A control method and system for unmanned aerial vehicle (UAV) to inspect and attack ground stations

Abstract

The present application provides a control method and system for a drone to check and hit a ground station. Among them, the present application first collects real-time image data for terrain contour comparison to identify disaster risk areas, then generates a task marker file and compresses it into an encrypted data packet with a geotag, then sends the encrypted data packet to the ground station, receives rescue priority instructions at the same time, adjusts the drone's hovering height and patrol speed, and sends a device ready status code to the ground station, then verifies the integrity of the device ready status code to generate an action authorization identifier, then controls the drone to fly to a safe coordinate, and finally, when the positioning signal transmitter receives a synchronization verification signal, controls the drone to perform the task, and automatically deletes the data after the task is completed; the technical solution provided by the present application enables the drone to quickly respond to multiple target areas, accurately execute, and manage data in a closed loop in disaster rescue scenarios.

Description

Technical Field

[0001] The present application relates to the field of ground station control technology, and in particular to a control method and system for a drone to inspect and attack a ground station. Background Art

[0002] In civilian disaster rescue scenarios, for tasks such as geological disaster monitoring, fire warning, and personnel search and rescue, drones need to be able to quickly identify multiple types of disaster risk areas in complex terrain, transmit real-time data, and respond to dynamic tasks.

[0003] The current mainstream solution is a drone control method based on fixed-route inspections and single-band communication. Its technical features include: Area-wide scanning along a pre-set fixed route, using a single-band communication module to transmit coordinate data of the disaster area. Ground station operators manually adjust the drone's altitude and speed based on this data to complete a secondary inspection. This method utilizes an offline geographic database comparison mechanism and periodic data packet transmission to achieve preliminary marking of the disaster area.

[0004] However, it still has the following shortcomings. First, the fixed route and manual adjustment mechanism make it impossible for the drone to dynamically respond to the priority changes of multiple disaster areas, resulting in scanning blind spots and the inability to simultaneously handle the collaborative scanning needs of at least two disaster areas; second, single-band communication is susceptible to interference in complex electromagnetic environments, resulting in interruption or delay in the transmission of encrypted data packets, affecting the real-time issuance of rescue instructions from the ground station; third, offline database comparison and manual parameter adjustment cause delayed response to disaster identification and task execution, and there is a lack of an automatic closed-loop management mechanism for task marking files, which poses a risk of data leakage or residual. Summary of the Invention

[0005] The present application provides a control method and system for drone inspection of ground stations, which is used to solve the problems of poor timeliness of disaster response, low multi-target scanning coverage, and insufficient reliability of data closed-loop management in the existing technology.

[0006] In a first aspect, the present application provides a control method for a drone to inspect and attack a ground station, comprising:

[0007] Using a multispectral sensor carried by a drone to collect real-time image data of the ground area along the planned route, the real-time image data is compared with a pre-stored geographic feature database for terrain contours to identify disaster risk areas;

[0008] During the flight of the UAV, a mission marking file containing the coordinates of the disaster risk area is synchronously generated, and the mission marking file is compressed into an encrypted data packet with a geotag;

[0009] Sending the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and receiving a rescue priority instruction returned by the ground station;

[0010] Dynamically adjust the drone's hovering altitude and patrol speed according to the rescue priority instructions, so that the drone's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement;

[0011] Verify the integrity of the device ready status code, and when the verification is successful, generate an action authorization identifier;

[0012] When the parsed action authorization identifier matches the scan completion time, the drone's onboard positioning signal transmitter is controlled to generate an evacuation path based on the real-time location data, and the drone is controlled to fly in the reverse direction along the evacuation path to a safe coordinate;

[0013] During the reverse flight, the synchronization verification signal sent by the ground station is continuously detected. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the drone is controlled to perform a preset rescue mission on the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed.

[0014] Optionally, a multispectral sensor carried by a drone collects real-time image data of the ground area along the planned route, compares the real-time image data with a pre-stored geographic feature database for terrain contours, and identifies disaster risk areas, including:

[0015] The multispectral sensor collects real-time image data of a ground area using three band combinations, wherein the band combinations include a first band group for distinguishing vegetation coverage, a second band group for detecting changes in surface temperature, and a third band group for identifying the material of artificial structures;

[0016] Dividing the real-time image data into a preset number of geographic blocks, where the size of each geographic block is dynamically adjusted based on the current flight altitude of the drone and the sensor field of view, so that the distribution of surface cover types within each geographic block is consistent;

[0017] Performing resolution adaptation on the real-time image data of each geographic block, adjusting the pixel spacing of the real-time image data according to the density of altitude changes in the corresponding area in the geographic feature database, and generating real-time image data after resolution adaptation;

[0018] Extracting a set of terrain contour lines corresponding to the current geographic block from the geographic feature database, comparing the real-time image data after resolution adaptation with the set of terrain contour lines layer by layer, and calculating the spatial correlation between the pixel point feature and the terrain contour based on the band combination data corresponding to each pixel point in the real-time image data and the slope change direction between adjacent altitude mutation points in the set of terrain contour lines;

[0019] Filtering out a set of pixels whose spatial correlation is lower than a dynamic threshold as a candidate area for abnormal heat sources;

[0020] Detecting fracture areas in the slope change direction between consecutive altitude mutation points in the terrain contour line set, and marking them as candidate structural deformation areas when the extension direction of the fracture area coincides with the offset direction of the material boundary line of the artificial structure at the same location in the real-time image data;

[0021] The spatially overlapping portion of the abnormal heat source candidate region and the structural deformation candidate region and the independent region within a preset distance between the abnormal heat source candidate region and the structural deformation candidate region are merged into a disaster risk region.

[0022] Optionally, during the flight of the UAV, a mission marking file containing the coordinates of the disaster risk area is synchronously generated, and the mission marking file is compressed into an encrypted data packet with a geo-tag, including:

[0023] Converting the absolute geographic coordinates of the disaster risk area into relative coordinates relative to the boundary of the current geographic block, and generating a task tag file containing a disaster type identifier based on the relative coordinates;

[0024] Adding the geographic block number and the current flight altitude data of the UAV to the mission tag file to generate an original mission file with a geographic tag;

[0025] The original task file is divided into multiple independent data blocks, each data block corresponds to a set of coordinates of a disaster risk area within a geographical block, and the compression rate is dynamically adjusted according to the distribution density of the coordinate points in the data block to obtain a processed data block;

[0026] Perform redundancy elimination on each processed data block, delete coordinate points that overlap with adjacent geographic blocks, and generate a hash chain containing the correspondence between data block numbers and compression ratios;

[0027] Arrange the processed data blocks in the order of geographic block numbers, and insert the corresponding hash chain into the header of each processed data block to generate a compressed task file;

[0028] Performing layered encryption on the compressed task file using a built-in encryption module of the drone to obtain an encrypted compressed task file, wherein the first layer of encryption generates a dynamic key based on the geographic block number, and the second layer of encryption combines the hash chain node and the drone's current timestamp to generate a verification identifier;

[0029] Encapsulate the encrypted compressed task file into an encrypted data packet with a timestamp and a sequence of geographic block numbers.

[0030] Optionally, dynamically adjusting the hovering height and patrol speed of the drone according to the rescue priority instruction so that the drone's onboard monitoring equipment can perform multi-angle coverage scanning of at least two disaster risk areas, and sending a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement, including:

[0031] parsing the weight value of each disaster risk area in the rescue priority instruction;

[0032] Calculating a hovering height adjustment gradient for the UAV between the disaster risk areas according to a distribution ratio of the weight values;

[0033] The scanning range of each disaster risk area is divided into multiple angular coverage sectors, the center line of each angular coverage sector forms a preset angle with the current flight direction of the drone, and the scanning dwell time of each angular coverage sector is dynamically allocated according to the gradient of the hovering height adjustment;

[0034] During the flight of the UAV, the number of scans of each angular coverage sector is monitored in real time. When the number of scans in the same angular coverage sector reaches a preset coverage threshold and the scanning time interval between adjacent angular coverage sectors is less than a preset tolerance, the angular coverage sector is marked as a valid coverage area.

[0035] Counting the total proportion of effective coverage areas in all disaster risk areas, and when the total proportion exceeds a preset coverage threshold, generating raw state data including the scanning completion timestamps of all current effective coverage areas and the corresponding hovering height adjustment gradients;

[0036] Performing time series encoding on the original state data, binding the scanning completion timestamp with the real-time position coordinates of the drone, and generating an intermediate state file with a time series identifier;

[0037] Sending the intermediate state file to a ground station for verification through a preset encrypted channel of the multi-band communication module, and receiving a verification sequence result returned by the ground station after the sending is completed, wherein the verification sequence result includes an encrypted check code that matches the timing identifier;

[0038] The encrypted verification code is verified, and when the encrypted verification code passes the integrity verification, the intermediate state file is converted into a device ready state code, where the device ready state code includes a mapping relationship between the scan completion timestamp and the position of the effective coverage area.

[0039] Optionally, verify the integrity of the device ready status code, and when the verification is successful, generate an action authorization identifier, including:

[0040] Performing layered decryption on the device readiness status code using a decryption key pre-stored on the drone, extracting the original timestamp and effective coverage area location mapping table of the device readiness status code from the decrypted data, and calculating the deviation from the reference time window of the ground station, wherein the reference time window is determined based on a preset tolerance range before and after the scan completion timestamp;

[0041] When the deviation is less than a preset threshold and the number of missing areas in the effective coverage area location mapping table does not exceed a preset ratio, it is determined that the integrity verification of the device ready status code has passed;

[0042] Generate a dynamic authorization factor based on the scan completion timestamp and the effective coverage area location mapping table, perform a superposition operation on the dynamic authorization factor and the weight value distribution ratio in the rescue priority instruction, and generate an intermediate authorization file including a timestamp binding code and an area coverage identifier;

[0043] The intermediate authorization file is double-encrypted, and the double-encrypted data is encapsulated as an action authorization identifier with a timestamp sequence, and is transmitted back to the drone through the response channel of the multi-band communication module, wherein the action authorization identifier includes an encrypted timestamp check segment that matches the scan completion timestamp.

[0044] Optionally, when the parsed action authorization identifier matches the scan completion time, controlling the drone's onboard positioning signal transmitter, generating an evacuation path based on the real-time position data, and controlling the drone to fly in the reverse direction along the evacuation path to a safe coordinate, including:

[0045] Extracting the encrypted timestamp check segment from the action authorization identifier, performing cyclic shift decryption in combination with the millisecond-level precision value of the scan completion timestamp, and obtaining the original time series including the check code;

[0046] Comparing the deviation between the original time series and the current time system of the drone, and activating the multi-band positioning function of the onboard positioning signal transmitter when the deviation value is less than a preset time window threshold;

[0047] Generate a set of key points for the evacuation route based on the elevation change rate and horizontal displacement rate in the real-time position data of the UAV;

[0048] performing redundancy elimination processing on the set of key points on the path, deleting turning points whose distance from the boundary of a preset safety restricted area is less than a warning threshold, and recalculating the reverse flight trajectory based on the distribution density of the remaining turning points;

[0049] The reverse flight trajectory is divided into a preset number of flight segments, and the flight segments are sorted according to the turning points in the path key point set to generate a flight segment sequence, and the UAV is controlled to perform reverse flight according to the flight segment sequence.

[0050] Optionally, during the reverse flight, the synchronization verification signal sent by the ground station is continuously detected. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the UAV is controlled to perform a preset rescue mission on the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed, including:

[0051] During the reverse flight, the positioning signal transmitter continuously receives a synchronization verification signal broadcast by a ground station, wherein the synchronization verification signal includes an encrypted set of path key points and corresponding verification timestamps;

[0052] Perform frame parsing on the received synchronization verification signal, extract the path key point set in each frame signal, and match the path key point set with the path key point set of the UAV's current evacuation path point by point;

[0053] When the number of successfully matched path key points exceeds a preset ratio and the deviation between the verification timestamp and the scan completion timestamp in the device ready status code is less than a preset threshold, it is determined that the synchronization verification signal matches the evacuation path;

[0054] Controlling the rescue mission execution module carried by the UAV to read the coordinate set of the target area from the mission marking file, and dynamically adjust the execution order of the rescue mission according to the distribution density of the key points of the path in the synchronization verification signal;

[0055] During the execution of the rescue mission, the on-site environmental data of the target area is collected in real time, and the on-site environmental data is compared with the parameters of the disaster risk area in the mission marking file for abnormal fluctuations. When the amplitude of the abnormal fluctuation exceeds the preset tolerance, the rescue mission execution is suspended and an abnormal interrupt request is sent to the ground station;

[0056] After receiving the mission continuation instruction returned by the ground station, updating the remaining flight segments of the evacuation path according to the latest path key point set in the synchronization verification signal, and restarting the rescue mission execution module;

[0057] When the rescue mission execution module triggers a completion status flag, it traverses all coordinate entries associated with the target area in the mission mark file, filters out coordinate entries that have been covered by the path key point set in the synchronization verification signal, and generates a list to be deleted;

[0058] Perform data relevance verification on the coordinate entries in the to-be-deleted list, and permanently remove the coordinate entry from the task marking file when the disaster risk area corresponding to the coordinate entry does not generate any new abnormal data during the execution of the rescue mission.

[0059] In a second aspect, the present application provides a control system for a drone to inspect and attack a ground station, comprising:

[0060] An acquisition module, configured to collect real-time image data of the ground area along the planned route using a multispectral sensor carried by the drone, compare the real-time image data with a pre-stored geographic feature database for terrain contours, and identify disaster risk areas;

[0061] A compression module is used to synchronously generate a mission marking file containing the coordinates of the disaster risk area during the flight of the UAV, and compress the mission marking file into an encrypted data packet with a geo-tag;

[0062] a sending module, configured to send the encrypted data packet to the ground station in an adaptive frequency hopping mode via a multi-band communication module, and simultaneously receive a rescue priority instruction returned by the ground station;

[0063] A scanning module is used to dynamically adjust the hovering height and patrol speed of the drone according to the rescue priority instruction, so that the drone's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement;

[0064] A verification module, configured to verify the integrity of the device ready status code and generate an action authorization identifier when the verification is successful;

[0065] a first control module, configured to control an onboard positioning signal transmitter of the UAV when the action authorization identifier is parsed to match the scanning completion time, generate an evacuation path based on the real-time position data, and control the UAV to fly in the reverse direction along the evacuation path to a safe coordinate;

[0066] The second control module is used to continuously detect the synchronization verification signal sent by the ground station during the reverse flight. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the second control module controls the UAV to perform a preset rescue mission on the target area, and automatically deletes the data associated with the target area in the mission marking file after the mission is completed.

[0067] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a control method for a drone to inspect a ground station as described in the first aspect above.

[0068] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a control method for a drone to inspect a ground station as described in the first aspect.

[0069] The embodiment of the present application uses a multispectral sensor to collect ground images in real time and dynamically compares them with a geographic feature database, thereby achieving accurate identification and dynamic marking of disaster risk areas; adopts encrypted data packets with geo-tags and an adaptive frequency hopping communication mode to ensure the secure transmission of disaster data and the efficiency of command interaction in complex electromagnetic environments; dynamically adjusts the flight parameters of the drone based on rescue priority instructions to ensure multi-angle coverage scanning of at least two disaster areas, significantly improving the scanning coverage rate and response speed; through the reverse flight of the evacuation path and the synchronous verification signal trigger mechanism, the drone can seamlessly switch between emergency mission execution and safe evacuation, and automatically delete sensitive data, forming a full-process closed loop from disaster identification, mission execution to data management, which solves the technical defects of traditional solutions such as response lag, insufficient multi-target coordination capabilities and data residual risks.

[0070] Furthermore, by analyzing the weight value distribution in the rescue priority instruction, the hovering height gradient and the scanning sector dwell time are dynamically calculated, so that the UAV can adaptively allocate scanning resources according to the degree of disaster threat; by real-time monitoring of the number of scans and time intervals of the angle coverage sector, the effective coverage area is accurately determined and the coverage rate is calculated to ensure the integrity and efficiency of multi-target area scanning; the scanning completion timestamp and the real-time position coordinates are bound to generate an intermediate state file with a timing identifier, and the integrity of the checksum is verified through an encrypted channel to form a trusted link from data acquisition, state generation to instruction feedback; finally, the verified intermediate state file is converted into a device ready state code to realize dynamic matching of the scanning state and the ground station instruction, which effectively solves the problems of scanning blind spots, instruction verification lag and low efficiency of multi-target collaboration caused by fixed flight parameters in the existing technology.

[0071] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0073] Figure 1 A flow chart showing a control method for a drone to inspect and attack a ground station provided by the present application is shown;

[0074] Figure 2 A schematic diagram of the structure of a control system for a drone ground station provided by the present application is shown;

[0075] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0076] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0077] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0078] In civilian disaster relief scenarios, existing drone control methods rely on fixed-route inspections and single-band communication mechanisms, which have the following key bottlenecks: First, fixed routes and manual adjustment modes cannot allocate scanning resources according to the dynamic changes in disaster risks, resulting in inefficient collaborative scanning of multiple target areas and the existence of blind spots; second, single-band communication has insufficient anti-interference capabilities in complex electromagnetic environments, and the encrypted data packet transmission is unstable, seriously affecting the real-time performance of ground station instructions; third, offline geographic database comparison and manual task management mode lead to a significant lag in the response cycle from disaster identification to task execution, and task marking files rely on manual operations, resulting in data management loopholes.

[0079] In response to the above problems, this application proposes a control method for drones to inspect and attack ground stations, and realizes full-process optimization through the technical architecture of dynamic priority response-multimodal encrypted transmission-adaptive adjustment of flight parameters-data closed-loop management. Specifically, based on the contour comparison of real-time multispectral images and geographic feature libraries, disaster areas are dynamically identified to generate encrypted data packets with geo-tags; the reliability and anti-interference capability of command transmission are improved through multi-band adaptive frequency hopping communication; the efficient collaborative scanning of multiple disaster areas is achieved by combining rescue priority command parsing and hovering height gradient allocation mechanism; relying on the reverse generation of evacuation paths and synchronous verification signal triggering logic, it is ensured that drones accurately perform rescue tasks and automatically clear related data during safe evacuation. This solution systematically solves the problems of scanning blind spots, command delays and data residues in the existing technology through dynamic priority matching, anti-interference transmission links and automated data closed-loop mechanisms, significantly improving the efficiency of disaster response and the safety of task execution.

[0080] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0081] Figure 1 The present invention provides a flowchart of a method for controlling a drone to check and hit a ground station. Figure 1 As shown, the method includes:

[0082] Step 101: using a multispectral sensor carried by a drone to collect real-time image data of the ground area along a planned route, comparing the real-time image data with a pre-stored geographic feature database for terrain contours to identify disaster risk areas;

[0083] This step involves parameters and features such as multispectral sensors, planned routes, a geographic feature database, terrain contour comparison, and disaster risk areas. A multispectral sensor is a device mounted on a drone that simultaneously collects data from multiple specific band combinations. The first band group is used to enhance the identification of differences in vegetation cover, the second band group strengthens the detection of surface temperature changes, and the third band group distinguishes the material characteristics of man-made structures. The planned route is the trajectory of the drone's flight along a preset path, with the spacing between waypoints dynamically adjusted based on flight altitude and sensor coverage. The geographic feature database is a three-dimensional spatial dataset pre-stored with terrain elevation, land cover type, and geological stability indicators for the target area. Terrain contour comparison refers to the process of spatially matching the surface relief characteristics of real-time imagery with elevation breakpoints and slope transition lines in the database. Disaster risk areas are geographic areas that simultaneously exhibit thermal radiation anomalies, structural deformation characteristics, and match historical disaster patterns.

[0084] In this embodiment, a multispectral sensor first collects ground imagery data using preset band combinations. The first band group analyzes vegetation density differences by calculating a vegetation index, the second band group inverts surface temperature distribution based on radiometric calibration, and the third band group uses a spectral matching algorithm to identify structural materials. Next, the real-time imagery is dynamically segmented into geographic blocks. The block size is dynamically calculated based on the drone's flight altitude and the sensor's field of view to ensure a consistent distribution of surface types within each block. Resolution adaptation is then performed on each block, adjusting the image pixel density based on the elevation variation density of the corresponding area in the database to ensure that the spatial accuracy of the real-time imagery matches that of the terrain data. During the terrain contour comparison phase, 3D grid node data for the corresponding block is extracted from the database. Spatial correlations are calculated between the adapted image pixels and the grid nodes. Pixels whose band characteristics do not match the terrain slope direction are selected as candidate anomalous heat sources. Spatially overlapping areas of continuous slope breaks in the terrain grid and image material boundaries are detected as candidate structural deformation areas. Finally, the spatially overlapping portions of the two candidate areas and adjacent areas are merged to output a coordinate set of disaster risk areas.

[0085] For example, in a forest fire monitoring scenario, the drone flies along a planned route, and the multispectral sensor simultaneously collects vegetation, temperature, and structure data. The first band group detects a sudden drop in the vegetation index in a certain area, the second band group shows that the surface temperature is significantly higher than the surrounding area, and the third band group identifies the reflective characteristics of metal materials. The image is dynamically segmented into uniform blocks, and after the resolution is increased based on the elevation density of the database, surface depressions and slope mutations are clearly captured. Terrain contour comparison found that the spatial correlation between high-temperature pixels and steep slope areas is lower than the threshold, and there is a continuous slope fault zone that coincides with the material boundary line. The system determines that the area is a complex disaster risk and outputs the center coordinates to the task marker file.

[0086] Step 102: synchronously generate a mission marking file containing the coordinates of the disaster risk area during the flight of the UAV, and compress the mission marking file into an encrypted data packet with a geographic tag;

[0087] In this step, the task marking file is a structured data file that records the coordinates and type identification of the disaster risk area, which includes the relative coordinate conversion results based on the geographic block boundary, the unique number of the geographic block and the real-time flight altitude parameters of the drone; the geographic tag refers to the spatial location identification information embedded in the data packet, which is generated by the dynamic combination of the unique number of the geographic block and the flight altitude, and is used to establish the spatial mapping relationship between the data packet and the collection location; the encrypted data packet is a compressed file processed using a layered dynamic encryption mechanism, which includes a dynamic key generated based on the geographic block number and a timestamp verification identifier to ensure the anti-interference and integrity of data transmission.

[0088] In this embodiment, the absolute geographic coordinates of the disaster risk area are first converted to relative coordinates relative to the current geographic block boundary. During this conversion, an offset is calculated based on the preset block side length and the center coordinates. Next, a disaster type identifier is assigned to each relative coordinate, and a geotagged original mission file is generated by combining the unique geographic block number and the flight altitude. The original mission file is then segmented into independent data blocks by geographic block. The compression ratio is dynamically adjusted based on the density of coordinate points within the data block—a high compression ratio strategy is used for densely populated areas, and a low compression ratio strategy is used for sparse areas. Redundancy elimination is performed on each data block, removing points that overlap with coordinates of adjacent blocks. A hash chain is generated that records the correlation between data block number and compression ratio. The processed data blocks are arranged in block number order, and a hash chain node is inserted into the header of each data block. Finally, a layered encryption process is performed: the data block is first encrypted using a dynamic key generated by the geographic block number. A second layer combines the hash chain node with the current timestamp to generate a verification identifier, which is then re-encrypted, forming an encrypted data packet with a timestamp sequence.

[0089] For example, in a forest fire monitoring scenario, the disaster risk area identified in step 101 belongs to a geographic block numbered G-23. The system converts the absolute coordinates of this area into relative coordinates relative to the boundary of the G-23 block and marks it as a composite disaster type. The original mission file is tagged with the G-23 number and flight altitude and then segmented into data blocks. Due to the dense distribution of coordinate points in this block, a high compression ratio strategy is used for compression. Redundancy is eliminated by removing any overlapping points with adjacent blocks, and a hash chain node is generated. The data block is encrypted with a dynamic key, and a verification identifier is generated based on the current timestamp before being encapsulated into an encrypted data packet.

[0090] Step 103, sending the encrypted data packet to the ground station in an adaptive frequency hopping mode through the multi-band communication module, and receiving the rescue priority instruction returned by the ground station;

[0091] In this step, the multi-band communication module refers to a hardware unit that supports dynamic switching of multiple communication frequency bands, which is used to maintain data transmission stability in complex electromagnetic environments; the adaptive frequency hopping mode refers to a technology that dynamically selects the optimal communication frequency band based on the real-time channel interference detection results, and realizes anti-interference transmission through a preset frequency band switching sequence and interference avoidance strategy; the encrypted data packet is the layered encrypted data with geo-tags and timestamps generated in step 102; the rescue priority instruction refers to the action instruction generated by the ground station based on the disaster type, regional threat level and resource scheduling strategy, which includes the target area scanning order, flight parameter adjustment threshold and mission execution authorization information.

[0092] In this embodiment, the channel interference intensity of the current airspace is first detected by the spectrum scanning unit of the multi-band communication module, and a dynamic frequency hopping sequence is generated - the frequency band with interference intensity lower than the threshold is preferentially selected to form the transmission channel. Then, the encrypted data packet is divided into multiple data frames according to the preset frame length, and a geographic tag and a timestamp are added to the header of each data frame. When sending, it switches cyclically between multiple frequency bands according to the frequency hopping sequence, and updates the frequency hopping sequence based on the confirmation signal of the ground station after each frame is sent. The command receiving channel is opened synchronously, and the response frequency band specified by the ground station is monitored during the data transmission interval. After receiving the rescue priority command, the command content is parsed: the target area scanning sequence code, the hovering height adjustment gradient and the speed threshold parameters are extracted, and a spatial matching check is performed with the real-time position data of the drone to ensure the executability of the command.

[0093] For example, in a forest fire monitoring scenario, the encrypted data packet generated in step 102 carries the disaster coordinates for block G-23. The multi-band communication module detects strong interference in some frequency bands in the current airspace, generates a frequency hopping sequence to eliminate the interfering frequency bands, and then divides the data packet into multiple frames and transmits them cyclically through the clean frequency band. The ground station receives the data packet and parses it, generating priority instructions based on the fire spread trend and rescue resource distribution. It designates block G-23 as a primary scanning target and issues the instructions through a low-interference frequency band. The drone monitors the response frequency band between transmissions. Upon receiving the instruction, it verifies its spatial compatibility with block G-23 and, after confirming the legitimacy of the instruction, stores it in the flight control unit.

[0094] Step 104: Dynamically adjust the drone's hovering altitude and patrol speed based on the rescue priority instruction, so that the drone's onboard monitoring equipment performs a multi-angle coverage scan of at least two disaster risk areas, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement;

[0095] In this step, the hovering height adjustment gradient refers to the dynamic change parameter of the flight height calculated according to the weight value distribution of different disaster areas in the rescue priority instruction; the inspection speed refers to the adaptive adjustment threshold of the horizontal movement speed of the UAV during the scanning process, which is inversely correlated with the hovering height to maintain the stability of the sensor; multi-angle coverage scanning refers to the technology of dividing the scanning sectors into multiple sectors and dynamically allocating the dwell time to enable the airborne monitoring equipment to collect multi-directional data on the same target area; the scanning coverage rate is the ratio threshold of the effective scanning area to the total area of ​​the target area, which is used to determine the integrity of the scan; the equipment ready status code is an encrypted verification identifier containing the scan completion timestamp, the effective coverage area position mapping relationship and the flight parameter adjustment record.

[0096] In this embodiment, the disaster area weight value in the rescue priority instruction is first parsed, and the hovering altitude adjustment gradient is calculated based on the weight ratio - the area with a high weight corresponds to a lower altitude and a smaller speed fluctuation range. Each disaster area is divided into multiple angular coverage sectors, and the centerline of the sector forms a fixed angle with the flight direction. The scanning dwell time of each sector is dynamically allocated according to the altitude gradient. During the flight, the number of scans and time intervals of each sector are monitored in real time. When the number of scans of the same sector meets the standard and the switching time of adjacent sectors is less than the tolerance threshold, it is marked as an effective coverage area. The effective coverage rate of all disaster areas is counted, and when it meets the standard, the original state data containing the scanning completion timestamp and the altitude adjustment gradient is generated. Time series encoding is performed on the original data, and the timestamp is bound to the real-time position of the drone to generate an intermediate state file, which is sent to the ground station for verification through an encrypted channel. After receiving the verification sequence returned by the ground station, the encrypted checksum is verified and converted to generate a device ready status code.

[0097] For example, in a forest fire monitoring scenario, the ground station issued a command designating G-23 (fire) and G-24 (landslide) as priority scanning areas. The drone interpreted the command and calculated the hovering altitude gradient: G-23 descended to a low altitude and reduced speed; G-24 maintained a medium-altitude, constant-speed scan. G-23 was divided into six scanning sectors, each allocated a longer dwell time to capture fire details; G-24 was divided into four sectors to rapidly scan for structural deformation. During flight, all six sectors of G-23 met the required scan count and switching time, and three sectors of G-24 met the requirements. Once the system calculated that the total coverage exceeded a preset threshold, it generated a status file containing a timestamp and location mapping and sent it to the ground station in an encrypted format.

[0098] Step 105: Verify the integrity of the device ready status code. If the verification is successful, generate an action authorization identifier.

[0099] In this step, the hovering altitude adjustment gradient is a flight altitude change parameter dynamically calculated based on the weight distribution of the disaster area in the rescue priority instruction, which is used to optimize data collection accuracy; the inspection speed refers to the adaptive adjustment threshold of the horizontal movement speed of the drone, which is inversely correlated with the hovering altitude to ensure sensor stability; the multi-angle coverage scan realizes all-round monitoring of the target area by the airborne equipment by dividing the scanning sectors and dynamically allocating the dwell time; the scanning coverage rate is used to measure the proportion of the effective scanning area in the total target area, which serves as the basis for determining the integrity of the scan; the equipment ready status code is an encrypted verification identifier that includes the scanning completion time, the coverage area location map and the flight parameter record.

[0100] In this embodiment, the disaster weight data in the rescue priority instruction is first parsed, and the corresponding hovering height gradient is calculated based on the weight - the area with higher weight is assigned a lower flight altitude and a smaller speed fluctuation range to improve the scanning resolution. Each disaster area is then divided into multiple angle scanning sectors, with the sector centerline maintaining a fixed angle with the flight direction, and the dwell time of each sector is dynamically allocated according to the height gradient. During flight, the scanning frequency and switching interval of each sector are monitored in real time. If the scanning frequency of the same sector meets the standard and the switching time of adjacent sectors meets the requirements, it is marked as an effective coverage area. The effective coverage rate of all disaster areas is counted, and after meeting the standard, the raw data of the integrated scanning completion time, coverage position and flight parameters is generated. The timestamp and real-time position are bound by time coding to form an intermediate file, which is encrypted and sent to the ground station for verification. After the verification code feedback from the ground station is verified, the device ready status code is generated.

[0101] For example, during forest fire rescue, the ground station designated G-23 (fire) and G-24 (landslide) as priority areas. After interpreting the instructions, the drone lowered the G-23's altitude to capture detailed fire data, dividing it into six sectors and extending its dwell time. The G-24 maintained a medium altitude to rapidly scan for structural deformation, dividing it into four sectors. Flight monitoring revealed that all six sectors met the criteria for G-23 and three for G-24. Once the total coverage exceeded the threshold, a status file was generated. The encrypted file was sent to the ground station, where, upon verification, it was converted to a ready status code, triggering the subsequent evacuation mission.

[0102] Step 106: When the parsed action authorization identifier matches the scan completion time, the drone's onboard positioning signal transmitter is controlled to generate an evacuation path based on the real-time location data, and the drone is controlled to fly in the reverse direction along the evacuation path to a safe coordinate.

[0103] In this step, the action authorization identifier is an encrypted instruction generated by the ground station after verifying the equipment ready status code, which includes the scan completion timestamp, authorized execution period and target area coordinate binding information; the scan completion time refers to the scan end time recorded in the equipment ready status code in step 104, which needs to be matched with the timestamp in the action authorization identifier to verify the timeliness; the evacuation path refers to the flight trajectory generated based on the real-time position data of the drone and the safety coordinates, which is composed of a set of path key points, and the distance between the key points is dynamically adjusted according to the flight attitude of the drone; reverse flight refers to the control mode of the drone flying in the opposite direction from the current coordinates to the safety coordinates along the evacuation path, which is used to avoid sudden obstacles and shorten the return time.

[0104] In this embodiment, the encrypted timestamp checksum in the action authorization identifier is first parsed. The original time series is restored using a cyclic shift decryption algorithm combined with the millisecond-level precision of the scan completion timestamp. The decrypted time series is then compared against the drone's local system time. If the deviation falls below a preset threshold, the multi-band positioning function of the onboard positioning signal transmitter is activated. Based on the elevation change rate and horizontal displacement rate in the real-time position data, a set of key points along the evacuation path is generated. The spacing between key points is dynamically calculated based on the maximum pitch angle to ensure flight stability. Redundancy is eliminated from the key points, turning points adjacent to the safety exclusion zone are removed, and the reverse flight trajectory is replanned based on the distribution density of the remaining key points. The trajectory is then segmented into several flight segments, each with a length proportional to the remaining battery power. Elevation constraints and speed limits for the turning points are then inserted into each segment. Finally, the drone is controlled to perform reverse flight according to the sequence of flight segments, while the positioning signal frequency band is adjusted to match the signal attenuation curve of the ground station's receiving equipment.

[0105] For example, in a forest fire rescue scenario, the action authorization identifier generated in step 105 includes the scan completion timestamp for block G-23. When parsing, if the timestamp deviation meets the requirements, the positioning module is activated to obtain the real-time location and generate a set of key points for the evacuation path. After removing dangerous turning points near the fire spread area, the remaining key points form the reverse flight trajectory. The trajectory is divided into three flight segments, with segment lengths allocated based on the remaining battery power. The first segment lowers the flight altitude to avoid dense smoke, while the final segment increases speed to shorten evacuation time. The drone then flies in the reverse direction of the path, switching the positioning signal to an anti-interference frequency band to ensure continuous communication with the ground station.

[0106] Step 107: continuously detecting the synchronization verification signal sent by the ground station during the reverse flight process. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the UAV is controlled to execute the preset rescue mission to the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed.

[0107] In this step, the synchronization verification signal is an encrypted verification instruction periodically sent by the ground station, which contains a set of evacuation path key points and a timestamp identifier, and is used to confirm the spatiotemporal consistency of the UAV flight trajectory and the mission execution; the evacuation path matching refers to the complete overlap between the set of path key points received by the UAV and the key point sequence of its current flight trajectory; the preset rescue mission refers to a predefined action plan for the disaster type, including but not limited to the delivery of emergency supplies, high-precision image acquisition or positioning beacon deployment; the mission marking file is an encrypted data file containing disaster coordinates generated in step 102; the automatic deletion mechanism refers to the irreversible operation of removing the processed data in the mission marking file based on the data correlation verification rules after the mission is completed.

[0108] In this embodiment, the drone continuously monitors the synchronization verification signal transmitted by the ground station during reverse flight. The multi-band receiving unit of the positioning signal transmitter analyzes the signal frame structure, extracting the encrypted path key point set and verification timestamp. The parsed key points are spatially matched point by point with the turning point sequence of the current evacuation path, and the deviation between the timestamp and the scan completion timestamp is verified to be within the tolerance range. After a successful match, the coordinates of the target area are read from the mission marker file, and the pre-set rescue mission module is invoked based on the disaster type—for example, a fire scenario initiates the fire extinguishing agent release program, while a landslide area triggers a 3D modeling scan. During the mission, environmental parameters (such as wind speed and temperature) are collected in real time and compared with the disaster parameters in the mission marker file for fluctuations. If fluctuations exceed the specified limits, the mission is paused and an exception interrupt request is sent to the ground station. Upon receiving a continue command, the remaining evacuation path is updated based on the latest synchronization verification signal, and the mission module is restarted until the completion state is triggered. Finally, the mission marker file is traversed, and the coordinate entries overlapped by the path key points are filtered to create a list to be deleted. After verifying that no new anomalies have been added, the relevant data is permanently removed.

[0109] For example, during a forest fire rescue, when the drone flies in the opposite direction of the evacuation path generated in step 106, it receives a synchronization verification signal from the ground station. After analyzing the signal, it is found that the key points of the path are consistent with the current trajectory and the timestamp is valid, triggering the fire extinguishing agent delivery task. During execution, a sudden increase in wind speed is detected, causing abnormal diffusion of the fire extinguishing agent. The system suspends the task and sends an interrupt request. After evaluation, the ground station issues a continue command. The drone updates the path to avoid the strong wind area and restarts the task, successfully completing the fire extinguishing agent delivery. After the task is completed, the system screens the processed fire coordinates in the G-23 block, verifies that there are no new fire points, and deletes the relevant data from the task marker file.

[0110] To address the issues of incomplete surface feature recognition caused by the use of a single band in traditional multispectral data, resolution adaptation deviation caused by the mismatch between geographic block division and sensor field of view parameters, and the inability of static terrain contour comparison to capture dynamic deformation features, in some embodiments, according to step 101, real-time image data of the ground area is collected along the planned route by a multispectral sensor carried by a drone, and the terrain contour is compared with a pre-stored geographic feature database to identify disaster risk areas, including:

[0111] Step 201: collecting real-time image data of a ground area using the multispectral sensor in three band combinations, wherein the band combinations include a first band group for distinguishing vegetation coverage, a second band group for detecting surface temperature changes, and a third band group for identifying the material of artificial structures.

[0112] In this step, the first band group is the acquisition channel of the multispectral sensor, which enhances the vegetation reflectance difference through a specific combination of visible light and near-infrared bands, and is used to distinguish areas with different vegetation densities such as forests and grasslands; the second band group is a thermal infrared band combination, which captures temperature anomaly areas through differences in surface thermal radiation energy; the third band group is a hyperspectral band combination, which identifies artificial structure types such as concrete and metal based on differences in spectral reflectance curves of different materials.

[0113] In this embodiment, the multispectral sensor's multi-channel synchronous acquisition mode is first activated: the first band group captures vegetation reflectance data in a continuous scanning mode, and the Normalized Vegetation Index calculation module generates a real-time vegetation density distribution map. The second band group collects surface thermal radiation data in an intermittent triggering mode, and combines it with radiation calibration parameters to invert the temperature distribution map. The third band group acquires material reflectance spectrum data in a line-by-line high-resolution scanning mode, and a spectral angle matching algorithm is used to generate a material classification map. The three data sets are then aligned according to geographic coordinates and fused into a three-dimensional image data block containing vegetation index, temperature gradient, and material type using a spatial overlay algorithm. This data is then output to the geographic feature database comparison module.

[0114] Step 202: Segment the real-time image data into a preset number of geographic blocks, where the size of each geographic block is dynamically adjusted based on the current flight altitude of the drone and the sensor field of view, so that the distribution of surface cover types within each geographic block is consistent.

[0115] In this step, the geographic block refers to the image processing unit dynamically divided according to the UAV flight parameters. Its size is dynamically calculated and generated by the geometric relationship between the flight altitude and the sensor field of view angle to ensure that the distribution fluctuation of the surface cover type (such as vegetation, bare soil, and water bodies) in the block does not exceed the preset threshold; consistent distribution of surface cover types means that the proportion of the main surface types in a single geographic block exceeds the set proportion, and there is no significant spatial aggregation of the distribution of secondary types.

[0116] In this embodiment, the theoretical side length of a geographic block is first calculated based on the drone's real-time flight altitude and the sensor's field of view. As altitude increases, the block size is expanded to improve scanning efficiency, while as altitude decreases, the size is reduced to enhance resolution. The real-time image data is then divided into rectangular grids based on the calculated size. A statistical analysis of the land cover types within each grid is performed. If the proportion of a particular type exceeds a threshold and the remaining types are evenly distributed, the block is retained. If the types are mixed or the secondary types are clustered, the block boundaries are recalculated and the division is repeated until the distribution consistency condition is met. The final output is a set of uniquely numbered geographic blocks, providing standardized input for subsequent resolution adaptation.

[0117] Step 203: performing resolution adaptation on the real-time image data of each geographic block, adjusting the pixel spacing of the real-time image data according to the altitude variation density of the corresponding area in the geographic feature database, and generating real-time image data after resolution adaptation;

[0118] In this step, resolution adaptation refers to the process of dynamically adjusting the pixel density of the image based on the terrain complexity index pre-stored in the geographic feature database; the intensity of altitude changes is quantified by the number and distribution gradient of elevation mutation points in a unit area, which is used to characterize the severity of terrain undulations; pixel spacing refers to the actual distance on the ground represented by adjacent pixel points in the image. The smaller the spacing, the higher the resolution and the stronger the ability to capture terrain details.

[0119] In this embodiment, the distribution data of elevation mutation points for the current geographic block is first extracted from the geographic feature database, and its density level is calculated. If the density exceeds a preset threshold, it is determined to be complex terrain, and pixel interpolation encryption is performed on the real-time image, reducing the pixel spacing to enhance detail resolution. If the density is below the threshold, it is determined to be flat terrain, and pixel aggregation is used to increase the spacing to optimize storage efficiency. During the adaptation process, based on the spatial distribution pattern of elevation mutation points, the resolution is locally increased in key areas (such as steep slopes and gullies) rather than uniformly adjusted. After resolution adaptation is completed, the adjusted image pixel coordinates are aligned with the three-dimensional terrain grid nodes in the geographic feature database to generate adapted image data that passes the spatial consistency check.

[0120] Step 204: extracting a set of terrain contour lines corresponding to the current geographic block from the geographic feature database, comparing the real-time image data after resolution adaptation with the set of terrain contour lines layer by layer, and calculating the spatial correlation between the pixel feature and the terrain contour based on the band combination data corresponding to each pixel in the real-time image data and the slope change direction between adjacent altitude mutation points in the set of terrain contour lines.

[0121] In this step, the terrain contour line set refers to the three-dimensional spatial grid lines formed by connecting the elevation mutation points in the geographic feature database, which is used to describe the surface undulation and slope turning characteristics; the altitude mutation point is the spatial point where the elevation difference between adjacent grid nodes exceeds the terrain stability threshold; the slope change direction refers to the extension trend of the line connecting adjacent mutation points on the horizontal plane, which is used to quantify the steepness and spatial continuity of the terrain trend; the spatial correlation is an indicator of the degree of matching between the multispectral characteristics of the pixel point (such as temperature, material reflectance spectrum) and the terrain slope direction, which is used to evaluate the coupling relationship between surface anomalies and terrain structure.

[0122] In this embodiment, the terrain contour lines of the current geographic block are first extracted from the geographic feature database to obtain the coordinates of all elevation change points and the slope direction data between adjacent points. The real-time image pixels, after resolution adaptation, are mapped to terrain grid cells according to their spatial coordinates. Multispectral band combination data (such as vegetation index, temperature gradient, and material classification results) is extracted for each pixel. For each pixel, the slope change direction of the grid cell in which it is located is calculated, and a correlation model is established based on the band data characteristics. For example, when a high-temperature pixel is located in a steep slope, the correlation decreases due to the conflict with the thermal convection law; when a metal pixel is located in a flat area, the correlation increases due to the consistency with the distribution law of artificial structures. If the correlation falls below the dynamically adjusted anomaly threshold, the point is marked as a candidate anomaly. Finally, a layer containing the distribution of anomaly points and their correlation levels is generated and output to the disaster area merging module.

[0123] Step 205, filtering out the pixel point set whose spatial correlation is lower than the dynamic threshold as the abnormal heat source candidate area;

[0124] In this step, the dynamic threshold refers to the critical value for abnormal judgment dynamically calculated based on the terrain complexity of the current geographical block and the historical disaster pattern. Its value is adjusted inversely with the severity of terrain fluctuations and the probability of disaster occurrence; the abnormal heat source candidate area refers to a set of pixel points with a spatial correlation lower than the dynamic threshold, indicating that the coupling relationship between the surface thermal radiation characteristics and the terrain contour in this area deviates significantly from the normal pattern, which may indicate a potential thermal disaster risk.

[0125] In this embodiment, an anomaly threshold for the current geographic region is first dynamically calculated based on the region's terrain complexity (e.g., the density of elevation change points and slope gradient) and the frequency of anomalous heat sources in similar terrain as reported in the historical disaster database. The spatial correlation layer generated in step 204 is then traversed, extracting all pixels with correlation values ​​below the dynamic threshold to form initial candidate regions. A morphological closing operation is performed on the candidate regions to eliminate isolated noise points, and a spatial clustering algorithm is used to merge adjacent pixels into continuous anomaly regions. Finally, a set of candidate regions with heat source intensity levels and spatial boundary information is output, providing input for subsequent merging of disaster risk regions.

[0126] Step 206: Detecting fracture areas in the slope change direction between consecutive altitude mutation points in the terrain contour line set. When the extension direction of the fracture area coincides with the offset direction of the material boundary line of the artificial structure at the same position in the real-time image data, mark it as a candidate structural deformation area.

[0127] In this step, the slope change direction fracture area refers to the area where the slope extension trend between consecutive altitude mutation points in the terrain contour line set undergoes discontinuous jumps, which is manifested as the horizontal extension direction of the line connecting adjacent mutation points suddenly exceeding the geological stability threshold; the offset direction of the material boundary line of artificial structures refers to the spatial offset trend of the reflectance spectrum characteristic boundary line of artificial materials such as concrete and metal in real-time image data relative to the original position recorded in the historical database, which is used to indicate the deformation or displacement of the structure; the structural deformation candidate area is the geographical block where the slope fracture area coincides with the offset direction of the material boundary line, representing the potential risk of geological structure instability or damage to artificial facilities.

[0128] In this embodiment, a sequence of slope directions of continuous elevation change points is first extracted from a set of terrain contour lines. Fracture segments whose direction changes exceed a stability threshold are detected, and their horizontal extension directions are recorded. Simultaneously, the material boundary lines of artificial structures are extracted from real-time image data. Using an edge detection algorithm, they are spatially offset and compared with the original boundary lines in a historical database to calculate the offset direction vector. The extension direction of the slope fracture zone is spatially overlaid with the offset direction of the material boundary line. If the two coincide within a preset tolerance, the candidate region is identified as a structural deformation candidate. Morphological dilation is performed on the candidate region to cover the deformation impact range, and labeled data with the deformation level and boundary coordinates is output.

[0129] Step 207 , merging the spatially overlapping portion of the abnormal heat source candidate region and the structural deformation candidate region and the independent region within a preset distance between the abnormal heat source candidate region and the structural deformation candidate region into a disaster risk region;

[0130] In this step, the preset distance refers to the spatial proximity judgment threshold that is dynamically adjusted according to the disaster type and terrain complexity, and is used to identify the potential correlation between abnormal heat sources and structural deformations; the disaster risk area is the spatial overlap of the abnormal heat source candidate area and the structural deformation candidate area and the collection of adjacent independent areas, which characterizes the spatial coupling characteristics of the occurrence of complex disasters; the spatial proximity effect refers to the fact that although the two types of candidate areas do not directly overlap, there is still a risk of disaster chain reaction when the distance between them is less than the preset threshold.

[0131] In this embodiment, spatial overlay analysis is first performed on the abnormal heat source candidate areas and the structural deformation candidate areas to generate completely overlapping disaster core areas. Next, a buffer zone analysis is performed on the non-overlapping parts of the two types of candidate areas to generate adjacent areas with an interval distance within a preset threshold. The core area and the adjacent area are merged to form an initial disaster risk area set. Subsequently, based on the historical disaster pattern data in the geographic feature database, the disaster coupling weight is calculated for the initial set - if the area has both high temperature anomalies and structural deformation features, the weight is increased; if only a single condition is met but there are historical disaster-related records in the adjacent area, the weight is maintained. Finally, a disaster risk area set with weight levels and spatial boundary information is output.

[0132] To address the issues of low transmission efficiency caused by redundant storage of disaster coordinate data, the inability of a fixed compression ratio to adapt to differences in coordinate distribution density, and the vulnerability of statically generated encryption keys to cracking, in some embodiments, according to step 102, a mission marker file containing the coordinates of the disaster risk area is synchronously generated during the flight of the UAV, and the mission marker file is compressed into an encrypted data packet with a geotag, including:

[0133] Step 301: converting the absolute geographic coordinates of the disaster risk area into relative coordinates relative to the boundary of the current geographic block, and generating a task tag file containing a disaster type identifier based on the relative coordinates;

[0134] In this step, the absolute geographic coordinates are the longitude and latitude coordinates based on the global positioning system, which are used to uniquely identify the spatial location of the disaster risk area; the relative coordinates are the local coordinates with the current geographic block boundary as the reference system, which are generated by calculating the offset of the block center point or boundary, and are used to simplify subsequent data compression and spatial correlation analysis; the disaster type identifier is a classification code generated based on the results of multispectral feature and terrain coupling analysis, which is used to identify disaster types such as fire, landslide, and collapse.

[0135] In this embodiment, the absolute geographic coordinates of the disaster risk area are first acquired, and the boundary coordinates of the geographic block to which it belongs (e.g., the latitude and longitude of the southwest corner of the block) are extracted. Using a coordinate system conversion algorithm, the absolute coordinates are converted into relative coordinates with the block boundary as the origin, preserving the elevation information of the coordinate points. Next, based on the results of a coupled multispectral and terrain analysis, a disaster type identifier (e.g., "F" for fire, "L" for landslide) is assigned to each disaster risk area. The relative coordinates, elevation values, and identifiers are packaged into a structured data entry according to a pre-set format. The geographic block number and the drone's current flight altitude parameters are then added to generate a mission tag file.

[0136] Step 302: Add the geographic block number and the current flight altitude data of the UAV to the mission tag file to generate an original mission file with a geographic tag;

[0137] In this step, the geographic block number is the unique identifier generated when the geographic blocks are dynamically divided in step 202, which is used to establish a mapping relationship between the mission file and the geographic spatial location; the current flight altitude data of the drone refers to the real-time flight altitude parameters when collecting the coordinates of the disaster risk area, which is used for subsequent resolution adaptation and dynamic calibration of path planning; the original mission file with geo-tags is a structured data set that integrates relative coordinates, disaster type identifiers, geographic block numbers and flight altitude parameters to form a standardized input for subsequent processing.

[0138] In this embodiment, the unique number of the block to which the current disaster risk area belongs is first extracted from the geographic block division record and associated with the relative coordinates and disaster type identifier in the mission tag file. The flight altitude data recorded in real time by the drone's flight control system is simultaneously read and appended to the mission tag file header as metadata. All data entries in the file are sorted and sorted by geographic block number, ensuring that disaster coordinate entries within the same block are stored consecutively. A checksum of the block number and flight altitude is added to the end of the file to generate the original geotagged mission file.

[0139] Step 303: Segment the original task file into multiple independent data blocks, each data block corresponding to a set of coordinates of a disaster risk area within a geographical block, and dynamically adjust the compression rate according to the distribution density of the coordinate points in the data block to obtain processed data blocks;

[0140] In this step, independent data blocks are independent data processing units divided by geographic block numbers, containing a set of disaster coordinates within a single block and its metadata; distribution density refers to the spatial clustering density of coordinate points within the data block, which is quantified by calculating the number of coordinate points per unit area or the average distance between adjacent points; dynamic adjustment of compression rate refers to selecting differentiated compression algorithm parameters according to density, with high-density areas using a high-compression rate algorithm to reduce redundancy and low-density areas using a low-compression rate algorithm to retain details.

[0141] In this embodiment, the original task file is first divided into multiple subfiles based on geographic block numbers. Each subfile contains only coordinate entries within the same block. A spatial distribution analysis of the coordinate points is performed on each subfile: the ratio of the minimum bounding rectangle area of ​​the coordinate points to the number of points is calculated. If the ratio is below a density threshold, the area is considered high-density; otherwise, it is considered low-density. High-density areas use a compression algorithm based on spatial difference coding, converting the relative offsets of adjacent coordinate points into a sequence of differences for storage. Low-density areas use Huffman coding for lossless compression of absolute coordinates. After compression, the block number, compression algorithm identifier, and metadata checksum are added to the header of each data block to generate a processed, standardized data block.

[0142] Step 304: performing redundancy elimination processing on each processed data block, deleting coordinate points that overlap with adjacent geographic blocks, and generating a hash chain containing a correspondence between data block numbers and compression ratios;

[0143] In this step, redundancy elimination processing refers to deleting duplicate coordinate points across geographic block boundaries through spatial overlap analysis to avoid the same disaster risk area being recorded repeatedly in multiple adjacent data blocks; the hash chain is a chain data structure that records the data block number, compression ratio and data block content summary, which is used to verify data integrity and compression parameter consistency; the correspondence between the data block number and the compression ratio refers to the compression algorithm identifier and compression ratio level information stored in each node in the hash chain, ensuring that the data can be correctly restored during subsequent decryption.

[0144] In this embodiment, spatial boundary analysis is first performed on each processed data block. The latitude and longitude ranges of all coordinate points within the data block are extracted and spatially overlaid with the boundary coordinates of adjacent geographic blocks to identify coordinate points located in the block boundary buffers. A spatial index fast query mechanism is used to filter out coordinate points that fall within two or more block buffers, marking them as redundant and deleting them. Next, the compression ratio parameter is extracted based on the data block number, and a hash function is used to generate a unique digest value for the data block content (including the remaining coordinate points, block number, and compression ratio). The digest value, number, and compression ratio are linked in a pre-set order to form hash chain nodes, and the nodes are then connected in series according to the block number order, forming an unalterable hash chain structure.

[0145] Step 305 , arranging the processed data blocks in order of geographic block numbers, and inserting the corresponding hash chain into the header of each processed data block to generate a compression task file;

[0146] In this step, the geographic block numbering sequence refers to the rule for sorting the block numbers according to the spatial continuity of the UAV route planning, ensuring that adjacent geographic blocks are stored continuously in the compressed mission file to optimize reading efficiency; the hash chain header is a data structure inserted into the starting position of the data block, which contains a hash summary, compression rate identifier and data block length information for rapid positioning and integrity verification; the compressed mission file is a standardized file that integrates all sorted data blocks and corresponding hash chains to form a traceable and verifiable disaster data transmission unit.

[0147] In this embodiment, a sorting rule is first established based on the spatial distribution of geographic block numbers. Data blocks are arranged in the order of the geographic grid covered by the drone's route (e.g., a serpentine path), ensuring that adjacent blocks are stored contiguously within the file. For each data block, its hash chain node is extracted, and the node data (hash digest, compression ratio, and data block length) is serialized into a fixed-format byte stream, which is then inserted into the data block's starting position as a header identifier. Subsequently, the data blocks with hash headers are written sequentially to the file stream according to the sorting rule. A global index table is added to the end of the file, recording the starting offset, number, and compression ratio parameters of each data block. Ultimately, a compressed task file is generated that supports random access and fast verification.

[0148] Step 306: Perform layered encryption on the compressed task file using the drone's built-in encryption module to obtain an encrypted compressed task file, wherein the first layer of encryption generates a dynamic key based on the geographic block number, and the second layer of encryption combines the hash chain node and the drone's current timestamp to generate a verification identifier;

[0149] In this step, layered encryption refers to the technology of using a multi-level encryption mechanism to protect data layer by layer. The first layer of encryption uses a dynamic key generated based on the geographic block number to achieve data content obfuscation. The second layer of encryption generates a verification identifier by binding the hash chain node and the timestamp to ensure the integrity and timeliness of data transmission. The dynamic key is an encryption factor calculated in real time based on the unique characteristics of the geographic block number, and its value is dynamically updated as the block number changes. The verification identifier is an encryption check code generated by combining the uniqueness of the hash chain node data and the irreversibility of the timestamp to prevent data tampering and replay attacks.

[0150] In this embodiment, the first layer of encryption is performed on the compressed task file: the geographic block number of the data block is extracted, a dynamic key is generated using a hash function and a random number generator, and the data block content is encrypted using a symmetric encryption algorithm (such as AES). Next, the second layer of encryption is performed: the hash chain node data at the data block header is extracted, concatenated with the drone's current timestamp, and an asymmetric encryption algorithm (such as RSA) is used to generate a verification identifier, which is appended to the end of the encrypted data block. Finally, the doubly encrypted data blocks are reassembled in their original order to generate an encrypted, compressed task file with a timestamp sequence and a dynamic key identifier.

[0151] Step 307 , encapsulating the encrypted compressed task file into an encrypted data packet with a timestamp and a geographic block number sequence;

[0152] In this step, the timestamp is the precise time mark when the UAV generates the encrypted data packet, which is recorded based on the Universal Coordinated Time (UTC) format and is used to verify the timeliness of the data and the timing consistency of the command execution; the geographic block number sequence is a set of unique identifiers of geographic blocks arranged in the order of the UAV route planning, reflecting the spatial coverage order of the encrypted mission files in the data packet; the encrypted data packet is a standardized transmission unit that integrates the encrypted compressed mission file, timestamp and geographic block number sequence. Its encapsulation structure supports rapid parsing and multi-task parallel processing by the ground station.

[0153] In this embodiment, all geographic block numbers of the encrypted and compressed mission file are first extracted, and a number sequence is generated based on the spatial continuity of the drone's route coverage (e.g., G-23 → G-24 → G-25 in a serpentine path). Next, a start identifier and timestamp field are inserted into the data packet header. The timestamp is derived from the millisecond-level UTC time of the drone's timing module. The geographic block number sequence is converted into binary code and written sequentially into the timestamp field in the data packet header to form a joint space-time index. Subsequently, the encrypted and compressed mission files are written into the data packet body in numerical order, and a global checksum and data packet length field based on a hash chain are appended to the end of the data packet to complete the encapsulation. The resulting encrypted data packet simultaneously meets the requirements of spatial traceability, temporal anti-counterfeiting, and transmission integrity.

[0154] In order to solve the problems of uneven scanning angle coverage of multiple target areas due to a fixed hovering altitude, disconnection between weight analysis and flight parameter adjustment, and command delay caused by reliance on manual operation for status verification, in some embodiments, according to step 104, the hovering altitude and inspection speed of the UAV are dynamically adjusted according to the rescue priority instruction, so that the UAV's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement, including:

[0155] Step 401: parsing the weight value of each disaster risk area in the rescue priority instruction;

[0156] In this step, the rescue priority instruction is an action instruction file generated by the ground station that contains the rescue urgency level of each disaster risk area. The weight value is a numerical indicator that quantifies the rescue priority of the disaster area in the instruction. It is generated by comprehensively calculating the hazard level of the disaster type, the density of trapped people and the environmental diffusion risk. The higher the weight value, the higher the level of priority response.

[0157] In this embodiment, the command parser first reads the encrypted content of the rescue priority command and extracts the weight value field from each disaster risk area entry. A data type verification module filters out non-numeric values ​​or abnormal weight values ​​outside a reasonable range to ensure the legitimacy of the input data. Legal weight values ​​are normalized, and the distribution ratio of each area's weight to the total weight is calculated to generate a weight distribution table. Finally, the weight distribution table is bound to the regional geographic coordinates to form a set of disaster areas with priority identifiers, which is then output to the flight parameter adjustment module.

[0158] Step 402: Calculate the hovering height adjustment gradient of the UAV between the disaster risk areas according to the distribution ratio of the weight values;

[0159] In this step, the hovering height adjustment gradient refers to the drone's flight height change parameter dynamically calculated based on the weight value distribution. Areas with high weight values ​​correspond to lower hovering heights to improve monitoring accuracy, while areas with low weight values ​​are raised to expand coverage. The gradient calculation needs to be combined with the drone's maximum climb rate and the sensor field of view angle constraints to ensure a balance between flight stability and data acquisition efficiency.

[0160] In this embodiment, the weight distribution table output in step 401 is first read to set the drone's minimum safe hovering altitude and maximum operating altitude thresholds. Based on the principle of weight reverse mapping, the weight distribution is converted into altitude gradient parameters—the higher the weight, the lower the hovering altitude. A linear interpolation algorithm is used to calculate the target altitude for each region, and a gradient smoothing algorithm dynamically constrains the altitude differences between adjacent regions to prevent sudden changes in flight attitude. Finally, a flight control instruction set containing the altitude gradient parameters and region coordinates is generated and sent to the drone's navigation system.

[0161] Step 403: Divide the scanning range of each disaster risk area into multiple angular coverage sectors, wherein the centerline of each angular coverage sector forms a preset angle with the current flight direction of the UAV, and dynamically allocate the scanning dwell time of each angular coverage sector according to the hovering height adjustment gradient;

[0162] In this step, the angle coverage sector refers to the sector-shaped area that divides the scanning range of the disaster risk area into equal parts according to the azimuth angle. The center line forms a fixed angle with the current flight direction of the UAV, which is used to realize multi-angle data collection; the dynamic allocation of scanning dwell time refers to the allocation of differentiated dwell time to different sectors according to the gradient of the hovering height adjustment, allocating longer dwell time to low-altitude areas to improve resolution, and shortening the time to high-altitude areas to optimize efficiency.

[0163] In this embodiment, the scanning range radius is first calculated based on the current position of the drone and the geometric center of the disaster risk area, and the scanning range is divided into multiple angular coverage sectors according to a preset angle. The centerline direction of each sector is generated by superimposing the drone's current flight direction and the preset angle. The gradient is adjusted according to the hovering altitude, and an initial dwell time is assigned to each sector - the lower the altitude, the longer the dwell time. Subsequently, a dynamic scheduling algorithm is used in combination with real-time wind speed and sensor stability data to fine-tune the dwell time to ensure data collection quality and flight safety. The adjusted dwell time sequence is sent to the flight control system, which controls the drone to perform multi-angle scanning in sequence.

[0164] Step 404: During the flight of the UAV, the number of scans of each angular coverage sector is monitored in real time. When the number of scans within the same angular coverage sector reaches a preset coverage threshold and the scanning time interval between adjacent angular coverage sectors is less than a preset tolerance, the angular coverage sector is marked as a valid coverage area.

[0165] In this step, the effective coverage area refers to the sector that meets both the scanning number standards and the time interval compliance, indicating the integrity and timeliness of data collection in this area; the scanning number threshold refers to the minimum number of scans that a single sector needs to complete to ensure data confidence; the scanning time interval tolerance refers to the maximum allowable time difference between adjacent sector switches to prevent data gaps caused by environmental interference.

[0166] In this embodiment, the drone's flight control system records the number of scans for each sector and the handoff timestamps for adjacent sectors in real time. When a sector's scan count reaches a threshold, the system checks whether the time interval between the previous scan and the sector is within a tolerance range. If both the number and interval conditions are met, the area is marked as valid and a coverage status indicator is generated. For sectors that fail to meet the requirements, rescan tasks are dynamically inserted into the flight queue until the conditions are met or the task timeout mechanism is triggered. Finally, a location mapping table of valid coverage areas is output for subsequent device readiness status code generation.

[0167] Step 405: Count the total proportion of effective coverage areas in all disaster risk areas. When the total proportion exceeds a preset coverage threshold, generate raw state data including scanning completion timestamps of all current effective coverage areas and corresponding hovering height adjustment gradients.

[0168] In this step, the total proportion of effective coverage area refers to the ratio of the sum of the areas of sectors marked as effectively covered in all disaster risk areas to the total area of ​​the target area; the coverage threshold is the minimum ratio requirement for determining the completeness of the scan, which must meet both spatial coverage adequacy and temporal continuity; the original status data is a structured data set containing the boundary coordinates of the effective coverage area, the scan completion timestamp, and the corresponding hovering height adjustment gradient, which is used for subsequent status verification and task tracing.

[0169] In this embodiment, the effective coverage area marker table for all disaster risk areas is first traversed, and the ratio of their total area to the total area of ​​the preset target area is calculated. If the total ratio exceeds a threshold, the boundary coordinates of the current effective coverage area are extracted from the flight control system and associated with the scan completion timestamp and hovering altitude gradient parameter. The three types of data are then packaged into structured entries according to the area number, and a global checksum is added to generate the original state data file.

[0170] Step 406: performing time series encoding on the original state data, binding the scanning completion timestamp with the real-time position coordinates of the drone, and generating an intermediate state file with a time series identifier;

[0171] In this step, time series encoding refers to the technology of converting discrete timestamps into continuous time series identifiers, ensuring the traceability of the sequence of data events through timeline mapping; the intermediate state file is an encrypted pre-processed file that integrates time series identifiers, effective coverage area coordinates, and hovering height parameters, and is used for cross-system verification and command authorization.

[0172] In this example, the scan completion timestamps in the raw state data are first aligned with millisecond-level precision. A timeline bucketing algorithm is then used to encode consecutive timestamps into time interval identifiers. Next, the time interval identifiers are bound to the longitude and latitude coordinates provided by the drone's real-time positioning system at the time of acquisition to generate spatiotemporally coupled data entries. Finally, all entries are sorted in ascending order by time interval identifier, and a metadata summary is added to the file header to generate an intermediate state file.

[0173] Step 407: Send the intermediate state file to the ground station through the preset encrypted channel of the multi-band communication module for verification, and receive the verification sequence result returned by the ground station after the sending is completed, wherein the verification sequence result includes an encrypted check code that matches the timing identifier;

[0174] In this step, the preset encryption channel refers to the dedicated transmission link reserved for high-priority data in the multi-band communication module, which adopts anti-interference modulation technology and layered dynamic encryption mechanism; the verification sequence result is the response data generated after the ground station verifies the intermediate status file, which contains an encryption check code that matches the file timing identifier, and is used to bidirectionally verify the integrity and timeliness of data transmission.

[0175] In this embodiment, an intermediate state file is first transmitted via a dedicated channel within the multi-band communication module. A location check code and timestamp signature for the current drone are added to the file header. Upon receiving the file, the ground station decrypts the file and verifies the continuity of the timing identifier and the consistency of the coordinate space. A verification sequence result, consisting of a hash check code and timestamp, is generated and returned to the drone via a clean frequency band. Upon receiving the verification result, the drone compares the check code with a locally calculated hash value. If they match, the check code is bound to the device ready state code.

[0176] Step 408: Verify the encrypted verification code. When the encrypted verification code passes the integrity check, convert the intermediate state file into a device ready state code. The device ready state code includes a mapping relationship between the scan completion timestamp and the location of the effective coverage area.

[0177] In this step, the encryption check code is a verification mark returned by the ground station containing a hash summary and a timestamp signature, which is used to confirm that the intermediate state file has not been tampered with during the transmission process and is timely; integrity verification refers to the process of verifying data integrity by comparing the hash summary returned by the ground station with the hash value calculated locally by the drone; the device ready status code is an encrypted instruction code generated after the verification is passed, which contains the time-space coordinate mapping relationship of the effective coverage area and the scanning completion timestamp, which is used to authorize the execution of subsequent tasks.

[0178] In this embodiment, an encrypted checksum is first extracted from the verification sequence result. The hash digest and timestamp signature in the checksum are decrypted using the public key of an asymmetric encryption algorithm. Next, the same hash function is used to calculate the contents of the locally stored intermediate state file to generate a local hash value. The local hash value is compared with the decrypted ground station hash digest. If they are consistent and the timestamp signature is within the validity period, the verification is considered passed. Subsequently, the location mapping relationship of the effective coverage area and the scan completion timestamp are extracted from the intermediate state file, converted into a binary instruction sequence according to preset encoding rules, and the flight control system authorization identifier is added to generate the device ready status code.

[0179] To address the issues of invalid command authorization caused by the asynchrony between the device readiness status code and the time base, the lack of spatial constraints in the generation of dynamic authorization factors, and the vulnerability of encrypted identifiers to forgery, in some embodiments, according to step 105, the integrity of the device readiness status code is verified. When the verification is successful, an action authorization identifier is generated, including:

[0180] Step 501: Perform hierarchical decryption on the device readiness status code using a decryption key pre-stored in the drone. Extract the original timestamp and effective coverage area location mapping table of the device readiness status code from the decrypted data, and calculate the deviation from the reference time window of the ground station. The reference time window is determined based on a preset tolerance range before and after the scan completion timestamp.

[0181] In this step, layered decryption refers to the operation of decrypting the encrypted content of the device ready status code layer by layer using the different levels of keys pre-stored in the drone; the reference time window is the time verification interval defined by the ground station based on the legal timeliness requirements of the scan completion timestamp, which is used to determine the legitimacy of the decrypted timestamp; the deviation is the absolute time difference between the original timestamp after decryption and the center point of the reference time window, which is used to quantify the degree of data timeliness deviation.

[0182] In this embodiment, the first layer of decryption is performed on the device ready state code using the dynamic key generated by the geographic block number to restore the intermediate data with the timestamp signature. Then, the second layer of decryption is performed using the hash chain key associated with the timestamp to extract the original timestamp and the effective coverage area location mapping table. The original timestamp is compared with the reference time window issued by the ground station to calculate the time deviation from the window center point.

[0183] Step 502: When the deviation is less than a preset threshold and the number of missing areas in the effective coverage area location mapping table does not exceed a preset ratio, it is determined that the integrity verification of the device ready status code has passed;

[0184] In this step, the number of missing areas refers to the number of geographical blocks in the effective coverage area location mapping table that are not actually scanned and covered, reflecting the integrity defects of data collection; integrity verification is a verification process that determines whether the device ready status code meets the task execution conditions through the dual indicators of deviation and missing area; the preset ratio refers to the maximum percentage threshold of the allowed number of missing areas to the total number of target areas. If it exceeds the percentage, the verification is deemed to have failed.

[0185] In this embodiment, the number of blocks marked "covered" in the effective coverage area location map is first counted. The difference between this number and the total number of target areas is calculated to determine the number of missing areas. If the number of missing areas does not exceed a preset ratio and the deviation is less than a threshold, a verification pass flag is triggered. Otherwise, a missing area rescan command is generated and transmitted back to the ground station, triggering the mission replanning process. After verification passes, the device readiness status code is bound to the flight control system, authorizing the execution of subsequent rescue missions.

[0186] Step 503: Generate a dynamic authorization factor based on the scan completion timestamp and the effective coverage area location mapping table, and perform a superposition operation on the dynamic authorization factor and the weight value distribution ratio in the rescue priority instruction to generate an intermediate authorization file containing a timestamp binding code and an area coverage identifier;

[0187] In this step, the dynamic authorization factor is an encrypted verification parameter generated based on the scanning completion timestamp and the effective coverage area location mapping table, which is used to quantify the spatiotemporal authorization strength of the drone mission execution; the timestamp binding code is a unique identifier generated by hashing the scanning completion timestamp and the geographic block number; the area coverage identifier is a ratio level code of the effective coverage area to the total area of ​​the target area, reflecting the integrity of data collection.

[0188] In this embodiment, timeline slicing encoding is first performed on the scan completion timestamp to generate a timestamp binding code. Simultaneously, the coverage ratio of each area is calculated based on the effective coverage area location mapping table to generate a regional coverage identifier. The dynamic authorization factor is initialized as the product of the timestamp binding code and the coverage identifier, and then the weight value distribution ratio in the rescue priority instruction is superimposed. An intermediate authorization file is generated using a weighted fusion algorithm. The file is internally divided into a timestamp binding segment, a coverage identifier segment, and a dynamic authorization factor segment, each segment separated by a delimiter.

[0189] Step 504: Double encrypt the intermediate authorization file, encapsulate the double-encrypted data into an action authorization identifier with a timestamp sequence, and transmit it back to the drone via the response channel of the multi-band communication module, wherein the action authorization identifier includes an encrypted timestamp check segment that matches the scan completion timestamp;

[0190] In this step, double encryption processing refers to performing symmetric and asymmetric encryption operations on the intermediate authorization file in sequence. The first layer of encryption uses a dynamic key generated by the geographic block number, and the second layer of encryption uses an asymmetric key bound to the timestamp check segment.

[0191] In this embodiment, the intermediate authorization file is first encrypted using AES using a dynamic key generated from the geographic block number, generating the first-level ciphertext. The timestamp field in the timestamp binding code is then extracted and concatenated with the drone's real-time location coordinates. This timestamp checksum is then generated using RSA public key encryption. This checksum is appended to the end of the first-level ciphertext, and the entire data is encrypted again using an asymmetric key to generate a doubly encrypted action authorization identifier. This is then transmitted to the ground station via the multi-band communication module's dedicated response channel. The ground station decrypts the timestamp, verifies its validity, and returns a response with a checksum.

[0192] To address the issues of insufficient obstacle avoidance capability caused by static evacuation path planning, increased flight energy consumption due to redundant turning points, and lack of real-time feedback on path updates, in some embodiments, according to step 106, when the parsed action authorization identifier matches the scan completion time, the onboard positioning signal transmitter of the UAV is controlled, and an evacuation path is generated based on the real-time position data. Simultaneously, the UAV is controlled to fly in the reverse direction along the evacuation path to a safe coordinate, including:

[0193] Step 601: extract the encrypted timestamp check segment from the action authorization identifier, perform cyclic shift decryption based on the millisecond-level precision value of the scan completion timestamp, and obtain the original time series including the check code;

[0194] In this step, cyclic shift decryption refers to a decryption technology that performs cyclic bit operations on encrypted data segments based on the millisecond value of the timestamp. The original time series is restored by cyclically shifting the check code according to the displacement amount corresponding to the millisecond value. The original time series is a data set that contains a complete timestamp check code and time axis event mark after decryption, which is used for subsequent timeliness verification.

[0195] In this embodiment, the timestamp check segment is first extracted from the encrypted field of the action authorization identifier and its byte stream structure is read. A cyclic shift is determined based on the millisecond value of the scan completion timestamp. A cyclic right shift is then performed on the check segment byte stream to restore the encrypted and disrupted time series structure. The decrypted original time series, including the check code and timeline event markers, is output to the time deviation comparison module.

[0196] Step 602: Compare the deviation between the original time series and the current time system of the UAV. When the deviation value is less than a preset time window threshold, activate the multi-band positioning function of the onboard positioning signal transmitter.

[0197] In this step, the time window threshold is the maximum deviation value allowed between the drone system time and the authorized timestamp. If it exceeds the value, the clock is considered to be out of synchronization; the multi-band positioning function refers to a mode that improves positioning accuracy by simultaneously enabling multiple positioning signal bands, which is used for high-precision navigation in complex terrain.

[0198] In this embodiment, the decrypted timestamp is first extracted from the original time series and the offset between it and the current time of the drone's local timing module is calculated. If the offset is less than a threshold, the positioning signal transmitter's multi-band collaborative positioning mode is triggered: it synchronously receives positioning signals from multiple satellite systems, eliminates ionospheric errors through carrier phase difference technology, and generates real-time position data with centimeter-level accuracy.

[0199] Step 603: Generate a set of key points of the evacuation path based on the elevation change rate and horizontal displacement rate in the real-time position data of the UAV;

[0200] In this step, the elevation change rate refers to the climb / descent rate of the UAV in the vertical direction, and the horizontal displacement rate is the flight speed in the horizontal plane. The path key point set is a sequence of spatial turning points generated in the evacuation path according to the terrain complexity and flight stability requirements. The key point spacing is dynamically optimized according to the real-time flight attitude.

[0201] In this embodiment, a set of key points of the evacuation path is first generated through a path optimization algorithm based on the elevation change rate and horizontal displacement rate in the real-time position data of the drone, combined with the terrain undulation features in the geographic feature database.

[0202] Step 604: performing redundancy elimination processing on the set of key points on the path, deleting turning points whose distance from the boundary of the preset safety restricted area is less than a warning threshold, and recalculating the reverse flight trajectory based on the distribution density of the remaining turning points;

[0203] In this step, the warning threshold is the preset minimum safe distance between the drone and the safety restricted area. Exceeding this threshold triggers the turning point deletion operation; the reverse flight trajectory refers to the flight path generated by the remaining key points that is opposite to the original evacuation direction, which is used for emergency obstacle avoidance or rapid return scenarios.

[0204] In this embodiment, a spatial index of the restricted area is first constructed. The shortest distance between each turning point in the set of key points on the path and the restricted area boundary is calculated. A spatial proximity query is then performed to filter out turning points with distances less than a warning threshold, marking them as redundant and deleting them. Density clustering analysis is then performed on the remaining turning points. The trajectory generation algorithm parameters are adaptively adjusted based on their distribution density: spline interpolation is used to smooth the trajectory in high-density areas, while linear fitting is used to complete the path in low-density areas. The resulting reverse flight trajectory maintains spatial topology consistency with the original path and satisfies safety obstacle avoidance constraints.

[0205] Step 605: Segment the reverse flight trajectory into a preset number of flight segments, sort the segments according to the turning points in the path key point set, generate a flight segment sequence, and control the UAV to perform reverse flight according to the flight segment sequence;

[0206] In this step, the flight segment refers to the continuous flight unit divided by key points in the reverse flight trajectory, and its length is positively correlated with the remaining power of the drone and the stability of the sensor; the flight segment order is the key point execution sequence sorted by the trajectory direction to ensure a smooth transition of the drone's attitude; reverse flight control refers to the navigation mode of the drone flying in the opposite direction from the end point of the path to the starting point, and the speed and altitude need to be dynamically adjusted in combination with real-time positioning data.

[0207] In this embodiment, a reasonable length threshold for a single flight segment is first calculated based on the remaining battery power and the maximum flight endurance of the aircraft. The reverse flight trajectory is then segmented into multiple flight segments based on this threshold. Key points within each flight segment are sorted by spatial proximity to generate a sequence of flight segments from the endpoint to the starting point. The flight control system generates control commands based on real-time positioning data and flight segment parameters (such as the maximum climb angle and horizontal speed limit within the segment), dynamically adjusting motor power and control surface deflection angles to ensure stable sensor data acquisition as the drone executes sequential reverse flight.

[0208] In order to solve the problems of mission conflicts caused by the asynchronous execution of the rescue mission and the path verification signal, operation interruption caused by the lag in abnormal fluctuation monitoring, and residual risks caused by the reliance on manual verification of data deletion, in some embodiments, according to step 107, the synchronization verification signal sent by the ground station is continuously detected during the reverse flight. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the drone is controlled to execute the preset rescue mission to the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed, including:

[0209] Step 701: During the reverse flight, the positioning signal transmitter continuously receives a synchronization verification signal broadcasted by a ground station, wherein the synchronization verification signal includes an encrypted set of path key points and corresponding verification timestamps.

[0210] In this step, the verification timestamp is the precise time mark when the signal is generated, which is used to verify the timeliness of the instruction.

[0211] In this embodiment, the drone continuously monitors the synchronization verification signal broadcast by the ground station via the multi-band receiving unit of the positioning signal transmitter. Upon receiving the signal, the drone first decrypts the signal frame header using a pre-stored geographic block key to extract the path keypoint set and verification timestamp. This keypoint set is then converted to the drone's local navigation coordinate system using a geographic coordinate system conversion algorithm. This keypoint set is then spatially aligned with the path keypoint set of the current evacuation path for subsequent use by the matching module.

[0212] Step 702: Frame parsing is performed on the received synchronization verification signal to extract a set of path key points in each frame of the signal, and the path key point set is matched point by point with the path key point set of the current evacuation path of the UAV;

[0213] In this step, frame parsing refers to the technology of dividing the continuous signal stream into independent data frames according to the communication protocol, ensuring that each frame contains a complete set of key points; point-by-point matching refers to the process of comparing the received key points with the local path key points one by one according to the spatial coordinates and calculating the coordinate offset.

[0214] In this embodiment, the received synchronization check signal is first segmented into independent frames according to the data frame length and delimiter defined by the communication protocol. A cyclic redundancy check (CRC) is performed on each frame to verify data integrity, and then a set of keypoints is extracted. A spatial hashing algorithm is used to establish a keypoint index. Each received keypoint is then searched for its nearest neighbor against the keypoints in the drone's local path by coordinates, and the Euclidean distance between them is calculated. If the distance is less than the positioning error threshold, the point is marked as a match.

[0215] Step 703: When the number of successfully matched path key points exceeds a preset ratio and the deviation between the verification timestamp and the scan completion timestamp in the device ready status code is less than a preset threshold, it is determined that the synchronization verification signal matches the evacuation path;

[0216] In this embodiment, the number of matching keypoints is first counted, and their proportion to the total number of keypoints is calculated. Simultaneously, the scan completion timestamp in the device ready status code is extracted, and the offset between this and the verification timestamp is calculated. If the matching ratio exceeds a threshold and the offset is less than a preset time window, the synchronization verification signal is deemed valid, triggering the flight control system to execute an evacuation according to the updated path. Otherwise, a verification failure indication is sent to the ground station, requesting retransmission or path replanning.

[0217] Step 704: Control the rescue mission execution module carried by the UAV to read the coordinate set of the target area from the mission marking file, and dynamically adjust the execution order of the rescue mission according to the distribution density of the key points of the path in the synchronization verification signal;

[0218] In this embodiment, the target area coordinates are first extracted from the mission tag file. A spatial clustering algorithm is then used to group and sort the coordinates by disaster type and weight. The distribution density of key points along the path is then read from the synchronization verification signal, and a dynamic priority scheduling algorithm is used to adjust the order of rescue mission execution: high-density areas (with dense key points) prioritize rapid scanning tasks (such as heat source location), while low-density areas prioritize wide-area coverage tasks (such as supply delivery). This adjusted task sequence is mapped to the spatial distribution of key points along the path, ensuring that the flight trajectory is coordinated with the mission execution rhythm.

[0219] Step 705: During the execution of the rescue mission, real-time on-site environmental data of the target area is collected, and the on-site environmental data is compared with the parameters of the disaster risk area in the mission marking file for abnormal fluctuations. When the amplitude of the abnormal fluctuation exceeds a preset tolerance, the rescue mission execution is suspended and an abnormal interrupt request is sent to the ground station;

[0220] In this embodiment, the drone collects temperature, smoke concentration and terrain deformation data of the target area in real time through multispectral sensors and gas detectors, and compares them in real time with the disaster parameter thresholds stored in the mission marking file: when it is detected that the temperature fluctuation exceeds the standard deviation of the historical mean or the terrain deformation rate exceeds the preset tolerance, the current mission is immediately suspended and a multi-dimensional interrupt request containing the anomaly type and coordinates is sent to the ground station.

[0221] Step 706: After receiving the mission continuation instruction returned by the ground station, the remaining flight segments of the evacuation path are updated according to the latest path key point set in the synchronization verification signal, and the rescue mission execution module is restarted;

[0222] In this embodiment, after receiving the mission continuation instruction issued by the ground station, the drone parses the updated set of path key points in the instruction, uses the incremental path planning algorithm to smoothly stitch the new key points with the remaining evacuation path using Bezier curves, and automatically skips the completed scan area when resuming execution from the mission interruption point. At the same time, the sensor power consumption and flight speed are dynamically allocated according to the remaining power and path length.

[0223] Step 707: When the rescue mission execution module triggers the completion status flag, it traverses all coordinate entries associated with the target area in the mission mark file, filters out coordinate entries that have been covered by the path key point set in the synchronization verification signal, and generates a list to be deleted;

[0224] In this step, the completion status is identified as a status mark triggered by the drone rescue mission execution module after completing the preset mission objectives (such as material delivery and heat source scanning); the list to be deleted is a set of coordinate entries that have been completely covered by path key points and have no new disaster risks, which are screened out through spatial coverage analysis. It is used to dynamically clean up redundant data in the mission marking file.

[0225] In this embodiment, when the rescue mission execution module triggers the completion status flag, the system traverses all target area coordinate entries in the mission marker file and uses a spatial coverage analysis algorithm to determine whether each coordinate entry is completely covered by the path key points set in the synchronization verification signal (for example, if the coordinate point is within the buffer range of the key point line). After filtering out all covered coordinate entries, a list of pending deletions is generated and added to a temporary cache queue, awaiting relevance verification.

[0226] Step 708: Perform data relevance verification on the coordinate entries in the to-be-deleted list. If the disaster risk area corresponding to the coordinate entry does not generate any new abnormal data during the execution of the rescue mission, permanently remove the coordinate entry from the mission marking file.

[0227] In this step, data relevance verification refers to the process of retrospectively verifying abnormal data in the disaster risk area corresponding to the coordinate entry to be deleted. By comparing the real-time monitoring data during the task execution cycle with the historical benchmark data, it is confirmed whether the area has generated new abnormal fluctuations; permanent removal refers to completely deleting the coordinate entry that has passed the verification from the storage structure of the task mark file and releasing the resource index it occupies.

[0228] In this embodiment, the system extracts all monitoring data (such as temperature and deformation rate) for a coordinate entry in the to-be-deleted list during the mission execution cycle from the anomaly database and compares it to the baseline parameters in the mission marker file. If no new anomalies are detected during the cycle (e.g., temperature fluctuations within tolerance, or deformation rate stability), the coordinate entry is marked as deletable and its associated metadata is removed from the mission marker file's storage tree. If new anomalies are detected, the entry is retained and the mission replanning process is triggered.

[0229] Figure 2 The present invention provides a schematic diagram of a control system for a drone to check and hit a ground station. Figure 2 As shown, the system includes:

[0230] The acquisition module 21 is configured to collect real-time image data of the ground area along the planned route using a multispectral sensor carried by the drone, compare the real-time image data with a pre-stored geographic feature database for terrain contours, and identify disaster risk areas;

[0231] A compression module 22 is configured to synchronously generate a mission marking file containing the coordinates of the disaster risk area during the flight of the UAV, and compress the mission marking file into an encrypted data packet with a geographic tag;

[0232] The sending module 23 is used to send the encrypted data packet to the ground station in an adaptive frequency hopping mode through the multi-band communication module, and simultaneously receive the rescue priority instruction returned by the ground station;

[0233] Scanning module 24 is configured to dynamically adjust the hovering altitude and patrol speed of the UAV according to the rescue priority instruction, so that the UAV's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement;

[0234] A verification module 25 is used to verify the integrity of the device ready status code and generate an action authorization identifier when the verification is successful;

[0235] The first control module 26 is configured to control the drone's onboard positioning signal transmitter when the action authorization identifier matches the scan completion time, generate an evacuation path based on the real-time position data, and control the drone to fly in the reverse direction along the evacuation path to a safe coordinate;

[0236] The second control module 27 is used to continuously detect the synchronization verification signal sent by the ground station during the reverse flight. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, it controls the UAV to perform a preset rescue mission on the target area and automatically deletes the data associated with the target area in the mission marking file after the mission is completed. Figure 2 The control system of the UAV ground station can execute Figure 1 The implementation principles and technical effects of the control method for a drone ground station inspection described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the control system for a drone ground station inspection described in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0237] In one possible design, Figure 2 The control system of a drone detecting and attacking ground station in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0238] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0239] The processing component 32 is used for the above Figure 1 The embodiment provides a control method for a drone to inspect and attack a ground station.

[0240] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0241] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0242] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0243] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0244] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0245] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0246] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a control method for a UAV to inspect and attack a ground station.

[0247] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0248] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0249] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0250] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for a drone to check and hit a ground station, characterized in that: include: Using a multispectral sensor carried by a drone to collect real-time image data of the ground area along the planned route, the real-time image data is compared with a pre-stored geographic feature database for terrain contours to identify disaster risk areas; During the flight of the UAV, a mission marking file containing the coordinates of the disaster risk area is synchronously generated, and the mission marking file is compressed into an encrypted data packet with a geotag; Sending the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and receiving a rescue priority instruction returned by the ground station; Dynamically adjust the drone's hovering altitude and patrol speed according to the rescue priority instructions, so that the drone's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement; Verify the integrity of the device ready status code, and when the verification is successful, generate an action authorization identifier; When the parsed action authorization identifier matches the scan completion time, the drone's onboard positioning signal transmitter is controlled to generate an evacuation path based on the real-time location data, and the drone is controlled to fly in the reverse direction along the evacuation path to a safe coordinate; During the reverse flight, the synchronization verification signal sent by the ground station is continuously detected. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the drone is controlled to perform a preset rescue mission on the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed.

2. The method according to claim 1, characterized in that The multispectral sensor carried by the drone collects real-time image data of the ground area along the planned route, compares the real-time image data with the pre-stored geographic feature database for terrain contours, and identifies disaster risk areas, including: The multispectral sensor collects real-time image data of a ground area using three band combinations, wherein the band combinations include a first band group for distinguishing vegetation coverage, a second band group for detecting changes in surface temperature, and a third band group for identifying the material of artificial structures; Dividing the real-time image data into a preset number of geographic blocks, where the size of each geographic block is dynamically adjusted based on the current flight altitude of the drone and the sensor field of view, so that the distribution of surface cover types within each geographic block is consistent; Performing resolution adaptation on the real-time image data of each geographic block, adjusting the pixel spacing of the real-time image data according to the density of altitude changes in the corresponding area in the geographic feature database, and generating real-time image data after resolution adaptation; Extracting a set of terrain contour lines corresponding to the current geographic block from the geographic feature database, comparing the real-time image data after resolution adaptation with the set of terrain contour lines layer by layer, and calculating the spatial correlation between the pixel point feature and the terrain contour based on the band combination data corresponding to each pixel point in the real-time image data and the slope change direction between adjacent altitude mutation points in the set of terrain contour lines; Filtering out a set of pixels whose spatial correlation is lower than a dynamic threshold as a candidate area for abnormal heat sources; Detecting fracture areas in the slope change direction between consecutive altitude mutation points in the terrain contour line set, and marking them as candidate structural deformation areas when the extension direction of the fracture area coincides with the offset direction of the material boundary line of the artificial structure at the same location in the real-time image data; The spatially overlapping portion of the abnormal heat source candidate region and the structural deformation candidate region and the independent region within a preset distance between the abnormal heat source candidate region and the structural deformation candidate region are merged into a disaster risk region.

3. The method according to claim 2, characterized in that During the flight of the UAV, a mission marker file containing the coordinates of the disaster risk area is synchronously generated, and the mission marker file is compressed into an encrypted data packet with a geotag, including: Converting the absolute geographic coordinates of the disaster risk area into relative coordinates relative to the boundary of the current geographic block, and generating a task tag file containing a disaster type identifier based on the relative coordinates; Adding the geographic block number and the current flight altitude data of the UAV to the mission tag file to generate an original mission file with a geographic tag; The original task file is divided into multiple independent data blocks, each data block corresponds to a set of coordinates of a disaster risk area within a geographical block, and the compression rate is dynamically adjusted according to the distribution density of the coordinate points in the data block to obtain a processed data block; Perform redundancy elimination on each processed data block, delete coordinate points that overlap with adjacent geographic blocks, and generate a hash chain containing the correspondence between data block numbers and compression ratios; Arrange the processed data blocks in the order of geographic block numbers, and insert the corresponding hash chain into the header of each processed data block to generate a compressed task file; Performing layered encryption on the compressed task file using a built-in encryption module of the drone to obtain an encrypted compressed task file, wherein the first layer of encryption generates a dynamic key based on the geographic block number, and the second layer of encryption combines the hash chain node and the drone's current timestamp to generate a verification identifier; Encapsulate the encrypted compressed task file into an encrypted data packet with a timestamp and a sequence of geographic block numbers.

4. The method according to claim 1, wherein Dynamically adjust the drone's hovering altitude and patrol speed based on the rescue priority instructions, so that the drone's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage reaches the preset requirements, including: parsing the weight value of each disaster risk area in the rescue priority instruction; Calculating a hovering height adjustment gradient for the UAV between the disaster risk areas according to a distribution ratio of the weight values; The scanning range of each disaster risk area is divided into multiple angular coverage sectors, the center line of each angular coverage sector forms a preset angle with the current flight direction of the drone, and the scanning dwell time of each angular coverage sector is dynamically allocated according to the gradient of the hovering height adjustment; During the flight of the UAV, the number of scans of each angular coverage sector is monitored in real time. When the number of scans in the same angular coverage sector reaches a preset coverage threshold and the scanning time interval between adjacent angular coverage sectors is less than a preset tolerance, the angular coverage sector is marked as a valid coverage area. Counting the total proportion of effective coverage areas in all disaster risk areas, and when the total proportion exceeds a preset coverage threshold, generating raw state data including the scanning completion timestamps of all current effective coverage areas and the corresponding hovering height adjustment gradients; Performing time series encoding on the original state data, binding the scanning completion timestamp with the real-time position coordinates of the drone, and generating an intermediate state file with a time series identifier; Sending the intermediate state file to a ground station for verification through a preset encrypted channel of the multi-band communication module, and receiving a verification sequence result returned by the ground station after the sending is completed, wherein the verification sequence result includes an encrypted check code that matches the timing identifier; The encrypted verification code is verified, and when the encrypted verification code passes the integrity verification, the intermediate state file is converted into a device ready state code, where the device ready state code includes a mapping relationship between the scan completion timestamp and the position of the effective coverage area.

5. The method according to claim 1, characterized in that Verify the integrity of the device ready status code. If the verification is successful, generate an action authorization identifier, including: Performing layered decryption on the device readiness status code using a decryption key pre-stored on the drone, extracting the original timestamp and effective coverage area location mapping table of the device readiness status code from the decrypted data, and calculating the deviation from the reference time window of the ground station, wherein the reference time window is determined based on a preset tolerance range before and after the scan completion timestamp; When the deviation is less than a preset threshold and the number of missing areas in the effective coverage area location mapping table does not exceed a preset ratio, it is determined that the integrity verification of the device ready status code has passed; Generate a dynamic authorization factor based on the scan completion timestamp and the effective coverage area location mapping table, perform a superposition operation on the dynamic authorization factor and the weight value distribution ratio in the rescue priority instruction, and generate an intermediate authorization file including a timestamp binding code and an area coverage identifier; The intermediate authorization file is double-encrypted, and the double-encrypted data is encapsulated as an action authorization identifier with a timestamp sequence, and is transmitted back to the drone through the response channel of the multi-band communication module, wherein the action authorization identifier includes an encrypted timestamp check segment that matches the scan completion timestamp.

6. The method according to claim 1, characterized in that When the parsed action authorization identifier matches the scan completion time, the drone's onboard positioning signal transmitter is controlled to generate an evacuation path based on the real-time position data, and the drone is controlled to fly in the reverse direction along the evacuation path to a safe coordinate, including: Extracting the encrypted timestamp check segment from the action authorization identifier, performing cyclic shift decryption in combination with the millisecond-level precision value of the scan completion timestamp, and obtaining the original time series including the check code; Comparing the deviation between the original time series and the current time system of the drone, and activating the multi-band positioning function of the onboard positioning signal transmitter when the deviation value is less than a preset time window threshold; Generate a set of key points for the evacuation route based on the elevation change rate and horizontal displacement rate in the real-time position data of the UAV; performing redundancy elimination processing on the set of key points on the path, deleting turning points whose distance from the boundary of a preset safety restricted area is less than a warning threshold, and recalculating the reverse flight trajectory based on the distribution density of the remaining turning points; The reverse flight trajectory is divided into a preset number of flight segments, and the flight segments are sorted according to the turning points in the path key point set to generate a flight segment sequence, and the UAV is controlled to perform reverse flight according to the flight segment sequence.

7. The method according to claim 1, characterized in that During the reverse flight, the synchronization verification signal sent by the ground station is continuously detected. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the drone is controlled to perform a preset rescue mission to the target area, and the data associated with the target area in the mission marker file is automatically deleted after the mission is completed, including: During the reverse flight, the positioning signal transmitter continuously receives a synchronization verification signal broadcast by a ground station, wherein the synchronization verification signal includes an encrypted set of path key points and corresponding verification timestamps; Perform frame parsing on the received synchronization verification signal, extract the path key point set in each frame signal, and match the path key point set with the path key point set of the UAV's current evacuation path point by point; When the number of successfully matched path key points exceeds a preset ratio and the deviation between the verification timestamp and the scan completion timestamp in the device ready status code is less than a preset threshold, it is determined that the synchronization verification signal matches the evacuation path; Controlling the rescue mission execution module carried by the UAV to read the coordinate set of the target area from the mission marking file, and dynamically adjust the execution order of the rescue mission according to the distribution density of the key points of the path in the synchronization verification signal; During the execution of the rescue mission, the on-site environmental data of the target area is collected in real time, and the on-site environmental data is compared with the parameters of the disaster risk area in the mission marking file for abnormal fluctuations. When the amplitude of the abnormal fluctuation exceeds the preset tolerance, the rescue mission execution is suspended and an abnormal interrupt request is sent to the ground station; After receiving the mission continuation instruction returned by the ground station, updating the remaining flight segments of the evacuation path according to the latest path key point set in the synchronization verification signal, and restarting the rescue mission execution module; When the rescue mission execution module triggers a completion status flag, it traverses all coordinate entries associated with the target area in the mission mark file, filters out coordinate entries that have been covered by the path key point set in the synchronization verification signal, and generates a list to be deleted; Perform data relevance verification on the coordinate entries in the to-be-deleted list, and permanently remove the coordinate entry from the task marking file when the disaster risk area corresponding to the coordinate entry does not generate any new abnormal data during the execution of the rescue mission.

8. A control system for a drone to inspect and attack a ground station, characterized in that: include: An acquisition module, configured to collect real-time image data of the ground area along the planned route using a multispectral sensor carried by the drone, compare the real-time image data with a pre-stored geographic feature database for terrain contours, and identify disaster risk areas; A compression module is used to synchronously generate a mission marking file containing the coordinates of the disaster risk area during the flight of the UAV, and compress the mission marking file into an encrypted data packet with a geo-tag; a sending module, configured to send the encrypted data packet to the ground station in an adaptive frequency hopping mode via a multi-band communication module, and simultaneously receive a rescue priority instruction returned by the ground station; A scanning module is used to dynamically adjust the hovering height and patrol speed of the drone according to the rescue priority instruction, so that the drone's onboard monitoring equipment can scan at least two disaster risk areas from multiple angles, and send a device ready status code to the ground station when the scanning coverage rate reaches a preset requirement; A verification module, configured to verify the integrity of the device ready status code and generate an action authorization identifier when the verification is successful; a first control module, configured to control an onboard positioning signal transmitter of the UAV when the action authorization identifier is parsed to match the scanning completion time, generate an evacuation path based on the real-time position data, and control the UAV to fly in the reverse direction along the evacuation path to a safe coordinate; The second control module is used to continuously detect the synchronization verification signal sent by the ground station during the reverse flight. When the positioning signal transmitter receives the synchronization verification signal matching the evacuation path, the second control module controls the UAV to perform a preset rescue mission on the target area, and automatically deletes the data associated with the target area in the mission marking file after the mission is completed.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a control method for a drone to inspect a ground station as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a control method for a drone to inspect a ground station as described in any one of claims 1 to 7 is implemented.

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