Air-ground integration method and system
By using an integrated air-ground approach and system, and by analyzing UAV images and dispatch capabilities, UAV dispatch can be automatically negotiated or manually arbitrated. This solves the problem of UAV dispatch relying on the synchronous movement of pilots, and realizes intelligent and efficient collaboration in UAV dispatch, reducing dispatch request time and improving fire response efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-05
- Publication Date
- 2026-04-07
AI Technical Summary
Drone dispatching requires pilots to move in sync, dispatch requests take a long time, and the lack of intelligent systems to provide brief summaries leads to delays in responding to fires.
This paper provides an integrated air-ground method and system that obtains evaluation parameters and weights by receiving UAV images and analyzing dispatching forces, and automatically negotiates or manually arbitrates UAV dispatching to achieve remote airport control and avoid the need for pilots to move synchronously.
It has enabled intelligent and efficient coordination of drone dispatching, reduced dispatch request time, and improved fire response efficiency.
Smart Images
Figure CN121809867A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of drone-based collaborative rescue, specifically to an integrated air-ground method and system. Background Technology
[0002] Drones have formed a complete application system in the field of fire protection, encompassing fire reconnaissance, fire fighting and rescue, and emergency communication. By being equipped with devices such as infrared thermal imaging and visible light cameras, they can achieve dynamic perception of fire scenes in dense smoke environments and transmit the data back.
[0003] Currently, firefighting drones mainly rely on manual operation by pilots. They receive real-time images and thermal data through external display devices, requiring manual analysis of the fire scene dynamics and feedback of critical information to the command system. Fire brigades then transport the drones and pilots to the fire scene to carry out operations. When multiple fires require coordination, the transfer of pilots and drones between different teams takes time, as the drones and pilots belong to the same unit.
[0004] Although intelligent dispatch systems have improved efficiency through algorithmic optimization, they still rely heavily on manual intervention in practical applications. The remote control and dispatch of drone airports have not been integrated into fire rescue operations. Furthermore, when conflicts arise in multi-departmental collaborative dispatching, requiring manual intervention from higher management, the lack of a concise summary provided by the intelligent system necessitates manual reassessment of the situation for allocation and arbitration of rescue resources, leading to delays in responding to fires. Summary of the Invention
[0005] Based on this, the purpose of this invention is to provide an integrated air-ground method and system to solve the technical problems of current UAV scheduling requiring pilots to move synchronously and long scheduling request times.
[0006] To achieve the above objectives, the present invention provides the following technical solution: an integrated air-ground method, comprising the following steps: S100, receiving drone images and obtaining fire status parameters; S200, collecting dispatch force analysis and obtaining drone coordination quantity parameters, wherein the drone coordination quantity parameters include the number of dispatchable drones, remaining battery power, single-drone monitoring radius, and basic drone performance parameters; S300, obtaining human dispatch parameters; S400, obtaining evaluation parameters based on fire status parameters, drone basic performance parameters, and human dispatch parameters; S500, receiving other dispatch requests and obtaining corresponding weights based on preset permission parameters of the original dispatcher and the dispatch requester; S600, obtaining dispatch values for both parties based on the evaluation parameters and corresponding weights; S710A, if the party with the higher dispatch value initiates a request, the party with the lower dispatch value can contact and negotiate within a preset time; if they cannot be contacted and the response time is exceeded, they can forcibly obtain permissions; S710B, if the party with the lower dispatch value or the party with missing values initiates a request and the response time is exceeded, a manual judgment is requested from the superior authority.
[0007] This invention also provides an integrated air-ground system, which is used to carry out the above-mentioned method, comprising: a first processing unit for receiving UAV images and obtaining fire status parameters; a second processing unit for collecting dispatch force analysis and obtaining UAV collaborative quantity parameters, wherein the UAV collaborative quantity parameters include the number of dispatchable UAVs, remaining battery power, single-unit monitoring radius, and basic performance parameters of UAVs; a first receiving unit for obtaining human dispatch parameters; a third processing unit for obtaining evaluation parameters based on fire status parameters, UAV basic performance parameters, and human dispatch parameters; a fourth processing unit for receiving other dispatch requests and obtaining corresponding weights based on preset permission parameters of the original dispatcher and the dispatch requester; a fifth processing unit for obtaining dispatch values of both parties based on the evaluation parameters and corresponding weights; a first judgment unit for forcibly obtaining permissions if the party with the higher dispatch value initiates a request and the party with the lower dispatch value can contact and negotiate within a preset time, or if they cannot be contacted after the response time has expired; and a second judgment unit for requesting manual judgment from a higher-level authority department if the party with the lower dispatch value or the party with missing values initiates a request and the response time has expired.
[0008] In summary, this invention receives other scheduling requests and obtains corresponding weights based on preset permission parameters of the original scheduler and the scheduling requester; it obtains the scheduling values of both parties based on the evaluation parameters and corresponding weights; if the party with the higher scheduling value initiates a request, the party with the lower scheduling value can contact and negotiate within a preset time; if they cannot contact each other and the response time is exceeded, they can forcibly obtain permission; if the party with the lower scheduling value or the party with missing values initiates a request and the response time is exceeded, it requests the superior authority to manually judge and assist in the scheduling of the drone. The entire process utilizes remote airport control to avoid the technical drawbacks of drone scheduling requiring pilots to move synchronously and the long scheduling request time. Attached Figure Description
[0009] Figure 1 This is a logical schematic diagram of the present invention. Detailed Implementation
[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0011] The embodiments of the present invention will now be described.
[0012] An integrated air-ground method includes the following steps:
[0013] S100: Receive drone images and obtain fire status parameters;
[0014] This system establishes network data connections with various drone airports and internal drones, and connects to drones requiring general data transmission as needed. Once connected, it can receive drone images. For example, after connecting to a local drone airport and internal drones, upon startup, the drone airport and drones arrive at the site under the control of remote control software and continuously transmit data back to this system. Optionally, this system can connect to an operator terminal, allowing local control of the drones from the terminal located within the system.
[0015] S100 includes the following steps:
[0016] S110. Receive drone images and acquire drone-transmitted data, wherein the drone-transmitted data includes visible light images and thermal infrared data.
[0017] S120. Perform median filtering noise reduction preprocessing on the visible light image to obtain processed image data;
[0018] S130. Based on the processed image data, establish an HSV color space flame segmentation model, set the H channel threshold to 0-30, the S channel threshold to 50-100%, use the triangulation method to perform three-dimensional reconstruction of the depth map, calculate the actual fire area ratio, and obtain fire status data, wherein the fire area ratio data includes fire line length, building visual data area judgment data, and fire area ratio data.
[0019] S140. Generate a fire temperature gradient map based on the processed image data, and mark areas with temperatures exceeding 600°C as hazardous areas.
[0020] S150. Use the hazardous area data and fire status data as fire status parameters.
[0021] Real-time dual-spectral data was acquired by connecting to a drone. The visible light images included flame morphology, smoke diffusion trajectory, and building structural damage. The thermal infrared data included temperature distribution matrix, spatial coordinates of high-temperature points, and thermal radiation characteristics of concealed fire sources.
[0022] Next, the flame color characteristics are analyzed. The analysis can employ any data analysis model validated in the art. This application primarily uses the HSV color space segmentation model. This model sets the H channel to a red spectral range of 0 to 30 and the S channel to a saturation threshold of 50% to 100%.
[0023] Simultaneously, 3D reconstruction technology is incorporated. Triangulation is used to transform the 2D image into a 3D model. This technology is then used to calculate key parameters such as the fire line length and the percentage of the surface area burned.
[0024] Thermal infrared data processing stage. A pseudo-color thermal map is generated through a temperature gradient mapping algorithm. The system automatically marks dangerous areas exceeding 600 degrees Celsius. The basis for setting the dangerous area marking threshold to 600℃ in this example includes: (1) According to GB / T 9978.1-2008 "Test Method for Fire Resistance of Building Components", the critical temperature at which steel structures lose their load-bearing capacity is 550℃, and setting 600℃ can reserve early warning redundancy for structural collapse; (2) The auto-ignition point of wood is about 300℃, but oil fires (such as gasoline ignition point > 400℃) or chemical fires require a higher threshold coverage; (3) According to historical data verification by the fire department (see "China Fire Statistics Yearbook"), the survival rate of people in areas with temperatures > 600℃ is less than 5%, which meets the criteria for major hazard judgment. Among them, the dangerous area temperature judgment threshold can be adjusted as needed. Finally, multi-source data are integrated to generate a structured fire report. The report contains core elements such as quantitative data on the fire range and coordinate information of high-temperature risk points. For subsequent use.
[0025] The depth map described in S130 is obtained through any of the following methods: (a) generating a disparity map and converting it into a depth map using a UAV equipped with a binocular vision camera; (b) generating a pseudo-depth map from a monocular image using a pre-trained depth estimation neural network (such as MiDaS or AdaBins); (c) generating a depth map by projecting LiDAR point cloud data. After obtaining the depth map, it can be converted into a 3D point cloud as needed through inverse perspective projection. Then, 3D reconstruction is performed from the 3D point cloud. Those skilled in the art will understand that the above-mentioned depth reconstruction technology is a mature existing solution. This invention only applies its results for 3D reconstruction and does not involve innovation in depth perception algorithms; it is merely an example.
[0026] S200: Collect and analyze dispatching forces to obtain drone coordination quantity parameters, which include the number of dispatchable drones, remaining battery power, single-drone monitoring radius, and basic drone performance parameters.
[0027] By collecting key status data from drone swarms in real time, a quantitative basis is provided for the scientific scheduling of fire monitoring resources. Obtaining the number of schedulable drones clarifies the total available resources, preventing task allocation from exceeding limits; remaining battery power determines the continuous operating time of a single drone, directly impacting rotation strategies; the monitoring radius of a single drone reflects its coverage capability and is a core variable for calculating the minimum formation size; basic drone performance parameters, including wind resistance and communication distance, determine their applicable scenarios. These parameters collectively form the basic input of the dynamic scheduling model, ensuring that the drone formation can meet fire monitoring needs while mitigating the risk of mission interruption due to battery depletion or insufficient performance. For example, in simulated test data, the system acquired 15 schedulable drones from 5 surrounding airports, 9 of which had batteries above 70%, a single-drone monitoring radius of 200 meters, and a wind resistance level of level 6. Based on a fire line length of 2.3 kilometers, the system automatically configured 12 drones (including 3 backup drones) to form a monitoring network. Drones with insufficient remaining battery power were automatically excluded from the formation, ensuring uninterrupted fire situational awareness for 8 hours.
[0028] S300, Obtain manpower dispatch parameters;
[0029] This step involves collecting structured data on rescue forces from external channels, including the fire command platform and IoT sensing devices. Depending on the situation, and if conditions permit, the data will be interfaced with the fire protection system. If interface integration with the fire protection system is not possible, calculations can be performed by manually entering known data. The following parameters should be included whenever possible:
[0030] Human resource dispatch parameters include the following core elements: Team affiliation information, including the fire brigade number, administrative jurisdiction, and direct command level; and personnel qualification data, including special disaster response certification type, emergency rescue experience level, and medical rescue qualification level.
[0031] Equipment configuration list. This includes the type of fire truck, the working height of the aerial ladder, the amount of extinguishing agent, and the set of special operations tools.
[0032] Real-time status parameters, including satellite positioning coordinates, mission workload, and equipment availability status codes.
[0033] Historical mission records. This includes the number of times similar fires have been successfully handled and the timeliness compliance rate for operations in high-risk environments.
[0034] Optionally, add a smart matching engine. Record all available resources and automatically filter teams within a 3-kilometer radius that are qualified to handle oil fires. For example, exclude teams without explosion-proof detection equipment. Experience-driven grouping. Calculate capability coefficients based on historical mission types. For example, assign a 1.5x weighting coefficient to experienced oil tank fire responders to optimize the composition of the task force.
[0035] Adding this engine will facilitate decision-making regarding subsequent increases, decreases, or transfers of power.
[0036] S400: Evaluation parameters are obtained based on fire status parameters, basic performance parameters of UAVs, and manpower dispatch parameters.
[0037] S400 includes the following steps:
[0038] S410. Calculate the rotation frequency based on the remaining battery power (manually set the rotation power threshold range) and obtain the rotation redundancy quantity.
[0039] S420. Calculate the minimum number of fire brigades based on the fire status parameters; the calculation formula is as follows:
[0040] N=(L / (2r))+A
[0041] Wherein, N represents the number of drones working together (rounded up), L represents the fire line length, r represents the single-drone monitoring radius, and A represents the number of rotational redundancies.
[0042] The system assigns weights to currently deployed drones based on the number of redundant drones and the number of drones in coordination, increasing the weight of necessary drones and decreasing the weight of redundant drones. The overall weight is also adjusted based on changes in fire status parameters, increasing the weight of fires that are worsening and decreasing the weight of fires that are under control and shrinking. For example, in a small fire, the system initially deploys two drones for fire monitoring. One necessary drone covers the core fire line, with a weight coefficient of 1.2 times the baseline value. The other redundant drone is on standby, with a weight coefficient of 0.8 times the baseline value. Given the limited surrounding combustibles and relative safety of the fire area, the overall fire weight is 0.5. If the system detects that the fire is rapidly decreasing due to the actions of rescue personnel, it automatically triggers a weight reorganization mechanism, reducing the overall fire weight to 0.3. The fire safety assessment can be based on any existing visual analysis library and supporting software, or by manually inputting weight parameters.
[0043] The rotation redundancy calculation can be implemented using any existing power management algorithm, such as remaining flight time prediction based on discharge curves, reinforcement learning optimization models, etc. This proposal does not innovate in this regard; any existing algorithm that can achieve this function is acceptable.
[0044] S500: Receive other scheduling requests and obtain the corresponding weights based on the preset permission parameters of the original scheduler and the scheduling requester;
[0045] S600: Obtain the scheduling values for both parties based on the evaluation parameters and corresponding weights;
[0046] Scheduling value calculation formula:
[0047] Score=α*Sfire / Smax+β*Ndrone / Ntotal+γ*Chuman / Cbase
[0048] Wherein, Sfire is the real-time fire area, Smax is the maximum monitoring area of the system; Ndrone is the number of available drones, Ntotal is the total number of registered drones; Chuman is the weighted value of human qualifications (assigned a value of 1-5 according to the qualification level), Cbase is the baseline value of 10, and the weight coefficients α, β, γ are preset according to the disaster type (forest fire: 0.6, 0.3, 0.1; chemical plant explosion: 0.3, 0.2, 0.5).
[0049] Sfire is obtained directly from the 3D reconstruction results; Smax is calculated based on the total coverage capability of the connected drone airports and the monitoring radius of a single drone. Ndrone is synchronized in real time from the airport management platform; Ntotal reads the system registration database, and this value is the regional configuration number, used to compare the number of currently occupied drones with the total number of schedulable drones in the region. Chuman is obtained through pre-setting manually; if it is not needed or cannot be obtained, this value is set to 0 and does not participate in the adjustment of the scheduling value; Cbase is used for normalization processing and is manually set as the default parameter. Parameter acquisition can adopt any mature technology, such as direct connection of IoT devices, API connection to government databases, OCR recognition of paper certificates, etc. This invention does not limit the specific implementation method.
[0050] S710A: If the party with the higher scheduling value initiates a request, the party with the lower scheduling value can contact and negotiate within a preset time. If they cannot contact each other and the response time is exceeded, they can forcibly obtain the permission.
[0051] Acquiring permissions refers to the transfer of control of the drone, but does not include data access permissions. If data access permissions are required, additional manual authorization is necessary, and the permission controller will assign permissions as needed.
[0052] Optionally, a redundancy scheduling value can be added according to the actual situation. For example, it is set to 10%, that is, when the difference between the scheduling values of the two parties is less than 10%, even if the party with the higher scheduling value initiates a request, it will enter into manual arbitration.
[0053] S710B: If the party with the low scheduling value or the party with the missing value initiates a request and the response time exceeds the specified time, a manual judgment is requested from the superior authority.
[0054] This application utilizes external third-party software for communication, which can be achieved using existing conventional communication software or direct telephone communication. Optionally, a communication unit can be added to this system to carry the built-in communication mode. This unit can use any existing conventional communication unit to build network communication. After negotiation and communication, manual intervention is performed based on the negotiation results.
[0055] Those skilled in the art can use any cross-platform communication scheme to implement the data exchange process at any step, such as ROS-based drone control middleware, WebSocket real-time communication, blockchain smart contracts, etc., and this invention does not impose any limitations.
[0056] The present invention also provides an integrated air-ground system for carrying out the above-described method, comprising:
[0057] The first processing unit is used to receive images from the drone and obtain fire status parameters.
[0058] The second processing unit is used to collect and analyze the dispatching force and obtain the drone coordination quantity parameters, which include the number of dispatchable drones, the remaining battery power, the single-drone monitoring radius, and the basic performance parameters of the drones.
[0059] The first receiving unit is used to acquire manpower dispatch parameters;
[0060] The third processing unit is used to obtain evaluation parameters based on fire status parameters, UAV basic performance parameters, and manpower dispatch parameters.
[0061] The fourth processing unit is used to receive other scheduling requests and obtain the corresponding weights based on the preset permission parameters of the original scheduler and the scheduling requester.
[0062] The fifth processing unit is used to obtain the scheduling values of both parties based on the evaluation parameters and corresponding weights;
[0063] The first judgment unit is used to determine whether the low scheduling value party can contact and negotiate within a preset time after the party with the high scheduling value initiates a request, or to forcibly obtain permissions after the response time has expired if the party with the low scheduling value fails to contact the low scheduling value party.
[0064] The second judgment unit is used to request manual judgment from the superior authority if the party with the low scheduling value or the party with missing value initiates a request and the response time has expired.
[0065] Preferred options also include:
[0066] The second receiving unit is used to receive drone images and acquire drone-transmitted data, wherein the drone-transmitted data includes visible light images and thermal infrared data.
[0067] The sixth processing unit is used to perform median filtering and noise reduction preprocessing on the data transmitted back by the UAV to obtain processed image data;
[0068] The seventh processing unit is used to establish an HSV color space flame segmentation model based on the processed image data, set the H channel threshold to 0-30 and the S channel threshold to 50-100%, use the triangulation method to perform three-dimensional reconstruction of the depth map, calculate the actual fire area ratio, and obtain fire status data, wherein the fire area ratio data includes fire line length, building visual data area judgment data, and fire area ratio data.
[0069] The eighth processing unit is used to generate a fire temperature gradient map based on the processed image data, and mark areas with temperatures exceeding 600°C as hazardous areas.
[0070] The ninth processing unit is used to use the hazardous area data and fire status data as fire status parameters.
[0071] Preferred options also include:
[0072] The tenth processing unit is used to calculate the rotation frequency based on the remaining battery power (the rotation power threshold range is manually set) and obtain the number of rotation redundancies.
[0073] The eleventh processing unit is used to calculate the minimum number of fire brigades based on the fire status parameters; the calculation formula is as follows:
[0074] N=(L / (2r))+A
[0075] Wherein, N represents the number of drones working together (rounded up), L represents the fire line length, r represents the single-drone monitoring radius, and A represents the number of rotational redundancies.
[0076] Although embodiments of the present invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions, and variations to the embodiments as needed without departing from the principles and spirit of the invention, but such modifications, substitutions, and variations are protected by patent law as long as they are within the scope of the claims of the present invention.
Claims
1. An integrated air-ground method, characterized in that, Includes the following steps: S100: Receive drone images and obtain fire status parameters; S200: Collect and analyze dispatching forces to obtain drone coordination quantity parameters, which include the number of dispatchable drones, remaining battery power, single-drone monitoring radius, and basic drone performance parameters. S300, Obtain manpower dispatch parameters; S400: Evaluation parameters are obtained based on fire status parameters, basic performance parameters of UAVs, and manpower dispatch parameters. S500: Receive other scheduling requests and obtain the corresponding weights based on the preset permission parameters of the original scheduler and the scheduling requester; S600: Obtain the scheduling values for both parties based on the evaluation parameters and corresponding weights; S710A: If the party with the higher scheduling value initiates a request, the party with the lower scheduling value can contact and negotiate within a preset time. If they cannot contact each other and the response time is exceeded, they can forcibly obtain the permission. S710B: If the party with the low scheduling value or the party with the missing value initiates a request and the response time exceeds the specified time, a manual judgment is requested from the superior authority.
2. The air-ground integrated method according to claim 1, characterized in that, The process of receiving drone images and obtaining fire status parameters includes the following steps: S110. Receive drone images and acquire drone-transmitted data, wherein the drone-transmitted data includes visible light images and thermal infrared data. S120. Perform median filtering noise reduction preprocessing on the visible light image to obtain processed image data; S130. Based on the processed image data, establish an HSV color space flame segmentation model, set the H channel threshold to 0-30, the S channel threshold to 50-100%, use the triangulation method to perform three-dimensional reconstruction of the depth map, calculate the actual fire area ratio, and obtain fire status data, wherein the fire area ratio data includes fire line length, building visual data area judgment data, and fire area ratio data. S140. Generate a fire temperature gradient map based on the processed image data, and mark areas with temperatures exceeding 600°C as hazardous areas. S150. The hazardous area data and fire status data are used as fire status parameters.
3. The air-ground integrated method according to claim 2, characterized in that, The process of obtaining the evaluation parameters based on fire status parameters, basic performance parameters of UAVs, and manpower dispatch parameters includes the following steps: S410. Calculate the rotation frequency based on the remaining battery power (manually set the rotation power threshold range) and obtain the rotation redundancy quantity. S420. Calculate the minimum number of fire brigades based on the fire status parameters; the calculation formula is as follows: N=(L / (2r))+A Wherein, N represents the number of drones working together (rounded up), L represents the fire line length, r represents the single-drone monitoring radius, and A represents the number of rotational redundancies.
4. An integrated air-ground system, characterized in that, The system is used to carry the methods of claims 1-4, including: The first processing unit is used to receive images from the drone and obtain fire status parameters. The second processing unit is used to collect and analyze the dispatching force and obtain the drone coordination quantity parameters, which include the number of dispatchable drones, the remaining battery power, the single-drone monitoring radius, and the basic performance parameters of the drones. The first receiving unit is used to acquire manpower dispatch parameters; The third processing unit is used to obtain evaluation parameters based on fire status parameters, UAV basic performance parameters, and manpower dispatch parameters. The fourth processing unit is used to receive other scheduling requests and obtain the corresponding weights based on the preset permission parameters of the original scheduler and the scheduling requester. The fifth processing unit is used to obtain the scheduling values of both parties based on the evaluation parameters and corresponding weights; The first judgment unit is used to determine whether the low scheduling value party can contact and negotiate within a preset time after the party with the high scheduling value initiates a request, or to forcibly obtain permissions after the response time has expired if the party with the low scheduling value fails to contact the low scheduling value party. The second judgment unit is used to request manual judgment from the superior authority if the party with the low scheduling value or the party with missing value initiates a request and the response time has expired.
5. The air-ground integrated system according to claim 4, characterized in that, include: The second receiving unit is used to receive drone images and acquire drone-transmitted data, wherein the drone-transmitted data includes visible light images and thermal infrared data. The sixth processing unit is used to perform median filtering and noise reduction preprocessing on the data transmitted back by the UAV to obtain processed image data; The seventh processing unit is used to establish an HSV color space flame segmentation model based on the processed image data, set the H channel threshold to 0-30 and the S channel threshold to 50-100%, use the triangulation method to perform three-dimensional reconstruction of the depth map, calculate the actual fire area ratio, and obtain fire status data, wherein the fire area ratio data includes fire line length, building visual data area judgment data, and fire area ratio data. The eighth processing unit is used to generate a fire temperature gradient map based on the processed image data, and mark areas with temperatures exceeding 600°C as hazardous areas. The ninth processing unit is used to use the hazardous area data and fire status data as fire status parameters.
6. The air-ground integrated system according to claim 4, characterized in that, include: The tenth processing unit is used to calculate the rotation frequency based on the remaining battery power (the rotation power threshold range is manually set) and obtain the number of rotation redundancies. The eleventh processing unit is used to calculate the minimum number of fire brigades based on the fire status parameters; the calculation formula is as follows: N=(L / (2r))+A Wherein, N represents the number of drones working together (rounded up), L represents the fire line length, r represents the single-drone monitoring radius, and A represents the number of rotational redundancies.