Unmanned aerial vehicle continuous monitoring method based on PoI priority

Abstract

The invention relates to the technical field of path planning, in particular to an unmanned aerial vehicle continuous monitoring method based on PoI priority, which comprises the following steps: S1, regarding each monitoring point in a to-be-monitored area as a point of interest (PoIs), and dividing all PoIs into K + 1 disjoint sets according to the monitoring priority; s2, determining a neighborhood of each PoIs; s3, respectively constructing K + 1 shortest loops with neighborhoods for the PoIs in each set; s4, carrying out K iterations from K + 1 shortest loops with neighborhoods, constructing 2K loops, constructing a group of initial monitoring paths in a monitoring period T, and enabling the PoIs in the set Vk to be accessed for 2K-k times in the monitoring period T; and S5, in consideration of the battery capacity of the unmanned aerial vehicle, cutting the initial monitoring path of the unmanned aerial vehicle, and increasing the path of the unmanned aerial vehicle to and from the base station to obtain a final continuous monitoring path of the unmanned aerial vehicle. According to the method, the monitoring path of the unmanned aerial vehicle can be planned according to the importance degrees of different monitoring point PoI, so that the monitoring time interval of the important PoI is shorter.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and more particularly to a method for continuous monitoring of unmanned aerial vehicles based on PoI priority. Background Art

[0002] Due to the advantages of low cost, fast deployment and high flexibility, drones have been widely used in disaster monitoring and rescue in recent years. Drones equipped with cameras are dispatched to take photos over the disaster area and the information is fed back to the base station in real time for dispatchers to make decisions.

[0003] Early drone continuous monitoring methods combined the location information of PoIs (Points of Interest) and performed drone scheduling based on heuristic algorithms and approximate algorithms to reduce the time interval between two adjacent PoIs being monitored. In recent years, with the in-depth research in the field of drone monitoring, the differences in the importance of PoIs have been taken into account in the scheduling of drones. Although such drone monitoring methods based on PoI priority can better meet actual scenarios, existing methods ignore the fact that drone-mounted cameras can usually shoot at a long distance, thus affecting the effect of drone continuous monitoring.

[0004] At present, most drone continuous monitoring methods assume that all PoIs in the disaster area have the same monitoring priority, so they just try to shorten the time interval between two adjacent monitoring of PoIs. Since different PoIs have different levels of importance, if the same monitoring time interval is used, it is easy to overlook important information. At the same time, all current algorithms ignore the fact that the camera mounted on the drone can usually shoot at a long distance.

[0005] Therefore, how to plan the monitoring path of the UAV according to the importance of different PoIs is a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0006] In view of this, the present invention provides a UAV continuous monitoring method based on PoI priority, which can plan the monitoring path of the UAV according to the importance of different monitoring points PoI, so that the monitoring time interval of important PoI is shorter.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A method for continuous monitoring of drones based on PoI priority includes the following steps:

[0009] S1. Each monitoring point in the monitored area is regarded as a point of interest (PoI). All PoIs are divided into K+1 non-overlapping sets according to the monitoring priority. The order of priority from high to low is V0>V1…>V K ;

[0010] S2, determine the neighborhood of each PoIs, and the drone can take photos when it reaches any point in the neighborhood of the PoIs;

[0011] S3, construct K+1 shortest circuits with neighbors for each PoI in each set;

[0012] S4, construct 2 from K+1 shortest loops with neighbors after K iterations K 2 loops, K loops are connected to construct a set of initial monitoring paths within a monitoring period T. In the initial monitoring path, the set V k The PoIs are visited within a monitoring period T2 K-k Second-rate;

[0013] S5. Considering the battery capacity of the UAV, the initial monitoring path of the UAV is cut, and the path of the UAV to and from the base station is added to obtain the final continuous monitoring path of the UAV.

[0014] Furthermore, in S1, it is assumed that there are n monitoring points PoIs, denoted as v1,v2,...,v n , V is the set of PoIs, that is, V = {v1,v2,...,v n}, monitoring point v i The coordinates of (x i, y i ,...,z i ), where 1≤i≤n, each monitoring point v i Each has a specified monitoring priority ρ i ; Divide all PoIs into K+1 disjoint sets according to monitoring priority, where: ρ max and ρ min Respectively represent the maximum monitoring priority and minimum monitoring priority of all PoIs in set V. The point is divided into V k middle.

[0015] Furthermore, in S2, the monitoring point v i Neighborhood D(v i ) is expressed as (x i ,y i ,...,h m) is the center and the radius is R, where x i and i They represent the monitoring points v i The horizontal and vertical coordinates, h m represents the flight altitude of the drone, R represents the monitoring radius, that is, D(v i )={(x,y,h m )|(xx i ) 2 +(yy i ) 2 ≤R 2}.

[0016] Furthermore, S3 includes:

[0017] For the monitoring point v in the set V0 i and V k PoI v j , 1≤k≤K, assuming v i and v j The distance is d(v i ,v j ), and v i and set V K The shortest distance is denoted as d(v i ,V k ), d(v i ,V k )=min{d(v i ,v j )}; Assume that v1 is the monitoring point in V0 with the minimum average distance to the other K sets,

[0018] in, Yes i The sum of the shortest distances to all monitoring points in the other K sets;

[0019] They are V0, V1, ... V K The PoIs in the construct K+1 shortest circuits with neighbors, denoted as C0, C1, …C K , and assume that v1 is included in all K+1 loops, and that the neighborhood D(v i ) has at least one point contained in the loop.

[0020] Furthermore, S4 includes:

[0021] In the first iteration, 2 is constructed by loops C0 and C1 1 Path C1 (1) and C2 (1), so that the PoIs in V0 can be monitored twice and the PoIs in V1 can be monitored once;

[0022] In the second iteration, through loop C1 (1) , C2 (1) and C2, build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) , so that the PoIs in V0 are monitored 4 times, the PoIs in V1 are monitored 2 times, and the nodes in V2 are monitored once;

[0023] And so on, at the Kth iteration, through loop C K and 2 constructed in the K-1th iteration K-1 Circuit, build 2 K loops, so that PoIs in V0 are monitored 2 K times, PoIs in V1 are monitored 2 times K-1 times, V k PoIs monitored in 2 K-k Second-rate;

[0024] During the entire iteration process, a total of 2 1 +2 2 +2 3 +…2 K =2 K+1 - 2 circuits.

[0025] Furthermore, in both S3 and S4, the TSPN algorithm is used to construct the loop.

[0026] Furthermore, in the second iteration, loop C2 is first cut into four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 , and then according to loop C1 (1) , C2 (1) and four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 Build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) ; Among them, the four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 The splitting process includes:

[0027] Assumptions Where p1 is the hovering monitoring position of monitoring point v1, n2 is the number of hovering positions in C2; Among them, c max represents the time spent at the hovering position with the longest round trip time with p1 in C2; t f (p1,p j ) is the drone flying from p1 to p j The time spent, t m (v j ) is the drone hovering at v i Monitoring time;

[0028] Find the three positions in loop C2 and So that in loop C2 from p1 to The time spent is no more than And p1 to The time spent is greater than Among them, 1≤j≤3, w(C2) is the total energy consumed by the UAV on loop C2;

[0029] will go from p1 to The path is taken as the first subpath P 2,1 , will be arrive The path is taken as the second subpath P 2,2 , will be arrive The path is taken as the third subpath P 2,3 , will be arrive The path is taken as the fourth subpath P 2,4 .

[0030] Furthermore, in the second iteration, the four loops C1 (2) , C2 (2) , C3 (2) , C4 (2) The build process includes:

[0031] According to the two circuits C1 (1) , C2 (1) And four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 , construct a weighted complete bidirectional graph G2 = (A2 ∪ B2, E2); where E2 represents; A2 = {a1, a2, a3, a4}, representing four loops C1 respectively (1) , C2 (1) , C1 (1) , C2(1) ; B2 = {b1, b2, b3, b4}, representing four subpaths P respectively 2,1 , P 2,2 , P 2,3 , P 2,4 ; Any node a in A2 i With any node b in B2 j The edge between i ,b j ) has a weight of a i With b j The time it takes to complete the circuit consisting of all PoIs in is denoted as t2(a i ,b j );

[0032] Find a perfect matching M2 in G2, so that the maximum weight of all edges in M2 is minimized;

[0033] Four loops C1 are found from the perfect match M2 (2) , C2 (2) , C3 (2) , C4 (2) , assuming that node a i With Node matches, where 1≤i≤4. At this time, a i and The hover position collection in is recorded as Using TSPN algorithm to construct loop C i (2) .

[0034] Furthermore, in S5, it is assumed that the initial monitoring path C' of the UAV within a monitoring cycle is expressed as Where s represents the base station, n' is the number of hovering positions in the loop C';

[0035] By cutting C', we can obtain a series of subpaths C1", C2", ..., C with root node s. Q ”;

[0036] During the cutting process, if the first circuit The energy w(C1") consumed does not exceed the battery capacity, and The energy consumed exceeds the battery capacity. Add base station node s between the paths;

[0037] And so on, until all base station nodes are added, the final continuous monitoring path of the drone is obtained.

[0038] Furthermore, in S5, a series of subpaths C1”, C2”, ..., C Q", the goal is to minimize the maximum value of the weighted monitoring time interval of all PoIs in the set V, which can be expressed as:

[0039]

[0040] And, w(C i ”)≤e CAP , 1≤i≤Q,

[0041] Among them, w(C i ”) is the UAV in C i "Energy consumption on the path; e CAP is the battery capacity of the drone; t(C i ”) is the UAV in C i ” The time spent on the path.

[0042] It can be seen from the above technical solutions that, compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention takes into account that different PoIs have different degrees of importance and gives different monitoring priorities to PoIs of different importance. For example, in disaster areas after an earthquake, densely populated places such as hospitals and schools need to be monitored more frequently, while sparsely populated places such as factories and outdoor farms require a lower monitoring frequency. In this way, important information can be discovered in a timely manner, thereby reducing the loss of life and property.

[0044] At the same time, the present invention takes into account the characteristic that the camera carried by the drone can usually take pictures at a long distance, and sets the neighborhood of each PoIs, so that the drone can complete the monitoring task without flying to the location of the PoI. Once the drone hovers in the area near the PoI, it can take high-quality and clear photos. This method can save the flight time and energy consumption of the drone, thereby shortening the monitoring time interval of PoIs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0046] Figure 1 A flowchart of the method for continuous monitoring of unmanned aerial vehicles based on PoI priority provided by the present invention;

[0047] Figure 2 A schematic diagram of the priority distribution of each point of interest (PoI) provided by the present invention;

[0048] Figure 3 The shortest loop for each monitoring priority level provided by the present invention;

[0049] Figure 4 In the first iteration provided by the present invention, the loop C1 is cut into two sub-paths P 1,1 and P 1,2 Schematic diagram of

[0050] Figure 5 Two loops generated after the first iteration provided by the present invention;

[0051] Figure 6 In the second iteration provided by the present invention, C2 is cut into four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 Schematic diagram of

[0052] Figure 7 A complete bidirectional graph provided for the present invention;

[0053] Figure 8 Four loops generated after the second iteration provided by the present invention;

[0054] Fig. 9 The initial monitoring path diagram generated when the battery capacity is not considered provided by the present invention;

[0055] Fig.10 The present invention provides a final continuous monitoring path map generated after considering the battery capacity. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, an embodiment of the present invention discloses a method for continuous monitoring of drones based on PoI priority, comprising the following steps:

[0058] S1. Each monitoring point in the monitored area is regarded as a point of interest (PoI). All PoIs are divided into K+1 non-overlapping sets according to the monitoring priority. The order of priority from high to low is V0>V1…>V K ;

[0059] S2, determine the neighborhood of each PoIs, and the drone can take photos when it reaches any point in the neighborhood of the PoIs;

[0060] S3, construct K+1 shortest circuits with neighbors for each PoI in each set;

[0061] S4, construct 2 from K+1 shortest loops with neighbors after K iterations K 2 loops, K loops are connected to construct a set of initial monitoring paths within a monitoring period T. In the initial monitoring path, the set V k The PoIs are visited within a monitoring period T2 K-k Second-rate;

[0062] S5. Considering the battery capacity of the UAV, the initial monitoring path of the UAV is cut, and the path of the UAV to and from the base station is added to obtain the final continuous monitoring path of the UAV.

[0063] The above steps are further described below.

[0064] S1. The present invention takes into account the importance differences of PoIs. In actual monitoring scenarios, there are multiple PoIs that require drones to take photos for data collection. However, PoIs have different degrees of importance due to their nature and geographical location. For example, densely populated places such as schools and hospitals are more important because they involve more life and property safety. Therefore, they need to be visited frequently and require a shorter monitoring time interval.

[0065] Assume there are n monitoring points PoIs, denoted as v1,v2,...,v n , V is the set of PoIs, that is, V = {v1,v2,...,v n}, monitoring point v i The coordinates of (x i ,y i ,...,z i ), where 1≤i≤n, each monitoring point v i Each has a specified monitoring priority ρ i , ρ i The larger the value, the higher the monitoring priority of the PoI, and the more frequently it will be visited than the PoI with lower monitoring priority. max =1, that is, ρ max =max1≤i≤n{ρ i}=1,0<ρ i ≤1, 1<i≤n.

[0066] Divide all PoIs into K+1 disjoint sets according to monitoring priority, where: ρ max and ρ min Respectively represent the maximum monitoring priority and minimum monitoring priority of all PoIs in set V. The point is divided into V k middle.

[0067] Assume K = 2, such as Figure 2 As shown, the black solid area, black shadow area and blank area represent PoIs with monitoring priorities of 1, 1 / 2 and 1 / 4 respectively, the dotted line part represents the neighborhood, and the triangle pattern represents the base station s.

[0068] S2, the present invention considers a method for continuous monitoring of drones with neighborhoods. It is understood that most drones are currently equipped with cameras with telephoto lenses, such as the DJI Mavic 3Pro with a 166mm telephoto lens. Such cameras can take high-quality clear images of PoIs from tens to hundreds of meters away, thereby reducing the flight path and energy consumption of the drone.

[0069] Monitoring point v i Neighborhood D(v i ) is expressed as (x i ,y i ,...,h m ) is the center and the radius is R, where x i and i They represent the monitoring points v i The horizontal and vertical coordinates, h m Indicates the flight altitude of the drone (e.g., h m =50m), R represents the monitoring radius (for example, R = 100m), that is

[0070] D(v i )={(x,y,h m )|(xx i ) 2 +(yy i ) 2 ≤R 2 As long as the drone reaches the vicinity of the PoI, it can take photos, which can effectively reduce the flight path length and energy consumption of the drone, thereby improving work efficiency.

[0071] S3 constructs K+1 shortest circuits with neighbors for PoIs in each set, including:

[0072] For the monitoring point v in the set V0 i and V kPoI v j , 1≤k≤K, assuming v i and v j The distance is d(v i ,v j ), and v i and set V K The shortest distance is denoted as d(v i ,V k ), d(v i ,V k )=min{d(v i ,v j )}; Assume that v1 is the monitoring point in V0 with the minimum average distance to the other K sets,

[0073] in, Yes i The sum of the shortest distances to all monitoring points in the other K sets;

[0074] They are V0, V1, ... V K The PoIs in the construct K+1 shortest circuits with neighbors, denoted as C0, C1, …C K , and assume that v1 is included in all K+1 loops, and that the neighborhood D(v i ) has at least one point contained in the loop. That is, for every set V k , using the TSPN algorithm (shortest loop algorithm with neighborhood) for V k ∪v1 constructs the shortest circuit, such as Figure 3 As shown in the figure, TSPN is similar to the TSP algorithm, except that it adds a concept of neighborhood, which refers to the neighborhood D(v1), D(v2), ..., D(v n ), find a shortest path so that each D(v i ) has at least one point contained in the loop.

[0075] S4, construct 2 from K+1 shortest loops with neighbors after K iterations K 2 loops, K loops are connected to construct a set of initial monitoring paths within a monitoring period T. In the initial monitoring path, the set V k The PoIs are visited within a monitoring period T2 K-k times; specifically including:

[0076] S41. In the first iteration, construct 2 through loops C0 and C1 1 Path C1 (1) and C2 (1), so that the PoIs in V0 can be monitored twice and the PoIs in V1 can be monitored once. The specific implementation is: Figure 3 The loop C1 in is cut into two subpaths P 1,1 and P 1,2 , and make the time consumed by the two subpaths almost equal, such as Figure 4 Next, C0 and P 1,1 The PoIs included in the loop C1 are constructed using the TSPN algorithm (1) ,like Figure 5 As shown in the left figure, C0 and P 1,2 The PoIs included in the loop C2 are constructed using the TSPN algorithm (1) ,like Figure 5 As shown in the picture on the right.

[0077] S42. In the second iteration, through loop C1 (1) , C2 (1) and C2, build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) , so that the PoIs in V0 are monitored 4 times, the PoIs in V1 are monitored 2 times, and the nodes in V2 are monitored once; the specific implementation method is: in the second iteration, first Figure 3 The loop C2 in is cut into four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 , so that P 2,1 , P 2,2 , P 2,3 , P 2,1 The time spent on the four sub-paths is roughly the same, such as Figure 6 As shown, according to loop C1 (1) , C2 (1) and four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 Build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) .

[0078] Among them, the four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 The splitting process includes:

[0079] 1) Assumptions Where p1 is the hovering monitoring position of monitoring point v1, n2 is the number of hovering positions in C2; Among them, c max represents the time spent at the hovering position with the longest round trip time with p1 in C2; t f (p1,p j ) is the drone flying from p1 to p j The time spent, t m (v j ) is the drone hovering at v i monitoring time.

[0080] 2) Starting from the first position in loop C2, traverse the entire loop in sequence to find the three positions in loop C2 and So that in loop C2 from p1 to The time spent is no more than And p1 to The time spent is greater than Among them, 1≤j≤3, w(C2) is the total energy consumed by the UAV on loop C2.

[0081] 3) Based on the three positions found in 2) and The loop C2 can be cut into four sub-paths, from p1 to The path is taken as the first subpath P 2,1 , will be arrive The path is taken as the second subpath P 2,2 , will be arrive The path is taken as the third subpath P 2,3 , will be to p n2 The path is taken as the fourth subpath P 2,4 ,like Figure 6 shown.

[0082] Next, according to loop C1 (1) , C2 (1) and four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 Build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) , including:

[0083] 1) According to the two circuits C1(1) , C2 (1) And four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 , construct a weighted complete bidirectional graph G2 = (A2 ∪ B2, E2); where E2 represents the set of edges connecting the points in A2 and the points in B2; A2 = {a1, a2, a3, a4}, representing the four loops C1 respectively (1) , C2 (1) , C1 (1) , C2 (1) ; B2 = {b1, b2, b3, b4}, representing four subpaths P respectively 2,1 , P 2,2 , P 2,3 , P 2,4 ,like Figure 7 As shown. Any node a in A2 i With any node b in B2 j The edge between i ,b j ) has a weight of a i With b j The time it takes to complete the circuit consisting of all PoIs in is denoted as t2(a i ,b j );

[0084] 2) Find a perfect match M2 in G2, so that the maximum weight of all edges in M2 is the smallest. The specific implementation process is: arrange the weights of the 16 edges in G2 in ascending order, that is,

[0085] t2(e1)≤t2(e2)≤...≤t2(e 16 ). Assume that there is an optimal solution OPT* for the perfect matching M2, where the maximum weight of the matching edge is e j *. For any weight greater than or equal to e j *Edge l , G2 l Denoted as the edges e1, e2,…, e l The subgraph formed by G2 l There exists a perfect match M in l , so that every node in A2 matches B2. However, if e l Less than e j *, there is no perfect matching, because it is impossible to find a solution that is better than the optimal solution. Therefore, the perfect matching M2 can be found using the bisection method.

[0086] 3) Four loops C1 are found from the perfect match M2 (2) , C2(2) , C3 (2) , C4 (2) ,like Figure 8 Assume that node a i With Node matches, where 1≤i≤4. At this time, a i and The hover position collection in is recorded as Using TSPN algorithm to construct loop C i (2) .

[0087] S43, and so on, find the remaining 2 3 +2 4 +…+2 K loops. At the Kth iteration, through loop C K and 2 constructed in the K-1th iteration K-1 Circuit, build 2 K loops, so that PoIs in V0 are monitored 2 K times, PoIs in V1 are monitored 2 times K-1 times, V k PoIs monitored in 2 K-k Second-rate;

[0088] During the entire iteration process, a total of 2 1 +2 2 +2 3 +…2 K =2 K+1 - 2 circuits.

[0089] S5, considering the battery capacity of the drone, cuts the initial monitoring path of the drone and adds the path of the drone to and from the base station to obtain the final continuous monitoring path of the drone. Specifically includes:

[0090] After the above steps, we get 2 K+1 -2 circuits, by repeating the last 2 K A UAV initial continuous monitoring path covering the monitoring period T can be found by using the paths. Assume that v2 is the PoI closest to base station s in V0, and assume that v2 is within 2 K The hovering positions in the loops are Rule 2 K The trajectory of the loop is

[0091] like Fig. 9 The continuous monitoring path C' of the UAV with monitoring period T can be obtained by repeating W, that is, C' = s→W→W→...W→s.

[0092] Assume that the initial monitoring path C' of the UAV within a monitoring cycle is expressed as Where s represents the base station, n' is the number of hovering positions in the loop C';

[0093] Since the battery capacity of the drone is limited, it needs to return to the base station s for charging or battery replacement multiple times within the period T. By cutting C', a series of subpaths C1", C2", ..., C with root nodes s are obtained. Q ”;

[0094] During the cutting process, if the first circuit The energy w(C1") consumed does not exceed the battery capacity, and The energy consumed exceeds the battery capacity. Add base station node s between the paths;

[0095] And so on, until all base station nodes are added, the final continuous monitoring path of the drone is obtained, such as Fig.10 shown.

[0096] The present invention takes into account the limitation of the battery capacity of the drone. In order to better fit the actual application scenario, the present invention assumes that the drone has a certain amount of battery capacity. The drone must return to the base station to charge or replace the battery before the battery is exhausted, and then continue to perform the monitoring task. The energy consumption model is as follows:

[0097] PoI v j The hovering monitoring time is recorded as t m (v j ), usually with PoI v j Assume that each time the drone takes off, it accelerates from the initial speed 0 to the maximum speed v max (For example, v max =15m / s), and then at a constant speed v max Fly to PoI v j+1 . Then the drone moves from position p i,j Fly to p i,j+1 The time is:

[0098]

[0099] Among them, a UAV is the acceleration of the drone, d j,j+1 Yes i,j and p i,j+1 The Euler distance of The drone accelerates from 0 to v max The distance from v max The distance to decelerate to 0. Therefore, loop C iThe total time taken is:

[0100]

[0101] Among them, s is the base station, n i For loop C i The number of hover positions.

[0102] Loop C i The total energy consumption in is denoted as w(C i ), including the energy consumed by the drone when hovering m (v j ) = η·t m (v i,j ), where η is the energy consumption rate of the drone when it is hovering, t m (v i,j ) is the drone in v i，j On the other hand, the drone moves from position p i,j Fly to p i,j+1 The energy consumed is:

[0103]

[0104] Among them, e acc and e dec represents the energy consumed during acceleration and deceleration, and λ represents the speed of the drone v max The rate at which energy is consumed during flight, Indicates the speed v max Time to fly. Loop C i The total energy consumed in is:

[0105]

[0106] Among them, w(C i ) is not greater than the drone battery capacity e CAP , that is, w(C i )≤e CAP .

[0107] The present invention will i Denoted as PoI v i The monitoring interval within the monitoring period T, that is, PoI v i The research goal of this invention is to find a series of flight loops C1, C2, ... C for the UAV. Q , so that the maximum value of the weighted monitoring time interval (i.e. the product of weight and time interval) of all PoIs in set V is minimized, that is:

[0108]

[0109] And, w(C i ”)≤e CAP , 1≤i≤Q,

[0110] Among them, the total monitoring time of Q-1 loops is less than the detection period T, and the total monitoring time of Q loops is greater than or equal to the detection period T, which ensures that the entire monitoring process covers the monitoring period T as exactly as possible.

[0111] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0112] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for continuous monitoring of drones based on PoI priority, characterized in that: The following steps are involved: S1. Each monitoring point in the area to be monitored is regarded as a point of interest (PoI). All PoIs are divided into K+1 non-overlapping sets according to the monitoring priority. The order of priority from high to low is V0>V1…>V K ; S2, determine the neighborhood of each PoIs, and the drone can take photos when it reaches any point in the neighborhood of the PoIs; S3, construct K+1 shortest circuits with neighbors for each PoI in each set; S4, construct 2 from K+1 shortest loops with neighbors after K iterations K 2 loops, K loops are connected to construct a set of initial monitoring paths within a monitoring period T. In the initial monitoring path, the set V k The PoIs are visited within a monitoring period T2 K-k Second-rate; S5. Considering the battery capacity of the UAV, the initial monitoring path of the UAV is cut, and the path of the UAV to and from the base station is added to obtain the final continuous monitoring path of the UAV.

2. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 1, characterized in that: In S1, it is assumed that there are n monitoring points PoIs, denoted as v1,v2,...,v n , V is the set of PoIs, that is, V = {v1,v2,...,v n }, monitoring point v i The coordinates of (x i ,y i ,...,z i ), where 1≤i≤n, each monitoring point v i Each has a specified monitoring priority ρ i ; Divide all PoIs into K+1 disjoint sets according to monitoring priority, where: ρ max and ρ min Respectively represent the maximum monitoring priority and minimum monitoring priority of all PoIs in set V. The point is divided into V k middle.

3. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 2, characterized in that: In S2, the monitoring point v i Neighborhood D(v i ) is expressed as (x i ,y i ,...,h m ) is the center and the radius is R, where x i and i They represent the monitoring points v i The horizontal and vertical coordinates, h m represents the flight altitude of the drone, R represents the monitoring radius, that is, D(v i )={(x,y,h m )|(xx i ) 2 +(yy i ) 2 ≤R 2 }.

4. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 1, characterized in that S3 include: For the monitoring point v in the set V0 i and V k PoI v j , 1≤k≤K, assuming v i and v j The distance is d(v i ,v j ), and v i and set V K The shortest distance is denoted as d(v i ,V k ), d(v i ,V k )=min{d(v i ,v j )}; Assume that v1 is the monitoring point in V0 with the minimum average distance to the other K sets, in, Yes i The sum of the shortest distances to all monitoring points in the other K sets; They are V0, V1, ... V K The PoIs in the construct K+1 shortest circuits with neighbors, denoted as C0, C1, …C K , and assume that v1 is included in all K+1 loops, and that the neighborhood D(v i ) has at least one point contained in the loop.

5. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 4, characterized in that S4 include: In the first iteration, 2 is constructed by loops C0 and C1 1 Path C1 (1) and C2 (1) , so that the PoIs in V0 can be monitored twice and the PoIs in V1 can be monitored once; In the second iteration, through loop C1 (1) , C2 (1) and C2, build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) , so that the PoIs in V0 are monitored 4 times, the PoIs in V1 are monitored 2 times, and the nodes in V2 are monitored once; And so on, at the Kth iteration, through loop C K and 2 constructed in the K-1th iteration K-1 Circuit, build 2 K loops, so that PoIs in V0 are monitored 2 K times, PoIs in V1 are monitored 2 times K-1 times, V k PoIs monitored in 2 K-k Second-rate; During the entire iteration process, a total of 2 1 +2 2 +2 3 +…2 K =2 K+1 - 2 circuits.

6. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 1, characterized in that: In both S3 and S4, the TSP algorithm is used to construct the loop.

7. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 6, characterized in that: In the second iteration, the loop C2 is first cut into four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 , and then according to loop C1 (1) , C2 (1) and four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 Build 2 2 Circuit C1 (2) , C2 (2) , C3 (2) , C4 (2) ; Among them, the four sub-paths P 2,1 , P 2,2 , P 2,3 , P 2,4 The splitting process includes: Assumptions Where p1 is the hovering monitoring position of monitoring point v1, n2 is the number of hovering positions in C2; Among them, c max represents the time spent at the hovering position with the longest round trip time with p1 in C2; t f (p1,p j ) is the drone flying from p1 to p j The time spent, t m (v j ) is the drone hovering at v i Monitoring time; Find the three positions in loop C2 and So that in loop C2 from p1 to The time spent is no more than And p1 to The time taken is greater than Among them, 1≤j≤3, w(C2) is the total energy consumed by the UAV on loop C2; will go from p1 to The path is taken as the first subpath P 2,1 , will be arrive The path is taken as the second subpath P 2,2 , will be arrive The path is taken as the third subpath P 2,3 , will be arrive The path is taken as the fourth subpath P 2,4 .

8. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 7, characterized in that: In the second iteration, the four loops C1 (2) , C2 (2) , C3 (2) , C4 (2) The build process includes: According to the two circuits C1 (1) , C2 (1) And four subpaths P 2,1 , P 2,2 , P 2,3 , P 2,4 , construct a weighted complete bidirectional graph G2 = (A2 ∪ B2, E2); where E2 represents; A2 = {a1, a2, a3, a4}, representing four loops C1 respectively (1) , C2 (1) , C1 (1) , C2 (1) ; B2 = {b1, b2, b3, b4}, representing four subpaths P respectively 2,1 , P 2,2 , P 2,3 , P 2,4 ; Any node a in A2 i With any node b in B2 j The edge between i ,b j ) has a weight of a i With b j The time it takes to complete the circuit consisting of all PoIs in is denoted as t2(a i ,b j ); Find a perfect matching M2 in G2, so that the maximum weight of all edges in M2 is minimized; Four loops C1 are found from the perfect match M2 (2) , C2 (2) , C3 (2) , C4 (2) , assuming that node a i With Node matches, where 1≤i≤4. At this time, a i and The hover position collection in is recorded as Using TSPN algorithm to construct loop C i (2) .

9. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 1, characterized in that: In S5, it is assumed that the initial monitoring path C' of the UAV within a monitoring cycle is expressed as Where s represents the base station, n' is the number of hovering positions in the loop C'; By cutting C', we can obtain a series of subpaths C1", C2", ..., C with root node s. Q ”; During the cutting process, if the first circuit The energy w(C1") consumed does not exceed the battery capacity, and The energy consumed exceeds the battery capacity. Add base station node s between the paths; And so on, until all base station nodes are added, the final continuous monitoring path of the drone is obtained.

10. The method for continuous monitoring of unmanned aerial vehicles based on PoI priority according to claim 9, characterized in that: In S5, a series of subpaths C1”, C2”, ..., C with root node s are constructed. Q ", the goal is to minimize the maximum value of the weighted monitoring time interval of all PoIs in the set V, which can be expressed as: And, w(C i ”)≤e CAP , 1≤i≤Q, Among them, w(C i ”) is the UAV in C i "Energy consumption on the path; e CAP is the battery capacity of the drone; t(C i ”) is the UAV in C i ” The time spent on the path.

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