Three-dimensional layout optimization method, system and equipment for deep-sea target range detection nodes

Abstract

The invention discloses a three-dimensional laying optimization method, system and device for deep sea target range detection nodes, and belongs to the technical field of underwater acoustic signal processing and underwater unmanned system laying optimization. The method comprises the following steps: performing three-dimensional space resolution grid division on an operation sea area; a comprehensive detection node laying optimization model is constructed by combining a deep sea sound propagation law, spatial distribution characteristics of an underwater target, types of detection nodes and an effective detection distance of an array, optimization solution is performed by adopting a differential evolution-particle swarm hybrid algorithm, and an optimal laying scheme of underwater multiple detection nodes is determined. According to the method, efficient coverage and reliable detection of an underwater target in an operation sea area are realized by using a limited number of underwater sound subsurface buoys and buoy equipment, and a deep sea sound propagation rule, the type and performance of detection nodes, the spatial distribution characteristics of the target and the effective detection range of the detection nodes are comprehensively considered; and an optimal node laying scheme can be provided for continuous monitoring of underwater targets.

Description

Technical Field

[0001] The present invention relates to the technical fields of underwater acoustic signal processing and optimization of the deployment of underwater unmanned systems, and particularly relates to a three-dimensional deployment optimization method, system and device for deep-sea range detection nodes. Background Art

[0002] The deep-sea environment has complex and variable acoustic propagation characteristics, which pose a major challenge to the optimal deployment of multiple underwater detection nodes. The propagation of sound waves in the ocean is affected by various environmental factors, including reflection, refraction, scattering, and the effects of parameters such as seawater temperature, salinity, depth, and seabed topography. These complex factors not only significantly affect the law of sound propagation but also cause great interference to the detection performance of underwater equipment. Therefore, combining the acoustic propagation characteristics in a complex ocean environment is an important prerequisite for improving the detection ability of an underwater acoustic array and optimizing the deployment strategy.

[0003] With the scientific development process of people discovering, understanding, exploring, and managing the ocean, how to monitor the ocean environment and underwater targets efficiently, stably, and economically has become an important research direction. As an important part of the underwater acoustic detection system, buoys are widely used in the field of underwater target detection due to their flexible deployment and convenient operation. However, there are still many deficiencies in the current research on the deployment of underwater detection nodes: (1) Existing research mostly focuses on planar (two-dimensional) deployment methods and does not fully consider the influence of the position of the target in three-dimensional space (such as target depth) on the detection performance. The deployment depth of the detection node has an important impact on the detection performance, but the existing solutions fail to effectively combine the performance of the detection node with the target depth characteristics for optimization, restricting the detection ability in a complex deep-sea environment; (2) Currently, in underwater unmanned target detection systems, the deployment of detection nodes such as acoustic buoys and moored buoys is mostly based on experience and lacks systematic and scientific theoretical support for optimal deployment. This deployment method has great blindness, resulting in low detection efficiency and being unable to fully meet the scientific research needs of continuous monitoring of targets in a complex deep-sea environment.

[0004] With the continuous improvement of the demand for underwater target monitoring and the rapid development of underwater unmanned target detection systems, it has become an inevitable trend to study an intelligent and efficient optimization strategy for the deployment of underwater acoustic detection nodes. By reasonably combining detection devices such as sonar buoys and moored buoys, the detection range and reliability of underwater targets can be significantly enhanced. However, how to give full play to the performance advantages of various detection devices under the constraint of a limited number of node deployments and achieve efficient coverage and detection of the operation sea area is still a technical problem to be solved urgently. Summary of the Invention

[0005] In view of the above problems, the present invention aims to provide a three-dimensional deployment optimization method, system and device for deep-sea range detection nodes, which can achieve efficient coverage and reliable detection of targets in the operation area by using a limited number of mooring buoys and floating buoys. This method comprehensively considers the deep-sea sound propagation law, the type and performance of detection nodes, the spatial distribution characteristics of targets, and the effective detection range of detection nodes, and constructs a deployment model based on intelligent optimization algorithms, which can provide the optimal node deployment scheme for continuous monitoring of underwater targets.

[0006] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0007] On the one hand, the present invention provides a three-dimensional deployment optimization method for deep-sea range detection nodes, including the following steps:

[0008] S1: Determine the three-dimensional space and detection indexes of the operation area;

[0009] S2: Conduct three-dimensional space resolution grid division on the operation area;

[0010] S3: Obtain the acoustic environment of the operation area and calculate the underwater target signal propagation loss data set;

[0011] S4: Estimate the depth of the underwater target;

[0012] S5: Calculate the effective detection distance of the detection node according to the underwater signal propagation loss;

[0013] S6: Based on the depth of the underwater target and the effective detection distance of the detection node, establish a three-dimensional deployment optimization model for underwater target detection;

[0014] S7: Use the differential evolution-particle swarm hybrid algorithm to optimize and solve the three-dimensional deployment optimization model for underwater target detection, and determine the optimized deployment scheme of the detection node.

[0015] Further, the specific operation of step S1 is as follows: One corner of the rectangular sea area O is used as the origin, the length and width are the x-axis and y-axis respectively, and the direction from the sea surface to the seabed is the z-axis to establish a Cartesian coordinate system O-xyz.

[0016] Further, the specific operation of step S2 includes the following steps:

[0017] Set the length, width and average depth of the operation area to be Xm, Ym and Zm respectively; evenly divide the entire operation area into square grids with a side length of c; the maximum sea depth in the operation area is Z max m, and divide it into grids with a layer spacing of d in the vertical direction. Then the number of grid points in the x direction in the operation area is n x = X / c, and the number of grid points in the y direction is n y= Y / c, the maximum number of grid points in the z direction is n z = Z max / d.

[0018] Furthermore, the specific operations of step S5 include the following steps

[0019] S501: Determine the directivity index DI and detection threshold DT of the detection node, and construct a function of the detection signal margin SE varying with the distance r between the underwater target and the detection node and the depth h of the underwater target

[0020]

[0021] In the formula, SL represents the radiation noise level of the underwater target, NL represents the sea noise spectral level, I0 represents the reference sound intensity at a distance of 1 m from the underwater target sound source; I(r, h) is the received sound intensity when the underwater target is at a depth h and the distance from the detection node is r

[0022] S502: Let the coordinate position of the detection node in O-xyz be (x i , y j , z k ), estimate the depth h of the underwater target t , and the corresponding depth grid is k = h t / d;

[0023] S503: According to the spatial grid (i, j, k) where the underwater target is located, take the minimum detection distance of several detection nodes of the same type for the spatial grid (i, j, k) as the effective detection distance R of this type of detection node i,j,k , that is

[0024] R i,j,k = min[R]

[0025] In the formula, R represents the detection distance of several detection nodes of the same type for the spatial grid (i, j, k);

[0026] S504: Determine the effective detection distance of the mooring buoy for the spatial grid (i, j, k) according to step S503 and the effective detection distance of the floating buoy for the spatial grid (i, j, k)

[0027] Furthermore, the three-dimensional deployment optimization model for underwater target detection described in step S6 is

[0028]

[0029] In the formula, U is the number of detection nodes set for the system to achieve the detection performance and can simultaneously detect underwater targets, γ i，j，k$n_{(i,j,k)}$ is the actual number of detection nodes that simultaneously detect an underwater target at the spatial grid $(i, j, k)$, $M$ is the total number of moored buoys, and $N$ is the total number of floating buoys;

[0030] $P_{\gamma}$ is the probability that the target is simultaneously detected by $\gamma$ detection nodes when the target is in the operation sea area, $p(x, y, z)$ is the probability that the underwater target is at any coordinate $(x, y, z)$; $f$ represents the optimization function for the underwater target located in the three-dimensional space $(x, y, z)$

[0031] $P$ o $>P$ m , $P$ m is the detection probability of the set detection node system.

[0032] Furthermore, the specific operations in step S7 include the following steps

[0033] S701: Initialize the population; randomly generate an initial population containing multiple individuals, and each individual represents the underwater position $(x$ i , $y$ j , $z$ k ) of a detection node. For each individual, substitute it into the three-dimensional deployment optimization model for underwater target detection to calculate the fitness;

[0034] S702: Differential evolution operation;

[0035] S703: Particle swarm optimization operation;

[0036] S704: Repeat steps S702 - 703 until the iteration condition is reached, thereby obtaining the minimum number of nodes for combined array detection that satisfies the underwater target detection probability $P$ m , and the underwater position information $(x$ i , $y$ j , $z$ k ) of each detection node.

[0037] On the other hand, the present invention also provides a three-dimensional deployment optimization system for deep-sea range detection nodes, including an operation sea area grid division module, an operation sea area environment data storage module, a detection node effective detection distance analysis module, a three-dimensional deployment optimization model construction module, and a target optimization output module;

[0038] The operation sea area grid division module is used to determine the three-dimensional space and detection indexes of the operation sea area, and perform three-dimensional space resolution grid division on the operation sea area;

[0039] The operation sea area environment data storage module is used to store the acoustic environment data of the operation sea area, the underwater target signal propagation loss data, and the depth range data of different types of underwater targets;

[0040] The effective detection distance analysis module of the detection node is used to calculate the effective detection distance of the detection node;

[0041] The three-dimensional deployment optimization model construction module is used to establish a three-dimensional deployment optimization model for underwater target detection;

[0042] The target optimization output module uses the differential evolution-particle swarm hybrid algorithm to optimize and solve the three-dimensional deployment optimization model for underwater target detection, and outputs the optimized deployment plan of the detection node;

[0043] The operation sea area grid division module, the operation sea area environment data storage module, the effective detection distance analysis module of the detection node, the three-dimensional deployment optimization model construction module, and the target optimization output module all adopt the methods described above.

[0044] On the other hand, the present invention also provides an electronic device, which includes at least one processor; and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the methods described above.

[0045] The beneficial effects of the present invention are:

[0046] 1. The present invention proposes a three-dimensional deployment optimization method for detection nodes in a deep-sea range. By combining the deep-sea sound propagation law, the spatial distribution characteristics of underwater targets, the types of detection nodes, and the effective detection distance of the array, a comprehensive detection node deployment optimization model is constructed. Under the condition of limited number of nodes, the differential evolution-particle swarm hybrid algorithm suitable for high-dimensional global search is used for optimization and solution to generate the optimal deployment plan. Compared with the existing methods, the present invention integrates the deep-sea sound propagation law into the optimized deployment plan of the detection node, breaks through the limitations of traditional two-dimensional deployment, proposes a three-dimensional deployment plan, can better adapt to the three-dimensional movement characteristics of the target, enhances the reliability of detection, has intelligence, high efficiency and practicability, improves the detection ability in complex environments, and can be widely applied to fields such as underwater target detection, marine environment monitoring, resource exploration, etc., and has high popularization value.

[0047] 2. The present invention realizes complementary performance of nodes by combining multiple types of detection nodes such as buoys and submersible buoys, improves the overall detection ability of the system; introduces an intelligent optimization algorithm to maximize the target detection probability under the condition of limited number of nodes, generates the optimal deployment plan, and reduces blindness.

[0048] 3. The detection node optimization deployment method in the present invention reduces the number requirement of detection nodes, reduces the deployment cost, and at the same time ensures continuous and reliable monitoring of the target in the operation sea area. Description of the Drawings

[0049] Figure 1 This is the flow chart of the three-dimensional deployment optimization method for the deep-sea range detection nodes in the present invention.

[0050] Figure 2 This is the schematic diagram of the three-dimensional coordinate system in the operation sea area of the present invention.

[0051] Figure 3 This is the pseudo-color map of the sea depth in the operation sea area in the simulation experiment of the present invention.

[0052] Figure 4 This is the sound velocity profile information of the assimilation data in the operation sea area in the simulation experiment of the present invention.

[0053] Figure 5 This is the schematic diagram of the three-dimensional detection area coverage of the subsurface mooring buoy and the surface buoy in the simulation experiment of the present invention.

[0054] Figure 6 This is the graph of the propagation loss and signal margin varying with the spatial position when the depth of the underwater target is 70 m in the simulation experiment of the present invention.

[0055] Figure 7 This is the graph of the detection probability results obtained under the deployment mode of the subsurface mooring buoy and the surface buoy in the simulation experiment of the present invention.

[0056] Figure 8 This is the graph of the optimal deployment results meeting the detection requirements in the simulation experiment of the present invention. Detailed implementation manners

[0057] In order to enable those of ordinary skill in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] Embodiment 1:

[0059] Embodiment 1 provides a three-dimensional deployment optimization method for deep-sea range detection nodes. As shown in the attached Figure 1 figure, it specifically includes the following steps.

[0060] S1: Determine the three-dimensional space and detection indexes of the operation sea area;

[0061] Specifically, set the operation sea area as a rectangular sea area O. By deploying each detection node in the operation sea area, the detection probability of the detection node system is not less than P m . Taking a corner of the rectangular sea area O as the origin, with the length and width being the x-axis and y-axis respectively, and the direction from the sea surface to the seabed as the z-axis, establish a Cartesian coordinate system O-xyz. The scene is as shown in the attached Figure 2 figure.

[0062] S2: Conduct three-dimensional space resolution grid division on the operation sea area;

[0063] In a three-dimensional space, the length, width, and average depth of the operation sea area are set as X m, Y m, and Z m respectively. The entire operation sea area is evenly divided into square grids with a side length of c, and the geometric center of the grid is denoted as C; the maximum sea depth within the operation sea area is Z max m. In the vertical direction, it is divided into grids with a layer spacing of d, and the geometric center of the grid is denoted as H. Thus, the number of grid points in the x direction within the operation sea area is n x = X / c, the number of grid points in the y direction is n y = Y / c, and the maximum number of grid points in the z direction is n z = Z max / d.

[0064] S3: Obtain the acoustic environment of the operation sea area and calculate the propagation loss of underwater target signals;

[0065] Specifically, S301: Conduct a marine environment survey on the operation sea area through the underwater topographic and geomorphic measurement equipment of the scientific research vessel; measure hydrological information such as sound velocity profiles and ocean currents using hydrological environment survey equipment; sample the seabed sediments through in-situ measurement devices, and through acoustic inversion, obtain bottom sediment parameters such as sound velocity, density, and acoustic attenuation of the seabed.

[0066] S302: Supplement the missing information in S301 by searching global marine database materials such as WOA13, SODA, ETOPO1, GEBCO, and NOAA, so as to obtain complete marine environment parameters such as hydrology, bottom sediment, and topography.

[0067] S303: Calculate the propagation loss matching field data set for underwater targets: According to the acoustic field reciprocity principle, place the underwater target sound source at the geometric center of the three-dimensional grid points in turn, set parameters such as sound velocity profiles, sea surface and seabed attenuation, and refraction, calculate the acoustic propagation loss of the three-dimensional acoustic field, and establish a database according to the three-dimensional coordinates. According to the signal frequency, the Kraken model, Ray model, or Ram model can be selected as the acoustic propagation loss calculation tool.

[0068] The propagation loss varies with the distance between the underwater target and the detection node and the depth of the underwater target, and can be expressed as

[0069]

[0070] In the formula, TL represents the propagation loss of the underwater target, I0 represents the reference sound intensity 1 m away from the underwater target sound source, r represents the distance between the underwater target and the detection node, h represents the depth of the underwater target; I(r, h) is the received sound intensity when the underwater target is at a depth of h and the distance from the detection node is r; the signal margin unit is dB.

[0071] So far, a complete dataset of the propagation loss of underwater target radiation signals has been established. It should be noted that this process is a prior art and will not be elaborated in detail in the present invention; the acoustic environment of the operation sea area and the dataset of the propagation loss of underwater target signals are complete datasets collected and stored in advance, and only need to be queried and retrieved during use.

[0072] S4: Estimate the depth of the underwater target;

[0073] Since the depth range of different types of underwater targets during underwater activities is relatively fixed, in the present invention, after determining the type of underwater target, the depth range of the underwater target can be obtained as [h1, h2], and thus the possible depth grid range of the underwater target can be calculated. Using the mean function n t = mean[h1 / d, h2 / d].

[0074] S5: Calculate the effective detection distance of the detection node according to the underwater signal propagation loss;

[0075] In an environment with background interference, while the detection node receives the underwater target signal, it also receives the background interference signal. If the difference between the received signal level and the background interference level is exactly equal to the detection threshold of the detection node, that is, signal level - background interference level = detection threshold, according to the definition of the detection threshold, at this time the detection node can just complete the predetermined function. Therefore, "signal level - background interference level = detection threshold" is usually used as the basic principle for forming the sonar equation. For the passive detection of underwater targets, the method for calculating the detection signal margin SE of the detection node for detecting underwater targets is

[0076] SE = SL - TL - NL + DI - DT (2)

[0077] In the formula, SL represents the radiation noise level of the underwater target, NL represents the sea noise spectral level, and usually SL and NL are constants; TL represents the propagation loss from the underwater target to the position of the detection node; DI represents the directivity index of the detection node, and DT represents the detection threshold of the detection node. For the detection performance of buoy and submersible detection nodes, the corresponding directivity index DI and detection threshold DT can be determined.

[0078] Combining formula (1) and formula (2), it can be obtained that the relationship between the detection signal margin SE of the detection node for detecting underwater targets and the distance r between the detection node and the underwater target and the depth h of the underwater target is

[0079]

[0080] Therefore, according to the specified value of the detection signal margin, the detection distance of the corresponding detection node can be determined. In the present invention, with the criterion of minimizing the detection blind area of the combined array, it is ensured that the detection range of the detection node covers when the underwater target moves in the operation sea area. That is, when the distance from the underwater target to the detection node is r and the depth of the underwater target is h, the detection node can detect the underwater target, and the detection signal margin at this position must remain greater than 0 dB. And within a certain range beyond this position, the detection signal margin still needs to be higher than 0 dB to ensure that there is still sufficient coverage when the underwater target moves, so as to confirm the underwater target.

[0081] When the underwater target enters the operation sea area, a certain buoy or submersible buoy within O-xyz detects the underwater target signal. At this time, this detection node starts to estimate the depth of the underwater target and obtains the depth estimated value h of the underwater target. t , then the depth grid corresponding to the underwater target depth is k = h t / d. Mark the minimum detection distance of each detection node in the operation sea area at the depth grid k as the effective detection distance of the detection node. By adjusting the position of the detection node in the (x, y) plane, ensure that U detection nodes can all detect the underwater target at the position of the spatial grid (i, j, k) at the same time. That is, the effective detection distance of the detection node at the spatial grid (i, j, k) is expressed as

[0082] R i,j,k = min[R] (4)

[0083] In the formula, R represents the detection distance of several detection nodes of the same type at the spatial grid (i, j, k). For example, if the detection distances of three submersible buoys are R1, R2, and R3 respectively, then take the minimum value among R1, R2, and R3 as the effective detection distance of the submersible buoy, as shown in the appendix Figure 2 as shown.

[0084] According to formula (4), the effective detection distance of the submersible buoy at the spatial grid (i, j, k) can be obtained and the effective detection distance of the buoy at the spatial grid (i, j, k) Thus, further optimize the deployment of the combined array of submersible buoys and buoys.

[0085] S6: Establish a three-dimensional deployment optimization model for detection nodes;

[0086] Specifically, in the underwater three-dimensional space, for the submersible buoy and buoy detection nodes, assume the number of submersible buoys is M, and the coordinates are respectively expressed as [x m , y m, , z m (m = 1, 2,..., M), and the directivity index is DI q; The number of buoys is N, and the coordinates are respectively expressed as [x n , y n , z n (n = 1, 2,..., N), and the directivity index is DI f ..

[0087] Assume that within the region O, the probability that the underwater target is at any coordinate (x, y, z) is p(x, y, z), then the total probability P of the underwater target within the region O o is:

[0088]

[0089] Thus, in order to complete the detection task, it is necessary to ensure that P o > P m . To ensure the detection accuracy, the detection ranges of each detection node need to overlap, so that when the underwater target enters the detection area, U detection nodes can detect it simultaneously. Let γ represent the actual number of detection nodes that can detect the underwater target at the coordinate (x, y, z). Therefore, for the position where the target is located, the proportion of detection nodes that can complete the detection simultaneously is ζ / (M + N). Transforming the continuous integral in formula (5) into a discrete summation approximation, that is, for the comprehensive anti-submarine search success probability P o of the underwater target, it can be further expressed as

[0090]

[0091] In the formula, γ i，j，k is the actual number of detection nodes that simultaneously detect the underwater target at the spatial grid (i, j, k).

[0092] Therefore, based on the three-dimensional deployment optimization objective of the detection nodes in the operation sea area, the objective function for maximizing the detection probability of the underwater target can be obtained as

[0093]

[0094] where f represents the optimization function for the underwater target located in the three-dimensional space (x, y, z),

[0095]

[0096] S7: Use the differential evolution - particle swarm hybrid algorithm to optimize and solve the three-dimensional deployment optimization model for underwater target detection, and determine the optimized deployment scheme of the detection nodes.

[0097] Specifically, S701: Initialize the population;

[0098] Randomly generate an initial population containing multiple individuals, and each individual represents a possible three-dimensional solution (xi , y j , z k ). In the particle swarm optimization part, each individual also needs to include a velocity vector (v xi , v yj , v zk ) for updating the position. For each individual, use the objective function to evaluate its fitness.

[0099] S702: Differential evolution (DE) operation;

[0100] Perform a mutation operation on each individual in the population to generate a mutant vector. Cross the mutated individual with the current individual to generate a new candidate solution. The commonly used crossover method is binary crossover. Compare the new individual with the current individual and select the individual with better fitness to enter the next generation.

[0101] S703: Particle swarm optimization (PSO) operation;

[0102] Update the velocity vector according to the historical best position and the global best position of the individual. Update the position of the individual according to the velocity vector. Update the historical best position and the global best position of each individual.

[0103] The operations of DE and PSO can be carried out alternately, or the mechanisms of both can be applied separately in a certain proportion in each generation. For example, the differential evolution operation can be applied first in each iteration, and then the particle swarm optimization operation, or which operation to apply can be selected according to a certain probability. DE is good at global search and increases the population diversity through the mutation operation, while PSO can accelerate the local convergence speed through the update of velocity and position. The combination of the two can take into account both global exploration and local exploitation in the optimization process.

[0104] S704: Repeat steps S702 - 703 until the iteration condition is reached, thereby obtaining the minimum number of nodes for combined array detection that satisfies the underwater target detection probability P m , and the underwater position information (x i , y j , z k ) of each detection node. The iteration condition includes reaching the maximum number of iterations or the change in population fitness being less than the preset threshold, and the algorithm converges.

[0105] Simulation experiment:

[0106] For a certain deep - sea area environment with an average sea depth of 2000m, refer to the appendix Figure 2The operation sea area is demarcated. The area range is a three-dimensional underwater scene with length, width, and depth. The x, y, and z axes are respectively along the north-south, east-west, and seabed directions of the sea area. The length and width are X = 50 km and Y = 40 km respectively, and the average sea depth Z = 2000 m (depth range from 1960 m to 2100 m). The demarcated sea area is divided into unit spaces. The grid length of the length and width is 0.5 km, and the grid length of the depth is 20 m. Thus, an approximate three-dimensional unit space of 100 * 80 * 100 is obtained. The seabed environment of the operation sea area is as shown in Appendix Figure 3 and the sound speed profile information of the sea area is as shown in Appendix Figure 4 . All the environmental information in this simulation experiment is obtained from the global ocean environmental information database.

[0107] According to the sound speed profile shown in Appendix Figure 4 , the parameters such as sea surface and seabed attenuation and refraction of the operation sea area are retrieved from the database, and the Ray model is selected to calculate the sound propagation loss when the sound source signal frequency is 100 Hz; the schematic diagram of the three-dimensional detection area coverage of the moored buoys and surface buoys is as shown in Appendix Figure 5 . The specific operation is to traverse the geometric centers of the cells of the underwater target sound source to calculate the sound propagation loss, thereby obtaining a matching field data set of the propagation loss and the three-dimensional space position. And according to the array signal processing and underwater target depth determination theory, the depth h of the underwater target within the effective detection range of the node is estimated t . Assume that the underwater target sound source level SL = 120 dB, the directivity index of both the moored buoy and the surface buoy is 5 dB, the environmental noise level NL = 76 dB, and the detection threshold DT = 3 dB. When the signal margin is just 0 dB, the detection node reaches the detection performance. In Appendix Figure 6 , (a) is the propagation loss of the underwater target when the underwater target depth is 70 m and it reaches the sensor array section plane, and in Appendix Figure 6 , (b) is the variation of the signal margin with the three-dimensional space position. According to Appendix Figure 6 , the effective detection distance sets of the moored buoys at different deployment depths can be obtained i = 1, 2,..., X; j = 1, 2,..., Y; k = 1, 2,..., Z, and the effective detection distance sets of the surface buoys at different deployment depths i = 1, 2,..., X; j = 1, 2,..., Y; k = 1, 2,..., Z.

[0108] According to the optimization method in Embodiment 1, the differential evolution - particle swarm hybrid algorithm is used to solve the optimal scheme of the number of detection nodes and the deployment method to maximize the detection probability of the sea area, and the detection probability is required to be not less than 95%. Set U = 3, that is, it is required that the target on each unit resolution should be detected by at least 3 detection nodes simultaneously. The constraint conditions are that the number of surface buoys M < 25 and the number of moored buoys N < 25, and when satisfying P mWhen it is > 0.95, min(M + N). Set the termination conditions of the algorithm: 1. Reach the maximum number of iterations, which is 60 times; 2. Satisfy P m < 0.95, and M < 25, N < 25.

[0109] Through the optimization method in the present invention, the global optimal solution is finally output as shown in the appendix Figure 7 and the appendix Figure 8 as shown. Figure 7 In (a) of the appendix, when the detection conditions are met, it shows the deployment method and quantity of the buoys, as well as the number of detection nodes covered when projected in the depth direction; Figure 7 In (b) of the appendix, when the detection conditions are met, it shows the deployment method and quantity of the moored buoys, as well as the number of detection nodes covered when projected in the depth direction; The appendix Figure 8 shows the optimal node deployment method and the number of nodes to meet the detection requirements of the test range. From the appendix Figure 7 and the appendix Figure 8 it can be seen that when the underwater target moves at a depth of 70m, within the range enclosed by the red rectangle, the number of nodes that simultaneously detect the underwater target is greater than or equal to 3. Among them, the number of moored buoys is 20, and the number of buoys is 24.

[0110] From the results of this simulation experiment, it can be seen that the method proposed in the present invention can work with the least number of nodes in a complex deep - sea environment, achieve the maximum target detection probability, reduce blindness, lower the deployment cost, and at the same time ensure the continuous monitoring of the target in the operation sea area.

[0111] Embodiment 2:

[0112] Embodiment 2 provides a three - dimensional deployment optimization system for deep - sea test range detection nodes, including an operation sea area grid division module, an operation sea area environmental data storage module, a detection node effective detection distance analysis module, a three - dimensional deployment optimization model construction module, and a target optimization output module;

[0113] The operation sea area grid division module is used to determine the three - dimensional space of the operation sea area and detection indexes, and conduct three - dimensional space resolution grid division on the operation sea area;

[0114] The operation sea area environmental data storage module is used to store the acoustic environment data of the operation sea area, the underwater target signal propagation loss data, and the depth range data of different types of underwater targets;

[0115] The detection node effective detection distance analysis module is used to calculate the effective detection distance of the detection nodes;

[0116] The three - dimensional deployment optimization model construction module is used to establish a three - dimensional deployment optimization model for underwater target detection;

[0117] The target optimization output module uses a differential evolution-particle swarm hybrid algorithm to optimize and solve the three-dimensional deployment optimization model for underwater target detection, and outputs an optimized deployment plan for detection nodes;

[0118] The operation sea area grid division module, the operation sea area environment data storage module, the effective detection distance analysis module of detection nodes, the three-dimensional deployment optimization model construction module, and the target optimization output module all adopt the methods described in Embodiment 1.

[0119] Embodiment 3:

[0120] Embodiment 3 provides an electronic device, which includes at least one processor; and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the methods described in Embodiment 1.

[0121] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional deployment optimization method for deep-sea range detection nodes, characterized in that including the following steps, S1: Determine the three-dimensional space and detection indexes of the operation sea area; S2: Conduct three-dimensional space resolution grid division on the operation sea area; S3: Obtain the acoustic environment of the operation sea area and calculate the underwater target signal propagation loss data set; S4: Estimate the depth of the underwater target; S5: Calculate the effective detection distance of the detection node according to the underwater signal propagation loss; S6: Based on the depth of the underwater target and the effective detection distance of the detection node, establish a three-dimensional placement optimization model for underwater target detection; S7: Use the differential evolution-particle swarm hybrid algorithm to optimize and solve the three-dimensional placement optimization model for underwater target detection, and determine the optimized placement scheme of the detection node.

2. The three-dimensional deployment optimization method for the deep-sea range detection node according to claim 1, wherein The specific operation of step S1 is: Take a corner of the rectangular sea area O as the origin, with the length and width as the x-axis and y-axis respectively, and the direction from the sea surface to the seabed as the z-axis to establish a Cartesian coordinate system O-xyz.

3. The three-dimensional deployment optimization method of the deep-sea range detection node according to claim 2, characterized in that The specific operation of step S2 includes the following steps: Set the length, width, and average depth of the working sea area to X m, Y m, and Z m respectively; evenly divide the entire working sea area into square grids with a side length of c; the maximum sea depth in the working sea area is Z max m. Divide it into grids with a layer spacing of d in the vertical direction. Then the number of grid points in the x-direction within the working sea area is n x = X / c, and the number of grid points in the y-direction is n y = Y / c, and the maximum number of grid points in the z-direction is n z = Z max / d.

4. The three-dimensional deployment optimization method of the deep-sea range detection node according to claim 3, characterized in that The specific operation of step S5 includes the following steps, S501: Determine the directivity index DI and detection threshold DT of the detection node, and construct a function of the detection signal margin SE varying with the distance r between the underwater target and the detection node and the depth h of the underwater target, where SL represents the underwater target radiated noise level, NL represents the sea noise spectral level, I0 represents the reference sound intensity at a distance of 1 m from the underwater target sound source; I(r,h) is the received sound intensity when the underwater target is at a depth h and the distance from the detection node is r; S502: Let the coordinate position of the detection node within O-xyz be (x i , y j , z k ), estimate the depth h of the underwater target t , and the corresponding depth grid is k = h t / d; S503: According to the spatial grid (i, j, k) where the underwater target is located, the minimum detection distance of several detection nodes of the same type to the spatial grid (i, j, k) is taken as the effective detection distance R of this type of detection node i,j,k , that is R i,j,k = min[R] where R represents the detection distance of several detection nodes of the same type to the spatial grid (i,j,k); S504: Determine the effective detection distance of the submersible buoy at the spatial grid (i, j, k) according to step S503 and the effective detection distance of the surface buoy at the spatial grid (i, j, k) 5. The three-dimensional deployment optimization method for the deep-sea range detection node according to claim 4, wherein The three-dimensional placement optimization model for underwater target detection described in step S6 is where U is the number of detection nodes set for the system to achieve detection performance, which can simultaneously detect underwater targets, and γ i，j，k is the actual number of detection nodes that simultaneously detect the underwater target at the spatial grid (i, j, k), M is the total number of submersibles, and N is the total number of buoys; is the probability that the target is detected by γ detection nodes simultaneously when the target is in the operation sea area, and p(x, y, z) is the probability that the underwater target is at any coordinate (x, y, z); f represents the optimization function for the underwater target located in the three-dimensional space of (x, y, z). P o >P m ,P m is the detection probability of the set detection node system.

6. The three-dimensional deployment optimization method of the deep-sea range detection node according to claim 5, characterized in that The specific operation of step S7 includes the following steps, S701: Initialize the population; randomly generate an initial population consisting of multiple individuals, where each individual represents the underwater position (x i , y j , z k ) of a detection node. For each individual, substitute it into the three-dimensional placement optimization model for underwater target detection to calculate the fitness; S702: Differential evolution operation; S703: Particle swarm optimization operation; S704: Repeat steps S702 - 703 until the iteration condition is met, thereby obtaining the minimum number of nodes for combined array detection that satisfies the underwater target detection probability P m , and the underwater position information (x i , y j , z k ) of each detection node.

7. A three-dimensional deployment optimization system for deep-sea range detection nodes, characterized in that: including an operation sea area grid division module, an operation sea area environmental data storage module, a detection node effective detection distance analysis module, a three-dimensional placement optimization model construction module, and a target optimization output module; The operation sea area grid division module is used to determine the three-dimensional space and detection indexes of the operation sea area and conduct three-dimensional space resolution grid division on the operation sea area; The operation sea area environmental data storage module is used to store the acoustic environment data of the operation sea area, the underwater target signal propagation loss data, and the depth range data of different types of underwater targets; The detection node effective detection distance analysis module is used to calculate the effective detection distance of the detection node; The three-dimensional placement optimization model construction module is used to establish a three-dimensional placement optimization model for underwater target detection; The target optimization output module uses the differential evolution-particle swarm hybrid algorithm to optimize and solve the three-dimensional placement optimization model for underwater target detection and output the optimized placement scheme of the detection node; The operation sea area grid division module, the operation sea area environmental data storage module, the detection node effective detection distance analysis module, the three-dimensional placement optimization model construction module, and the target optimization output module all adopt the method described in any one of claims 1-6.

8. An electronic device, characterized in that: The electronic device includes at least one processor; and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor is enabled to execute the method described in any one of claims 1-6.

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