Control method and system for unmanned aerial vehicle to check and hit ground station

Abstract

The invention provides a control method and system for an unmanned aerial vehicle to check and hit a ground station. The method comprises the following steps: firstly, acquiring real-time image data, carrying out terrain contour comparison, identifying a disaster risk area, secondly, generating a task mark file, compressing the task mark file into an encrypted data packet with a geographic label, then sending the encrypted data packet to a ground station, simultaneously receiving a rescue priority instruction, and adjusting the hovering height and the inspection navigational speed of the unmanned aerial vehicle. The method comprises the steps of receiving a device ready state code, sending the device ready state code to a ground station, verifying the integrity of the device ready state code to generate an action authorization identifier, controlling an unmanned aerial vehicle to fly to a safe coordinate, controlling the unmanned aerial vehicle to execute a task when a positioning signal transmitter receives a synchronous verification signal, and automatically deleting data after the task is completed; according to the technical scheme provided by the invention, rapid response, accurate execution and data closed-loop management of the unmanned aerial vehicle to the multi-target area in a disaster rescue scene are realized.

Description

Technical Field

[0001] This application relates to the technical field of ground station control, and particularly to a control method and system for an unmanned aerial vehicle (UAV) search-and-strike ground station. Background Art

[0002] In civilian disaster rescue scenarios, for tasks such as geological disaster monitoring, fire warning, and personnel search and rescue, it is necessary to enable UAVs to quickly identify multi-type disaster risk areas in complex terrains, transmit real-time data, and dynamically respond to tasks.

[0003] The current mainstream solution is a UAV control method based on fixed-route inspection and single-band communication. Its technical features include: performing area coverage scanning through preset fixed routes, using a single-band communication module to transmit back the coordinate data of the disaster area, and manually adjusting the flight altitude and speed of the UAV by ground station operators according to the transmitted data to complete the secondary detailed inspection task. This method uses an offline geographic database comparison mechanism to preliminarily mark the disaster area through timed data packet transmission.

[0004] However, it still has the following deficiencies. First, the fixed route and manual adjustment mechanism cause the UAV to be unable to dynamically respond to the priority changes of multi-disaster areas, resulting in scanning blind spots and being unable to synchronously process the collaborative scanning requirements of at least two disaster areas. Second, single-band communication is vulnerable to interference in complex electromagnetic environments, leading to interruptions or delays in encrypted data packet transmission and affecting the real-time issuance of rescue instructions from the ground station. Third, offline database comparison and manual parameter adjustment result in a lag in disaster identification and task execution response, and lack an automatic closed-loop management mechanism for task marking files, posing risks of data leakage or residue. Summary of the Invention

[0005] This application provides a control method and system for a UAV search-and-strike ground station to solve the problems of poor timeliness of disaster response, low multi-target scanning coverage rate, and insufficient reliability of data closed-loop management in the prior art.

[0006] In a first aspect, this application provides a control method for a UAV search-and-strike ground station, including: Collecting real-time image data of a ground area along a planned route through a multi-spectral sensor carried by the UAV, comparing the real-time image data with a pre-stored geographic feature database for terrain contour comparison to identify disaster risk areas; Synchronously generating a task marking file containing the coordinates of the disaster risk areas during the flight of the UAV, and compressing the task marking file into an encrypted data packet with geographic tags; Sending the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and simultaneously receiving a rescue priority instruction returned by the ground station; Dynamically adjust the hovering height and inspection speed of the drone according to the rescue priority instructions, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and sends a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement; Verify the integrity of the device ready status code, and generate an action authorization identifier after passing the verification; When parsing that the action authorization identifier matches the scanning completion time, control the on-board positioning signal transmitter of the drone, generate an evacuation path according to the real-time position data, and at the same time control the drone to fly backward along the evacuation path to a safe coordinate; Continuously detect the synchronization check signal sent by the ground station during the backward flight. When the positioning signal transmitter receives the synchronization check signal that matches the evacuation path, control the drone to perform the preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed.

[0007] Optionally, collect real-time image data of the ground area along the planned flight path through the multi-spectral sensor carried by the drone, compare the real-time image data with the pre-stored geographical feature database for terrain contour comparison, and identify disaster risk areas, including: Collect real-time image data of the ground area through the multi-spectral sensor under three band combinations. Among them, the band combination includes a first band group for distinguishing the vegetation coverage degree, a second band group for detecting the surface temperature change, and a third band group for identifying the material of artificial structures; Divide the real-time image data into a preset number of geographical blocks, and the size of each geographical block is dynamically adjusted according to the current flight height of the drone and the sensor field of view angle, so that the distribution of surface coverage types within each geographical block is consistent; Perform resolution adaptation on the real-time image data of each geographical block, and adjust the pixel pitch of the real-time image data according to the altitude change density of the corresponding area in the geographical feature database to generate the resolution-adapted real-time image data; Extract the terrain contour line set corresponding to the current geographical block from the geographical feature database, compare the resolution-adapted real-time image data with the terrain contour line set layer by layer, and calculate the spatial correlation degree between the pixel point feature and the terrain contour for the band combination data corresponding to each pixel point in the real-time image data, in combination with the slope change direction between adjacent altitude mutation points in the terrain contour line set; Screen out the set of pixel points with a spatial correlation degree lower than the dynamic threshold as the candidate area for abnormal heat sources; Detect the slope change direction fracture area 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 artificial structure material boundary line at the same position in the real-time image data, mark it as a candidate area for structural deformation; Merge the spatial overlapping part of the abnormal heat source candidate area and the structural deformation candidate area, and the independent areas where the abnormal heat source candidate area and the structural deformation candidate area are separated within a preset distance, into a disaster risk area.

[0008] Optionally, during the flight of the drone, synchronously generate a task marking file containing the coordinates of the disaster risk area, and compress the task marking file into an encrypted data packet with geographical tags, including: Convert the absolute geographical coordinates of the disaster risk area into relative coordinates relative to the boundary of the current geographical block, and generate a task marking file containing disaster type identifiers based on the relative coordinates; Add the geographical block number and the current flight altitude data of the drone to the task marking file to generate a raw task file with geographical tags; Split the raw task file into multiple independent data blocks, each data block corresponding to a set of coordinates of the disaster risk area within a geographical block, and dynamically adjust the compression ratio according to the distribution density of the coordinate points in the data block to obtain the processed data block; Perform redundancy elimination processing on each processed data block, delete the coordinate points overlapping with adjacent geographical blocks, and generate a hash chain containing the corresponding relationship between the data block number and the compression ratio; Arrange the processed data blocks in the order of geographical block numbers, and insert the corresponding hash chain at the head of each processed data block to generate a compressed task file; Perform hierarchical encryption on the compressed task file through the encryption module built in the drone to obtain the encrypted compressed task file. Among them, the first layer of encryption generates a dynamic key based on the geographical block number, and the second layer of encryption combines the hash chain node and the current timestamp of the drone to generate a verification identifier; Package the encrypted compressed task file into an encrypted data packet with a timestamp and a geographical block number sequence.

[0009] Optionally, dynamically adjust the hovering height and inspection flight speed of the drone according to the rescue priority instruction, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and sends a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement, including: Analyze the weight value of each disaster risk area in the rescue priority instruction; Calculate the hovering height adjustment gradient of the drone among the disaster risk areas according to the distribution ratio of the weight values; Divide the scanning range of each disaster risk area 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 dynamically allocate the scanning residence time of each angular coverage sector according to the hovering height adjustment gradient; During the flight of the drone, monitor the scanning times of each angular coverage sector in real time. When the scanning times in the same angular coverage sector reach the preset coverage times threshold and the scanning time interval of the adjacent angular coverage sector is less than the preset tolerance, mark the angular coverage sector as an effective coverage area; Statistically calculate the total proportion of the effective coverage areas in all disaster risk areas. When the total proportion exceeds the preset coverage rate threshold, generate the original status data including the scanning completion timestamp of the current all effective coverage areas and the corresponding hovering height adjustment gradient; Perform time series encoding on the original status data, bind the scanning completion timestamp with the real-time position coordinates of the drone, and generate an intermediate status file with time series identification; Send the intermediate status file to the ground station through the preset encryption 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. The verification sequence result includes an encryption check code matching the time series identification; Verify the encryption check code. When the encryption check code passes the integrity verification, convert the intermediate status file into a device ready status code, and the device ready status code includes the position mapping relationship between the scanning completion timestamp and the effective coverage area.

[0010] Optionally, verify the integrity of the device ready status code. When the verification passes, generate an action authorization identifier, including: Perform hierarchical decryption on the device ready status code through the decryption key pre-stored in the drone, extract the original timestamp of the device ready status code and the effective coverage area position mapping table from the decrypted data, and calculate the deviation amount with the reference time window of the ground station, where the reference time window is determined according to the preset tolerance range before and after the scanning completion timestamp; When the deviation amount is less than the preset threshold and the number of missing areas in the effective coverage area position mapping table does not exceed the preset ratio, determine that the integrity verification of the device ready status code passes; Generate a dynamic authorization factor based on the scanning completion timestamp and the effective coverage area position 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 including a timestamp binding code and a regional coverage degree identifier; Perform double encryption processing on the intermediate authorization file, encapsulate the data after double encryption into an action authorization identifier with a timestamp sequence, and transmit it back to the drone through the response channel of the multi-band communication module. Among them, the action authorization identifier includes an encrypted timestamp verification segment that matches the scanning completion timestamp.

[0011] Optionally, when it is parsed that the action authorization identifier matches the scanning completion time, control the on-board positioning signal transmitter of the drone, generate an evacuation path based on the real-time position data, and at the same time control the drone to fly backward along the evacuation path to a safe coordinate, including: Extract the encrypted timestamp verification segment from the action authorization identifier, perform cyclic displacement decryption in combination with the millisecond-level precision value of the scanning completion timestamp to obtain the original time sequence containing the verification code; Compare the deviation between the original time sequence and the current time system of the drone. When the deviation value is less than the preset time window threshold, activate the multi-band positioning function of the on-board positioning signal transmitter; Generate a set of path key points for the evacuation path based on the elevation change rate and horizontal displacement rate in the real-time position data of the drone; Perform redundancy elimination processing on the set of path key points, delete the turning points whose distance from the boundary of the preset safety restricted area is less than the warning threshold, and recalculate the reverse flight trajectory according to the distribution density of the remaining turning points; Divide the reverse flight trajectory into a preset number of flight segments, sort them according to the turning points in the set of path key points to generate a flight segment sequence, and control the drone to perform reverse flight according to the flight segment sequence.

[0012] Optionally, continuously detect the synchronization verification signal sent by the ground station during the reverse flight. When the positioning signal transmitter receives a synchronization verification signal that matches the evacuation path, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed, including: Continuously receive the synchronization verification signal broadcast by the ground station through the positioning signal transmitter during the reverse flight. The synchronization verification signal includes an encrypted set of path key points and the corresponding verification timestamp; Perform frame-by-frame parsing on the received synchronization verification signal, extract the set of path key points in each frame of the signal, and perform point-by-point matching of the set of path key points with the set of path key points of the current evacuation path of the drone; When the number of successfully matched path key points exceeds the preset ratio and the deviation between the verification timestamp and the scanning completion timestamp in the device ready status code is less than the preset threshold, determine that the synchronization verification signal matches the evacuation path; Control the rescue mission execution module carried by the drone to read the coordinate set of the target area from the mission marker file, and dynamically adjust the execution order of the rescue mission according to the distribution density of the path key points in the synchronization verification signal; During the execution of the rescue mission, collect the on-site environment data of the target area in real time, compare the abnormal fluctuations of the on-site environment data with the parameters of the disaster risk area in the mission marker file. When the amplitude of the abnormal fluctuation exceeds the preset tolerance, suspend the execution of the rescue mission and send an abnormal interruption request to the ground station; After receiving the mission continuation instruction returned by the ground station, update the remaining flight segments of the evacuation path according to the latest path key point set in the synchronization verification signal, and restart the rescue mission execution module; When the rescue mission execution module triggers the completion status flag, traverse all the coordinate entries associated with the target area in the mission marker file, and filter out the coordinate entries covered by the path key point set in the synchronization verification signal to generate a list of coordinates to be deleted; Perform data relevance verification on the coordinate entries in the list of coordinates to be deleted. When no new abnormal data is generated in the disaster risk area corresponding to the coordinate entry during the execution of the rescue mission, permanently remove the coordinate entry from the mission marker file.

[0013] In a second aspect, the present application provides a control system for a drone to search and strike a ground station, including: An acquisition module, configured to collect real-time image data of a ground area along a planned flight path through a multi-spectral sensor carried by the drone, compare the real-time image data with a pre-stored geographical feature database for terrain contour comparison, and identify disaster risk areas; A compression module, configured to synchronously generate a mission marker file containing the coordinates of the disaster risk area during the flight of the drone, and compress the mission marker file into an encrypted data packet with geographical tags; A sending module, configured to send the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and at the same time receive the rescue priority instruction returned by the ground station; A scanning module, configured to dynamically adjust the hovering height and inspection speed of the drone according to the rescue priority instruction, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and send an equipment ready status code to the ground station when the scanning coverage rate reaches the preset requirement; A verification module, configured to verify the integrity of the equipment ready status code, and generate an action authorization flag when the verification is passed; The first control module is used to control the onboard positioning signal transmitter of the drone when the parsed action authorization identifier matches the scan completion time, generate an evacuation path based on real-time position data, and at the same time control the drone to fly backward 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 backward flight. When the positioning signal transmitter receives the synchronization verification signal that matches the evacuation path, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed.

[0014] In a third aspect, an embodiment of the present application provides a computing device, including 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 search and attack a ground station as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a control method for a drone to search and attack a ground station as described in the first aspect.

[0016] In the embodiment of the present application, real-time ground images are collected through a multispectral sensor and dynamically compared with a geographic feature database, realizing accurate identification and dynamic marking of disaster risk areas; using encrypted data packets with geographic tags and an adaptive frequency hopping communication mode to ensure the secure transmission of disaster data and the instruction interaction efficiency in a complex electromagnetic environment; dynamically adjusting 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 backward flight along the evacuation path and the synchronization verification signal trigger mechanism, realizing seamless switching between the execution of emergency tasks and safe evacuation of the drone, and at the same time automatically deleting sensitive data, forming a full-process closed loop from disaster identification, task execution to data management, and solving the technical defects of lagging response, insufficient multi-target collaboration ability and data residue risk in the traditional solution.

[0017] Furthermore, by analyzing the weight value distribution in the rescue priority instruction, the hover height gradient and the scanning sector residence time are dynamically calculated, enabling the UAV to adaptively allocate scanning resources according to the degree of disaster threat; by real-time monitoring the scanning times and time intervals of the angle coverage sector, the effective coverage area is accurately determined and the coverage rate is statistically 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 status file with a time sequence identifier, and the integrity of the verification code is verified through an encrypted channel, forming a trusted link from data collection, status generation to instruction feedback; finally, the verified intermediate status file is converted into a device ready status code to achieve dynamic matching of the scanning status and the ground station instruction, effectively solving the problems of scanning blind spots, instruction verification lag, and low multi-target cooperation efficiency caused by fixed flight parameters in the prior art.

[0018] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 The flowchart showing a control method of a UAV search and strike ground station provided by the present application is shown; Figure 2 The structural schematic diagram showing a control system of a UAV search and strike ground station provided by the present application is shown; Figure 3 The structural schematic diagram showing a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0022] In some processes described in the specification, claims, and the above-mentioned drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, 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 such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0023] In the civilian disaster rescue scenario, the existing UAV control methods rely on fixed-route inspection and single-band communication mechanisms, and there are the following key bottlenecks: First, the fixed route and manual adjustment mode cannot allocate scanning resources according to the dynamic changes of disaster risks, resulting in low efficiency of collaborative scanning in multi-target areas and blind spots; Second, the single-band communication has insufficient anti-interference ability in complex electromagnetic environments, and the transmission stability of encrypted data packets is poor, seriously affecting the real-time performance of ground station instructions; Third, the offline geographical database comparison and manual task management mode lead to a significant lag in the response cycle from disaster identification to task execution, and the task marking files rely on manual operations, resulting in data management loopholes.

[0024] To address the above problems, this application proposes a control method for a UAV search-and-strike ground station, which realizes the full-process optimization through a technical architecture of dynamic priority response - multi-modal encrypted transmission - flight parameter adaptive adjustment - data closed-loop management. Specifically, based on the contour comparison of real-time multi-spectral images and geographical feature libraries, disaster areas are dynamically identified, and encrypted data packets with geographical tags are generated; the reliability and anti-interference ability of command transmission are improved through multi-band adaptive frequency hopping communication; combined with the rescue priority command parsing and hover height gradient allocation mechanism, efficient collaborative scanning of multi-disaster areas is realized; relying on the reverse generation of evacuation paths and the trigger logic of synchronous verification signals, it is ensured that the UAV accurately executes rescue tasks during safe evacuation and automatically deletes associated data. This solution systematically solves the problems of scanning blind spots, command delays, and data residues in the prior art through dynamic priority matching, anti-interference transmission links, and automated data closed-loop mechanisms, significantly improving the disaster response efficiency and task execution safety.

[0025] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] Figure 1 The following is a flowchart of a control method for an unmanned aerial vehicle (UAV) reconnaissance and strike ground station provided by an embodiment of the present application. As Figure 1 shown, the method includes: Step 101, collect real-time image data of a ground area along a planned flight path by a multispectral sensor carried by the UAV, compare the real-time image data with a pre-stored geographic feature database for terrain contour comparison, and identify a disaster risk area; In this step, the parameters and features involved include a multispectral sensor, a planned flight path, a geographic feature database, terrain contour comparison, and a disaster risk area. Among them, the multispectral sensor refers to a device carried on the UAV that can simultaneously collect data of multiple specific band combinations. The first band group is used to enhance the identification of vegetation cover differences, the second band group strengthens the detection of surface temperature changes, and the third band group distinguishes the material characteristics of artificial structures. The planned flight path refers to the trajectory of the UAV flying along a preset path, and the waypoint spacing is dynamically adjusted according to the flight altitude and the sensor coverage range. The geographic feature database is a three-dimensional spatial data set of the terrain elevation, surface cover type, and geological stability index of the pre-stored target area. Terrain contour comparison refers to the process of spatially matching the surface undulation characteristics of the real-time image with the elevation mutation points and slope break lines in the database. The disaster risk area refers to a geographic block that simultaneously meets the characteristics of abnormal thermal radiation, structural deformation, and matches the historical disaster pattern.

[0027] In this embodiment, first, the ground image data is collected by the multispectral sensor according to a preset band combination. The first band group analyzes the vegetation density difference by calculating the vegetation index, the second band group inversely calculates the surface temperature distribution based on radiometric calibration, and the third band group uses a spectral matching algorithm to identify the material of the structure. Then, the real-time image is dynamically segmented into geographic blocks, and the block size is dynamically calculated according to the UAV flight altitude and the sensor field of view angle to ensure the consistent distribution of surface types within each block. Subsequently, resolution adaptation is performed on each block, and the image pixel density is adjusted according to the elevation change density in the corresponding area of the database to match the spatial accuracy of the real-time image and the terrain data. In the terrain contour comparison stage, the three-dimensional grid node data of the corresponding block is extracted from the database, and the spatial correlation degree between the adapted image pixel points and the grid nodes is calculated. The pixel points with inconsistent band characteristics and the terrain slope direction are selected as candidate areas for abnormal heat sources, and at the same time, the spatial coincidence area between the continuous slope fracture area in the terrain grid and the image material demarcation line is detected as a candidate area for structural deformation. Finally, the spatial overlapping part and the adjacent areas of the two types of candidate areas are merged to output the coordinate set of the disaster risk area.

[0028] For example, in the scenario of forest fire monitoring, the drone flies along the planned route, and the multispectral sensor synchronously collects data on vegetation, temperature, and structures. 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 reflection characteristics of metal materials. The image is dynamically segmented into uniform blocks. After the resolution is improved according to the elevation density of the database, the surface depression and slope mutation are clearly captured. Through terrain contour comparison, it is found that the spatial correlation between the high-temperature pixel points and the steep slope area is lower than the threshold, and there is a coincidence between the continuous slope fracture zone and the material boundary line. The system determines that this area is a complex disaster risk and outputs the center coordinates to the task marking file.

[0029] Step 102, during the flight of the drone, synchronously generate a task marking file containing the coordinates of the disaster risk area, and compress the task marking file into an encrypted data packet with geographical tags; In this step, the task marking file is a structured data file recording the coordinates and type identifiers of the disaster risk area, including the relative coordinate conversion result based on the geographical block boundary, the unique number of the geographical block, and the real-time flight altitude parameter of the drone; the geographical tag refers to the spatial position identification information embedded in the data packet, which is dynamically combined by the unique number of the geographical block and the flight altitude, and is used to establish the spatial mapping relationship between the data packet and the acquisition position; the encrypted data packet is a compressed file processed by a hierarchical dynamic encryption mechanism, including a dynamic key generated based on the geographical block number and a timestamp verification identifier, ensuring the anti-interference and integrity of data transmission.

[0030] In this embodiment, first, convert the absolute geographical coordinates of the disaster risk area into relative coordinates relative to the current geographical block boundary. When converting, calculate the offset based on the preset side length of the block and the coordinates of the center point. Then, assign a disaster type identifier to each relative coordinate, and generate an original task file with geographical tags by combining the unique number of the geographical block and the flight altitude. Subsequently, divide the original task file into independent data blocks according to geographical blocks, and dynamically adjust the compression ratio according to the distribution density of the coordinate points in the data block - adopt a high compression ratio strategy for dense areas and a low compression ratio strategy for sparse areas. Perform redundancy elimination processing on each data block, delete the points that overlap with the coordinates of adjacent blocks, and generate a hash chain recording the corresponding relationship between the data block number and the compression ratio. Arrange the processed data blocks in the order of block numbers, and insert a hash chain node at the head of each data block. Finally, through hierarchical encryption processing: the first layer encrypts the data block with the dynamic key generated by the geographical block number, and the second layer performs secondary encryption by combining the hash chain node and the current timestamp to generate a verification identifier, forming an encrypted data packet with a timestamp sequence.

[0031] For example, in the scenario of forest fire monitoring, the geographical block number of the disaster risk area identified in step 101 is G-23. The system converts the absolute coordinates of this area into relative coordinates relative to the boundary of block G-23 and marks it as a composite disaster type. After adding the G-23 number and flight altitude label to the original task file, it is segmented into data blocks. Since the coordinate points in this block are densely distributed, a high compression ratio strategy is adopted for compression. After redundancy elimination deletes some overlapping points with adjacent blocks, hash chain nodes are generated. After the data blocks are encrypted with a dynamic key, a verification identifier is generated in combination with the current timestamp, and finally encapsulated into an encrypted data packet.

[0032] Step 103, send the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and at the same time receive the rescue priority instruction returned by the ground station; In this step, the multi-band communication module refers to a hardware unit that supports dynamic switching of multiple communication bands and is used to maintain data transmission stability in a complex electromagnetic environment; the adaptive frequency hopping mode refers to a technology that dynamically selects the optimal communication band according to the results of real-time channel interference detection, and realizes anti-interference transmission through a preset frequency band switching sequence and interference avoidance strategy; the encrypted data packet is the hierarchical encrypted data with geographical tags and timestamps generated in step 102; the rescue priority instruction refers to the action instruction generated by the ground station according to the disaster type, regional threat level and resource scheduling strategy, and includes the target area scanning order, flight parameter adjustment threshold and task execution authorization information.

[0033] In this embodiment, first, the channel interference intensity of the current airspace is detected by the spectrum scanning unit of the multi-band communication module to generate a dynamic frequency hopping sequence - preferentially select the frequency bands with interference intensity lower than the threshold to form a transmission channel. Then, the encrypted data packet is segmented into multiple data frames according to the preset frame length, and a geographical tag and timestamp identifier are added to the header of each data frame. When sending, it cycles and switches 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 instruction receiving channel is synchronously opened, and the response frequency band specified by the ground station is listened to during the data transmission gap. After receiving the rescue priority instruction, the instruction content is parsed: the target area scanning order code, hovering height adjustment gradient and airspeed threshold parameters are extracted, and spatial matching verification is performed with the real-time position data of the UAV to ensure the executability of the instruction.

[0034] For example, in the scenario of forest fire monitoring, the encrypted data packet generated in step 102 carries the disaster coordinates of block G-23. After the multi-band communication module detects strong interference in some frequency bands in the current airspace and generates a frequency hopping sequence to exclude the interference bands, the data packet is segmented into multiple frames and cyclically sent through the clean frequency bands. After receiving the data packet, the ground station parses it, generates a priority instruction based on the fire spread trend and the distribution of rescue resources, designates block G-23 as the primary scanning target, and issues the instruction through the low-interference frequency band. The UAV listens to the response frequency band during the sending gap. After receiving the instruction, it verifies its spatial matching with block G-23. After confirming the legality of the instruction, it stores it in the flight control unit.

[0035] Step 104: Dynamically adjust the hovering height and inspection speed of the UAV according to the rescue priority instruction, so that the on-board monitoring equipment of the UAV performs multi-angle coverage scanning on at least two disaster risk areas, and sends a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement; 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, and it has an inverse correlation with the hovering height to maintain the stability of the sensor; the multi-angle coverage scanning refers to a technology that makes the on-board monitoring equipment collect multi-directional data on the same target area by dividing multiple scanning sectors and dynamically allocating the stay time; 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 scanning integrity; the device ready status code is an encrypted verification identifier containing the scanning completion timestamp, the position mapping relationship of the effective coverage area, and the flight parameter adjustment record.

[0036] In this embodiment, first, parse the weight values of the disaster areas in the rescue priority instruction, and calculate the hovering height adjustment gradient based on the weight ratio - the area with a higher weight corresponds to a lower height and a smaller speed fluctuation range. Divide each disaster area into multiple angle coverage sectors, with a fixed angle formed between the center line of the sector and the flight direction, and dynamically allocate the scanning stay time of each sector according to the height gradient. During the flight, continuously monitor the scanning times and time intervals of each sector. When the scanning times of the same sector reach the standard and the switching time between adjacent sectors is less than the tolerance threshold, mark it as an effective coverage area. Statistically calculate the effective coverage rate of all disaster areas. After reaching the standard, generate the original status data containing the scanning completion timestamp and the height adjustment gradient. Perform time series encoding on the original data, bind the timestamp with the real-time position of the UAV to generate an intermediate status file, and send it to the ground station for verification through an encrypted channel. After receiving the verification sequence returned by the ground station, verify the encrypted verification code and convert it to generate the device ready status code.

[0037] For example, in the scenario of forest fire monitoring, the ground station issues an instruction to specify G-23 (fire) and G-24 (landslide) as the priority scanning areas. After the UAV parses the instruction, it calculates the hovering height gradient: the height of G-23 drops to low altitude and the flight speed decreases; G-24 maintains medium altitude and scans at a constant speed. G-23 is divided into six scanning sectors, and each sector is allocated a longer stay time to capture the details of the fire; G-24 is divided into four sectors for rapid scanning of structural deformation. During the flight, when it is monitored that all six sectors of G-23 have reached the scanning times and the switching time meets the requirements, and three sectors of G-24 meet the standards. After the system calculates that the total coverage rate exceeds the preset threshold, it generates a status file containing the timestamp and position mapping, and encrypts and sends it to the ground station.

[0038] Step 105: Verify the integrity of the device ready status code. After the verification passes, generate an action authorization identifier. In this step, the hovering height adjustment gradient is a flight height change parameter dynamically calculated according to the weight distribution of the disaster areas in the rescue priority instruction, which is used to optimize the data acquisition accuracy; the inspection flight speed refers to the adaptive adjustment threshold of the UAV's horizontal movement speed, which is inversely related to the hovering height to ensure the stability of the sensor; the multi-angle coverage scanning realizes the omnidirectional monitoring of the target area by the airborne equipment by dividing multiple scanning sectors and dynamically allocating the stay time; the scanning coverage rate is used to measure the proportion of the effective scanning area in the total target area and is used as the basis for judging the scanning integrity; the device ready status code is an encrypted verification identifier containing the scanning completion time, the position mapping of the covered area, and the flight parameter record.

[0039] In this embodiment, first, parse the disaster weight data in the rescue priority instruction, and calculate the corresponding hovering height gradient based on the weight level - the areas with higher weights are allocated lower flight heights and smaller speed fluctuation ranges to improve the scanning resolution. Then, each disaster area is divided into multiple angular scanning sectors, the center line of the sector maintains a fixed angle with the flight direction, and the stay duration of each sector is dynamically allocated according to the height gradient. During the 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 between adjacent sectors meets the requirements, it is marked as an effectively covered area. Statistically calculate the effective coverage rate of all disaster areas. After meeting the standard, generate the original data integrating the scanning completion time, the covered position, and the flight parameters, form an intermediate file by binding the timestamp and the real-time position through time coding, and encrypt and send it to the ground station for verification. After receiving the verification code feedback from the ground station and passing the verification, generate the device ready status code.

[0040] For example, in forest fire rescue, the ground station designates G-23 (fire) and G-24 (landslide) as priority areas. After the UAV parses the instructions, it reduces the altitude for G-23 to capture fire details, divides it into six sectors and extends the stay time; for G-24, it maintains a medium altitude to quickly scan for structural deformation and divides it into four sectors. Flight monitoring shows that all six sectors of G-23 meet the standards, and three sectors of G-24 meet the standards. After the total coverage rate exceeds the threshold, a status file is generated. The file is encrypted and sent to the ground station. After passing the verification, it is converted into a ready status code to trigger the subsequent evacuation mission.

[0041] Step 106, when the parsed action authorization identifier matches the scan completion time, control the on-board positioning signal transmitter of the UAV, generate an evacuation path based on the real-time position data, and at the same time control the UAV to fly backward along the evacuation path to the safety coordinates; In this step, the action authorization identifier is an encrypted instruction generated after the ground station verifies the device ready status code, including the scan completion timestamp, the authorized execution period, and the target area coordinate binding information; the scan completion time refers to the scan end time recorded in the device ready status code in step 104, which needs to match the timestamp in the action authorization identifier to verify the timeliness; the evacuation path refers to the flight trajectory generated based on the UAV real-time position data and the safety coordinates, which is composed of a set of path key points, and the distance between key points is dynamically adjusted according to the UAV flight attitude; flying backward refers to the control mode in which the UAV flies backward from the current coordinates to the safety coordinates along the evacuation path, which is used to avoid sudden obstacles and shorten the return time.

[0042] In this embodiment, first parse the encrypted timestamp verification segment in the action authorization identifier, and restore the original time series through the cyclic shift decryption algorithm combined with the millisecond-level precision data of the scan completion timestamp. Compare the deviation between the decrypted time series and the UAV local system time. If the deviation value is lower than the preset threshold, activate the multi-band positioning function of the on-board positioning signal transmitter. Based on the elevation change rate and horizontal displacement rate in the real-time position data, generate a set of key points for the evacuation path - the distance between key points is dynamically calculated according to the maximum pitch angle to ensure flight stability. Perform redundancy elimination on the set of key points, delete the turning points near the safety restricted area, and re-plan the backward flight trajectory according to the distribution density of the remaining key points. Divide the trajectory into several flight segments, and the length of each segment is positively correlated with the remaining battery power and insert the elevation constraint and speed limit parameters of the turning points within the segment. Finally, control the UAV to perform backward flight in the order of flight segments, and synchronously adjust the positioning signal frequency band to match the signal attenuation curve of the ground station receiving device.

[0043] For example, in a forest fire rescue scenario, the operation authorization identifier generated in step 105 includes the scan completion timestamp of Block G-23. When the timestamp deviation value meets the requirements during parsing, the positioning module is activated to obtain the real-time position, and a set of key points for the evacuation path is generated. After deleting the dangerous turning points near the fire spread area, the remaining key points form a reverse flight trajectory. The trajectory is segmented into three flight segments, and the segment lengths are allocated according to the remaining battery power. The flight altitude is reduced in the first segment to avoid the thick smoke area, and the speed is increased in the last segment to shorten the evacuation time. The drone flies in reverse along the path, and the positioning signal is switched to the anti-interference frequency band to ensure continuous communication with the ground station.

[0044] Step 107, 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 that matches the evacuation path, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed; In this step, the synchronization verification signal is an encrypted verification instruction periodically sent by the ground station, which includes the set of key points of the evacuation path and the timestamp identifier, and is used to confirm the spatio-temporal consistency of the drone flight trajectory and task execution; Evacuation path matching means that the set of key points received by the drone is exactly the same as the key point sequence of its current flight trajectory; The preset rescue task refers to an action plan predefined for the type of disaster, including but not limited to emergency material delivery, high-precision image acquisition, or positioning beacon deployment; The task marker file is an encrypted data file containing the disaster coordinates generated in step 102; The automatic deletion mechanism refers to an irreversible operation that removes the processed data in the task marker file based on the data correlation verification rule after the task is completed.

[0045] In this embodiment, the drone continuously listens to the synchronization verification signal sent by the ground station during the reverse flight, parses the signal frame structure through the multi-band receiving unit of the positioning signal transmitter, and extracts the encrypted set of key points of the path and the verification timestamp. The parsed key points are spatially matched point by point with the turning point sequence of the current evacuation path, and at the same time, it is verified whether the deviation between the timestamp and the scan completion timestamp is within the tolerance range. After successful matching, the coordinates of the target area are read from the task marker file, and the preset rescue task module is called according to the type of disaster - for example, the fire extinguishing agent delivery program is started in the fire scene, and the three-dimensional modeling scan is triggered in the landslide area. During the task execution, the environmental parameters (such as wind speed, temperature) are collected in real time and compared with the disaster parameters in the task marker file for fluctuations. If the fluctuations exceed the limit, the task is suspended and an abnormal interruption request is sent to the ground station. After receiving the continue instruction, the remaining evacuation path is updated based on the latest synchronization verification signal, and the task module is restarted until the completion state is triggered. Finally, the task marker file is traversed, the coordinate entries covered by the key points of the path are screened to generate a list to be deleted, and the relevant data is permanently removed after verifying no new anomalies.

[0046] For example, during forest fire rescue, when the drone flies in the reverse direction along the evacuation path generated in step 106, it receives a synchronization verification signal sent by the ground station. After analyzing the signal, it is found that the path key points are consistent with the current trajectory and the timestamp is valid, triggering the fire extinguishing agent delivery task. During the execution, it is monitored that the sudden increase in wind speed causes abnormal diffusion of the fire extinguishing agent, and the system suspends the task and sends an interruption request. After the ground station evaluates and issues a continue instruction, the drone updates the path to avoid the strong wind area and then restarts the task, successfully completing the delivery of the fire extinguishing agent. After the task ends, the system filters the processed fire coordinates in block G-23, and deletes the relevant data from the task marking file after verifying that there are no new fire points.

[0047] To solve the problems that the traditional multi-spectral data uses a single band, resulting in incomplete recognition of surface features, the mismatch between geographical block division and sensor field of view parameters causing resolution adaptation deviation, and the static comparison of terrain contours being unable to capture dynamic deformation features, in some embodiments, according to step 101, real-time image data of the ground area is collected by a multi-spectral sensor carried by the drone along the planned flight path, and the real-time image data is compared with the pre-stored geographical feature database for terrain contour comparison to identify disaster risk areas, including: Step 201, collect real-time image data of the ground area by the multi-spectral sensor under three band combinations, where the band combinations include a first band group for distinguishing vegetation coverage degree, a second band group for detecting surface temperature changes, and a third band group for identifying the materials of artificial structures; In this step, the first band group is a collection channel in the multi-spectral sensor that enhances the vegetation reflection difference through a specific combination of visible light and near-infrared bands, and is used to distinguish different vegetation density areas such as forests and grasslands; the second band group is a thermal infrared band combination that captures temperature anomaly areas through the difference in surface thermal radiation energy; the third band group is a hyperspectral band combination that identifies artificial structure types such as concrete and metal based on the difference in spectral reflection curves of different materials.

[0048] In this embodiment, first start the multi-channel synchronous acquisition mode of the multi-spectral sensor: the first band group captures vegetation reflection data in a continuous scanning manner, and a vegetation density distribution map is generated in real time through a normalized difference vegetation index calculation module; the second band group collects surface thermal radiation data in an intermittent trigger mode, and combines the radiation calibration parameters to invert the temperature distribution map; the third band group obtains material reflection spectrum data in a high-resolution line-by-line scanning manner, and generates a material classification map using a spectral angle matching algorithm. Subsequently, the three groups of data are aligned according to geographical coordinates, and are fused into a three-dimensional image data block containing vegetation index, temperature gradient and material type through a spatial overlay algorithm, and output to the geographical feature database comparison module.

[0049] Step 202: Divide the real-time image data into a preset number of geographical blocks. The size of each geographical block is dynamically adjusted according to the current flight altitude of the drone and the sensor field of view angle, so that the distribution of surface cover types within each geographical block is consistent. In this step, a geographical block refers to an image processing unit dynamically divided according to the drone flight parameters. Its size is dynamically calculated based on the geometric relationship between the flight altitude and the sensor field of view angle to ensure that the distribution fluctuation of surface cover types (such as vegetation, bare soil, water bodies) within the block does not exceed a preset threshold. The consistent distribution of surface cover types means that the proportion of the main surface type within a single geographical block exceeds a set ratio, and the distribution of secondary types has no significant spatial aggregation.

[0050] In this embodiment, first, calculate the theoretical side length of the geographical block according to the real-time flight altitude of the drone and the sensor field of view angle. When the altitude increases, the block size is enlarged to improve the scanning efficiency; when the altitude decreases, the size is reduced to enhance the resolution. Then, divide the real-time image data into a rectangular grid according to the calculated size, and conduct statistical analysis on the surface cover types within each grid. If the proportion of a certain type exceeds the threshold and the distribution of the remaining types is uniform, then retain the block; if the types are mixed or the secondary types show an aggregated distribution, then recalculate the block boundary and iterate the division until the distribution consistency condition is met. Finally, output a set of geographical blocks with unique numbers to provide a standardized input for subsequent resolution adaptation.

[0051] Step 203: Perform resolution adaptation on the real-time image data of each geographical block. Adjust the pixel pitch of the real-time image data according to the density of altitude changes in the corresponding area in the geographical feature database to generate real-time image data after resolution adaptation. In this step, resolution adaptation refers to the process of dynamically adjusting the image pixel density according to the terrain complexity index pre-stored in the geographical feature database. The density of altitude changes is quantified by the number and distribution gradient of elevation mutation points within a unit area, and is used to characterize the severity of terrain undulation. The pixel pitch refers to the actual ground distance represented by adjacent pixel points in the image. The smaller the pitch, the higher the resolution and the stronger the ability to capture terrain details.

[0052] In this embodiment, first, extract the elevation mutation point distribution data of the current geographical block from the geographical feature database and calculate its density level. If the density is higher than the preset threshold, it is determined as a complex terrain, and pixel interpolation encryption is performed on the real-time image to reduce the pixel spacing to enhance the detail resolution; if the density is lower than the threshold, it is determined as a flat terrain, and pixel aggregation is used to increase the spacing to optimize the storage efficiency. During the adaptation process, based on the spatial distribution pattern of the elevation mutation points, the resolution is locally increased in key areas (such as steep slopes and gullies), rather than adjusted uniformly. After completing the resolution adaptation, align the adjusted image pixel coordinates with the three-dimensional terrain grid nodes in the geographical feature database to generate adapted image data that passes the spatial consistency check.

[0053] Step 204: Extract the set of terrain contour lines corresponding to the current geographical block from the geographical feature database, layer-by-layer compare the real-time image data after resolution adaptation with the set of terrain contour lines, and for the band combination data corresponding to each pixel point in the real-time image data, calculate the spatial correlation degree between the pixel point features and the terrain contour in combination with the slope change direction between adjacent altitude mutation points in the set of terrain contour lines. In this step, the set of terrain contour lines refers to the three-dimensional space grid lines formed by connecting the elevation mutation points in the geographical feature database, which is used to describe the surface undulation form 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 connection line between adjacent mutation points on the horizontal plane, which is used to quantify the steepness and spatial continuity of the terrain trend; the spatial correlation degree is an index of the matching degree between the multi-spectral features (such as temperature, material reflection spectrum) of the pixel point and the terrain slope direction, which is used to evaluate the coupling relationship between surface anomalies and terrain structures.

[0054] In this embodiment, first, extract the set of terrain contour lines of the current geographical block from the geographical feature database, obtain the coordinates of all elevation mutation points and the slope direction data between adjacent points. Map the real-time image pixels after resolution adaptation to the terrain grid units according to the spatial coordinates, and extract the multi-spectral band combination data (such as vegetation index, temperature gradient, material classification result) for each pixel point. For each pixel point, calculate the slope change direction of the grid unit where it is located, and establish a correlation model in combination with the band data characteristics - for example, when a high-temperature pixel point is located in a steep slope area, the correlation degree decreases due to conflict with the heat convection law; when a metal material pixel point is located in a gentle area, the correlation degree increases due to consistency with the distribution law of artificial structures. If the correlation degree is lower than the dynamically adjusted anomaly threshold, it is marked as a candidate anomaly point. Finally, generate a layer containing the distribution of anomaly points and their correlation degree levels, and output it to the disaster area merging module.

[0055] Step 205: Screen out the set of pixel points with a spatial correlation degree lower than the dynamic threshold as the candidate area for abnormal heat sources. In this step, the dynamic threshold refers to the critical value for abnormal determination dynamically calculated based on the terrain complexity of the current geographical block and historical disaster patterns, and its value is adjusted inversely with the degree of terrain undulation and the probability of disaster occurrence; the candidate area for abnormal heat sources refers to the set of pixel points with a spatial correlation degree lower than the dynamic threshold, indicating that the coupling relationship between the surface heat radiation characteristics and the terrain contour in this area significantly deviates from the normal mode and may indicate potential heat disaster risks.

[0056] In this embodiment, first, the abnormal determination threshold of the current block is dynamically calculated according to the terrain complexity level of the geographical block (such as the density of elevation mutation points and the gradient of slope change) and the occurrence frequency of abnormal heat sources in the same type of terrain in the historical disaster database. Then, traverse the spatial correlation degree layer generated in step 204, extract all pixel points with correlation degree values lower than the dynamic threshold to form an initial candidate area. Perform morphological closing operations on the candidate area to eliminate isolated noise points, and merge adjacent pixel points into continuous abnormal areas through a spatial clustering algorithm. Finally, output a set of candidate areas with heat source intensity levels and spatial boundary information to provide input for subsequent disaster risk area merging.

[0057] Step 206: Detect the slope change direction fracture area between consecutive elevation mutation points in the set of terrain contour lines. When the extension direction of the fracture area coincides with the offset direction of the artificial structure material demarcation line at the same position in the real-time image data, mark it as the candidate area for structural deformation. In this step, the slope change direction fracture area refers to the area where the slope extension trend between consecutive elevation mutation points in the set of terrain contour lines undergoes a discontinuous jump, manifested as the horizontal extension direction of the line connecting adjacent mutation points mutating beyond the geological stability threshold; the offset direction of the artificial structure material demarcation line refers to the spatial offset trend of the reflection spectrum feature boundary line of artificial materials such as concrete and metal in the real-time image data relative to the original position recorded in the historical database, used to indicate the deformation or displacement of the structure; the candidate area for structural deformation is the geographical block where the slope fracture area coincides with the offset direction of the material demarcation line, representing the potential risk of geological structure instability or artificial facility damage.

[0058] In this embodiment, first, a slope direction sequence of continuous elevation mutation points is extracted from the terrain contour line set, and fracture sections with direction mutations exceeding the stability threshold are detected, and their horizontal extension directions are recorded. At the same time, the material demarcation lines of artificial structures are extracted from the real-time image data, and spatial offset comparison is performed with the original demarcation lines in the historical database through an edge detection algorithm to calculate the offset direction vector. Spatial overlay analysis is performed on the extension direction of the slope fracture zone and the offset direction of the material demarcation line. If the two coincide within a preset tolerance range, it is determined as a candidate area for structural deformation. Morphological dilation processing is performed on the candidate area to cover the deformation influence range, and marked data with deformation levels and boundary coordinates is output.

[0059] Step 207: Combine the spatially overlapping part of the abnormal heat source candidate area and the structural deformation candidate area, and the independent areas where the abnormal heat source candidate area and the structural deformation candidate area are separated by a preset distance into a disaster risk area; In this step, the preset distance refers to a spatially adjacent determination threshold dynamically adjusted according to the disaster type and terrain complexity, which is used to identify the potential correlation between the abnormal heat source and the structural deformation; the disaster risk area is a set of the spatially overlapping part of the abnormal heat source candidate area and the structural deformation candidate area and the adjacent independent areas, which characterizes the spatial coupling characteristics of the occurrence of compound disasters; the spatial proximity effect means 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.

[0060] In this embodiment, first, spatial overlay analysis is performed on the abnormal heat source candidate area and the structural deformation candidate area to generate a disaster core area with complete overlap. Then, 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 the preset threshold. The core area and the adjacent areas are combined to form an initial set of disaster risk areas. Subsequently, based on the historical disaster pattern data in the geographical feature database, disaster coupling weights are calculated for the initial set - if a region has both high-temperature anomaly and structural deformation characteristics, the weight is increased; if only one condition is met but there is a historical disaster association record in the adjacent area, the weight is maintained. Finally, a set of disaster risk areas with weight levels and spatial boundary information is output. To solve the problems of low transmission efficiency caused by redundant storage of disaster coordinate data, inability of a fixed compression ratio to adapt to the difference in coordinate distribution density, and easy cracking of statically generated encryption keys, in some embodiments, according to what is described in step 102, during the flight of the unmanned aerial vehicle, a task marking file containing the coordinates of the disaster risk area is synchronously generated, and the task marking file is compressed into an encrypted data packet with geographical tags, including: Step 301: Convert the absolute geographic coordinates of the disaster risk area into relative coordinates with respect to the boundary of the current geographic block, and generate a task marker file containing disaster type identifiers based on the relative coordinates. In this step, the absolute geographic coordinates are longitude and latitude coordinates based on the Global Positioning System (GPS), which are used to uniquely identify the spatial location of the disaster risk area; the relative coordinates are local coordinates with the boundary of the current geographic block as the reference system, 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 multi-spectral feature and terrain coupling analysis, and is used to identify disaster types such as fires, landslides, and collapses.

[0061] In this embodiment, first, obtain the set of absolute geographic coordinates of the disaster risk area, and extract the boundary coordinates of the geographic block to which it belongs (such as the longitude and latitude of the southwest corner of the block). Through the coordinate system conversion algorithm, convert the absolute coordinates into relative coordinates with the block boundary as the origin, and retain the elevation information of the coordinate points during the conversion. Then, according to the results of multi-spectral feature and terrain coupling analysis, assign a disaster type identifier to each disaster risk area (such as "F" representing fire and "L" representing landslide). Package the relative coordinates, elevation values, and identifiers into structured data entries in a preset format, add the geographic block number and the current flight altitude parameter of the drone, and generate a task marker file.

[0062] Step 302: Add the geographic block number and the current flight altitude data of the drone to the task marker file to generate a raw task file with geographic tags. In this step, the geographic block number is a unique identifier generated when dynamically dividing geographic blocks in step 202, and is used to establish the mapping relationship between the task file and the geographic spatial location; the current flight altitude data of the drone refers to the real-time flight altitude parameter when collecting the coordinates of the disaster risk area, and is used for subsequent resolution adaptation and dynamic calibration of path planning; the raw task file with geographic tags is a structured data set integrating relative coordinates, disaster type identifiers, geographic block numbers, and flight altitude parameters, forming a standardized input for subsequent processing.

[0063] In this embodiment, first, extract the unique number of the block to which the current disaster risk area belongs from the geographic block division record, and associate it with the relative coordinates and disaster type identifiers in the task marker file. Synchronously read the flight altitude data recorded in real time by the drone flight control system, and append it as metadata to the head of the task marker file. Classify and sort all data entries in the file according to the geographic block number to ensure that the disaster coordinate entries within the same block are continuously stored, and add a check code for the block number and flight altitude at the end of the file to generate a raw task file with geographic tags.

[0064] Step 303: Split the original task file into multiple independent data blocks. Each data block corresponds to a set of coordinates of disaster risk areas within a geographical block, and dynamically adjust the compression ratio according to the distribution density of the coordinate points in the data block to obtain the processed data block; In this step, the independent data blocks are independent data processing units numbered by geographical blocks, containing the set of disaster coordinates and their metadata within a single block; the distribution density refers to the spatial aggregation density of the coordinate points in the data block, quantified by calculating the number of coordinate points per unit area or the average distance between adjacent points; dynamically adjusting the compression ratio means selecting different compression algorithm parameters according to the density. A high-compression ratio algorithm is used in high-density areas to reduce redundancy, and a low-compression ratio algorithm is used in low-density areas to retain details.

[0065] In this embodiment, first, the original task file is split into multiple sub-files according to the geographical block numbers, and each sub-file only contains the coordinate entries within the same block. Perform spatial distribution analysis of the coordinate points for each sub-file: calculate the ratio of the area of the minimum bounding rectangle of the coordinate points to the number of points. If the ratio is lower than the density threshold, it is determined as a high-density area, otherwise it is a low-density area. For high-density areas, a compression algorithm based on spatial difference coding is used to convert the relative offsets of adjacent coordinate points into a difference sequence for storage; for low-density areas, Huffman coding is used for lossless compression of the absolute coordinates. After compression, add the block number, compression algorithm identifier, and metadata checksum to the head of each data block to generate the processed standardized data block.

[0066] Step 304: Perform redundancy elimination processing on each processed data block, delete the coordinate points that overlap with adjacent geographical blocks, and generate a hash chain containing the correspondence between the data block number and the compression ratio; In this step, redundancy elimination processing means deleting duplicate coordinate points across geographical block boundaries through spatial overlap analysis to avoid the same disaster risk area being repeatedly recorded by multiple adjacent data blocks; the hash chain is a chained data structure that records the data block number, compression ratio, and data block content summary, 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 of the hash chain to ensure that the data can be correctly restored during subsequent decryption.

[0067] In this embodiment, first, perform spatial boundary analysis on each processed data block: extract the latitude and longitude ranges of all coordinate points within the data block, perform spatial overlay calculation with the boundary coordinates of adjacent geographical blocks, and identify the coordinate points located in the buffer zone at the block intersection. Filter out the coordinate points that fall into the buffer zones of two or more blocks simultaneously through the spatial index fast query mechanism, mark them as redundant points, and delete them. Then, extract the compression ratio parameter according to the data block number, and use a hash function to generate a unique digest value for the data block content (including the remaining coordinate points, block number, and compression ratio). Link the digest value, number, and compression ratio into a hash chain node in a preset order, and concatenate the nodes in the order of block numbers to form an immutable hash chain structure.

[0068] Step 305: Arrange the processed data blocks in the order of geographical block numbers, and insert the corresponding hash chain at the head of each processed data block to generate a compressed task file; In this step, the order of geographical block numbers refers to the rule of sorting the block numbers according to the spatial continuity of the UAV flight path planning, ensuring that adjacent geographical blocks are continuously stored in the compressed task file to optimize the reading efficiency; the head of the hash chain is a data structure inserted at the starting position of the data block, including the hash digest, compression ratio identifier, and data block length information, which is used for quick positioning and integrity verification; the compressed task file is a standardized file integrating all sorted data blocks and the corresponding hash chains, forming a traceable and verifiable disaster data transmission unit.

[0069] In this embodiment, first, establish a sorting rule according to the spatial distribution characteristics of the geographical block numbers - arrange the data blocks in the order of the geographical space grid covered by the UAV flight path (such as a serpentine reciprocating path), ensuring that adjacent blocks are continuously stored in the file. Extract the hash chain nodes of each data block, serialize the node data (hash digest, compression ratio, data block length) into a byte stream of a fixed format, and insert it at the starting position of the data block as the head identifier. Subsequently, write the data blocks with hash headers into the file stream in the sorting order, and add a global index table at the end of the file to record the starting offset, number, and compression ratio parameter of each data block, and finally generate a compressed task file that supports random access and quick verification.

[0070] Step 306: Perform hierarchical encryption on the compressed task file through the encryption module built in the UAV to obtain the encrypted compressed task file, where the first layer of encryption generates a dynamic key based on the geographical block number, and the second layer of encryption combines the hash chain node and the current timestamp of the UAV to generate a verification identifier; In this step, hierarchical encryption refers to a technology that uses a multi-level encryption mechanism to protect data layer by layer. The first layer of encryption achieves data content confusion based on a dynamic key generated from the geographical block number. The second layer of encryption generates a verification identifier by binding the hash chain node with the timestamp to ensure the integrity and timeliness of data transmission. The dynamic key is an encryption factor calculated in real time according to the uniqueness feature of the geographical block number, and its value is dynamically updated as the block number changes. The verification identifier is an encrypted check code generated by combining the uniqueness of the hash chain node data and the irreversibility of the timestamp, which is used to prevent data tampering and replay attacks.

[0071] In this embodiment, first, perform the first layer of encryption on the compressed task file: extract the geographical block number of the data block, generate a dynamic key through a hash function and a random number generator, and encrypt the data block content using a symmetric encryption algorithm (such as AES). Then perform the second layer of encryption: extract the hash chain node data from the data block header, splice it with the current timestamp of the drone, generate a verification identifier through an asymmetric encryption algorithm (such as RSA), and append it to the end of the encrypted data block. Finally, reorganize the double-encrypted data blocks in the original order to generate an encrypted compressed task file with a timestamp sequence and a dynamic key identifier.

[0072] Step 307, encapsulate the encrypted compressed task file into an encrypted data packet with a timestamp and a geographical block number sequence; In this step, the timestamp is an accurate time identifier when the drone generates the encrypted data packet, recorded in the Coordinated Universal Time (UTC) format, which is used to verify the timeliness of data and the timing consistency of instruction execution. The geographical block number sequence is a set of unique identifiers of geographical blocks arranged in the order of the drone's flight route planning, which reflects the spatial coverage order of the encrypted task file in the data packet. The encrypted data packet is a standardized transmission unit that integrates the encrypted compressed task file, the timestamp, and the geographical block number sequence, and its encapsulation structure supports fast parsing by the ground station and multi-task parallel processing.

[0073] In this embodiment, first, extract all the geographical block numbers of the encrypted compressed task file, and generate a number sequence according to the spatial continuity covered by the drone's flight route (such as G-23→G-24→G-25 under a serpentine path). Then, insert a start identifier and a timestamp field into the data packet header, and the timestamp is taken from the millisecond-level UTC time of the drone's timing module. Convert the geographical block number sequence into binary coding and write it in order after the timestamp field in the data packet header to form a space-time joint index. Subsequently, write the encrypted compressed task file into the data packet body in the order of the numbers, and append a global check code based on the hash chain and a data packet length field to the end of the data packet to complete the encapsulation. The finally generated encrypted data packet meets the requirements of spatial traceability, time anti-counterfeiting, and transmission integrity at the same time.

[0074] To address the issues of uneven scanning angle coverage in multi-target areas caused by a fixed hovering height, the disconnection between weight analysis and flight parameter adjustment, and the command delay caused by the dependence on manual operation for status verification, in some embodiments, as described in step 104, the hovering height and inspection flight speed of the drone are dynamically adjusted according to the rescue priority instruction, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and when the scanning coverage rate reaches the preset requirement, a device ready status code is sent to the ground station, including: Step 401, parse the weight value of each disaster risk area in the rescue priority instruction; 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 index that quantifies the rescue priority of the disaster area in the instruction, and is generated by comprehensively calculating the hazard degree of the disaster type, the density of trapped people, and the environmental diffusion risk. The higher the weight value, the higher the priority level that needs to be responded to first.

[0075] In this embodiment, first, the encrypted content of the rescue priority instruction is read by the instruction parser, and the weight value field in each disaster risk area entry is extracted. The data type verification module is used to filter out abnormal weight values that are non-numerical or outside the reasonable range to ensure the legality of the input data. The legal weight values are normalized, and the distribution ratio of the weight values of each area to the total weight is calculated to generate a weight value distribution table. Finally, the weight value distribution table is bound to the regional geographical coordinates to form a set of disaster areas with priority identifiers, which is output to the flight parameter adjustment module.

[0076] Step 402, calculate the hovering height adjustment gradient of the drone between the disaster risk areas according to the distribution ratio of the weight values; In this step, the hovering height adjustment gradient refers to the drone flight height change parameter dynamically calculated according to the weight value distribution. The area with a high weight value corresponds to a lower hovering height to improve the monitoring accuracy, and the area with a low weight value is raised to expand the coverage range; the gradient calculation needs to combine the maximum climb rate of the drone and the sensor field of view angle constraint to ensure the balance between flight stability and data acquisition efficiency.

[0077] In this embodiment, first, the weight value distribution table output in step 401 is read, and the minimum safe hovering height and the maximum operation height threshold of the drone are set. Based on the principle of inverse mapping of weight values, the weight value distribution is converted into height gradient parameters - the higher the weight value, the lower the hovering height. The linear interpolation algorithm is used to calculate the target height of each area, and the gradient smoothing algorithm is used to dynamically constrain the height difference between adjacent areas to prevent sudden changes in the flight attitude. Finally, a flight control instruction set containing height gradient parameters and regional coordinates is generated and sent to the drone navigation system.

[0078] Step 403: Divide the scanning range of each disaster risk area into multiple angular coverage sectors. The center line of each angular coverage sector forms a preset angle with the current flight direction of the UAV, and dynamically allocate the scanning stay time of each angular coverage sector according to the hovering height adjustment gradient; In this step, the angular coverage sector refers to a fan-shaped area obtained by equally dividing the scanning range of the disaster risk area by azimuth. Its center line forms a fixed angle with the current flight direction of the UAV, which is used to achieve multi-angle data collection. Dynamically allocating the scanning stay time means allocating different stay durations for different sectors according to the hovering height adjustment gradient. A longer time is allocated to the low-altitude area to improve the resolution, and the time is shortened in the high-altitude area to optimize the efficiency.

[0079] In this embodiment, first calculate the scanning range radius based on the current position of the UAV and the geometric center of the disaster risk area, and divide the scanning range into multiple angular coverage sectors according to the preset angle. The center line direction of each sector is generated by superimposing the current flight direction of the UAV and the preset angle. According to the hovering height adjustment gradient, allocate an initial stay time for each sector - the lower the height, the longer the stay time. Subsequently, use a dynamic scheduling algorithm to fine-tune the stay time in combination with real-time wind speed and sensor stability data to ensure data collection quality and flight safety. The adjusted stay time sequence is sent to the flight control system to control the UAV to perform multi-angle scanning in sequence.

[0080] Step 404: During the flight of the UAV, monitor the scanning times of each angular coverage sector in real time. When the scanning times in the same angular coverage sector reach the preset coverage times threshold and the scanning time interval between adjacent angular coverage sectors is less than the preset tolerance, mark the angular coverage sector as an effective coverage area; In this step, the effective coverage area refers to a sector that meets both the scanning times standard and the time interval compliance, indicating the integrity and timeliness of data collection in this area. The scanning times 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 for switching between adjacent sectors to prevent data breaks caused by environmental interference.

[0081] In this embodiment, the UAV flight control system records the scanning times of each sector and the switching timestamps of adjacent sectors in real time. When the scanning times of a certain sector reach the threshold, the system detects whether the time interval from the previous scan is within the tolerance range. If both the times and interval conditions are met, it is marked as an effective coverage area and a coverage status identifier is generated. For sectors that do not meet the standard, a supplementary scan task is dynamically inserted into the flight queue until the conditions are met or the task timeout mechanism is triggered. Finally, output the position mapping table of the effective coverage area for subsequent generation of device ready status codes.

[0082] Step 405: Statistically calculate the total proportion of the effectively covered areas among all disaster risk areas. When the total proportion exceeds the preset coverage rate threshold, generate the original status data including the scan completion timestamp of all current effectively covered areas and the corresponding hover height adjustment gradient. In this step, the total proportion of effectively covered areas refers to the ratio of the sum of the sector areas marked as effectively covered among all disaster risk areas to the total area of the target area. The coverage rate threshold is the minimum proportion requirement for determining the scan integrity and needs to satisfy both spatial coverage sufficiency and time continuity. The original status data is a structured data set including the boundary coordinates of the effectively covered areas, the scan completion timestamp, and the corresponding hover height adjustment gradient, which is used for subsequent status verification and task traceability.

[0083] In this embodiment, first traverse the effective coverage area marking table of all disaster risk areas and calculate the ratio of its total area to the total area of the preset target area. If the total proportion exceeds the threshold, extract the set of boundary coordinates of the current effectively covered areas from the flight control system and associate the scan completion timestamp and the hover height adjustment gradient parameters. Package the three types of data into structured entries according to the area number, and generate the original status data file after adding the global check code.

[0084] Step 406: Perform time series encoding on the original status data, bind the scan completion timestamp with the real-time position coordinates of the UAV, and generate an intermediate status file with time series identifiers. In this step, time series encoding refers to the technology of converting discrete timestamps into continuous time series identifiers, ensuring that the sequence of data events can be traced through the time axis mapping. The intermediate status file is an encrypted preprocessing file integrating time series identifiers, the coordinates of the effectively covered areas, and the hover height parameters, which is used for cross-system verification and instruction authorization.

[0085] In this embodiment, first align the scan completion timestamp in the original status data with millisecond-level precision, and use the time axis bucketing algorithm to encode the continuous timestamps into time series interval identifiers. Then, bind the time series identifiers with the longitude and latitude coordinates provided by the UAV real-time positioning system according to the acquisition time points to generate spatio-temporal coupling data entries. Finally, sort all the entries in ascending order of the time series identifiers and add the metadata summary at the head of the file to generate the intermediate status file.

[0086] Step 407: Send the intermediate status file to the ground station through the preset encryption 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. The verification sequence result includes the encrypted check code matching the time series identifier. In this step, the preset encryption channel refers to a dedicated transmission link reserved for high-priority data in the multi-band communication module, which adopts anti-jamming modulation technology and a hierarchical dynamic encryption mechanism; the verification sequence result is the response data generated after the ground station verifies the intermediate state file, including an encryption check code matching the file timing identifier, which is used to bidirectionally verify the integrity and timeliness of data transmission.

[0087] In this embodiment, first, the intermediate state file is sent through the dedicated channel of the multi-band communication module, and the position check code and timestamp signature of the current UAV are added to the file header during transmission. After receiving the file, the ground station decrypts the file and verifies the continuity of the timing identifier and the coordinate space consistency, generates a verification sequence result containing a hash check code and a timestamp identifier, and returns it to the UAV through the clean frequency band. After receiving the verification result, the UAV compares the check code with the locally calculated hash value. If they are the same, the check code is bound to the device ready status code.

[0088] Step 408, verify the encryption check code. When the encryption check code passes the integrity verification, convert the intermediate state file into a device ready status code, where the device ready status code includes the position mapping relationship between the scan completion timestamp and the effective coverage area; In this step, the encryption check code is a verification identifier containing a hash digest and a timestamp signature returned by the ground station, which is used to confirm that the intermediate state file has not been tampered with during transmission and has timeliness; the integrity verification refers to the process of verifying data integrity by comparing the hash digest returned by the ground station with the locally calculated hash value of the UAV; the device ready status code is an encrypted instruction code generated after the verification passes, including the spatio-temporal coordinate mapping relationship of the effective coverage area and the scan completion timestamp, which is used to authorize the execution of subsequent tasks.

[0089] In this embodiment, first, extract the encryption check code from the verification sequence result, and decrypt the hash digest and timestamp signature in the check code through the public key of the asymmetric encryption algorithm. Then, use the same hash function to calculate the content of the intermediate state file stored locally to generate a local hash value. Compare the local hash value with the decrypted ground station hash digest. If they are the same and the timestamp signature is within the valid period, it is determined that the verification passes. Subsequently, extract the position mapping relationship of the effective coverage area and the scan completion timestamp from the intermediate state file, convert them into a binary instruction sequence according to the preset coding rule, and generate a device ready status code after adding the flight control system authorization identifier.

[0090] To solve the problems of instruction authorization failure caused by the asynchrony between the device ready status code and the time reference, the lack of spatial constraints in the generation of dynamic authorization factors, and the easy forgery of encryption identifiers, in some embodiments, according to what is described in step 105, verify the integrity of the device ready status code. When the verification passes, generate an action authorization identifier, including: Step 501: Perform hierarchical decryption on the device ready status code using the decryption key pre-stored in the drone, extract the original timestamp of the device ready status code and the effective coverage area location mapping table from the decrypted data, and calculate the deviation amount from the reference time window of the ground station, where the reference time window is determined according to the preset tolerance range before and after the scan completion timestamp; In this step, hierarchical decryption refers to the operation of gradually decrypting the encrypted content of the device ready status code using different hierarchical keys pre-stored in the drone; the reference time window is a time verification interval defined by the ground station according to the legal timeliness requirements of the scan completion timestamp, used to determine the legality of the decrypted timestamp; the deviation amount is the absolute time difference between the decrypted original timestamp and the center point of the reference time window, used to quantify the degree of data timeliness offset.

[0091] In this embodiment, first, perform the first-layer decryption on the device ready status code using the dynamic key generated by the geographical block number to restore the intermediate data with a timestamp signature. Then, perform the second-layer decryption through the hash chain key associated with the timestamp to extract the original timestamp and the effective coverage area location mapping table. Compare the original timestamp with the reference time window issued by the ground station and calculate the time deviation amount from the center point of the window Step 502: When the deviation amount is less than the preset threshold and the number of missing areas in the effective coverage area location mapping table does not exceed the preset ratio, it is determined that the integrity verification of the device ready status code passes; 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 defect of data collection; integrity verification is a verification process for determining whether the device ready status code meets the task execution conditions through dual indicators of deviation amount and missing areas; the preset ratio refers to the maximum percentage threshold of the allowable number of missing areas in the total number of target areas, and if it exceeds, the verification fails.

[0092] In this embodiment, first, count the number of blocks marked as "covered" in the effective coverage area location mapping table, calculate the difference between it and the total number of target areas to obtain the number of missing areas. If the number of missing areas does not exceed the preset ratio and the deviation amount is less than the threshold, trigger the verification passed flag. Otherwise, generate a missing area rescan instruction and send it back to the ground station to trigger the task replanning process. After the verification passes, bind the device ready status code to the flight control system and authorize the execution of subsequent rescue tasks.

[0093] Step 503: 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 containing a timestamp binding code and a regional coverage degree identifier; In this step, the dynamic authorization factor is an encrypted verification parameter generated based on the scanning completion timestamp and the effective coverage area position mapping table, which is used to quantify the spatio-temporal authorization intensity of the UAV mission execution; the timestamp binding code is a unique identifier generated by performing a hash operation on the scanning completion timestamp and the geographical block number; the area coverage degree identifier is a proportional level code of the area of the effective coverage area in the total area of the target area, reflecting the integrity of data collection.

[0094] In this embodiment, first, perform time-axis sharding encoding on the scanning completion timestamp to generate a timestamp binding code. At the same time, calculate the coverage ratio of each area according to the effective coverage area position mapping table to generate an area coverage degree identifier. Initialize the dynamic authorization factor as the product of the timestamp binding code and the coverage degree identifier, and then superimpose the weight value distribution ratio in the rescue priority instruction, and generate an intermediate authorization file through a weighted fusion algorithm. The internal part of the file is divided into a timestamp binding segment, a coverage degree identifier segment, and a dynamic authorization factor segment, and each segment is isolated by a separator.

[0095] Step 504, perform double encryption processing on the intermediate authorization file, encapsulate the double-encrypted data into an action authorization identifier with a timestamp sequence, and send it back to the UAV through the response channel of the multi-band communication module, where the action authorization identifier includes an encrypted timestamp verification segment matching the scanning completion timestamp; In this step, the double encryption processing refers to performing symmetric and asymmetric encryption operations on the intermediate authorization file successively. The first layer of encryption uses a dynamic key generated by the geographical block number, and the second layer of encryption uses an asymmetric key bound to the timestamp verification segment.

[0096] In this embodiment, first, perform AES encryption on the intermediate authorization file using a dynamic key generated by the geographical block number to generate the first-layer ciphertext. Then extract the timestamp field in the timestamp binding code, splice it with the real-time positioning coordinates of the UAV, and generate a timestamp verification segment through RSA public key encryption. Append the verification segment to the end of the first-layer ciphertext, and encrypt the overall data with the asymmetric key again to generate a double-encrypted action authorization identifier. Send it to the ground station through the dedicated response channel of the multi-band communication module. After the ground station decrypts it, verify the legality of the timestamp and return a response instruction with a verification code.

[0097] To solve the problems of insufficient obstacle avoidance ability caused by static evacuation path planning, increased flight energy consumption due to redundant turning points, and lack of real-time feedback for path updates, in some embodiments, as described in step 106, when parsing that the action authorization identifier matches the scanning completion time, control the on-board positioning signal transmitter of the UAV, generate an evacuation path according to the real-time position data, and at the same time control the UAV to fly backward along the evacuation path to a safe coordinate, including: Step 601: Extract the encrypted timestamp verification segment from the action authorization identifier, and perform cyclic shift decryption in combination with the millisecond-level precision value of the scan completion timestamp to obtain the original time series containing the verification code. In this step, cyclic shift decryption refers to a decryption technique that performs cyclic bit operations on the encrypted data segment based on the millisecond value of the timestamp. By cyclically shifting the verification code by the displacement amount corresponding to the millisecond value, the original time series is restored. The original time series is a data set containing the complete timestamp verification code and time axis event markers after decryption, which is used for subsequent timeliness verification.

[0098] In this embodiment, first, extract the timestamp verification segment from the encrypted field of the action authorization identifier and read its byte stream structure. Determine the cyclic shift amount according to the millisecond value of the scan completion timestamp, and perform a cyclic right shift operation on the verification segment byte stream to restore the encrypted and disrupted time series structure. The decrypted original time series contains the verification code and time axis event markers, and is output to the time deviation comparison module.

[0099] Step 602: Compare the deviation between the original time series and the current time system of the drone. When the deviation value is less than the preset time window threshold, activate the multi-band positioning function of the on-board positioning signal transmitter. In this step, the time window threshold is the maximum deviation allowed between the drone system time and the authorized timestamp. If it exceeds, it is determined that the clocks are not synchronized. The multi-band positioning function refers to a mode that improves the positioning accuracy by simultaneously enabling multiple positioning signal frequency bands and is used for high-precision navigation in complex terrains.

[0100] In this embodiment, first, extract the decrypted timestamp from the original time series and calculate the deviation value from the current time of the drone's local timing module. If the deviation value is less than the threshold, trigger the multi-band collaborative positioning mode of the positioning signal transmitter: synchronously receive the positioning signals of multiple satellite systems, eliminate the ionospheric error through carrier phase differential technology, and generate real-time position data with centimeter-level accuracy.

[0101] Step 603: Generate a set of path key points for the evacuation path based on the elevation change rate and horizontal displacement rate in the real-time position data of the drone. In this step, the elevation change rate refers to the climbing / descending rate of the drone in the vertical direction, and the horizontal displacement rate is the flight speed in the horizontal plane. The set of path key points is a sequence of spatial turning points generated in the evacuation path according to the terrain complexity and flight stability requirements, and the key point spacing is dynamically optimized according to the real-time flight attitude.

[0102] In this embodiment, first, based on the elevation change rate and horizontal displacement rate in the real-time position data of the drone, in combination with the terrain undulation characteristics in the geographic feature database, generate a set of evacuation path key points through a path optimization algorithm.

[0103] Step 604: Perform redundancy elimination processing on the set of path key points, delete the turning points whose distance from the boundary of the preset safety restricted area is less than the warning threshold, and recalculate the reverse flight trajectory according to the distribution density of the remaining turning points. In this step, the warning threshold is the preset minimum safety distance between the UAV and the safety restricted area. When the threshold is exceeded, the turning point deletion operation is triggered. The reverse flight trajectory refers to the flight path generated according to the remaining key points and opposite to the original evacuation direction, which is used for emergency obstacle avoidance or rapid return scenarios.

[0104] In this embodiment, first, construct a spatial index of the safety restricted area and calculate the shortest distance between each turning point in the set of path key points and the boundary of the restricted area. Screen out the turning points with a distance less than the warning threshold through spatial proximity query, mark them as redundant points and delete them. Perform density clustering analysis on the remaining turning points, and adaptively adjust the parameters of the trajectory generation algorithm according to the distribution density: use spline interpolation to generate a smooth trajectory in the high-density area, and complete the path by linear fitting in the low-density area. The finally generated reverse flight trajectory maintains spatial topological consistency with the original path and satisfies the safety obstacle avoidance constraint.

[0105] Step 605: Divide the reverse flight trajectory into a preset number of flight segments, sort them according to the turning points in the set of path key points to generate the flight segment order, and control the UAV to perform reverse flight according to the flight segment order. In this step, a flight segment refers to a continuous flight unit divided in the reverse flight trajectory according to key points, and its length is positively correlated with the remaining power of the UAV and the stability of the sensor. The flight segment order is the execution sequence of key points sorted according to the trajectory direction, ensuring a smooth transition of the UAV's attitude. Reverse flight control refers to the navigation mode in which the UAV flies reversely from the end point to the start point of the path, and the speed and altitude need to be dynamically adjusted in combination with real-time positioning data.

[0106] In this embodiment, first, calculate a reasonable length threshold for a single flight segment according to the remaining power and the maximum endurance time of the aircraft, and divide the reverse flight trajectory into multiple flight segments according to this threshold. Sort the key points within each flight segment according to spatial proximity to generate the flight segment order from the end point to the start point. The flight control system generates control commands according to real-time positioning data and flight segment parameters (such as the maximum climb angle and horizontal speed limit within the segment), and dynamically adjusts the motor power and rudder surface deflection angle to ensure the stability of sensor data acquisition when the UAV performs reverse flight in sequence.

[0107] To address the issues of task conflicts caused by the asynchrony between rescue mission execution and path verification signals, the lag in abnormal fluctuation monitoring leading to operation interruptions, and the residual risk of data deletion relying on manual verification, in some embodiments, as described in step 107, during the reverse flight process, continuously detect the synchronous verification signal sent by the ground station. When the positioning signal transmitter receives a synchronous verification signal that matches the evacuation path, control the drone to perform a preset rescue mission on the target area, and automatically delete the data associated with the target area in the task marker file after the mission is completed, including: Step 701, during the reverse flight process, continuously receive the synchronous verification signal broadcast by the ground station through the positioning signal transmitter. The synchronous verification signal includes an encrypted set of path key points and the corresponding verification timestamp; In this step, the verification timestamp is an accurate time identifier when the signal is generated, used to verify the timeliness of the instruction.

[0108] In this embodiment, the drone continuously listens for the synchronous verification signal broadcast by the ground station through the multi-band receiving unit of the positioning signal transmitter. After receiving the signal, first use the pre-stored geographical block key to decrypt the signal frame header, extract the set of path key points and the verification timestamp. The set of key points is converted to the drone's local navigation coordinate system through a geographical coordinate transformation algorithm, and is preprocessed for spatial alignment with the set of path key points of the current evacuation path for subsequent calls by the matching module.

[0109] Step 702, perform frame-by-frame parsing on the received synchronous verification signal, extract the set of path key points in each frame of the signal, and perform point-by-point matching between the set of path key points and the set of path key points of the drone's current evacuation path; In this step, frame-by-frame parsing refers to the technique of splitting a continuous signal stream into independent data frames according to the communication protocol to ensure 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.

[0110] In this embodiment, first, according to the data frame length and delimiter defined by the communication protocol, split the received synchronous verification signal into independent frames. Perform cyclic redundancy check (CRC) on each frame of data, extract the set of key points after verifying the data integrity. Establish a key point index through a spatial hashing algorithm, perform a nearest neighbor search on each received key point and the drone's local path key points according to the coordinates, and calculate the Euclidean distance between the two. If the distance is less than the positioning error threshold, mark it as a matching point.

[0111] 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, determine that the synchronous verification signal matches the evacuation path; In this embodiment, first, the number of matching key points is counted, and the proportion of the total number of key points is calculated. At the same time, the scan completion timestamp in the device ready status code is extracted, and the deviation amount is calculated with the verification timestamp. If the matching ratio exceeds the threshold and the deviation amount is less than the preset time window, it is determined that the synchronization verification signal is valid, and the flight control system is triggered to execute the evacuation according to the updated path; otherwise, a verification failure instruction is sent to the ground station to request retransmission or path replanning.

[0112] Step 704, control the rescue mission execution module carried by the UAV to read the coordinate set of the target area from the mission marker file, and dynamically adjust the execution order of the rescue mission according to the distribution density of the path key points in the synchronization verification signal; In this embodiment, first, the coordinate set of the target area is extracted from the mission marker file, and grouped and sorted according to the disaster type and weight value through a spatial clustering algorithm. Read the distribution density of the path key points in the synchronization verification signal, and use the dynamic priority scheduling algorithm to adjust the execution order of the rescue mission: high-density areas (dense key points) give priority to executing fast-scanning tasks (such as heat source localization), and wide-area coverage tasks (such as material delivery) are postponed in low-density areas. The adjusted task sequence forms a mapping relationship with the spatial distribution of the path key points to ensure the coordination of the flight trajectory and the task execution rhythm.

[0113] Step 705, during the execution of the rescue mission, the on-site environmental data of the target area is collected in real time, and the abnormal fluctuations of the on-site environmental data are compared with the parameters of the disaster risk area in the mission marker file. When the amplitude of the abnormal fluctuation exceeds the preset tolerance, the execution of the rescue mission is suspended and an abnormal interruption request is sent to the ground station; In this embodiment, the UAV collects the temperature, smoke concentration and terrain deformation data of the target area in real time through a multi-spectral sensor and a gas detector, and compares them with the pre-stored disaster parameter thresholds in the mission marker file in real time: when it is detected that the temperature fluctuation exceeds the historical mean standard deviation or the terrain deformation rate breaks through the preset tolerance, the current task is immediately suspended and a multi-dimensional interruption request including the abnormal type and coordinates is sent to the ground station.

[0114] Step 706, after receiving the task continuation instruction returned by the ground station, update the remaining flight segments of the evacuation path according to the latest path key point set in the synchronization verification signal, and restart the rescue mission execution module; In this embodiment, after the UAV receives the task continuation instruction issued by the ground station, it parses the updated path key point set in the instruction, uses the incremental path planning algorithm to smoothly stitch the new key points with the remaining evacuation path by Bezier curve, automatically skips the scanned area that has been completed when resuming execution from the task interruption point, and dynamically allocates the sensor power consumption and flight speed according to the remaining battery power and path length.

[0115] Step 707: When the rescue mission execution module triggers the completion status flag, traverse all coordinate entries associated with the target area in the mission marker file, and filter out the coordinate entries that have been covered by the set of path key points in the synchronization verification signal to generate a list of coordinates to be deleted; In this step, the completion status flag is a status marker triggered by the UAV rescue mission execution module after completing the preset mission objectives (such as material delivery, heat source scanning); the list of coordinates to be deleted is a set of coordinate entries that have been completely covered by the path key points and have no new disaster risks screened through spatial coverage analysis, and is used to dynamically clean up redundant data in the mission marker file.

[0116] In this embodiment, after the rescue mission execution module triggers the completion status flag, the system traverses all the target area coordinate entries in the mission marker file, and uses the spatial coverage analysis algorithm to determine whether each coordinate entry is completely covered by the set of path key points in the synchronization verification signal (such as the coordinate point is within the buffer range of the connection line of the key points). After screening out all the covered coordinate entries, a list of coordinates to be deleted is generated and added to the temporary cache queue, waiting for correlation verification.

[0117] Step 708: Perform data correlation verification on the coordinate entries in the list of coordinates to be deleted. When no new abnormal data is generated in the disaster risk area corresponding to the coordinate entry during the execution of the rescue mission, permanently remove the coordinate entry from the mission marker file; In this step, data correlation verification refers to the process of backtracking and verifying abnormal data for the disaster risk area corresponding to the coordinate entry to be deleted. By comparing the real-time monitoring data and historical benchmark data within the mission execution cycle, it is confirmed whether there is a new abnormal fluctuation in this area; permanent removal means completely deleting the coordinate entry that passes the verification from the storage structure of the mission marker file and releasing the resource index it occupies.

[0118] In this embodiment, the system extracts all the monitoring data (such as temperature, deformation rate) of the coordinate entries in the list of coordinates to be deleted from the abnormal database during the mission execution cycle, and performs a time series comparison with the benchmark parameters in the mission marker file. If there is no new abnormal data in this cycle (such as the temperature fluctuation does not exceed the tolerance, and the deformation rate remains stable), the coordinate entry is marked as deletable, and the entry and its associated metadata are removed from the storage tree of the mission marker file; if there is new abnormal data, the entry is retained and the mission replanning process is triggered.

[0119] Figure 2 The following is a schematic structural diagram of a control system for a UAV search and strike ground station provided by an embodiment of the present application, as Figure 2 shown. The system includes: The acquisition module 21 is used to collect real-time image data of the ground area along the planned flight path through a multispectral sensor carried by the drone, compare the real-time image data with a pre-stored geographical feature database, and identify disaster risk areas; The compression module 22 is used to synchronously generate a task marker file containing the coordinates of the disaster risk areas during the flight of the drone, and compress the task marker file into an encrypted data packet with geographical tags; The sending module 23 is used to send the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and at the same time receive the rescue priority instruction returned by the ground station; The scanning module 24 is used to dynamically adjust the hovering height and inspection speed of the drone according to the rescue priority instruction, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and sends a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement; The verification module 25 is used to verify the integrity of the device ready status code, and generate an action authorization identifier after passing the verification; The first control module 26 is used to control the on-board positioning signal transmitter of the drone when parsing that the action authorization identifier matches the scanning completion time, generate an evacuation path according to the real-time position data, and at the same time control the drone to fly backward along the evacuation path to a safe coordinate; The second control module 27 is used to continuously detect the synchronization check signal sent by the ground station during the backward flight. When the positioning signal transmitter receives the synchronization check signal matching the evacuation path, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed. Figure 2 The described control system of the drone for searching and attacking the ground station can execute Figure 1 The control method of the drone for searching and attacking the ground station shown in the embodiments described above, its implementation principle and technical effects will not be elaborated. For the control system of the drone for searching and attacking the ground station in the above embodiments, the specific ways for each module and unit to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0120] In a possible design, Figure 2 The control system of the drone for searching and attacking the ground station shown in the embodiments can be implemented as a computing device, such as Figure 3 shown, this computing device may include a storage component 31 and a processing component 32; 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.

[0121] The processing component 32 is used for the above Figure 1 control method of an unmanned aerial vehicle search-and-strike ground station in the above-described embodiment.

[0122] Among them, 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 by 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 for executing the above method.

[0123] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage 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.

[0124] Of course, the computing device may necessarily further include other components, such as input / output interfaces, display components, communication components, etc.

[0125] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0126] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0127] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from the cloud computing platform.

[0128] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 control method of an unmanned aerial vehicle search-and-strike ground station in the above-described embodiment.

[0129] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0130] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0131] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A control method for an unmanned aerial vehicle to search and strike a ground station, characterized in that Including: Collect real-time image data of the ground area along the planned route through a multi-spectral sensor carried by a drone, compare the real-time image data with a pre-stored geographical feature database for terrain contour comparison, and identify disaster risk areas; During the flight of the drone, synchronously generate a task marker file containing the coordinates of the disaster risk areas, and compress the task marker file into an encrypted data packet with geographical tags; Send the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and at the same time receive the rescue priority instruction returned by the ground station; Dynamically adjust the hovering height and inspection speed of the drone according to the rescue priority instruction, so that the on-board monitoring equipment of the drone performs multi-angle coverage scanning of at least two disaster risk areas, and send the equipment ready status code to the ground station when the scanning coverage rate reaches the preset requirement; Verify the integrity of the equipment ready status code, and generate an action authorization identifier when the verification passes; When parsing that the action authorization identifier matches the scanning completion time, control the on-board positioning signal transmitter of the drone, generate an evacuation path according to the real-time position data, and at the same time control the drone to fly backward along the evacuation path to a safe coordinate; Continuously detect the synchronous check signal sent by the ground station during the backward flight. When the positioning signal transmitter receives the synchronous check signal matching the evacuation path, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed.

2. The method according to claim 1, wherein Collect real-time image data of the ground area along the planned route through a multi-spectral sensor carried by a drone, compare the real-time image data with a pre-stored geographical feature database for terrain contour comparison, and identify disaster risk areas, including: Collect real-time image data of the ground area through the multi-spectral sensor under three band combinations, wherein the band combinations include a first band group for distinguishing vegetation coverage degree, a second band group for detecting surface temperature changes, and a third band group for identifying the materials of artificial structures; Divide the real-time image data into a preset number of geographical blocks, and dynamically adjust the size of each geographical block according to the current flight height of the drone and the sensor field of view angle, so that the distribution of surface cover types within each geographical block is consistent; Perform resolution adaptation on the real-time image data of each geographical block, and adjust the pixel pitch of the real-time image data according to the altitude change density of the corresponding area in the geographical feature database to generate resolution-adapted real-time image data; Extract the terrain contour line set corresponding to the current geographical block from the geographical feature database, compare the resolution-adapted real-time image data with the terrain contour line set layer by layer, and calculate the spatial correlation degree between the pixel point features and the terrain contour for the band combination data corresponding to each pixel point in the real-time image data, in combination with the slope change direction between adjacent altitude mutation points in the terrain contour line set; Screen out the set of pixel points with a spatial correlation degree lower than the dynamic threshold as the abnormal heat source candidate area; Detect the slope change direction fracture area between consecutive abrupt elevation change points in the set of terrain contour lines. When the extension direction of the fracture area coincides with the offset direction of the artificial structure material boundary at the same position in the real-time image data, mark it as a candidate area for structural deformation; Merge the spatially overlapping part of the abnormal heat source candidate area and the structural deformation candidate area, as well as the independent areas where the abnormal heat source candidate area and the structural deformation candidate area are separated within a preset distance, into a disaster risk area.

3. The method according to claim 2, characterized in that During the flight of the UAV, synchronously generate a task marking file containing the coordinates of the disaster risk area, and compress the task marking file into an encrypted data packet with geographical tags, including: Convert the absolute geographical coordinates of the disaster risk area into relative coordinates relative to the current geographical block boundary, and generate a task marking file containing disaster type identifiers based on the relative coordinates; Add the geographical block number and the current flight altitude data of the UAV to the task marking file to generate a raw task file with geographical tags; Divide the raw task file into multiple independent data blocks, each data block corresponding to a set of coordinates of the disaster risk area within a geographical block, and dynamically adjust the compression ratio according to the distribution density of the coordinate points in the data block to obtain the processed data block; Perform redundancy elimination processing on each processed data block, delete the coordinate points overlapping with adjacent geographical blocks, and generate a hash chain containing the correspondence between the data block number and the compression ratio; Arrange the processed data blocks in the order of geographical block numbers, and insert the corresponding hash chain at the head of each processed data block to generate a compressed task file; Perform hierarchical encryption on the compressed task file through the encryption module built in the UAV to obtain the encrypted compressed task file. Among them, the first layer of encryption generates a dynamic key based on the geographical block number, and the second layer of encryption combines the hash chain nodes and the current timestamp of the UAV to generate a verification identifier; Package the encrypted compressed task file into an encrypted data packet with a timestamp and a geographical block number sequence.

4. The method according to claim 1, wherein Dynamically adjust the hovering height and inspection speed of the UAV according to the rescue priority instruction, so that the on-board monitoring equipment of the UAV performs multi-angle coverage scanning of at least two disaster risk areas, and send a device ready status code to the ground station when the scanning coverage rate reaches the preset requirement, including: Analyze the weight value of each disaster risk area in the rescue priority instruction; Calculate the hovering height adjustment gradient of the UAV between the disaster risk areas according to the distribution ratio of the weight values; Divide the scanning range of each disaster risk area into multiple angle coverage sectors, the center line of each angle coverage sector forms a preset angle with the current flight direction of the UAV, and dynamically allocate the scanning residence time of each angle coverage sector according to the hovering height adjustment gradient; During the flight of the UAV, monitor the scanning times of each angle coverage sector in real time. When the scanning times in the same angle coverage sector reach the preset coverage times threshold and the scanning time interval between adjacent angle coverage sectors is less than the preset tolerance, mark the angle coverage sector as an effective coverage area; Statistically calculate the total proportion of the effectively covered area among all disaster risk areas. When the total proportion exceeds the preset coverage rate threshold, generate the original state data including the scan completion timestamp of all current effectively covered areas and the corresponding hover height adjustment gradient. Perform time series encoding on the original state data, bind the scan completion timestamp with the real-time position coordinates of the drone, and generate an intermediate state file with time series identification. Send the intermediate state file to the ground station through the preset encryption 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. The verification sequence result includes an encryption check code that matches the time series identification. Verify the encryption check code. When the encryption check code passes the integrity verification, convert the intermediate state file into a device ready status code, and the device ready status code includes the position mapping relationship between the scan completion timestamp and the effectively covered area.

5. The method according to claim 1, characterized in that, Verify the integrity of the device ready status code. When the verification passes, generate an action authorization identifier, including: Perform hierarchical decryption on the device ready status code using the decryption key pre-stored in the drone, extract the original timestamp of the device ready status code and the position mapping table of the effectively covered area from the decrypted data, and calculate the deviation amount with the reference time window of the ground station, where the reference time window is determined according to the preset tolerance range before and after the scan completion timestamp. When the deviation amount is less than the preset threshold and the number of missing areas in the position mapping table of the effectively covered area does not exceed the preset ratio, determine that the integrity verification of the device ready status code passes. Generate a dynamic authorization factor based on the scan completion timestamp and the position mapping table of the effectively covered area, 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 including a timestamp binding code and a regional coverage identification. Perform double encryption processing on 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 through the response channel of the multi-band communication module, where the action authorization identifier includes an encrypted timestamp verification segment that matches the scan completion timestamp.

6. The method according to claim 1, characterized in that, When it is parsed that the action authorization identifier matches the scan completion time, control the on-board positioning signal transmitter of the drone, generate an evacuation path based on the real-time position data, and at the same time control the drone to fly backward along the evacuation path to the safe coordinates, including: Extract the encrypted timestamp verification segment from the action authorization identifier, perform cyclic displacement decryption in combination with the millisecond-level precision value of the scan completion timestamp to obtain the original time series including the check code. Compare the deviation between the original time series and the current time system of the drone. When the deviation value is less than the preset time window threshold, activate the multi-band positioning function of the on-board positioning signal transmitter. Generate a set of path key points of the evacuation path based on the elevation change rate and horizontal displacement rate in the real-time position data of the drone. Perform redundancy elimination processing on the set of path key points, delete the turning points whose distance from the boundary of the preset safety restricted area is less than the warning threshold, and recalculate the reverse flight trajectory according to the distribution density of the remaining turning points; Divide the reverse flight trajectory into a preset number of flight segments, sort them according to the turning points in the set of path key points, generate the flight segment order, and control the drone to perform reverse flight according to the flight segment order.

7. The method according to claim 1, wherein 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, control the drone to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task is completed, including: During the reverse flight, continuously receive the synchronization verification signal broadcast by the ground station through the positioning signal transmitter. The synchronization verification signal contains an encrypted set of path key points and the corresponding verification timestamp; Perform frame-by-frame parsing on the received synchronization verification signal, extract the set of path key points in each frame of the signal, and perform point-by-point matching of the set of path key points with the set of path key points of the current evacuation path of the drone; When the number of successfully matched path key points exceeds the preset ratio and the deviation between the verification timestamp and the scan completion timestamp in the device ready status code is less than the preset threshold, determine that the synchronization verification signal matches the evacuation path; Control the rescue task execution module carried by the drone to read the coordinate set of the target area from the task marker file, and dynamically adjust the execution order of the rescue task according to the distribution density of the path key points in the synchronization verification signal; During the execution of the rescue task, collect the on-site environment data of the target area in real time, compare the abnormal fluctuations of the on-site environment data with the parameters of the disaster risk area in the task marker file. When the amplitude of the abnormal fluctuations exceeds the preset tolerance, pause the execution of the rescue task and send an abnormal interruption request to the ground station; After receiving the task continuation instruction returned by the ground station, update the remaining flight segments of the evacuation path according to the latest set of path key points in the synchronization verification signal, and restart the rescue task execution module; When the rescue task execution module triggers the completion status flag, traverse all the coordinate entries associated with the target area in the task marker file, and filter out the coordinate entries covered by the set of path key points in the synchronization verification signal to generate a list of coordinates to be deleted; Perform data relevance verification on the coordinate entries in the list of coordinates to be deleted. When no new abnormal data is generated in the disaster risk area corresponding to the coordinate entry during the execution of the rescue task, permanently remove the coordinate entry from the task marker file.

8. A control system for an unmanned aerial vehicle (UAV) to search and strike a ground station, characterized in that, Including: An acquisition module, which is used to collect real-time image data of the ground area along the planned flight path through a multi-spectral sensor carried by the drone, compare the real-time image data with the pre-stored geographical feature database for terrain contour comparison, and identify the disaster risk area; A compression module, configured to synchronously generate a task marker file containing the coordinates of the disaster risk area during the flight of the UAV, and compress the task marker file into an encrypted data packet with geographical tags; A sending module, configured to send the encrypted data packet to the ground station in an adaptive frequency hopping mode through a multi-band communication module, and simultaneously receive a rescue priority instruction returned by the ground station; A scanning module, configured to dynamically adjust the hovering height and inspection speed of the UAV according to the rescue priority instruction, so that the on-board monitoring equipment of the UAV performs multi-angle coverage scanning of at least two disaster risk areas, and sends 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 passed; A first control module, configured to control the on-board positioning signal transmitter of the UAV and generate an evacuation path according to the real-time position data when parsing that the action authorization identifier matches the scanning completion time, and simultaneously control the UAV to fly backward along the evacuation path to a safe coordinate; A second control module, configured to continuously detect a synchronous verification signal sent by the ground station during the backward flight. When the positioning signal transmitter receives a synchronous verification signal matching the evacuation path, control the UAV to perform a preset rescue task on the target area, and automatically delete the data associated with the target area in the task marker file after the task 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 UAV to search and attack a ground station according to 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, it implements a control method for a UAV to search and attack a ground station according to any one of claims 1 to 7.

Images