area of ​​the grid; The factory area in the building area of ​​each grid is counted, and the grid detection requirement is calculated using the following formula: Among them, Es is the grid detection demand of the corresponding grid, Sa is the flight detection area of ​​the corresponding grid, μa is the mean of the flight detection areas of all grids, SE is the factory area of ​​the corresponding grid, and μE is the mean of the factory area of ​​all grids; Assign one drone to each grid for monitoring. For grids with a detection requirement Es greater than 1.2 and less than 1.5, add one drone for monitoring. For grids with a detection requirement Es greater than or equal to 1.5, add 2-3 drones for monitoring.

3. The method for detecting air pollution from a drone according to claim 1, characterized in that: According to the topography of the management area, several adjacent grids are grouped into a computing task area, including the following steps: According to the topography of the management area, the grid complexity is calculated by the following formula: Wherein, Ed is the grid complexity of the corresponding grid, g is the number of buildings with a height greater than 50 meters in the corresponding grid, μg is the mean number of buildings with a height greater than 50 meters in all grids, Sr is the obstacle area of ​​the corresponding grid, μr is the mean obstacle area of ​​all grids, and Es is the grid detection requirement of the corresponding grid; The grid complexities of adjacent grids are added together, and the areas where the sum of the grid complexities of adjacent grids is less than 3 are assigned to the same computing task area, dividing the computing task areas of the management area.

4. The method for detecting air pollution from a drone according to claim 1, characterized in that: Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit evaluates the grid pollution index based on the air detection data of the drone in the task area, including the following steps: Each computing task area is assigned a drone equipped with an edge computing unit. The edge computing unit calculates the pollution index of each pollutant based on the air detection data of the drone in the grid of the task area, including: nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5: Faq = C / Cmax, where C is the maximum value of the pollutant concentration measured by the drone in the grid, and Cmax is the maximum allowable concentration in the corresponding standard of the pollutant in the management area; The grid pollution index is evaluated by the following formula: F0=max(FaqSO2,FaqNO2,FaqVOCs,FaqPM2.5) Among them, F is the grid pollution index, F0 is the maximum value of the pollution index of the pollutant, FaqSO2, FaqNO2, FaqVOCs and FaqPM2.5 are the pollution indices of nitrogen dioxide concentration, sulfur dioxide concentration, VOCs volatile organic compound concentration and PM2.5 pollutants respectively.

5. The method for detecting air pollution from a drone according to claim 4, characterized in that: The edge computing unit updates the grid detection task priority according to the real-time evaluated grid pollution index, including the following steps: The edge computing unit calculates the grid detection task priority coefficient based on the real-time evaluated grid pollution index and the grid complexity of the corresponding grid; According to the grid detection task priority coefficient G, if the grid detection task priority coefficient G is greater than 1 and less than or equal to 2, the corresponding grid priority is determined to be the second-level priority; if the grid detection task priority coefficient G is greater than 2, the corresponding grid priority is determined to be the first-level priority; if the grid detection task priority coefficient G is less than or equal to 1, the corresponding grid priority is determined to be the third-level priority, and the grid detection task priority is updated.

6. The method for detecting air pollution from a drone according to claim 1, characterized in that: By acquiring the task execution data of the UAVs in the corresponding computing task area and the status data of the corresponding UAVs, the task load of each UAV is dynamically evaluated, including the following steps: By obtaining the mission execution data of the UAV in the corresponding calculation mission area, including: mission completion ratio, route and location data, the mission load of the UAV is calculated using the following formula: Among them, Xa is the mission load of the UAV, fa is the mission completion ratio of the UAV, La is the total mission route length of the UAV, Lmax is the maximum value of the total mission route length of the UAV in the mission area, La1 is the completed mission route length of the UAV, Lb is the shortest distance to the obstacle area, and L0 is the maximum inner diameter of the grid where the UAV is located; Calculate the status data of the corresponding drone in the mission area, including: network status, remaining power and communication status data. Calculate the state load of the drone using the following formula: Among them, Xb is the state load degree of the UAV, Tp is the mean value of the signal propagation delay time of the UAV, Tmax is the maximum value of the signal propagation delay time of the UAV in the calculation mission area, Pa is the remaining power of the UAV, and P0 is the total power of the UAV; According to the real-time statistics of the mission load and state load of the UAV, the sum of the mission load and state load of the UAV is calculated, and the mission load of each UAV is dynamically evaluated; If the sum of the drone's mission load and state load is greater than 5, the drone's mission load is overloaded. If the sum of the drone's mission load and state load is greater than 3 and less than or equal to 5, the drone's mission load is balanced. If the sum of the drone's mission load and state load is less than or equal to 3, the drone's mission load is insufficient.

7. The method for detecting air pollution from a drone according to claim 6, characterized in that: Determine the unloading ratio of drone tasks according to the task priority, including the following steps: Unload 5% of the remaining navigation tasks of the drones with overloaded drone mission load in the grid priority level 1 in the calculation mission area; In the grid with a secondary priority level, the remaining navigation tasks of the drones with overloaded mission loads are unloaded by 10%; In the three-level priority grid, the remaining navigation tasks of the drones with overloaded drone missions are unloaded by 15%.

8. The method for detecting air pollution from a drone according to claim 1, characterized in that: Through the collaborative optimization algorithm within the drone cluster, the comprehensive target cost of all drones in the management area is minimized, and the tasks unloaded by the drones are allocated, including the following steps: The target costs of all drones in the management area are divided into the target cost of mission importance, the target cost of mission execution time, the target cost of energy consumption and the target cost of communication delay; The task execution time target cost CoT is calculated by the following formula: Where n is the number of UAVs participating in the task in the management area, i∈(1,2,…,n), Di is the optimized remaining flight distance of the i-th UAV, Vi is the average flight speed of the i-th UAV, and Ttask is the fixed time required for each UAV to perform a specific detection task; The energy consumption target cost CoE is calculated by the following formula: Among them, Pfi is the power consumption of the ith UAV during flight, Tfi is the time required for the ith UAV to fly, and Eti is the energy consumption of the ith UAV for data detection; The communication delay target cost CoD is calculated by the following formula: Communication delay is usually related to the packet size, network bandwidth and transmission distance. It can be expressed by the following formula: Where m is the number of communication links for communication in the management area, j∈(1,2,…,m), Lj is the estimated size of the data packet transmitted on the j-th link, in bits, Bj is the bandwidth of the j-th link, in bits / second, and Tpj is the mean signal propagation delay time of the drone; The comprehensive target cost Co of all drones is: Co=w1*CoT+w2*CoE+w3*CoD The comprehensive target cost of the UAV is the objective function, and the constraints are set as follows: the energy consumption of each UAV cannot exceed 80% of its maximum power, and all UAV tasks are completed within the specified time threshold; The objective function is minimized under given constraints through the PSO particle swarm algorithm until the number of particle iterations reaches a preset threshold or the objective function change is less than the set threshold, then the particle iteration is stopped; Get the comprehensive target cost of all UAVs and the minimum, and the optimized remaining route distance Di of the i-th UAV; Calculate the optimized remaining route distance of the i-th UAV minus Di and the actual remaining route distance of the UAV, and based on the difference, prioritize the unloaded tasks of the UAV to the UAV with the largest difference result, and the task allocation amount of the UAV shall not exceed 10% of the optimized remaining route distance.

9. An unmanned aerial vehicle air pollution detection system, characterized in that: The system uses a drone air pollution detection method as described in any one of claims 1-8 to realize drone air pollution detection in the management area.

Images