A forest area hidden danger identification method and device based on a UAV

Abstract

The application provides a forest area hidden danger identification method and device based on a UAV. In the method, a UAV controller compares inspection images collected on a predetermined inspection path with pre-stored inspection area images in real time. In the case that there is an abnormal inspection record in the comparison result and the abnormal level of the abnormal inspection record is higher than a threshold level, the arrival time difference between a planned charging bin in a charging plan corresponding to the predetermined inspection path and a pending charging bin at a current position is determined. The abnormal inspection record is used to determine a hidden danger area from the inspection area image. The pending charging bin includes a charging bin corresponding to a flight distance that is less than a threshold distance in a flight time from the current position to the position of the charging bin. Based on the arrival time difference, a selected charging bin is determined, and the UAV flies to the selected charging bin to send the abnormal inspection record to a server through the selected charging bin.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hidden danger identification, and in particular to a forest area hidden danger identification method and device based on a UAV. BACKGROUND

[0002] Forest areas are mainly distributed in mountainous, humid or hilly areas with low population density. As an important element of human living environment, forest areas need to be protected to avoid human or natural damage. Due to the difficult accessibility of forest area roads, dangers such as wild aggressive animals and natural disasters are prone to occur, and patrol personnel need to have sufficient emergency escape ability to patrol the forest area.

[0003] At present, with the development of UAV technology, patrol personnel can operate a UAV to patrol the forest area from above. However, since the UAV is an electronic product, the UAV has a power limit, and the large forest area still needs patrol personnel to operate the UAV to patrol the forest area.

[0004] Therefore, there is an urgent need for a method that can reduce the cost of patrolling the forest area, ensure the safety of patrol personnel, and efficiently patrol the forest area in real time. SUMMARY

[0005] The present application provides a forest area hidden danger identification method and device based on a UAV, which solves the problem of high labor cost and inefficient patrol in the current forest area patrol.

[0006] In one aspect, the present application provides a forest area hidden danger identification method based on a UAV, which comprises:

[0007] The UAV controller compares the patrol image collected on the predetermined patrol path with the pre-stored patrol area image in real time;

[0008] In the case that the comparison result has an abnormal patrol record and the abnormal level of the abnormal patrol record is higher than the threshold level, the arrival time difference between the planned charging bin in the predetermined patrol path and the pending charging bin at the current position is determined; wherein the abnormal patrol record is used to determine the hidden danger area from the patrol area image; and the pending charging bin includes the charging bin corresponding to the flight distance less than the threshold time length from the current position to the charging bin position;

[0009] Based on the arrival time difference, a selected charging bin is determined, and the selected charging bin is flown to, so as to send the abnormal patrol record to the server through the selected charging bin.

[0010] In an implementation of the present application, the abnormal inspection record comprises abnormal position information of the abnormal inspection record, and the charging plan is arranged at a charging bin on the predetermined inspection path or an extension line of the predetermined inspection path.

[0011] In an implementation of the present application, the abnormal inspection record comprises a UAV identifier, a predetermined inspection path of the UAV, and a corresponding inspection time of the predetermined inspection path; the inside or outside of the selected charging bin is provided with a Bluetooth module corresponding to the charging bin; the method further comprises:

[0012] The UAV controller receives a wireless signal emitted by the Bluetooth module.

[0013] In an implementation of the present application, the selected charging bin comprises a planned charging UAV, a charging UAV, and a temporarily staying UAV; the UAV controller establishes a connection with the Bluetooth module through the wireless signal as a temporarily staying signal.

[0014] In an implementation of the present application, the UAV controller sends a UAV identifier field through a wireless connection at a frequency value matched with an abnormal level of the abnormal inspection record according to a preset frequency indication table, so that the selected charging bin sends inspection path update information to the server based on a frequency value of receiving the UAV identifier field within a predetermined time and a power of the charging UAV; the inspection path update information comprises inspection path update information of the charging UAV and inspection path update information of a UAV corresponding to the UAV identifier field.

[0015] In an implementation of the present application, in a case where there is no abnormal inspection record or there is an abnormal inspection record but an abnormal level of the abnormal inspection record is lower than a threshold level in the comparison result, the UAV controller flies according to the predetermined inspection path and executes a charging plan.

[0016] In an implementation of the present application, the UAV establishes a power supply connection with the selected charging bin; the UAV controller sends the abnormal inspection record through the power supply connection port; and

[0017] The UAV controller receives abnormal level update information from the selected charging bin or the server through the power supply connection port, so as to update an abnormal level reference table in the UAV controller; wherein the abnormal level update information comprises a reduced abnormal level or an increased abnormal level.

[0018] In an implementation of the present application, the UAV controller determines the abnormal position information through GPS or inertial positioning.

[0019] In an implementation form of the application, the flight distance comprises a flight distance over terrain.

[0020] In another aspect, the embodiments of the application further provide a forest area hidden danger identification device based on a UAV, the device comprising:

[0021] a processor;

[0022] and a memory having executable code stored thereon, when the executable code is executed, causing the processor to perform:

[0023] The UAV controller compares the inspection images collected on the predetermined inspection path with the pre-stored inspection area images in real time;

[0024] In the case that the comparison result has an abnormal inspection record, and the abnormal level of the abnormal inspection record is higher than a threshold level, determining the arrival time difference between a planned charging bin in a charging plan corresponding to the predetermined inspection path and a pending charging bin at a current position; wherein the abnormal inspection record is used to determine a hidden danger area from the inspection area images; the pending charging bin comprises a charging bin corresponding to a flight distance less than a threshold time length from the current position to the position of the charging bin;

[0025] Based on the arrival time difference, determining a selected charging bin and flying to the selected charging bin to send the abnormal inspection record to the server through the selected charging bin.

[0026] The application can use a UAV to self-inspect a forest area and identify hidden danger areas in the forest area, which can reduce the labor cost of inspecting the forest area, ensure the safety of inspection personnel, and efficiently and timely inspect the forest area. The current inspection of the forest area has high labor cost and is difficult to efficiently inspect. In addition, the UAV of the application does not set a hardware module for real-time communication of data with the server, which reduces the hardware cost and reduces the cost loss caused by the UAV being artificially knocked down and damaged or damaged by bad weather. The automatic forest area inspection is realized at low cost. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which are included to provide a further understanding of the application, constitute a part of the application and illustrate the illustrative embodiments of the application and their description serve to explain the application, and do not constitute an improper limitation on the application. In the drawings:

[0028] Figure 1 is a flowchart of a forest area hidden danger identification method based on a UAV in an embodiment of the application;

[0029] Figure 2 is a structural diagram of a system corresponding to the forest area hidden danger identification method based on a UAV in an embodiment of the application;

[0030] Figure 3 This is a schematic diagram of another system structure corresponding to a method for identifying potential hazards in forest areas based on unmanned aerial vehicles (UAVs) in this application.

[0031] Figure 4 This is a schematic diagram of the structure of a forest hazard identification device based on a drone, as described in this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] This application provides a method and device for identifying potential hazards in forest areas based on unmanned aerial vehicles (UAVs), addressing the problems of high manpower costs and inefficient forest inspections. The system for identifying potential hazards in forest areas based on UAVs includes a charging compartment, a UAV, a cloud data server, and a large front-end data display screen. The charging compartment is used to charge the UAV (including wired and wireless charging) and provide IoT data transmission capabilities, enabling the UAV to transmit data to a remote cloud data server. In practical use, the charging compartments are deployed in multiple locations within the forest area; the location settings can be configured by the user and adjusted during actual use. Furthermore, the storage unit corresponding to the UAV controller stores the location information of the charging compartments.

[0034] The various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0035] This application provides a method for identifying potential hazards in forest areas based on unmanned aerial vehicles (UAVs), such as... Figure 1 As shown, the method may include steps S101-S103:

[0036] S101, the drone controller compares the inspection images collected along the predetermined inspection path with the pre-stored inspection area images in real time.

[0037] The drone controller is electrically connected to the drone's storage unit, enabling it to retrieve images of the inspection area from the storage unit in real time and compare the inspection images collected along the predetermined inspection path with the inspection area images.

[0038] The comparison between the inspection image and the inspection area image can be performed using existing image comparison algorithms. For example, the pixels of the inspection image can be compared with the pixels of the inspection area image, and the cosine similarity between the two can be calculated. Based on whether the calculated cosine similarity exceeds a predetermined threshold, it is determined whether an anomaly exists in the inspection image, and the type of the anomaly is preliminarily identified, resulting in a comparison result. The anomaly type can be identified using a pre-set neural network model, such as identifying fires, flammable materials, or trapped people. This neural network model is trained based on pre-set anomaly type samples. If an anomaly is confirmed, the anomaly, its type, and the inspection image are stored to generate an anomaly inspection record.

[0039] S102, if the drone controller finds abnormal inspection records in the comparison results and the abnormality level of the abnormal inspection records is higher than the threshold level, it determines the arrival time difference between the planned charging compartment in the charging plan corresponding to the predetermined inspection path and the undetermined charging compartment at the current location.

[0040] Among them, the abnormal inspection record is used to identify potential hazard areas from the inspection area images. The pending charging compartments include charging compartments corresponding to flight distances from the current location to the location of the charging compartment where the flight time is less than a threshold duration.

[0041] The flight distances mentioned above include distances over terrain. For example, if the charging compartment is located on the ground, the distance the drone travels over obstacles to reach the charging station is the terrain-over-terrain distance.

[0042] In this embodiment of the application, the abnormal inspection record includes abnormal location information of the abnormal inspection record, and the planned charging compartment of the charging plan is located on the predetermined inspection path or the extension of the predetermined inspection path.

[0043] The abnormal inspection records generated by the UAV controller may include abnormal location information of the collected inspection images. The abnormal location information can be obtained through the positioning module in the UAV, such as the Global Positioning System (GPS) or inertial positioning.

[0044] In practical use, abnormal location information can provide a location reference for identifying potential hazard areas, facilitating timely identification. The determination of the hazard area can be done by the server, or by the server retrieving matching information from a pre-set database of abnormal inspection records. This matching information is used to match the abnormal inspection records to obtain the hazard area. A hazard area refers to the area where a hazard requiring manual maintenance is located. Not all abnormalities in the inspection records are hazards requiring manual maintenance. The server's matching information can filter out hazards requiring manual maintenance and determine their locations. Hazard types include fire, flammable materials, people trapped, animal attacks, natural disasters, and deforestation.

[0045] In one embodiment of this application, such as Figure 2 As shown, the drone controller 210 sends an abnormal inspection record 310 to the server 230 via the charging compartment 220. After receiving the abnormal inspection record 310, the server 230 queries the matching information 320 from the preset database 240, and then determines the hidden danger area 330 corresponding to the abnormal inspection record. The server 230 then sends the information to the user terminal 250 or the front-end data display screen 260. The user terminal can be a mobile phone, computer, PDA, or other device used by the inspection personnel; this application does not specifically limit this. The hidden danger area can be an area marked on a forest map, and the forest map and the hidden danger area markers are displayed on the user terminal and the front-end data display screen. The markers can also include the type of hidden danger.

[0046] In another embodiment of this application, such as Figure 3 As shown, the potential hazard area 330 can also be determined within the charging compartment 220. For example, if there are inspection personnel near the location of the charging compartment 220, the charging compartment 220 can directly send information to the inspection personnel's user terminal or a large front-end data display screen that they can view. Specifically, the drone controller 210 sends an abnormal inspection record 310 to the server 230 through the charging compartment 220. After receiving the abnormal inspection record 310, the server 230 queries the matching information 320 corresponding to the abnormal inspection record from the preset database 240. The server sends the matching information 320 to the charging compartment 220, and the charging compartment 220 determines the potential hazard area 330 and sends the potential hazard area 330 to the user terminal 250 or the large front-end data display screen 260.

[0047] The planned charging compartment of the charging plan is located on the scheduled inspection path or its extension. This means that the location of the planned charging compartment is in a position that the drone can reach and that will not affect the normal inspection of the drone, such as on the scheduled inspection path or its extension.

[0048] In this embodiment, the abnormal inspection record may further include a drone identifier, the drone's predetermined inspection path, and the corresponding inspection time for the predetermined inspection path. A Bluetooth module corresponding to the selected charging compartment is installed inside or outside the charging compartment. Furthermore, it includes: a drone controller receiving wireless signals transmitted by the Bluetooth module.

[0049] Since the planned inspection path and flight speed of the drone are known, the server also calculates the abnormal location information of the abnormal inspection images of the drone based on the planned inspection path and the corresponding inspection time, thereby better identifying the potential hazard area.

[0050] The charging compartment may have a Bluetooth module installed inside or outside to transmit Bluetooth signals. This module is used to establish a wireless Bluetooth connection with drones that enter a predetermined area within the charging compartment. The communication range of this wireless Bluetooth connection is not very large; the drone can only establish a connection with the Bluetooth module when it is within the predetermined area. Correspondingly, the drone has a Bluetooth signal receiving module inside.

[0051] In this embodiment, the selected charging bay includes drones that are planned to be charged, drones that are currently being charged, and drones that are temporarily stopping. The drone controller establishes a connection with the Bluetooth module via wireless signals as a temporary stopping signal.

[0052] In other words, when the drone controller establishes a wireless Bluetooth connection with the Bluetooth module, it doesn't need to send data. The charging compartment generates a temporary stop signal based on the Bluetooth connection status to determine if a drone needs to temporarily stop there. For example, if the charging compartment's Bluetooth module doesn't have a connected drone, after establishing a wireless Bluetooth connection with a nearby drone that will be temporarily stopped, the charging compartment can generate a temporary stop signal and determine if there is a drone already scheduled for charging or currently charging in the selected charging compartment (the one corresponding to the drone currently being temporarily stopped). It then decides whether to allow the drone currently being charged to leave or to plan the charging duration for the temporarily stopped drone. The departure of a drone currently being charged includes leaving for another charging compartment, conducting an inspection, or flying to a predetermined location and waiting for a predetermined time before returning to the charging compartment to continue charging.

[0053] In this embodiment, the drone controller, based on a preset frequency indication table and a frequency value matching the anomaly level of the abnormal inspection record, wirelessly transmits the drone identification field. This enables the selected charging station to send inspection path update information to the server based on the frequency value of the drone identification field received within a predetermined time and the battery level of the drone being charged. The inspection path update information includes the inspection path update information for the drone being charged and the inspection path update information for the drone corresponding to the drone identification field.

[0054] In other words, via wireless Bluetooth connection, the drone controller can send the drone identification field, such as the binary code of a preset drone number: 001, 002, 003, etc., to the processor in the charging case. The drone controller continuously sends the drone identification field to the charging case at a frequency value matched to the anomaly level. Based on the frequency value of the drone identification field received within a predetermined time, the charging case can determine the urgency level of the drone's stoppage event. For example, a frequency value of 10 jumps per second corresponds to a moderate urgency level, and a frequency value of 20 jumps per second corresponds to an urgent urgency level. Based on the frequency value matched to the drone's current charge level, the charging case selects whether to update the drone's inspection path.

[0055] In actual use, when a drone's scheduled inspection path is fixed, its inspection path may overlap during temporary stops for uploading abnormal inspection records. Furthermore, if a drone is already charging in the temporary charging bay, the urgency and battery level must be considered to determine whether the charging drone should relinquish its charging position. For example, if the urgency is moderate and the charging drone has 50% battery (enough to fly to the next charging bay on its scheduled inspection path), the charging drone can enter inspection mode. If the urgency is urgent, the charging drone must relinquish its charging position, and based on its current battery level (less than 30%, but enough to fly to the nearest charging bay), the server updates the charging drone's inspection path to the nearest charging bay and sends the information to the charging drone via the charging bay.

[0056] The charging compartment can also determine whether to update the scheduled inspection path of drone A, which has uploaded abnormal inspection records, based on the urgency level corresponding to the frequency value and the battery level of the drone being charged. For example, if the urgency level of drone A is "urgent," its scheduled inspection path can be updated. If there is a fire, drone A can be prevented from continuing to fly along the scheduled inspection path, and the scheduled inspection path can be updated to go to the area with the fire for focused inspection.

[0057] In this embodiment of the application, if the comparison results show no abnormal inspection records or there are abnormal inspection records, but the abnormality level of the abnormal inspection records is lower than the threshold level, the UAV controller flies according to the predetermined inspection path and executes the charging plan.

[0058] For example, if the anomaly is a plastic bag or a balloon, which is below the threshold level, the drone controller does not need to perform an emergency anomaly inspection record reporting operation. Instead, it can continue to fly along the predetermined inspection path and upload the anomaly inspection record when the charging plan is reached.

[0059] Furthermore, the levels of the abnormal inspection records in this application can include not only single events or phenomena, but also combinations thereof. For example, this application encodes single events and single abnormal phenomena separately, such as fire being coded as 10, logging as 8, animal activity as 5, and so on. Abnormal levels include 1-4 for general, 4-7 for moderate, and 7-10 for emergency. When statistically analyzing abnormal levels, the UAV controller may contain abnormal inspection records for multiple abnormal events or phenomena. When an abnormal inspection record is a single abnormal event or phenomenon, the UAV controller can directly determine the abnormal level to which that single abnormality belongs; when an abnormal inspection record contains multiple abnormal events or phenomena, the UAV controller can set the highest level corresponding to the multiple abnormal events or phenomena as the abnormal level of the abnormal inspection record.

[0060] S103, the drone controller determines the selected charging compartment based on the time difference of arrival, and flies to the selected charging compartment to send the abnormal inspection record to the server through the selected charging compartment.

[0061] In this embodiment, the drone establishes a power connection with the selected charging compartment. The drone controller sends anomaly inspection records through the power connection port. The drone controller also receives anomaly level update information from the selected charging compartment or server through the power connection port to update the anomaly level lookup table in the drone controller. The anomaly level update information includes lowering or raising the anomaly level.

[0062] The anomaly level update information can be understood as follows: in certain scenarios, such as cold or high temperature environments, the anomaly level of certain abnormal events needs to be lowered or raised. For example, flammable materials might have a "moderate" anomaly level in cold conditions and an "emergency" anomaly level in high temperatures. This example is only for the purpose of understanding how to update the anomaly level in a specific scenario and does not limit this application to the technical solutions described above.

[0063] When an environmental sensor, such as a temperature sensor or a humidity sensor, is present in the selected charging compartment, the anomaly level update information can be generated by either the selected charging compartment or the server. For example, the anomaly level update information can be generated by the selected charging compartment.

[0064] This application, through the aforementioned solution, enables the use of drones for autonomous forest inspections and the identification of potential hazard areas. This reduces manpower costs for forest inspections, ensures the safety of inspection personnel, and allows for real-time and efficient forest inspections. It solves the current problems of high manpower costs and inefficient forest inspections.

[0065] Furthermore, the drone in this application does not have a hardware module for real-time remote data communication with a server, reducing hardware costs and minimizing losses from drone damage caused by being shot down or damaged by severe weather. This enables low-cost automated forest area inspection.

[0066] Figure 4 A forest hazard identification device based on unmanned aerial vehicles (UAVs) is provided for embodiments of this application. The device includes:

[0067] A processor and memory, on which executable code is stored, cause the processor to execute when the executable code is executed:

[0068] The drone controller compares the inspection images collected along the predetermined inspection path with the pre-stored inspection area images in real time.

[0069] If the comparison results show abnormal inspection records, and the abnormality level of the abnormal inspection records is higher than the threshold level, the arrival time difference between the planned charging compartment in the charging plan corresponding to the predetermined inspection path and the pending charging compartment at the current location is determined; wherein, the abnormal inspection records are used to identify the potential hazard area from the inspection area image; the pending charging compartment includes the charging compartment corresponding to the flight distance from the current location to the location of the charging compartment is less than the threshold time.

[0070] Based on the arrival time difference, a selected charging bay is determined, and the aircraft flies to the selected charging bay to send anomaly inspection records to the server through the selected charging bay.

[0071] In one embodiment, the abnormal inspection record includes abnormal location information of the abnormal inspection record, and the planned charging compartment of the charging plan is located on the predetermined inspection path or an extension of the predetermined inspection path.

[0072] In one embodiment, the abnormal inspection record includes a drone identifier, a predetermined inspection path for the drone, and an inspection time corresponding to the predetermined inspection path; the selected charging compartment is equipped with a Bluetooth module corresponding to the charging compartment, either inside or outside; the method further includes:

[0073] The drone controller receives the wireless signals transmitted by the Bluetooth module.

[0074] In one embodiment, the selected charging compartment includes a drone that is scheduled to be charged, a drone that is currently being charged, and a drone that is temporarily stopping; the drone controller establishes a connection with the Bluetooth module via the wireless signal as a temporary stopping signal.

[0075] In one embodiment, the drone controller, according to a preset frequency indication table, transmits a drone identification field via a wireless connection at a frequency value matching the anomaly level of the abnormal inspection record. This enables the selected charging compartment to send inspection path update information to the server based on the frequency value of the drone identification field received within a predetermined time and the battery level of the drone being charged. The inspection path update information includes the inspection path update information of the drone being charged and the inspection path update information of the drone corresponding to the drone identification field.

[0076] In one embodiment, if the comparison results show no abnormal inspection records or if there are abnormal inspection records but the abnormality level of the abnormal inspection records is lower than the threshold level, the drone controller flies according to the predetermined inspection path and executes the charging plan.

[0077] In one embodiment, the drone establishes a power connection with the selected charging compartment; the drone controller sends the abnormal inspection record through the power connection port; and

[0078] The drone controller receives anomaly level update information from the selected charging compartment or the server through the power supply connection port in order to update the anomaly level lookup table in the drone controller; wherein, the anomaly level update information includes lowering the anomaly level and raising the anomaly level.

[0079] In one embodiment, the drone controller determines the abnormal location information via GPS or inertial positioning.

[0080] In one embodiment, the flight distance includes the distance over terrain.

[0081] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0082] The devices and methods provided in this application are one-to-one correspondences. Therefore, the devices also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices will not be repeated here.

[0083] It should also be noted that 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 limitation, 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.

[0084] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for identifying potential hazards in forest areas based on unmanned aerial vehicles (UAVs), characterized in that, The method includes: The drone controller compares the inspection images collected along the predetermined inspection path with the pre-stored inspection area images in real time. If an abnormal inspection record is found in the comparison results, and the abnormality level of the abnormal inspection record is higher than the threshold level, the arrival time difference between the planned charging compartment and the pending charging compartment at the current location in the charging plan corresponding to the predetermined inspection path is determined; wherein, the abnormal inspection record is used to determine the potential hazard area from the inspection area image; the pending charging compartment includes the charging compartment corresponding to the flight distance from the current location to the location of the charging compartment where the flight time is less than the threshold time. Based on the arrival time difference, a selected charging bay is determined, and the aircraft flies to the selected charging bay to send the abnormal inspection record to the server through the selected charging bay; The selected charging compartment includes drones that are scheduled to be charged, drones that are currently being charged, and drones that are temporarily stopping; the drone controller establishes a connection with the Bluetooth module via wireless signal as a temporary stopping signal; The drone controller, according to a preset frequency indication table, sends a drone identification field via wireless connection at a frequency value matching the anomaly level of the abnormal inspection record. This enables the selected charging compartment to send inspection path update information to the server based on the frequency value of the drone identification field received within a predetermined time and the battery level of the drone being charged. The inspection path update information includes the inspection path update information of the drone being charged and the inspection path update information of the drone corresponding to the drone identification field.

2. The method according to claim 1, characterized in that, The abnormal inspection record includes abnormal location information of the abnormal inspection record, and the planned charging compartment of the charging plan is located on the predetermined inspection path or the extension of the predetermined inspection path.

3. The method according to claim 1, characterized in that, The abnormal inspection record includes the drone identifier, the drone's planned inspection path, and the inspection time corresponding to the planned inspection path; The selected charging compartment is equipped with a Bluetooth module corresponding to that charging compartment, either inside or outside; the method further includes: The drone controller receives the wireless signals transmitted by the Bluetooth module.

4. The method according to claim 1, characterized in that, The method further includes: If the comparison results show no abnormal inspection records or if there are abnormal inspection records but the abnormality level of the abnormal inspection records is lower than the threshold level, the UAV controller will fly according to the predetermined inspection path and execute the charging plan.

5. The method according to claim 1, characterized in that, The drone establishes a power connection with the selected charging compartment; the drone controller sends the abnormal inspection record through the power connection port. as well as The drone controller receives anomaly level update information from the selected charging compartment or the server through the power supply connection port in order to update the anomaly level lookup table in the drone controller; wherein, the anomaly level update information includes lowering the anomaly level and raising the anomaly level.

6. The method according to claim 2, characterized in that, The drone controller determines the abnormal location information using GPS or inertial positioning.

7. The method according to claim 1, characterized in that, The flight distance includes the distance over terrain.

8. A forest hazard identification device based on unmanned aerial vehicles (UAVs), characterized in that, The device includes: processor; The processor includes a memory storing executable code, which, when executed, causes the processor to perform a method for identifying potential hazards in forest areas based on unmanned aerial vehicles as described in any one of claims 1-7.

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