Design method of submerged buoy network layout for marine hydrological environment observation

Abstract

The present invention discloses a method for designing a network layout of a submerged buoy for marine hydrological environment observation, comprising the following steps: (1) gridding the sea area to be measured and collecting historical hydrological observation data of the sea area; (2) randomly generating the first submerged buoy position in the grid map, and then selecting the position with the largest detection range as the next submerged buoy position based on the condition that the uncovered points covering the boundary of its detection range are covered; (3) judging whether the underwater autonomous vehicle and the submerged buoy can complete real-time communication based on the current submerged buoy position, and where the communication requirement cannot be met; (4) performing multiple iterations, selecting the solution with the smallest overlap of the submerged buoy detection range as the optimal network layout of the underwater buoy. The present invention can maintain real-time communication between the underwater autonomous vehicle and the submerged buoy when conducting collaborative observation, so that the information measured by the underwater autonomous vehicle can be transmitted upward in real time through the submerged buoy.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater buoy network layout optimization, and in particular to a buoy network layout design method for ocean hydrological environment observation. Background Art

[0002] Submerged buoys are one of the important equipment for marine environmental observation. Submerged buoys are generally fixed to a specific position underwater by anchors. Different sensors can be installed on the ropes between the main float and the anchor to achieve regular and continuous observation of hydrological information such as temperature, salinity or ocean currents at various water depths. With the development and application of technologies such as satellite communications and radio communications, underwater buoys can now achieve real-time and long-distance transmission of observation data. At the same time, with the continuous development of underwater navigation technology in recent years, autonomous underwater vehicles (AUVs) have also been widely used in underwater detection, and they have the characteristics of good maneuverability, safety, convenience and intelligence. Autonomous underwater vehicles play an important role in marine resource exploration, seabed topography mapping and other aspects.

[0003] Here, collaborative observations will be conducted using submerged buoys and autonomous underwater vehicles (AUVs), providing a richer and more comprehensive understanding of the marine environment. This means that hydrological information such as temperature, salinity, and pressure at different depths in the surveyed sea area can be obtained, along with corresponding seabed topography information. The seabed information measured by the AUV will be transmitted upstream via communication with the submerged buoy. Therefore, a reasonable layout of the AUVs is essential. With traditional technology, it is difficult for an AUV to maintain communication with a single submerged buoy at any given location in the surveyed sea area. Furthermore, in areas with drastic changes in the waters, the accuracy of the hydrological information measured by the AUVs is difficult to maintain within a certain range. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for designing a submerged buoy network layout for marine hydrological environment observation, which uses a minimum number of submerged buoys to achieve real-time communication and accuracy of measured information in collaborative observation.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for designing a buoy network layout for marine hydrological environment observation, comprising the following steps: (1) gridding the sea area to be measured and collecting historical hydrological observation data of the sea area; (2) randomly generating the first buoy position in the grid map, and then selecting the position with the largest detection range as the next buoy position based on the condition of covering the uncovered points at the boundary of its detection range, and continuing to select until all sea areas are covered; (3) judging whether the underwater autonomous vehicle and the buoy can complete real-time communication based on the current buoy position, and continuing to select suitable buoys to cover the places where the communication requirements cannot be met until the requirements are met; (4) performing multiple iterations, and selecting the solution with the smallest overlap of the buoy detection range as the optimal network layout of the underwater buoy.

[0006] Preferably, in step (1), the detection range of each buoy position is determined by the extreme difference of hydrological information and the requirements for deploying buoys in actual situations.

[0007] Preferably, in step (2), a seed-filling method is used to obtain connected domains of uncovered pixels.

[0008] The present invention has the beneficial effect of maintaining real-time communication between an AUV and a submerged buoy during collaborative observation, allowing the AUV's measurement information to be transmitted upstream in real time via the submerged buoy. Furthermore, for areas with drastic underwater environmental changes, the buoy's detection range is scientifically calculated based on the variation in hydrological information, and a suitable buoy placement scheme is developed for these areas, enabling more accurate hydrological measurements by the buoys. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram showing the grid marked as 1 to randomly generate the first potential marker position;

[0010] Figure 2 A schematic diagram of the location of the buoy that meets the search conditions;

[0011] Figure 3 Schematic diagram of the buoy position with the largest r2;

[0012] Figure 4 The red box shows the area with the most severe environmental changes, with full coverage of buoy detection in the surveyed sea area.

[0013] Figure 5 A map of the coverage areas that currently meet communication requirements;

[0014] Figure 6 Schematic diagram of marking uncovered grids as 0 for the binary grid image;

[0015] Figure 7 To randomly generate a new buoy location map;

[0016] Figure 8 The figure shows that 95% of the measured sea areas meet the communication requirements. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] The present invention will be further described with reference to the accompanying drawings.

[0019] The communication distance between the autonomous vehicle and the buoy is set to D.

[0020] (1) The target observation sea area is gridded with a resolution of L = 1 km; based on historical observation data, the temperature and salinity values ​​within each grid are calculated.

[0021] (2) In the grid map, randomly generate the first potential marker position P1 = (x1, y1), and find the maximum radius r1 with P1 as the center that meets the following conditions:

[0022]

[0023] Where C1 represents the circular area with P1 as the center and r1 as the radius; These are the historical maximum and minimum temperature observations in the C1 region during the same historical period; and The highest and lowest salinity values ​​observed in the C1 region during the same historical period are shown in Figure 1. R(T) and R(S) are the maximum deviations of seawater temperature and salinity allowed within the control area of ​​a buoy.

[0024] The circular area with P1 as the center and r1 as the radius is as follows Figure 1 As shown, the number of pixels covered is calculated A1.

[0025] (3) Among the pixels not covered by the C1 boundary, select the pixel P that satisfies equation (2).

[0026] min[|PP1|-r1]#(2)

[0027] Where |PP1| is the Euclidean distance between the uncovered pixel positions P and P1 near the boundary of C1.

[0028] (4) To satisfy the condition of formula (3), search for the next potential marker position P2 = (x2, y2) in the grid map, as follows: Figure 2 shown.

[0029]

[0030] Where P′2 is the predicted position of the next buoy; is the maximum detection radius of the predicted position; |PP′2| is the Euclidean distance between the predicted position of the next buoy and position P.

[0031] (5) Select from all the predicted positions P′2 that meet the conditions The maximum position is the second buoy position P2 = (x2, y2), then The circular area with P2 as the center and r2 as the radius is recorded as C2, such as Figure 3 As shown, the number of pixels covered is calculated A2.

[0032] (6) Continue to select the next potential marker position using the method of steps (3), (4), and (5) until all pixels are covered. Figure 4 shown.

[0033] At this point, n potential buoy positions can be obtained:

[0034] P j =(x j ,y j )j=1,2,…,n#(4)

[0035] The number of covered pixels corresponding to n latent marker positions:

[0036] A j ={A1,A2,…,A n}#(5)

[0037] (7) Used for P j The circular area with the center as the circle and the communication distance D between the autonomous vehicle and the buoy as the radius covers the target observation sea area, such as Figure 5 As shown, the number of pixels Z in the covered area is calculated.

[0038] (8) Binarize the grid image and use the seed-filling method to obtain the connected domains of uncovered pixels, such as Figure 6 shown.

[0039] (9) Calculate the number of pixels E in each connected domain k :

[0040] E k ={E1,E2,E3,…}#(6)

[0041] In max[E k], a potential marker position O1 is randomly generated in the connected domain of . The circular area with O1 as the center and D as the radius is as follows Figure 7 As shown, the number of pixels covered is calculated.

[0042] (10) Calculate the proportion η of covered pixels.

[0043] F=Z+B1#(7)

[0044]

[0045] Among them, F is the number of currently covered pixels; Q is the total number of pixels in the grid map.

[0046] (11) Continue to select the next buoy position using the method of steps (8), (9), and (10) until the percentage of covered pixels η ≥ 0.95, that is, 95% of the target observation sea area is covered. Figure 8 shown.

[0047] At this point, m new buoy positions can be obtained:

[0048] O i ={O1, O2, ..., O m}#(9)

[0049] The number of covered pixels corresponding to m latent marker positions:

[0050] B j ={B1, B2, ..., B m}#(10)

[0051] The total number of latent buoys H is:

[0052] H=n+m#(11)

[0053] (12) After N iterations, the overlapping area G of all buoy detection ranges is calculated:

[0054]

[0055] but The corresponding solution is the optimal network layout of underwater buoys.

[0056] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for designing a submerged buoy network layout for marine hydrological environment observation, characterized by: The following steps are involved: (1) Grid the sea area to be measured and collect historical hydrological observation data of the sea area; (2) Randomly generate the first potential marker position in the grid map, P1 = (x1, y1), With P1 as the center, find the maximum radius r1 that satisfies the following conditions: Where C1 represents the circular area with P1 as the center and r1 as the radius; These are the historical maximum and minimum temperature observations in the C1 region during the same historical period; and are the highest and lowest salinity observation values ​​in the C1 region during the same historical period, respectively. R(T) and R(S) are the maximum deviations of seawater temperature and salinity allowed within the sea area controlled by a buoy, respectively. The circular area with P1 as the center and r1 as the radius is shown in Figure 1. Calculate the number of pixels A1 it covers; (3) Among the pixels not covered by the C1 boundary, select the pixel P that satisfies formula (2). min[|PP1|-r1] (2) Where |PP1| is the Euclidean distance between the uncovered pixel positions P and P1 near the boundary of C1; (4) To satisfy the condition of formula (3), search for the next potential marker position P2 = (x2, y2) in the grid map. Among them, P2 ′ is the predicted position of the next buoy; is the maximum detection radius of the predicted position; |PP′2| is the Euclidean distance between the predicted position of the next buoy and position P; (5) From all the predicted positions P2 that meet the conditions ′ Select The maximum position is the second buoy position P2 = (x2, y2), then The circular area with P2 as the center and r2 as the radius is recorded as C2, and the number of pixels it covers A2 is calculated; (6) Continue to select the next potential marker position using the method of steps (3), (4), and (5) until all pixels are covered. At this point, n potential buoy positions can be obtained: P j (x j ,y j )j=1,2,…,n (4) The number of covered pixels corresponding to n latent marker positions: <h2 style=";text-align:left;direction:ltr">A<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> (A1,A2,…,A)<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr">} (5) (7) Used for P j The target observation sea area is covered by a circular area with a center of φ and a communication distance D between the autonomous vehicle and the buoy as the radius. The number of pixels Z in the covered area is calculated. (8) Binarize the grid image and use the seed-filling method to obtain the connected domains of uncovered pixels; (9) Calculate the number of pixels E in each connected domain k : <h2 style=";text-align:left;direction:ltr">E<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr"> (6) {E1,E2,E3,…} In max[E k ], continue to randomly generate a potential marker position O1 in the connected domain, take O1 as the center and D as the radius of the circular area, and calculate the number of pixels B1 covered by it; (10) Calculate the proportion of covered pixels η F=Z+B1 (7) Where F is the number of currently covered pixels; Q is the total number of pixels in the grid map; (11) Continue selecting the next buoy position using the methods of steps (8), (9), and (10) until the percentage of covered pixels η ≥ 0.95, i.e., 95% of the target observation sea area is covered; At this point, m new buoy positions can be obtained: THE i ={O1,O2,…,O m } (9) The number of covered pixels corresponding to m latent marker positions: B j ={B1,B2,…,B m } (10) The total number of latent buoys H is: H=n+m (11) (12) After N iterations, the overlapping area G of all buoy detection ranges is calculated: but The corresponding solution is the optimal network layout of underwater buoys.

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