UAV emergency collaborative path planning method and system for extreme post-disaster scenarios

Abstract

This invention relates to a method for emergency collaborative path planning of unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios. Specific steps include: real-time priority ranking of UAVs based on their flight indicators and preset rules; allocation of UAV flight tasks based on the disaster severity level of each disaster point in the extreme post-disaster scenario using a hybrid UAV scheduling strategy; firstly, matching the UAV with the highest real-time priority to the disaster point with the highest disaster severity level using a greedy strategy, and then allocating the remaining UAVs to disaster points using a nearest neighbor allocation strategy; using the A* algorithm to plan the path from each UAV to the disaster point, and estimating the time for each UAV to reach each path point on the flight path based on the attributes of each UAV and its distance to the disaster point, and determining whether there is a collision risk.

Description

Technical Field

[0001] This invention relates to the field of wireless temperature measurement technology, specifically to a method and system for emergency collaborative path planning of unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios. Background Technology

[0002] As a portable device, drones can be either aircraft with autopilot systems or unmanned aerial vehicles controlled remotely by ground operators. They offer advantages such as ease of operation, high performance, strong adaptability, and high economic efficiency. In my country, most power transmission lines are located in the open and are affected by the natural environment. When a section of a power transmission line fails, it can cause large-scale power outages and other accidents. In practice, to ensure the normal operation of power transmission lines, regular manual inspections are required, resulting in a waste of human resources. Utilizing the small size, lack of susceptibility to terrain and environmental factors, and ease of operation of drones, their application in power line fault inspection can improve inspection efficiency, reduce maintenance costs, and ensure the safety of personnel.

[0003] In the aftermath of extreme disasters, power grids may experience numerous and dispersed failure points. Due to disruptions or closures of ground transportation, traditional human resources struggle to quickly reach disaster areas for rescue and assessment. The portability and autonomous flight capabilities of drones make them an ideal choice. By using drones for path planning, rescue needs and priorities in disaster areas can be quickly and accurately determined. Furthermore, drones can overcome terrain and environmental limitations, easily flying over damaged buildings, obstacles, and complex terrain. They can cover large areas in a short time, significantly improving search and rescue efficiency. Drones can also quickly identify closed roads and difficult rescue points, providing real-time image and video transmission, enabling command centers and rescue personnel to make rapid decisions. To provide technical support and optimize the power grid repair process, this invention proposes a collaborative method to develop an intelligent path planning algorithm, using drones as the execution tool.

[0004] The existing patent CN113359831A, "A Method for Generating Paths for Clustered Quadrotor UAVs Based on Task Logic Scheduling," proposes a method that decomposes a collaboratively scheduled UAV swarm into individual UAVs generating their own paths. It then considers the positions and flight speeds of neighboring UAVs to generate the desired flight speed for each aircraft, thus avoiding collisions. However, this method does not consider factors such as total path length and UAV status, making it difficult to guarantee path quality. Furthermore, existing methods rely on fixed UAV priorities, leading to deadlocks and overall inefficiency due to unreasonable priorities. Additionally, existing methods have limited allocation strategies in multi-tasking scenarios, lacking the flexibility and intelligence to adapt to diverse task requirements. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method and system for emergency collaborative path planning of unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, this invention proposes a method for emergency collaborative path planning of unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios, the specific steps of which include:

[0008] The drones are prioritized in real time based on their flight parameters and preset rules.

[0009] Based on the disaster severity level of each disaster point in extreme post-disaster scenarios, a hybrid scheduling strategy for drones is used to allocate drone flight missions. First, a greedy strategy is used to select the drone with the highest real-time priority and match it with the disaster point with the highest disaster severity level. Then, the nearest neighbor allocation strategy is used to allocate the remaining drones and disaster points.

[0010] The A* algorithm is used to plan the paths of each UAV to the disaster point. Based on the attributes of each UAV and the distance to the disaster point, the time for each UAV to reach each path point on the flight path is estimated, and the risk of collision is determined.

[0011] For multi-drone flight planning with collision risk, the flight paths of multiple drones are dynamically prioritized based on the urgency of each drone's flight mission and the drone's priority status, and the priority ranking among all drones is updated.

[0012] For multi-UAV flight planning where there is no risk of collision, it is necessary to further confirm whether there are dynamic changes in the flight missions of each UAV. If there are dynamic changes, the missions are reassigned, the path planning is revised, and the collision assessment is performed. If there are no dynamic changes, the final multi-UAV emergency collaborative path planning scheme is determined.

[0013] In a preferred embodiment, the flight indicators of the UAV include the collision risk of the UAV, the total flight path length of the UAV, the collision waiting time of the UAV, and the remaining battery power of the UAV.

[0014] In a preferred embodiment, the preset planning for real-time priority ranking of UAVs based on their flight indicators and preset rules specifically includes:

[0015] When the collision risk of drones is different, the greater the collision risk, the lower the priority.

[0016] When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows:

[0017]

[0018] In the formula, P is the priority comprehensive index. α is the normalized value of the total flight path length of the UAV, and α is the weight of the total flight path length index of the UAV. β is the normalized value of the drone's conflict waiting time, and β is the weight of the drone's conflict waiting time index. γ is the normalized value of the drone's remaining battery power, and γ is the weight of the drone's remaining battery power index.

[0019] Based on a comparison of the overall priority indicators of each drone, the drone with the higher the overall priority indicator is given a higher priority.

[0020] In a preferred embodiment, the step of dynamically adjusting the flight paths of multiple UAVs based on the urgency of the UAV flight mission and the priority status of the UAVs specifically includes:

[0021] The specific formula for calculating the urgency of a drone flight mission is as follows:

[0022] U i =A·(a*M i +b*d(t))

[0023] In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level.

[0024] Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows:

[0025]

[0026] In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient.

[0027] Finally, the overall dynamic priority of the UAV is calculated using the following formula:

[0028] P i =U i +S i

[0029] In the formula, P i The overall dynamic priority of UAV i;

[0030] The real-time priority ranking of each drone is based on its overall dynamic priority.

[0031] In a preferred embodiment, the step of updating the priority ranking among all drones specifically includes:

[0032] Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula:

[0033] P new,i =P old,i +γ(P i -P old,i )

[0034] In the formula, γ is the update rate, which controls the adjustment step size; P new,i This is the updated priority; P old,i It is the original priority.

[0035] On the other hand, this invention proposes an emergency collaborative path planning system for unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios, comprising:

[0036] The drone priority ranking module ranks drones in real time based on their flight indicators and preset rules.

[0037] The UAV flight mission allocation module allocates UAV flight missions based on the disaster severity level of each disaster point in extreme post-disaster scenarios through a hybrid UAV scheduling strategy. First, it selects the UAV with the highest real-time priority and matches it with the disaster point with the highest disaster severity level according to a greedy strategy. Then, it allocates the remaining UAVs and disaster points through a nearest neighbor allocation strategy.

[0038] The UAV flight path planning module uses the A* algorithm to plan the path from each UAV to the disaster point, and estimates the time for each UAV to reach each path point on the flight path based on the attributes of each UAV and the distance to the disaster point, and determines whether there is a risk of collision.

[0039] The flight mission dynamic adjustment module dynamically adjusts the flight paths of multiple UAVs for flight planning with collision risks based on the urgency of each UAV flight mission and the priority status of the UAVs, and updates the priority ranking among all UAVs.

[0040] The flight mission replanning module re-confirms whether there are dynamic changes in the flight missions of each UAV for multi-UAV flight plans that do not pose a collision risk. If there are dynamic changes, the missions are reassigned, paths are replanned, and collisions are assessed. If there are no dynamic changes, the final multi-UAV emergency collaborative path planning scheme is determined.

[0041] In a preferred embodiment, the flight indicators of the UAV include the collision risk of the UAV, the total flight path length of the UAV, the collision waiting time of the UAV, and the remaining battery power of the UAV.

[0042] In a preferred embodiment, the preset planning for real-time priority ranking of UAVs based on their flight indicators and preset rules specifically includes:

[0043] When the collision risk of drones is different, the greater the collision risk, the lower the priority.

[0044] When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows:

[0045]

[0046] In the formula, P is the priority comprehensive index. α is the normalized value of the total flight path length of the UAV, and α is the weight of the total flight path length index of the UAV. β is the normalized value of the drone's conflict waiting time, and β is the weight of the drone's conflict waiting time index. γ is the normalized value of the drone's remaining battery power, and γ is the weight of the drone's remaining battery power index.

[0047] Based on a comparison of the overall priority indicators of each drone, the drone with the higher the overall priority indicator is given a higher priority.

[0048] In a preferred embodiment, the step of dynamically adjusting the flight paths of multiple UAVs based on the urgency of the UAV flight mission and the priority status of the UAVs specifically includes:

[0049] The specific formula for calculating the urgency of a drone flight mission is as follows:

[0050] U i =A·(a*M i +b*d(t))

[0051] In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level.

[0052] Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows:

[0053]

[0054] In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient.

[0055] Finally, the overall dynamic priority of the UAV is calculated using the following formula:

[0056] P i =U i +S i

[0057] In the formula, P i The overall dynamic priority of UAV i;

[0058] The real-time priority ranking of each drone is based on its overall dynamic priority.

[0059] In a preferred embodiment, the step of updating the priority ranking among all drones specifically includes:

[0060] Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula:

[0061] P new,i =P old,i +γ(P i -P old,i )

[0062] In the formula, γ is the update rate, which controls the adjustment step size; P new,i This is the updated priority; P old,i It is the original priority.

[0063] The present invention has the following beneficial effects:

[0064] 1. This invention employs a multi-UAV collaborative inspection method, enabling rapid inspection of disaster sites and the entire power grid after extreme disasters. Through a greedy strategy and a nearest neighbor strategy, high-priority tasks are prioritized and assigned to the appropriate UAVs to ensure timely execution and minimize potential risks and problems.

[0065] 2. This invention uses the A* algorithm to plan a global path between the UAV and obstacles in the environment without collision; it solves the problem of collision between UAVs by using a dynamic priority update strategy, avoids repeated path replanning, and improves planning efficiency and result quality.

[0066] 3. This invention considers multiple indicators for dynamic priority determination, including collision risk, total flight path length, UAV waiting time, and remaining battery power. UAV priority is determined through weighted calculations, granting higher-priority UAVs greater rights of movement and preventing collisions with lower-priority UAVs. Simultaneously, path corrections are made considering inspection tasks and the operating environment, comprehensively taking multiple factors into account to improve the accuracy and practicality of path planning.

[0067] 4. This invention prioritizes inspection tasks based on the disaster level of disaster points, and achieves optimal task allocation for multiple UAVs by combining greedy and nearest neighbor strategies. It selects appropriate UAVs to perform tasks based on task priorities and UAV availability, taking into account both task priorities and spatial relationships between UAVs to improve task execution efficiency.

[0068] 5. This invention designs a dynamic priority adjustment strategy, which dynamically adjusts the priority of the UAV based on the real-time task and UAV status, comprehensively considering planning speed and quality, and improving the efficiency of conflict handling. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the steps of the present invention;

[0070] Figure 2 This is a diagram illustrating the hierarchical structure;

[0071] Figure 3 A schematic diagram showing the distribution and classification of disaster sites;

[0072] Figure 4 This is a diagram illustrating the dispatching of drones at disaster sites. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0075] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0076] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0077] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0078] Example 1:

[0079] See Figure 1 A method for emergency collaborative path planning of unmanned aerial vehicles (UAVs) in extreme post-disaster scenarios, the specific steps of which include:

[0080] Step 1: Prioritize the drones in real time according to their flight indicators and preset rules;

[0081] Step 2: Based on the disaster severity level of each disaster point in the extreme post-disaster scenario, the drone flight missions are allocated through a drone hybrid scheduling strategy. First, the drone with the highest real-time priority is selected and matched with the disaster point with the highest disaster severity level according to the greedy strategy. Then, the remaining drones are allocated to disaster points through the nearest neighbor allocation strategy.

[0082] In this embodiment, assume there are N tasks and M drones, with task set T and drone set U. Each task t belongs to a disaster point and has a corresponding disaster level d(t). Each drone u has an availability state s(u), where s(u) = 1 indicates that drone u is available, and s(u) = 0 indicates that drone u is unavailable.

[0083] Define a task-drone assignment matrix X, where X[t][u] is 1 if task t is assigned to drone u, and 0 if not assigned.

[0084] Define the priority P(t) of task t as the negative value of disaster level d(t), i.e., P(t) = -d(t), to ensure that a higher disaster level corresponds to a higher priority in the mathematical expression.

[0085] The goal of the hybrid scheduling strategy is to maximize overall priority. The specific expression is as follows:

[0086]

[0087] The constraints include:

[0088] 1. Each task can only be performed by one drone:

[0089]

[0090] 2. The mission can only be performed by available drones:

[0091]

[0092] The greedy strategy is implemented by selecting the highest priority task-drone combination:

[0093]

[0094] In the formula, if task t is the highest priority task and drone u is available, then X[t][u] is set to 1, indicating that task t is assigned to drone u. Otherwise, X[t][u] is set to 0, indicating that task t is not assigned to drone u.

[0095] The nearest neighbor strategy assigns other tasks to the nearest available drone:

[0096]

[0097] In the formula, if drone u is available and task t is the nearest unassigned task, then X[t][u] is set to 1, indicating that task t is assigned to drone u. Here, D(t,u′) is a distance metric between task t and drone u′ defined according to the actual situation.

[0098] In the formula, t′ represents the index of a task in the task set T, used to iterate through all tasks. u′ represents the index of a drone in the drone set U, used to iterate through all drones.

[0099] Step 3: Use the A* algorithm to plan the path from each UAV to the disaster point, and estimate the time for each UAV to reach each path point on the flight path based on the attributes of each UAV and the distance to the disaster point, and determine whether there is a risk of collision.

[0100] Step 4: For multi-drone flight planning with collision risk, dynamically adjust the flight paths of multiple drones according to the urgency of each drone's flight mission and the priority status of the drones, and update the priority ranking among all drones.

[0101] Step 5: For multi-UAV flight planning without collision risk, confirm whether there are dynamic changes in the flight missions of each UAV. If there are dynamic changes, reassign missions, replan paths, and make collision judgments. If there are no dynamic changes, confirm the final multi-UAV emergency collaborative path planning scheme.

[0102] In this embodiment, it should be noted that:

[0103] When processing drone path planning, approximate calculations are performed on the waypoints and their corresponding times. This means estimating the arrival time of each waypoint based on factors such as flight speed and flight path length. This helps to more accurately plan drone flight schedules and reduce errors during mission execution.

[0104] The urgency of the mission is determined based on the disaster level of the disaster site after the disaster. A hybrid strategy, which combines greedy and nearest neighbor strategies, is used to schedule drones to adapt to the flexibility and intelligence required for multiple missions.

[0105] The system will perform conflict assessments, detecting potential conflicts between drones, such as path intersections or mutual interference. Once a conflict is detected, a dynamic priority adjustment strategy is used to update priorities based on task urgency and drone status weights. The updated priorities are then sorted to ensure that higher-priority drones can avoid collisions first, thereby reducing collision risk and improving overall planning efficiency.

[0106] Path correction is the process of adjusting and optimizing the drone's path based on specific inspection tasks and environmental conditions. Through task- and environment-based path correction, drones can be made more flexible, safe, and efficient when performing tasks.

[0107] As a preferred embodiment of this example, the flight indicators of the UAV include the collision risk of the UAV, the total flight path length of the UAV, the collision waiting time of the UAV, and the remaining battery power of the UAV.

[0108] In this embodiment, the number of conflicts between drones is used as an indicator to measure the collision risk of drones; the total flight path length of drones is the cumulative flight distance of drones; the conflict waiting time of drones is the time that drones take to execute waiting strategies to resolve conflicts when they occur; and the remaining battery power of drones is the remaining battery power of drones when they occur.

[0109] In a preferred embodiment of this invention, the preset planning for real-time priority ranking of UAVs based on their flight indicators and preset rules specifically includes:

[0110] When the collision risk of drones is different, the greater the collision risk, the lower the priority.

[0111] When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows:

[0112]

[0113] In the formula, P is the priority comprehensive index. α is the normalized value of the total flight path length of the UAV, and α is the weight of the total flight path length index of the UAV. β is the normalized value of the drone's conflict waiting time, and β is the weight of the drone's conflict waiting time index. γ is the normalized value of the drone's remaining battery power, and γ is the weight of the drone's remaining battery power index.

[0114] Based on a comparison of the overall priority indicators of each drone, the drone with the higher the overall priority indicator is given a higher priority.

[0115] In this embodiment, and The normalization formula is as follows:

[0116]

[0117]

[0118]

[0119] Where D min and D max T represents the minimum and maximum normalized values ​​of the total flight path length of the UAV, where D is the total flight path length of the mission performed by the target UAV; min and T max Let T be the minimum and maximum normalized values ​​of the conflict waiting time for the UAV, where T is the conflict waiting time of the target UAV when a conflict occurs during mission execution; E min and E max The minimum and maximum values ​​of the normalized remaining battery power of the drone are denoted as , where E is the remaining battery power of the target drone before it performs the mission.

[0120] The weights α, β, and γ of each indicator are determined using the analytic hierarchy process (AHP), and the procedure is as follows:

[0121] 1) Establish a hierarchical structure, including a decision-making objective layer, an influencing factor layer, and an alternative solution layer, such as... Figure 2 As shown.

[0122] 2) Construct a judgment matrix to compare the importance of total route length D, waiting time T, and remaining battery power E.

[0123]

[0124] 3) Calculate the eigenvectors and the largest eigenvalue.

[0125] The weights a of the influencing factors layer to the decision objective layer ij The approximate value W of the eigenvector is calculated using the following formula. i ;

[0126]

[0127] After standardizing the feature vectors, we obtain the weight vector W as shown in the following formula.

[0128] W = (W1, W2, ..., W n )T

[0129] 4) Consistency check.

[0130] The following formula is used to perform a consistency check to see if the consistency ratio is within an acceptable range.

[0131]

[0132]

[0133]

[0134] Where, λ max is the largest eigenvalue; N is the order of the judgment matrix; CI is the consistency index; the average random consistency index RI can be given based on the order of the comparison matrix. When the random consistency ratio CR < 0.1, the calculated weights are considered correct and reasonable.

[0135] In a preferred embodiment of this invention, the step of dynamically adjusting the flight paths of multiple UAVs based on the urgency of the UAV flight mission and the priority status of the UAVs specifically includes:

[0136] The specific formula for calculating the urgency of a drone flight mission is as follows:

[0137] U i =A·(a*M i +b*d(t))

[0138] In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level.

[0139] Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows:

[0140]

[0141] In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient.

[0142] Finally, the overall dynamic priority of the UAV is calculated using the following formula:

[0143] P i =U i +S i

[0144] In the formula, P iThe overall dynamic priority of UAV i;

[0145] The real-time priority ranking of each drone is based on its overall dynamic priority.

[0146] In a preferred embodiment of this invention, the step of updating the priority ranking among all drones specifically includes:

[0147] Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula:

[0148] P new,i =P old,i +γ(P i -P old,i )

[0149] In the formula, γ is the update rate, which controls the adjustment step size; P new,i This is the updated priority; P old,i It is the original priority.

[0150] In this embodiment, the priority of each drone is updated according to the new sorting results, so that drones with higher priority have a higher initial priority in the next round of planning.

[0151] Example 2:

[0152] According to the scheme in Implementation Example 1, it is assumed that in a large-scale inspection mission involving multiple disaster points, there are six inspection missions (T1, T2, T3, T4, T5, T6) and five drones (A, B, C, D, E). Each inspection mission has different disaster point disaster levels and required inspection routes.

[0153] like Figure 3 As shown, disaster sites are classified according to their disaster severity. Inspection routes 2 and 3 intersect.

[0154] First, determine the tasks and drones that need to be assigned as follows.

[0155] Task list: T1, T2, T3, T4, T5, T6

[0156] List of drones: A, B, C, D, E

[0157] Second, the task is analyzed and coded.

[0158] T1: Disaster level 5, priority level 5, inspection route 1

[0159] T2: Disaster level 3, priority level 3, inspection route 2

[0160] T3: Disaster level 2, priority level 2, inspection route 3

[0161] T4: Disaster level 3, priority level 3, inspection route 1

[0162] T5: Disaster level 4, priority level 4, inspection route 2

[0163] T6: Disaster level 1, priority level 1, inspection route 3

[0164] Third, the allocation scheme of Scheme 1 is used for the scheduling and planning of drones.

[0165] A greedy strategy is used to select the highest priority task-drone combination. The highest priority task T1 is assigned to drone C, which is closest to inspection route 1; the second highest priority task T5 is assigned to drone B, which is closest to inspection route 2. This ensures that high-priority tasks are executed as quickly as possible, reducing potential risks and problems.

[0166] Subsequently, a nearest neighbor strategy was employed to assign other tasks. Based on the drone's current location and the task's location, the drone closest to it was selected to perform the task. For example, drone D is closest to inspection route 2, so task T2 was assigned to drone D. This reduces drone idle time and flight distance, improving task execution efficiency.

[0167] For inspection route 3, which contains two low-level disaster points, the remaining four drones are dispatched to inspect power transmission lines containing high-level disaster points. Therefore, drone E is first dispatched to inspect route 3 at point T3. After inspecting point T2, drone D is closest to point T6, so drone D is dispatched to inspect route 3 at point T6. This hybrid scheduling strategy balances task priority and spatial relationships between drones, achieving optimal task allocation. A complete diagram of multi-task drone inspection is shown below. Figure 4 As shown.

[0168] Finally, confirm the drone mission assignments.

[0169] T1: Assigned to drone C (highest priority task)

[0170] T2: Assigned to drone D (based on the nearest neighbor strategy)

[0171] T3: Assigned to drone E (based on the nearest neighbor strategy)

[0172] T4: Assigned to drone A (based on the nearest neighbor strategy)

[0173] T5: Assigned to drone B (based on the nearest neighbor strategy)

[0174] T6: Assigned to drone D (based on the nearest neighbor strategy)

[0175] The allocation of collaborative tasks is crucial for the rapid deployment of multiple drones for inspections after extreme disasters. Therefore, to quickly inspect the entire power grid in extreme disaster scenarios, a hybrid drone scheduling strategy is adopted. This involves first using a greedy strategy to select the highest-priority task-drone combination, and then using a nearest-neighbor strategy to allocate other tasks. The drone path planning algorithm uses the A* algorithm, prioritizing locations with higher disaster levels for inspection. After inspecting key disaster points, other inspection tasks are completed, thus achieving comprehensive power grid inspection after extreme disasters.

[0176] Example 3:

[0177] A drone-based emergency collaborative path planning system for extreme post-disaster scenarios includes:

[0178] The drone priority ranking module ranks drones in real time according to their flight indicators and preset rules; this module is used to implement the function of step one in embodiment one, and will not be described in detail here.

[0179] The UAV flight mission allocation module allocates UAV flight missions based on the disaster severity level of each disaster point in an extreme post-disaster scenario through a hybrid UAV scheduling strategy. First, it selects the UAV with the highest real-time priority and matches it with the disaster point with the highest disaster severity level according to a greedy strategy. Then, it allocates the remaining UAVs and disaster points through a nearest neighbor allocation strategy. This module is used to implement the function of step two in embodiment one, and will not be described in detail here.

[0180] The UAV flight path planning module uses the A* algorithm to plan the path from each UAV to the disaster point, and estimates the time for each UAV to reach each path point on the flight path based on the attributes of each UAV and the distance to the disaster point, and determines whether there is a collision risk; this module is used to implement the function of step three in embodiment one, and will not be described in detail here.

[0181] The flight mission dynamic adjustment module dynamically adjusts the flight paths of multiple UAVs for flight planning with collision risks based on the urgency of each UAV flight mission and the priority status of the UAVs, and updates the priority ranking among all UAVs.

[0182] The flight mission replanning module re-confirms whether there are dynamic changes in the flight missions of each UAV for multi-UAV flight plans that do not pose a collision risk. If there are dynamic changes, the missions are reassigned, paths are replanned, and collisions are assessed. If there are no dynamic changes, the final multi-UAV emergency collaborative path planning scheme is determined.

[0183] As a preferred embodiment of this example, the flight indicators of the UAV include the collision risk of the UAV, the total flight path length of the UAV, the collision waiting time of the UAV, and the remaining battery power of the UAV.

[0184] In a preferred embodiment of this invention, the preset planning for real-time priority ranking of UAVs based on their flight indicators and preset rules specifically includes:

[0185] When the collision risk of drones is different, the greater the collision risk, the lower the priority.

[0186] When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows:

[0187]

[0188] In the formula, P is the priority comprehensive index. α is the normalized value of the total flight path length of the UAV, and α is the weight of the total flight path length index of the UAV. β is the normalized value of the drone's conflict waiting time, and E is the normalized value of the drone's remaining battery power, and γ is the weight of the drone's remaining battery power.

[0189] Based on a comparison of the overall priority indicators of each drone, the drone with the higher the overall priority indicator is given a higher priority.

[0190] In a preferred embodiment of this invention, the step of dynamically adjusting the flight paths of multiple UAVs based on the urgency of the UAV flight mission and the priority status of the UAVs specifically includes:

[0191] The specific formula for calculating the urgency of a drone flight mission is as follows:

[0192] U i =A·(a*M i +b*d(t))

[0193] In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level.

[0194] Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows:

[0195]

[0196] In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient.

[0197] Finally, the overall dynamic priority of the UAV is calculated using the following formula:

[0198] P i =U i +S i

[0199] In the formula, P i The overall dynamic priority of UAV i;

[0200] The real-time priority ranking of each drone is based on its overall dynamic priority.

[0201] In a preferred embodiment of this invention, the step of updating the priority ranking among all drones specifically includes:

[0202] Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula:

[0203] P new,i =P old,i +γ(P i -P old,i )

[0204] In the formula, γ is the update rate, which controls the adjustment step size; P new,i This is the updated priority; P old,i It is the original priority.

[0205] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for UAV emergency collaborative path planning in extreme post-disaster scenarios, characterized in that, The specific steps include: The drones are prioritized in real time based on their flight parameters and preset rules. Based on the disaster severity level of each disaster point in extreme post-disaster scenarios, a hybrid scheduling strategy for drones is used to allocate drone flight missions. First, a greedy strategy is used to select the drone with the highest real-time priority and match it with the disaster point with the highest disaster severity level. Then, the nearest neighbor allocation strategy is used to allocate the remaining drones and disaster points. The A* algorithm is used to plan the paths of each UAV to the disaster point. Based on the attributes of each UAV and the distance to the disaster point, the time for each UAV to reach each path point on the flight path is estimated, and the risk of collision is determined. For multi-drone flight planning with collision risk, the flight paths of multiple drones are dynamically prioritized based on the urgency of each drone's flight mission and the drone's priority status, and the priority ranking among all drones is updated. For multi-UAV flight planning where there is no risk of collision, it is necessary to confirm whether there are dynamic changes in the flight missions of each UAV. If there are dynamic changes, the missions are reassigned, the path planning is re-planned, and the collision judgment is re-evaluated. If there are no dynamic changes, the final multi-UAV emergency collaborative path planning scheme is confirmed. The flight indicators of the drone include the drone's collision risk, the drone's total flight path length, the drone's collision waiting time, and the drone's remaining battery power. The specific preset rule for the drone is as follows: when the collision risk of the drone is different, the greater the collision risk of the drone, the lower the priority. When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows: In the formula, P is the priority comprehensive index. The normalized value for the total flight path length of the UAV. The weight of the total flight path length index for UAVs; Normalized values ​​for drone conflict waiting time. Weighting of drone conflict wait time metrics; The remaining battery power of the drone is normalized to a value. The weight of the drone's remaining battery power indicator; Based on the comparison of the overall priority index of each drone, the drone with the higher the overall priority index has the higher priority. The specific steps for dynamically adjusting the flight paths of multiple UAVs based on the urgency of each UAV flight mission and the priority status of the UAVs are as follows: The specific formula for calculating the urgency of a drone flight mission is as follows: In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level. Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows: In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient. Finally, the overall dynamic priority of the UAV is calculated using the following formula: In the formula, P i The overall dynamic priority of UAV i; The real-time priority ranking of each drone is based on its overall dynamic priority.

2. The UAV emergency collaborative path planning method for extreme post-disaster scenarios according to claim 1, characterized in that, The specific steps for updating the priority ranking among all drones are as follows: Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula: In the formula, It is the update rate, which controls the step size of the adjustment; This is the updated priority; It is the original priority.

3. A drone-based emergency collaborative path planning system for extreme post-disaster scenarios, characterized in that: include: The drone priority ranking module ranks drones in real time based on their flight indicators and preset rules. The UAV flight mission allocation module allocates UAV flight missions based on the disaster severity level of each disaster point in extreme post-disaster scenarios through a hybrid UAV scheduling strategy. First, it selects the UAV with the highest real-time priority and matches it with the disaster point with the highest disaster severity level according to a greedy strategy. Then, it allocates the remaining UAVs and disaster points through a nearest neighbor allocation strategy. The UAV flight path planning module uses the A* algorithm to plan the path from each UAV to the disaster point, and estimates the time for each UAV to reach each path point on the flight path based on the attributes of each UAV and the distance to the disaster point, and determines whether there is a risk of collision. The flight mission dynamic adjustment module dynamically adjusts the flight paths of multiple UAVs for flight planning with collision risks based on the urgency of each UAV flight mission and the priority status of the UAVs, and updates the priority ranking among all UAVs. The flight mission replanning module reconfirms whether there are dynamic changes in the flight missions of each UAV for multi-UAV flight plans that do not have a collision risk. If there are dynamic changes, the missions are reassigned, the path is replanned, and the collision is judged. If there are no dynamic changes, the final multi-UAV emergency collaborative path planning scheme is determined. The flight indicators of the drone include the drone's collision risk, the drone's total flight path length, the drone's collision waiting time, and the drone's remaining battery power. The specific preset rule for the drone is as follows: when the collision risk of the drone is different, the greater the collision risk of the drone, the lower the priority. When drones have the same collision risk, a priority calculation model based on multiple indicators is established using the drone's total flight path length, conflict waiting time, and remaining battery power. The specific formula is as follows: In the formula, P is the priority comprehensive index. The normalized value for the total flight path length of the UAV. The weight of the total flight path length index for UAVs; Normalized values ​​for drone conflict waiting time. Weighting of drone conflict wait time metrics; The remaining battery power of the drone is normalized to a value. The weight of the drone's remaining battery power indicator; Based on the comparison of the overall priority index of each drone, the drone with the higher the overall priority index has the higher priority. The specific steps for dynamically adjusting the flight paths of multiple UAVs based on the urgency of each UAV flight mission and the priority status of the UAVs are as follows: The specific formula for calculating the urgency of a drone flight mission is as follows: In the formula, U i Indicates the urgency of the mission for drone i; A is the weighting coefficient; M i d(t) represents the importance of the real-time task; d(t) represents the disaster level; a and b are the weighting coefficients for the importance of the real-time task and the disaster level. Next, the real-time state priority of the drone is calculated based on its priority status. The specific formula is as follows: In the formula, S i Let B be the state priority of drone i, and B be the weighting coefficient. Finally, the overall dynamic priority of the UAV is calculated using the following formula: In the formula, P i The overall dynamic priority of UAV i; The real-time priority ranking of each drone is based on its overall dynamic priority.

4. The UAV emergency collaborative path planning system for extreme post-disaster scenarios according to claim 3, characterized in that, The specific steps for updating the priority ranking among all drones are as follows: Using the real-time priority ranking of each drone, the drone priority status is updated according to the following formula: In the formula, It is the update rate, which controls the step size of the adjustment; This is the updated priority; It is the original priority.

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