A method for continuous monitoring of drones based on PoI priority

Abstract

The present invention relates to the field of path planning technology, and in particular to a method for continuous monitoring of unmanned aerial vehicles based on PoI priority, comprising: S1, regarding each monitoring point in the monitored area as a point of interest (PoI), and dividing all PoIs into K+1 disjoint sets according to the monitoring priority; S2, determining the neighborhood of each PoI; S3, constructing K+1 shortest loops with neighborhoods for the PoIs in each set; S4, constructing 2 shortest loops with neighborhoods from the K+1 shortest loops after K iterations. K loops, construct a set of initial monitoring paths within a monitoring period T, so that the set V k The PoIs are visited within a monitoring period T2 K‑k S5. Taking into account the drone's battery capacity, the drone's initial monitoring path is cut and the path to and from the base station is added to obtain the drone's final continuous monitoring path. This method can plan the drone's monitoring path based on the importance of different monitoring points (PoIs), shortening the monitoring interval for important PoIs.

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 Point of Interest (PoI) priority. Background Art

[0002] Drones have been widely used in disaster monitoring and rescue operations in recent years due to their low cost, rapid deployment, and high flexibility. Drones equipped with cameras are deployed over disaster-stricken areas to capture images and transmit real-time information to base stations for dispatchers to make decisions.

[0003] Early drone continuous monitoring methods combined the location information of Points of Interest (PoIs) with heuristic and approximate algorithms to schedule drones, reducing the time interval between monitoring of adjacent PoIs. In recent years, with the in-depth research in the field of drone monitoring, the importance differences between PoIs have been taken into account in drone scheduling. While these PoI priority-based drone monitoring methods can better meet real-world scenarios, existing methods ignore the fact that drone-mounted cameras can typically capture images from a distance, which affects the effectiveness of drone continuous monitoring.

[0004] Currently, most continuous drone monitoring methods assume that all Points of Interest (PoIs) in a disaster area have the same monitoring priority. Therefore, they simply minimize the time interval between monitoring of adjacent PoIs. However, since different PoIs have varying degrees of importance, using the same monitoring interval can easily overlook important information. Furthermore, all current algorithms ignore the fact that drone-mounted cameras can typically capture images from a distance.

[0005] Therefore, how to plan the monitoring path of the UAV according to the importance of different PoIs is a problem that those skilled in the art urgently need to solve. 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 shortened.

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

[0008] A method for continuous monitoring of drones based on PoI priority, comprising 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 disjoint sets according to the monitoring priority. The order of priority of each set is V0>V1…>V K ;

[0010] S2. Determine the neighborhood of each PoIs. 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 circuits with neighbors after K iterations K 2 loops, K The 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, represented 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 There is 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, and the monitoring priority satisfies The point is divided into V k middle.

[0015] Furthermore, in S2, 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 y i Represents the monitoring point v i The horizontal and vertical coordinates, h m represents the flight altitude of the UAV, 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 ), change 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, It is v 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, the loop C2 is first cut into four sub-paths P 2,1 、P 2,2 、P 2,3 、P 2,4 , 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; let Among them, c max The time spent at the longest hovering position in C2 traveling back and forth with p1; 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 taken is no more than And p1 to The time taken is greater than Where, 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 the second subpath P 2,2 , will be arrive The path is the third subpath P 2,3 , will be arrive The path is 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 formed by all PoIs in is t2(a i ,b j );

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

[0033] Four loops C1 are found from the perfect matching M2 (2) , C2 (2) , C3 (2) , C4 (2) , assuming that node a i With node Match, where 1≤i≤4. At this time, a i and The hover position collection in is recorded as Construct loop C using TSPN algorithm 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', a series of subpaths C1", C2", ..., C with root node s are obtained. 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, then Add base station node s between the paths;

[0037] This process is deduced in this way until all base station nodes are added, and the final continuous monitoring path of the drone is obtained.

[0038] Furthermore, 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:

[0039]

[0040] Moreover, 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 the varying importance of different PoIs and assigns them different monitoring priorities. For example, in a disaster area following an earthquake, densely populated places such as hospitals and schools require more frequent monitoring, while sparsely populated places such as factories and outdoor farms require less frequent monitoring. This allows important information to be discovered promptly, thereby reducing loss of life and property.

[0044] At the same time, the present invention takes into account the fact that drone-mounted cameras can usually capture images from a distance. By setting the neighborhood of each PoI, the drone can complete the monitoring task without flying to the PoI location. Once the drone hovers in the area near the PoI, it can take high-quality, clear photos. This method can save the drone's flight time and energy consumption, thereby shortening the monitoring 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0046] Figure 1 Flowchart of the method for continuous monitoring of UAVs based on PoI priority provided by the present invention;

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

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

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

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

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

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 disjoint sets according to the monitoring priority. The order of priority of each set is V0>V1…>V K ;

[0059] S2. Determine the neighborhood of each PoIs. 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 circuits with neighbors after K iterations K 2 loops, K The 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 explained below.

[0064] S1. This invention considers the differences in the importance of PoIs. In actual monitoring scenarios, there are many PoIs that require drones to take photos for data collection. However, PoIs have different levels of importance due to their nature and geographical location. For example, crowded 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 shorter monitoring intervals.

[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 There is 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, and the monitoring priority satisfies The point is divided into V k middle.

[0067] Assume K = 2, such as Figure 2 As shown, the black solid area, black shaded 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. This invention considers a method for continuous monitoring of a drone with a neighborhood. It is understood that most drones currently have cameras equipped with telephoto lenses. For example, the DJI Mavic 3Pro is equipped with a 166mm telephoto lens. Such cameras can capture high-quality, clear images of Points of Interest (PoIs) from tens to hundreds of meters away, thereby reducing the drone's flight path and energy consumption.

[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 y i Represents the monitoring point 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 arrives in the vicinity of the PoI, it can take photos. This can effectively reduce the drone's flight path length and energy consumption, thereby improving work efficiency.

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

[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 ), change 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, It is v 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 included in the loop. That is, for every set V k , using the TSPN algorithm (shortest path algorithm with neighborhood) for V k ∪v1 constructs the shortest circuit, such as Figure 3 As shown. TSPN is similar to TSP algorithm, the difference is that it adds a concept of neighborhood, which refers to the neighborhood D(v1), D(v2), ..., D(v n ), find a shortest circuit so that each D(v i ) has at least one point contained in the loop.

[0075] S4, construct 2 from K+1 shortest circuits with neighbors after K iterations K 2 loops, K The 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 the loop is cut into two subpaths P 1,1 and P 1,2 , and make the time consumed by the two sub-paths 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 taken by the four subpaths 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 subpaths P 2,1 、P 2,2 、P 2,3 、P 2,4 The splitting process includes:

[0079] 1) Assumption Where p1 is the hovering monitoring position of monitoring point v1, n2 is the number of hovering positions in C2; let Among them, c max The time spent at the longest hovering position in C2 traveling back and forth with p1; 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 taken 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.

[0081] 3) Based on the three locations 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 the second subpath P 2,2 , will be arrive The path is the third subpath P 2,3 , will be to p n2 The path is 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) , specifically 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 with 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 formed by all PoIs in is 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 matching M in l , so that every node in A2 matches B2. However, if e l Less than e j *, then 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 As shown. Assume that node a i With node Match, where 1≤i≤4. At this time, a i and The hover position collection in is recorded as Construct loop C using TSPN algorithm 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, taking into account 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, it 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 following 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 Figure 9 The continuous monitoring path C' of the UAV with a 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 multiple times within the period T to charge or replace the battery. By cutting C', a series of subpaths C1", C2", ..., C with the root node 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, then 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 Figure 10 shown.

[0096] This invention takes into account the limitations of drone battery capacity. To better suit actual application scenarios, the invention assumes that the drone has a certain battery capacity. The drone must return to the base station for charging or battery replacement 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, max =15m / s), and then at a constant speed v max Fly to PoI v j+1 Then the UAV 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 It is p 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 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 Energy consumption rate 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] Moreover, 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] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0112] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform 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 monitored area is regarded as a point of interest (PoI). All PoIs are divided into K+1 disjoint sets according to the monitoring priority. The order of priority of each set is V0>V1…>V K ; S2. Determine the neighborhood of each PoIs. 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 circuits with neighbors after K iterations K 2 loops, K The 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 drones based on PoI priority according to claim 1, characterized in that: In S1, it is assumed that there are n monitoring points PoIs, represented 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 There is 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, and the monitoring priority satisfies The point is divided into V k middle.

3. The method for continuous monitoring of drones based on PoI priority according to claim 2, characterized in that: In S2, 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 y i Represents the monitoring point v i The horizontal and vertical coordinates, h m represents the flight altitude of the UAV, 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 drones based on PoI priority according to claim 1, wherein 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 ), change 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, It is v 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 drones based on PoI priority according to claim 4, wherein 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 drones 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 drones 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 , 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; let Among them, c max The time spent at the longest hovering position in C2 traveling back and forth with p1; 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 taken is no more than And p1 to The time taken is greater than Where, 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 the second subpath P 2,2 , will be arrive The path is the third subpath P 2,3 , will be arrive The path is the fourth subpath P 2,4 .

8. The method for continuous monitoring of drones 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 formed by all PoIs in is t2(a i ,b j ); Find a perfect matching M2 in G2 such that the maximum weight of all edges in M2 is minimized; Four loops C1 are found from the perfect matching M2 (2) , C2 (2) , C3 (2) , C4 (2) , assuming that node a i With node Match, where 1≤i≤4; at this time, a i and The hover position collection in is recorded as Construct loop C using TSPN algorithm i (2) .

9. The method for continuous monitoring of drones 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', a series of subpaths C1", C2", ..., C with root node s are obtained. 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, then Add base station node s between the paths; This process is deduced in this way until all base station nodes are added, and the final continuous monitoring path of the drone is obtained.

10. The method for continuous monitoring of drones 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: Moreover, 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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