Unmanned aerial vehicle fog detection method

Abstract

The invention relates to an unmanned aerial vehicle fog detection method, and belongs to the technical field of unmanned aerial vehicle control. An unmanned aerial vehicle fog detection method is characterized in that unmanned aerial vehicle matrix distribution fixed point sampling is adopted for fog detection, an unmanned aerial vehicle takes the same matrix as a detection unit, n operation units are included in the same detection unit, different operation units are used for replacing fixed point sampling of the unmanned aerial vehicle, and in the same detection unit, n is an integer greater than or equal to 2. Comprising a first-class unmanned aerial vehicle used for signal transmission, a second-class unmanned aerial vehicle used for data acquisition and a third-class unmanned aerial vehicle used for data processing. The fog detection method comprises the following steps: S1, space division and assignment; s2, unmanned aerial vehicle group distribution; s3, establishing and transmitting a signal system; and S4, replacing the unmanned aerial vehicle. The method and the device are used for solving the technical problem that real-time transmission of data cannot be realized under long duration such as foggy weather in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a method for an unmanned aerial vehicle to detect fog. Background Art

[0002] In meteorological observation, with the development of technology, the air detection technology has more abundant and convenient detection technologies and means. Among them, it is more convenient to use a rotor unmanned aerial vehicle for meteorological detection, which can detect and sample data three-dimensionally for the formation of some phenomena. In the sampling and data detection of foggy weather, it is necessary to continuously sample each temperature layer and each flow layer before the formation of fog, and finally obtain the changes in each stage from the formation to the development, maturity, and finally dissipation of the complete fog.

[0003] The patent with the publication number CN117740480A discloses a meteorological unmanned aerial vehicle observation system, including an unmanned aerial vehicle body, a connecting frame, a collection mechanism, a sealing part, a reinforcement part, and an observation part. The connecting frame is installed at the lower part of the unmanned aerial vehicle body. An observation area and a collection area are opened on the connecting frame. The collection mechanism is installed inside the collection area for collecting atmospheric samples in a specified area. The collection mechanism includes an air intake part, a collection part, and a sampling part. The air intake part is installed at the bottom of the connecting frame. The collection part is installed in the collection area. The collection part is connected to the air intake part. The sampling part is installed inside the collection part. The sealing part is installed between the sampling part and the collection part for sealing the connection between the sampling part and the collection part. The reinforcement part is installed outside the collection part for fixing the position of the collection part. The observation part is installed in the observation area for observing the surrounding environment of the unmanned aerial vehicle.

[0004] The patent with the publication number: CN109115183B discloses a matrix-type unmanned aerial vehicle aerial survey method, including a consumer-grade unmanned aerial vehicle and an intelligent terminal with a GIS system; it also includes an external GPS navigation box, and the external GPS navigation box provides high-precision positioning services for ground operators and selects an optimal takeoff and landing point for the consumer-grade unmanned aerial vehicle at the same time; the intelligent terminal includes a mobile phone, a computer, and a tablet computer.

[0005] When the prior art is performing the work of an unmanned aerial vehicle detecting fog, there are at least the following problems:

[0006] During the process of sampling various types of data through a matrix, it is impossible to achieve real-time transmission of data in foggy weather with a relatively long duration. Summary of the Invention

[0007] The present invention provides a method for an unmanned aerial vehicle to detect fog, which is used to solve the technical problem that in the prior art, it is impossible to achieve real-time transmission of data in foggy weather with a relatively long duration.

[0008] In order to achieve the above object, the present invention is realized through the following technical solutions:

[0009] A method for a drone to detect fog, which uses a matrix distribution of drones for fixed-point sampling to detect fog. The drones use the same matrix as a detection unit, and within the same detection unit, there are n operating units. Different operating units are used to replace the drones for fixed-point sampling. In the same detection unit, there are: a first type of drone for signal transmission, a second type of drone for data collection, and a third type of drone for data processing.

[0010] Furthermore, a method for a drone to detect fog includes the following steps:

[0011] S1. Spatial division and assignment: Divide the working area according to the scope of the fog detection area, and assign the detection unit and the corresponding working area.

[0012] S2. Drone fleet distribution: The third type of drone guides the first type of drone to the assigned area. The first type of drone identifies the positions with the first type of drones in adjacent working areas and locates each other. The second type of drone is guided by the third type of drone to the corresponding working points.

[0013] S3. Signal system construction and transmission: The data collected by the second type of drone is transmitted within the working area to the corresponding third type of drone. The third type of drone integrates the data and sends it to the first type of drone. The first type of drone realizes data distribution and transmission by transmitting data with adjacent first type of drones.

[0014] S4. Drone replacement: Set corresponding safety thresholds for the working power of the first drone, the second drone, and the third drone. After reaching the safety threshold, a replacement signal is sent. Through the signal transmission of the first type of drone, after the detection unit receives the replacement signal, it sends an assignment signal to other batches of operating units, thereby guiding the corresponding type of drones for supplementary replacement.

[0015] Furthermore, in the above step S1, divide the working area according to the size of the area to be detected. The working area is set as a square matrix. After the control center of the ground unit divides the working area of the detection area, preset the arrangement spacing of the drones in each working area and send it to all the third type of drones in the corresponding detection unit.

[0016] Furthermore, in the above step S2, the following steps are also included:

[0017] S21. After the third type of drone enters the working area, it guides the first type of drone to disperse. The first type of drone pairs and locates with the first type of drones in adjacent working areas. The first type of drone is used to form the sides of the square matrix, and the first type of drone forms a communication network within its own working area and a communication route between different working areas. During this period, the third type of drone conducts inspections on the positioning situation of the first type of drones in the corresponding working area.

[0018] S22. When it is determined that the first type of drone has completed positioning, the third type of drone sends a first completion signal, which is transmitted to the corresponding detection unit via the communication route. The second type of drone enters the working area, and the third type of drone guides the second type of drone to their respective working points according to the preset arrangement spacing.

[0019] Furthermore, the above step S3 further includes the following steps:

[0020] S31. The drones of one type are distributed on the edges of the square matrix of their respective working areas. In the same working area, signal channels are established between the drones of one type to receive signals from the three types of drones and to send and receive signals transmitted from other working areas to the signal channels of other working areas;

[0021] S32, Category II drones sample data through various sensors on board, and form a signal internal communication network through self-organizing networks. Category II drones aggregate the collected data to Category III drones through the internal communication network;

[0022] S33, the third type of drone packages the data within a time unit and sends it to the signal channel, and transmits the data to the signal channels of other working areas through the signal channel;

[0023] S34, after receiving the data, other signal channels feed back the receiving signal. At this time, the first type of drone in the signal channel sends the corresponding receiving signal to the third type of drone. After the third type of drone obtains the receiving signal, it stores the packaged data locally.

[0024] S35, when other signal channels have reception anomalies, attempt to transmit data and report anomalies to signal channels on other paths;

[0025] S36. A communication grid is formed by data transmission between signal channels in each work area, and the packaged data is transmitted along each signal channel back to the ground unit for data reception.

[0026] Furthermore, the above step S4 further includes the following steps:

[0027] S41. When the Class II UAV reaches the safety threshold, it sends a Class II replacement signal to the Class III UAV. The Class III UAV sends the Class II replacement signal to the corresponding operation unit through multiple signal channels. The corresponding operation unit sends the Class II UAV to the corresponding point along the path guided by the signal channel.

[0028] S42. When a certain type of UAV reaches the safety threshold, it sends a replacement signal of this type to the third type of UAV. The third type of UAV sends the replacement signal of this type to the corresponding operation unit through multiple signal channels, and the corresponding operation unit sends the first type of UAV to the corresponding point along the path guided by the signal channel.

[0029] S43. When the third type of UAV reaches the safety threshold, it sends a replacement signal of the third type to the signal channel. Multiple signal channels send the replacement signal of the third type to the corresponding operation unit, and the corresponding operation unit sends the third type of UAV to the corresponding point along the path guided by the signal channel.

[0030] Furthermore, when the environmental fluctuations are large, the second type of UAV moves continuously within a certain range and intermittently establishes communication with the surrounding second type of UAVs during the movement.

[0031] Furthermore, after the second type of UAV samples data through continuous movement, it will be marked during data processing, so as to calculate and correct the data samples obtained by mobile sampling through general data fitting.

[0032] The present invention provides a method for a UAV to detect fog, and the beneficial effects are as follows:

[0033] 1. By means of the rotation and supplementation of multiple working units, the problem of short battery life of the UAV is effectively solved, and the overall working time of the system is extended.

[0034] 2. In response to different environmental challenges, the second type of UAV uses the fluctuation positioning method to continuously move for data sampling and communication transmission, ensuring short-distance signal communication to achieve stable data transmission, and improving the working ability of the system in complex environments.

[0035] 3. By performing separate matrix sampling in different areas to obtain saturated data and combining data fitting correction, the error caused by fluctuation positioning is effectively overcome, ensuring the reliability of signal acquisition.

[0036] 4. By classifying different types of UAVs, the data transmission efficiency is improved, and by building signal channels, the working efficiency and stability of the entire system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a flowchart of a method for a UAV to detect fog provided by an embodiment of the present invention.

[0039] Figure 2 Schematic diagram of the working structure distribution of multiple working areas provided by the embodiments of the present invention;

[0040] Figure 3 Schematic diagram of the distribution of a single working area provided by the embodiments of the present invention.

[0041] In the figure: 1 - signal channel; 2 - three types of unmanned aerial vehicles; 3 - working area. Specific embodiments

[0042] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0043] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present application.

[0044] The terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0045] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be welding, bolt connection, or riveting; it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0046] Embodiment:

[0047] An unmanned aerial vehicle fog detection method, which uses the matrix distribution of unmanned aerial vehicles for fixed-point sampling for fog detection. The unmanned aerial vehicles take the same matrix as a detection unit, and within the same detection unit, there are n operation units. Different operation units are used to replace the unmanned aerial vehicles for fixed-point sampling. In the same detection unit, there are: a first type of unmanned aerial vehicle for signal transmission, a second type of unmanned aerial vehicle for data collection, and a third type of unmanned aerial vehicle for data processing.

[0048] Further, a method for a drone to detect fog includes the following steps:

[0049] S1. Spatial division and assignment: Divide the working area according to the scope of the fog detection area, and assign the detection units and the corresponding working areas;

[0050] S2. Drone fleet distribution: One type of drone is guided by three types of drones to the assigned area. One type of drone identifies the positions with one type of drone in adjacent working areas and locates each other. Two types of drones are guided by three types of drones to the corresponding working points;

[0051] S3. Signal system construction and transmission: The data collected by two types of drones is transmitted within the working area to the corresponding three types of drones. The three types of drones integrate the data and send it to one type of drone. One type of drone realizes data distribution and transmission by transmitting data with adjacent one type of drones;

[0052] S4. Drone replacement: Set corresponding safety thresholds for the working power of the first drone, two types of drones, and three types of drones. After reaching the safety threshold, send a replacement signal. Through the signal transmission of one type of drone, after the detection unit receives the replacement signal, it sends an assignment signal to other batches of operation units, thereby guiding the corresponding type of drones to be supplemented and replaced.

[0053] Further, in the above step S1, the working area is divided according to the size of the area to be detected. The working area is set as a square matrix. After the control center of the ground unit divides the working area of the detection area, the arrangement spacing of the drones in each working area is preset and sent to all the third types of drones in the corresponding detection unit.

[0054] Further, in the above step S2, the following steps are further included:

[0055] S21. After the three types of drones enter the working area, guide the one type of drones to disperse. The one type of drones are paired and located with the one type of drones in adjacent working areas. The one type of drones are used to form the sides of the square matrix, and the one type of drones form the communication network within their respective working areas and the communication routes between different working areas. During this period, the three types of drones conduct inspections on the positioning of the one type of drones in the corresponding working area;

[0056] S22. When it is determined that the one type of drones have completed positioning, the three types of drones send a first completion signal. The first completion signal is transmitted to the corresponding detection unit via the communication route. The two types of drones enter the working area, and the three types of drones guide the two types of drones to their respective working points according to the preset arrangement spacing.

[0057] Furthermore, the above step S3 further includes the following steps:

[0058] S31. The drones of one type are distributed on the edges of the square matrix of their respective working areas. In the same working area, signal channels are established between the drones of one type to receive signals from the three types of drones and to send and receive signals transmitted from other working areas to the signal channels of other working areas;

[0059] S32, Category II drones sample data through various sensors on board, and form a signal internal communication network through self-organizing networks. Category II drones aggregate the collected data to Category III drones through the internal communication network;

[0060] S33, the third type of drone packages the data within a time unit and sends it to the signal channel, and transmits the data to the signal channels of other working areas through the signal channel;

[0061] S34, after receiving the data, other signal channels feed back the receiving signal. At this time, the first type of drone in the signal channel sends the corresponding receiving signal to the third type of drone. After the third type of drone obtains the receiving signal, it stores the packaged data locally.

[0062] S35, when other signal channels have reception anomalies, attempt to transmit data and report anomalies to signal channels on other paths;

[0063] S36. A communication grid is formed by data transmission between signal channels in each work area, and the packaged data is transmitted along each signal channel back to the ground unit for data reception.

[0064] Furthermore, the above step S4 further includes the following steps:

[0065] S41. When the Class II UAV reaches the safety threshold, it sends a Class II replacement signal to the Class III UAV. The Class III UAV sends the Class II replacement signal to the corresponding operation unit through multiple signal channels. The corresponding operation unit sends the Class II UAV to the corresponding point along the path guided by the signal channel.

[0066] S42, when the first type of UAV reaches the safety threshold, a first type of replacement signal is sent to the third type of UAV, and the third type of UAV sends the first type of replacement signal to the corresponding operation unit through multiple signal channels, and the corresponding operation unit sends the first type of UAV to the corresponding point along the path guided by the signal channel;

[0067] S43. When the three types of drones reach the safety threshold, three types of replacement signals are sent to the signal channel. Multiple signal channels send the three types of replacement signals to the corresponding operation units. The corresponding operation units send the three types of drones to the corresponding points along the paths guided by the signal channels.

[0068] In this implementation method, first, professionals use tools such as Geographic Information System (GIS) to accurately measure and analyze the fog detection area, and determine its scope and shape. According to the measurement results, the fog detection area is divided into multiple square working areas according to certain rules (such as area, terrain, etc.). The ground control center sends the division information of the working areas and the preset drone arrangement spacing parameters to the three types of drones of each detection unit through the wireless communication network. After receiving these information, the three types of drones are ready to guide other drones to enter the corresponding positions.

[0069] The three types of drones are equipped with high-precision positioning and navigation systems (such as GPS, inertial navigation systems, etc.) and take the lead in flying to the designated working areas. After arrival, the three types of drones send guiding instructions to the first type of drones through wireless signals, and the first type of drones disperse according to the instructions. During the dispersion process, the first type of drones perform pairing and positioning through their own positioning devices and signal interaction with the first type of drones in adjacent working areas. For example, the first type of drones can emit signals of a specific frequency, and adjacent drones receive and analyze the signals to determine their relative positions. After the first type of drones complete positioning, they automatically establish a communication network within the working area and communication routes between different working areas. The three types of drones check the positioning situation of the first type of drones according to the preset inspection path within the working area to ensure accurate positioning. During the inspection process, the three types of drones judge whether the first type of drones have completed positioning by monitoring the signals and confirming the position information of the first type of drones. When it is confirmed that the positioning is completed, the three types of drones send the first completion signal to the corresponding detection unit through the established communication route. After receiving the signal, the detection unit sends an instruction to let the second type of drones enter the working area. According to the preset arrangement spacing information received before, the three types of drones guide the second type of drones to accurately reach their respective working positions by continuously adjusting the relative positions with the second type of drones and sending guiding signals.

[0070] Furthermore, when the environment fluctuates greatly, the second type of drones continuously move within a certain range and intermittently establish communication with the surrounding second type of drones during the movement.

[0071] After the first type of drones complete positioning, they automatically start the signal channel establishment program. Each first type of drone establishes a signal channel with adjacent first type of drones through a specific communication protocol (such as ZigBee communication) at its position on the side of the square matrix where it is located. These signal channels have two-way communication capabilities, can stably receive the signals sent by the three types of drones, and transmit the signals to the signal channels in other working areas, while receiving the signals transmitted from other working areas.

[0072] Class II drones are equipped with a variety of sensors suitable for fog detection, such as humidity sensors, temperature sensors, visibility sensors, etc. After arriving at the working point, the Class II drones begin to collect data according to the preset sampling frequency and method. The collected data forms an internal communication network through self-organizing network technology. After each Class II drone performs preliminary processing on the collected data, it sends the data to the Class III drone through the internal communication network. Class II drones can integrate and encode the multiple data points collected, and then send them to the Class III drone in the form of data packets.

[0073] Furthermore, when the second type of drone samples through continuous movement, it will be marked during data processing, so that the data samples using mobile sampling can be calculated and corrected through general data fitting.

[0074] The Class II drone is equipped with an environmental perception module. When it detects large environmental fluctuations, it automatically starts the mobile sampling mode. During the movement, the Class II drone adjusts its flight attitude and speed to continuously move within a certain range, with a radius of 5 meters centered on the work point. At the same time, the Class II drone intermittently establishes communication with surrounding Class II drones through short-range wireless communication technology, exchanges location information and sampling data, and ensures the comprehensiveness and accuracy of the data.

[0075] During the mobile sampling process, the second-class drone will add special marking information to the collected data, indicating that the data was obtained in the mobile sampling mode. After receiving these data, the third-class drone will classify and store the marked data. In the data processing stage, the mobile sampling data is calculated and corrected using special data fitting algorithms (such as polynomial fitting, spline fitting, etc.). By comparing and analyzing the mobile sampling data with the sampling data at relatively stable positions around, the mobile sampling data is corrected to improve the accuracy of the data.

[0076] In summary, the method of multiple working units taking turns to supplement the work effectively solves the problem of short UAV flight time and extends the overall working time of the system; in response to different environmental challenges, the second type of UAV adopts the wave positioning method to continuously move for data sampling and transmission communication, ensuring short-distance signal communication to achieve stable data transmission and improve the system's ability to work in complex environments; through partitioning and separate matrix sampling, saturated data is obtained, combined with data fitting correction, the error caused by wave positioning is effectively overcome, and the reliability of signal acquisition is guaranteed; by classifying different types of UAVs, the data transmission efficiency is improved, and by building signal channels, the working efficiency and stability of the entire system are improved.

[0077] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope recorded in the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims described above.

Claims

1. A method for detecting fog using an unmanned aerial vehicle, characterized in that: The method uses the fixed-point sampling of the drone matrix distribution to detect fog. The drones use the same matrix as a detection unit. The same detection unit contains n operation units. Different operation units are used to replace the drones for fixed-point sampling. In the same detection unit, there are: a type of drone for signal transmission, a type of drone for data collection, and a type of drone for data processing. The fog detection method includes the following steps: S1. Space division and assignment: divide the working area according to the scope of the fog detection area, and assign the detection units and corresponding working areas; S2, UAV fleet distribution, the third type of UAV guides the first type of UAV to the assigned area, the first type of UAV and the first type of UAV in the adjacent working area identify and locate each other, and the second type of UAV is guided by the third type of UAV to the corresponding working point; S3. Construction and transmission of signal system. The data collected by the second-category UAV is transmitted to the corresponding third-category UAV in the working area. The third-category UAV integrates the data and sends it to the first-category UAV. The first-category UAV realizes data distribution and transmission by transmitting data with the adjacent first-category UAV. S4. Replacement of drones. Set corresponding safety thresholds of working power for the first type of drone, the second type of drone and the third type of drone. When the safety threshold is reached, a replacement signal is sent out and the signal is transmitted through the first type of drone. After the detection unit receives the replacement signal, it sends an assignment signal to the operating units of other batches, thereby guiding the corresponding types of drones to be supplemented and replaced.

2. A method for detecting fog using an unmanned aerial vehicle according to claim 1, characterized in that: In step S1, the working area is divided according to the size of the area to be detected. The working area is set as a square matrix. After the control center of the ground unit divides the detection area into working areas, the arrangement spacing of the drones in each working area is preset and sent to all the third-category drones in the corresponding detection unit.

3. A method for detecting fog using an unmanned aerial vehicle according to claim 2, characterized in that: Step S2 also includes the following steps: S21. After the three types of drones enter the work area, guide the first type of drone to disperse and pair and locate with the first type of drone in the adjacent work area. The first type of drone is used to form the edge of the square matrix. The first type of drone forms the communication network in their respective work areas and the communication routes between different work areas. During this period, the three types of drones inspect the positioning of the first type of drone in the corresponding work area; S22. When it is determined that the first type of drone has completed positioning, the third type of drone sends a first completion signal, which is transmitted to the corresponding detection unit via the communication route. The second type of drone enters the working area, and the third type of drone guides the second type of drone to their respective working points according to the preset arrangement spacing.

4. A method for detecting fog using an unmanned aerial vehicle according to claim 2, characterized in that: Step S3 further includes the following steps: S31. The drones of one type are distributed on the edges of the square matrix of their respective working areas. In the same working area, signal channels are established between the drones of one type to receive signals from the three types of drones and to send and receive signals transmitted from other working areas to the signal channels of other working areas; S32, Category II drones sample data through various sensors on board, and form a signal internal communication network through self-organizing networks. Category II drones aggregate the collected data to Category III drones through the internal communication network; S33, the third type of drone packages the data within a time unit and sends it to the signal channel, and transmits the data to the signal channels of other working areas through the signal channel; S34, after receiving the data, other signal channels feed back the receiving signal. At this time, the first type of drone in the signal channel sends the corresponding receiving signal to the third type of drone. After the third type of drone obtains the receiving signal, it stores the packaged data locally. S35, when other signal channels have reception anomalies, attempt to transmit data and report anomalies to signal channels on other paths; S36. A communication grid is formed by data transmission between signal channels in each work area, and the packaged data is transmitted along each signal channel back to the ground unit for data reception.

5. A method for detecting fog using an unmanned aerial vehicle according to claim 2, characterized in that: Step S4 also includes the following steps: S41. When the Class II UAV reaches the safety threshold, it sends a Class II replacement signal to the Class III UAV. The Class III UAV sends the Class II replacement signal to the corresponding operation unit through multiple signal channels. The corresponding operation unit sends the Class II UAV to the corresponding point along the path guided by the signal channel. S42, when the first type of UAV reaches the safety threshold, a first type of replacement signal is sent to the third type of UAV, and the third type of UAV sends the first type of replacement signal to the corresponding operation unit through multiple signal channels, and the corresponding operation unit sends the first type of UAV to the corresponding point along the path guided by the signal channel; S43. When the three types of drones reach the safety threshold, three types of replacement signals are sent to the signal channel. Multiple signal channels send the three types of replacement signals to the corresponding operation units. The corresponding operation units send the three types of drones to the corresponding points along the paths guided by the signal channels.

6. A method for detecting fog using an unmanned aerial vehicle according to claim 4, characterized in that: When the environment fluctuates greatly, the Class II UAV continues to move within a certain range, and intermittently establishes communication with the surrounding Class II UAVs during the movement.

7. A method for detecting fog using an unmanned aerial vehicle according to claim 6, characterized in that: When the second type of drone takes samples through continuous movement, it will be marked during data processing, so that the data samples using mobile sampling can be calculated and corrected through general data fitting.

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