A three-dimensional positioning accurate recognition method and system based on optimization algorithm

Abstract

The present invention discloses a three-dimensional positioning precision identification method and system based on an optimization algorithm, comprising: spatially clustering the underwater target area according to the area information of the underwater target area of ​​the ocean and the task information of the positioning task, obtaining multiple underwater target sub-areas and setting an anchor node, and using a dual communication radius method to count the minimum number of hops from all unknown nodes to each anchor node according to the three-dimensional spatial coordinates of each anchor node; after correcting the average hop distance of each unknown node according to the weight coefficient, using the improved gazelle optimization algorithm to determine the three-dimensional coordinates of each unknown node; after moving each unknown node according to the three-dimensional coordinates of each unknown node, positioning and identifying the object of the positioning task according to each anchor node and each unknown node. The present invention is used to deploy mobile nodes in an underwater network to build a dynamic UWSN, study the influence of water flow velocity on positioning accuracy, and improve positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of underwater sensor technology, and in particular to a three-dimensional positioning precision recognition method and system based on an optimization algorithm. Background Art

[0002] As the importance of ocean development gradually becomes more prominent, underwater wireless sensor networks (UWSN) are increasingly widely used in the marine field. The three-dimensional precise positioning of UWSN underwater nodes is one of the core technologies for realizing applications such as marine environment monitoring, resource exploration, and underwater target tracking. The complexity of the marine environment and the limitations of underwater signal transmission make the three-dimensional positioning of UWSN challenging.

[0003] At present, underwater robots (AUVs) will be introduced for collaborative positioning. However, although the introduction of AUVs improves the positioning capability of UWSN, it increases the positioning cost. Most algorithms are highly complex and the application environment is mostly static networks. The following problems are not solved: 1. The distance error between anchor nodes; 2. The optimization capability of node position calculation. 3. The positioning error caused by the drift of node position due to water flow and the interference of underwater noise on positioning are not considered. Various reasons will lead to inaccurate positioning. Summary of the invention

[0004] The embodiment of the present invention provides a three-dimensional positioning accurate identification method and system based on an optimization algorithm, which adjusts the ratio of the total number of nodes and anchor nodes, optimizes the node communication radius, weakens the positioning error caused by the calculation of the number of node hops and the hop distance, and then deploys mobile nodes in the network to build a dynamic UWSN, thereby improving the positioning accuracy.

[0005] A first aspect of an embodiment of the present application provides a three-dimensional positioning accurate recognition method based on an optimization algorithm, comprising:

[0006] According to the area information of the underwater target area of ​​the ocean and the task information of the positioning task, spatially clustering the underwater target area to obtain a plurality of underwater target sub-areas;

[0007] An anchor node is set at the center of each underwater target sub-area. According to the three-dimensional spatial coordinates of each anchor node, the minimum number of hops from all unknown nodes to each anchor node is counted using a dual communication radius method.

[0008] After correcting the average hop distance of each unknown node according to the weight coefficient, the hop distance between each unknown node and each anchor node is calculated in combination with the minimum hop number from the unknown node to each anchor node;

[0009] According to the hop distance between each unknown node and each anchor node, the three-dimensional coordinates of each unknown node are determined using the improved gazelle optimization algorithm;

[0010] After moving each unknown node according to its three-dimensional coordinates, the object of the positioning task is positioned and identified according to each anchor node and each unknown node.

[0011] In a possible implementation manner of the first aspect, spatially clustering the underwater target area according to the area information of the underwater target area of ​​the ocean and the task information of the positioning task to obtain a plurality of underwater target sub-areas specifically includes:

[0012] According to the water volume of the underwater target area of ​​the ocean, the underwater target area is divided into a plurality of initial sub-areas according to the same preset water volume, and a plurality of cluster centers having the same number of initial sub-areas are established;

[0013] Perform environmental sampling at the location of each cluster center, and obtain multiple center vectors based on the sampling results and the task information of the positioning task;

[0014] Multiple sampling points are sequentially set in each initial sub-region for multi-point environmental sampling, and multiple sub-region feature vectors are obtained in each initial sub-region according to the sampling results and the task information of the positioning task;

[0015] All sub-region feature vectors are clustered to obtain multiple new cluster centers as the centers of the underwater target sub-regions; during the clustering process, if the distance between two cluster centers is less than the maximum communication radius of the anchor node, one of the cluster centers is selected for deletion.

[0016] In a possible implementation of the first aspect, the sampling result includes a water flow velocity, a light value, and a DOI value at the sampling point; and the task information of the positioning task includes an importance index value of the location of the sampling point to the task objective of the positioning task.

[0017] In a possible implementation manner of the first aspect, counting the minimum number of hops from all unknown nodes to each anchor node in a dual communication radius manner according to the three-dimensional space coordinates of each anchor node specifically includes:

[0018] According to the three-dimensional spatial coordinates of each anchor node, the distances between multiple anchor nodes are calculated;

[0019] Select the smallest distance between multiple anchor nodes as the reference distance;

[0020] Determine a first communication radius according to a ratio of a square value of a maximum communication radius of the anchor node to the reference distance;

[0021] Using the maximum communication radius as the second communication radius;

[0022] The minimum number of hops from all unknown nodes to each anchor node is counted according to the first communication radius and the second communication radius in sequence.

[0023] In a possible implementation manner of the first aspect, correcting the average hop distance of each unknown node according to the weight coefficient specifically includes:

[0024] According to the distance error between every two anchor nodes, multiple propagation distance weight coefficients are obtained;

[0025] According to multiple propagation distance weight coefficients, the average hop distance of all unknown nodes is calculated.

[0026] In a possible implementation manner of the first aspect, determining the three-dimensional coordinates of each unknown node by using an improved gazelle optimization algorithm according to a hop distance between each unknown node and each anchor node specifically includes:

[0027] Use Logistic chaotic mapping to initialize the gazelle population;

[0028] Within a preset maximum number of iterations, the fitness of all gazelles is calculated, and an elite gazelle matrix is ​​constructed according to the fitness of all gazelles;

[0029] In each iteration, if the generated random number is greater than a preset stage threshold, a development activity simulation is performed on the elite gazelle matrix;

[0030] In each iteration, if the generated random number is less than or equal to the preset stage threshold and the current iteration number meets the preset iteration condition, the elite gazelle matrix performs Levy flight simulation; if the generated random number is less than or equal to the preset stage threshold and the current iteration number does not meet the preset iteration condition, the elite gazelle matrix performs Brownian motion simulation;

[0031] In each iteration, the fitness of the position is evaluated after each simulation, the elite gazelle matrix is ​​updated and the elite gazelles are updated.

[0032] In a possible implementation manner of the first aspect, it is characterized in that the reference formula when executing the development activity simulation is:

[0033] y n (t)=by n (t+1)(1-y n (t-1)), where y n ∈[0, 1], t represents the current iteration number, the value range of parameter b is: 1≤b≤4, b is a control parameter.

[0034] In a possible implementation manner of the first aspect, the reference formula when executing the Levy flight simulation is:

[0035] Where x = Normal (0, μ 2 x ),y=Normal(0,μ 2 y ), μ y and β are constants, μ x Defined as: Γ is the gamma function.

[0036] In a possible implementation manner of the first aspect, a reference formula when performing Brownian motion simulation is:

[0037] Ga i+1 =Ga i +s×R×R B ×(Elite i -R B ×Ga i ), where Elite is the elite gazelle matrix, Ga i+1 is the solution for the next iteration, s represents the speed of the gazelle, R B Represents a random vector based on Brownian motion, where R is a random number between 0 and 1.

[0038] A second aspect of the embodiment of the present application provides a three-dimensional positioning accurate recognition system based on an optimization algorithm, comprising:

[0039] A clustering module, used for spatially clustering the underwater target area of ​​the ocean according to the area information of the underwater target area and the task information of the positioning task, to obtain a plurality of underwater target sub-areas;

[0040] The statistical module is used to set an anchor node at the center of each underwater target sub-area, and count the minimum number of hops from all unknown nodes to each anchor node using a dual communication radius method according to the three-dimensional spatial coordinates of each anchor node;

[0041] The first calculation module is used to calculate the hop distance between each unknown node and each anchor node after correcting the average hop distance of each unknown node according to the weight coefficient and combining the minimum hop number from the unknown node to each anchor node;

[0042] The second calculation module is used to determine the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node;

[0043] The positioning and identification module is used to move each unknown node according to the three-dimensional coordinates of each unknown node, and then locate and identify the object of the positioning task according to each anchor node and each unknown node.

[0044] Compared with the prior art, the embodiment of the present invention provides a three-dimensional positioning and precise identification method and system based on an optimization algorithm. By uniformly sampling water sample information of the target water area, the target water area is clustered in combination with the task information to obtain multiple underwater target sub-areas. Each underwater target sub-area is a space with similar environment and similar monitoring requirements. An anchor node (fixed node) is placed in each underwater target sub-area, and other unknown nodes (mobile nodes) are arranged as needed. The ratio of the total number of nodes and anchor nodes is adjusted according to the clustering results to ensure positioning accuracy while saving operation and maintenance costs. Then, the dual communication radius method is used to count the minimum number of hops from all unknown nodes to each anchor node, optimize the node communication radius, weaken the positioning error caused by the node hop number and hop distance calculation, and finally use the optimized algorithm to locate and identify the object of the positioning task. Mobile nodes are deployed in the network to build a dynamic UWSN, and the influence of water flow velocity on positioning accuracy is studied to improve positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of a three-dimensional positioning accurate recognition method based on an optimization algorithm provided by an embodiment of the present invention;

[0046] Figure 2 The present invention is a flowchart of a three-dimensional positioning algorithm for improving the gazelle optimization algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Please refer to Figure 1 The embodiment of the present invention provides a three-dimensional positioning accurate recognition method based on an optimization algorithm, comprising:

[0049] S10. According to the area information of the underwater target area of ​​the ocean and the task information of the positioning task, spatially cluster the underwater target area to obtain a plurality of underwater target sub-areas.

[0050] S11. An anchor node is set at the center of each underwater target sub-area, and according to the three-dimensional spatial coordinates of each anchor node, a dual communication radius method is used to count the minimum number of hops from all unknown nodes to each anchor node.

[0051] S12. After correcting the average hop distance of each unknown node according to the weight coefficient, the hop distance between each unknown node and each anchor node is calculated in combination with the minimum number of hops from the unknown node to each anchor node.

[0052] S13. According to the hop distance between each unknown node and each anchor node, the three-dimensional coordinates of each unknown node are determined using the improved gazelle optimization algorithm.

[0053] S14, after moving each unknown node according to the three-dimensional coordinates of each unknown node, positioning and identifying the object of the positioning task according to each anchor node and each unknown node.

[0054] In S10, the underwater target area is spatially clustered, and the waters with certain commonalities are divided together. Subsequently, only one fixed anchor node and multiple movable unknown nodes need to be installed in each underwater target sub-area, thereby constructing a dynamic UWSN network structure. Since only one fixed anchor node needs to be installed in each underwater target sub-area, there is no need to place too many anchor nodes to reduce the total energy consumption of the nodes. It should be noted that the sensor nodes are deployed in the underwater three-dimensional space and are divided into fixed nodes (anchor nodes) and mobile nodes (unknown nodes). The mobile nodes will record the position of each positioning. According to the monitoring requirements, move to the location where the monitoring task needs to be performed.

[0055] The existing DV-Hop algorithm has three errors. First, when calculating the number of hops, the DV-Hop algorithm considers all neighbor nodes within the communication radius of the anchor node as 1 hop. In the case of random node deployment, when the distance between the neighbor node defined as 1 hop and the anchor node is not exactly the same, it will be recorded as 1 hop, which will cause errors. Second, the average hop distance. The average distance per hop is calculated by dividing the sum of the Euclidean distances between the anchor nodes by the sum of the minimum number of hops. The actual number of hops of the nodes is not in a straight line. In the case of multiple hops, there will be positioning errors in the space with low node density. The third is the calculation error. The errors generated in the first two stages will be magnified when the position of the unknown node is estimated by the least squares method in the third stage, and a large amount of errors will also accumulate.

[0056] For the above three errors, S11 uses a dual communication radius method to count the minimum number of hops from all unknown nodes to each anchor node, and S12 performs a weighted summation of the distance errors. The larger the value of the distance error, the greater the deviation between the actual propagation distance and the ideal path. Through weighted summation, the propagation distance between anchor nodes can be better measured and optimized. S11 and S12 reduce the positioning error caused by weakening the node hop count and hop distance calculation.

[0057] S13 aims at the third error mentioned above (because the least square method will cause error accumulation), so the improved IGOA (Improved Gazelle Optimization Algorithm) is used to replace the least square method for position calculation, which further reduces the error and improves the positioning accuracy.

[0058] Compared with the prior art, the embodiment of the present invention provides a three-dimensional positioning and precise identification method based on an optimization algorithm. By uniformly sampling water sample information of the target water area, the target water area is clustered in combination with the task information to obtain multiple underwater target sub-areas. Each underwater target sub-area is a space with similar environment and similar monitoring requirements. An anchor node (fixed node) is placed in each underwater target sub-area, and other unknown nodes (mobile nodes) are arranged as needed. The ratio of the total number of nodes and anchor nodes is adjusted according to the clustering results to ensure positioning accuracy while saving operation and maintenance costs. Then, the dual communication radius method is used to count the minimum number of hops from all unknown nodes to each anchor node, optimize the node communication radius, weaken the positioning error caused by the node hop number and hop distance calculation, and finally use the optimized algorithm to locate and identify the object of the positioning task. Mobile nodes are deployed in the network to build a dynamic UWSN, and the influence of water flow velocity on positioning accuracy is analyzed to improve positioning accuracy.

[0059] Exemplarily, the spatial clustering of the underwater target area according to the area information of the underwater target area of ​​the ocean and the task information of the positioning task to obtain a plurality of underwater target sub-areas specifically includes:

[0060] According to the water volume of the underwater target area of ​​the ocean, the underwater target area is divided into a plurality of initial sub-areas according to the same preset water volume, and a plurality of cluster centers having the same number of initial sub-areas are established;

[0061] Perform environmental sampling at the location of each cluster center, and obtain multiple center vectors based on the sampling results and the task information of the positioning task;

[0062] Multiple sampling points are sequentially set in each initial sub-region for multi-point environmental sampling, and multiple sub-region feature vectors are obtained in each initial sub-region according to the sampling results and the task information of the positioning task;

[0063] All sub-region feature vectors are clustered to obtain multiple new cluster centers as the centers of the underwater target sub-regions; during the clustering process, if the distance between two cluster centers is less than the maximum communication radius of the anchor node, one of the cluster centers is selected for deletion.

[0064] Exemplarily, the sampling result includes the water flow velocity, illumination value and DOI value of the sampling point; the task information of the positioning task includes the importance index value of the location of the sampling point to the task target of the positioning task.

[0065] The center vector and the sub-region feature vector are vectors of the same dimension and are in the same vector space. The composition structure of the center vector and the sub-region feature vector can be (water flow velocity at the sampling point, illumination value at the sampling point, DOI value at the sampling point, and importance index value). Since the water flow in the underwater environment is stable, the mobile node is affected by the water flow and moves horizontally, so the water flow velocity at the sampling point is introduced as one of the vector dimensions. In practical applications, the communication range of the sensor node is affected by factors such as multipath attenuation, unstable signal propagation, and noise, resulting in it not being a regular circular area, and the communication radius is smaller than the sensor communication radius. In order to model the irregularity of the sea area, the communication irregularity (Degree of Irregularity, DOI) indicator is introduced as one of the dimensions when constructing the vector. The DOI value directly reflects the severity of the impact of the environment on the communication range, that is, the higher the DOI value, the greater the impact of environmental interference on the communication, and the smaller the communication range. The importance index value refers to the importance of the underwater target sub-area to the task. The tasks generally include marine environment monitoring, resource exploration and underwater target tracking. Taking resource exploration as an example, if there are a large number of target resources in the underwater target sub-area, the importance index value of the underwater target sub-area can be increased. The sum of the importance index values ​​of all underwater target sub-areas is 1.

[0066] Then, based on clustering the feature vectors of all sub-areas, multiple new cluster centers are obtained as the centers of the underwater target sub-areas. In this way, each target sub-area can be regarded as a water area with certain environmental similarities and similarities in task requirements. Subsequently, placing an anchor node in the center of the area is more conducive to the collection of underwater environmental characteristics. The subsequent arrangement of other mobile nodes can also make different arrangements according to the different center vectors, which is conducive to optimizing the network architecture of UWSN.

[0067] Generally speaking, as the total number of nodes increases, the average positioning error of the subsequent positioning algorithm also decreases. Since the increase in the total number of nodes improves the network connectivity, unknown nodes can obtain more auxiliary positioning information.

[0068] Exemplarily, the method of counting the minimum number of hops from all unknown nodes to each anchor node in a dual communication radius manner according to the three-dimensional space coordinates of each anchor node specifically includes:

[0069] According to the three-dimensional spatial coordinates of each anchor node, the distances between multiple anchor nodes are calculated;

[0070] Select the smallest distance between multiple anchor nodes as the reference distance;

[0071] Determine a first communication radius according to a ratio of a square value of a maximum communication radius of the anchor node to the reference distance;

[0072] Using the maximum communication radius as the second communication radius;

[0073] The minimum number of hops from all unknown nodes to each anchor node is counted according to the first communication radius and the second communication radius in sequence.

[0074] Generally, the hop count correction can adopt a multi-level communication radius to reduce the hop count error and improve accuracy. However, multiple adjustments to the communication radius will increase node energy consumption. The dual communication radius has a small number of adjustments, so this embodiment uses the dual communication radius for hop count correction. The ratio of the square value of the maximum communication radius of the anchor node to the reference distance is a value smaller than the maximum communication radius. The advantage of using this value as the first communication radius is that it takes into account the two nearest anchor nodes. Different anchor nodes are in different environments, which can avoid counting nodes in different environments and speed up the adjustment.

[0075] Assuming that the first communication radius is 0.5R and the second communication radius is R, first, the anchor node broadcasts the data packet for the first time in the network with a communication radius of 0.5R, and sets the initial hop count to 0.5. Node J that receives the information sets the minimum hop count to 0.5, but does not forward the information. Then, the anchor node O uses the complete communication range R for propagation again. Node K that did not receive the first propagation sets the minimum hop count to 1, increases the saved hop count by 1, and then propagates it to the adjacent nodes. Finally, all nodes will save the minimum hop count information with each anchor node. In order to avoid flooding propagation that consumes node energy and shortens the network life, nodes that receive the information do not need to propagate again when propagating within the communication range of 0.5R.

[0076] Exemplarily, the average hop distance of each unknown node is corrected according to the weight coefficient, specifically including:

[0077] According to the distance error between every two anchor nodes, multiple propagation distance weight coefficients are obtained;

[0078] According to multiple propagation distance weight coefficients, the average hop distance of all unknown nodes is calculated.

[0079] The distance between two anchor nodes is:

[0080]

[0081] According to the three-dimensional coordinates of the anchor nodes, the distance error between two anchor nodes is obtained as follows:

[0082] ei，j =|H i ×h i，j -D i，j |;

[0083] In order to reduce the distance error between anchor nodes, the distance error can be weighted and summed. The larger the distance error value, the greater the deviation between the actual propagation distance and the ideal path. By weighted summation, the propagation distance between anchor nodes can be better measured and optimized. The weight coefficient ω is:

[0084]

[0085] By using the average hop distance Hi of the anchor node i that is closest to the unknown node u, the average hop distance of the unknown node u can be calculated as:

[0086]

[0087] Because the least squares method will cause error accumulation, the improved IGOA is used to replace the least squares method for position calculation. The objective function formula of the three-dimensional positioning algorithm is:

[0088]

[0089] Among them, fitness is the individual fitness value of gazelle, (x i ,y i , z i ) and (x u ,y u , z u ) are the three-dimensional coordinates of the anchor node and the unknown node respectively. d is the distance between the anchor node and the unknown node, and n is the number of anchor nodes. The IGOADV-Hop algorithm process is as follows: Figure 2 shown.

[0090] The operation flow of the IGOADV-Hop algorithm is explained below in conjunction with a specific embodiment.

[0091] Exemplarily, the determining the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node specifically includes:

[0092] Use Logistic chaotic mapping to initialize the gazelle population;

[0093] Within a preset maximum number of iterations, the fitness of all gazelles is calculated, and an elite gazelle matrix is ​​constructed according to the fitness of all gazelles;

[0094] In each iteration, if the generated random number is greater than a preset stage threshold, a development activity simulation is performed on the elite gazelle matrix;

[0095] In each iteration, if the generated random number is less than or equal to the preset stage threshold and the current iteration number meets the preset iteration condition, the elite gazelle matrix performs Levy flight simulation; if the generated random number is less than or equal to the preset stage threshold and the current iteration number does not meet the preset iteration condition, the elite gazelle matrix performs Brownian motion simulation;

[0096] In each iteration, the fitness of the position is evaluated after each simulation, the elite gazelle matrix is ​​updated and the elite gazelles are updated.

[0097] Population initialization has an important impact on the performance of swarm intelligence optimization algorithms. Traditional initialization methods usually randomize the positions and moving steps of gazelles, which ensures a certain degree of randomness, but may cause the escape positions of some gazelles to affect the convergence speed of the algorithm. In order to solve this problem, Logistic chaotic mapping is introduced to initialize the gazelle population. By utilizing the ergodic nature of chaotic motion, the initial population can be better selected, thereby accelerating the convergence speed of the algorithm and improving the performance of the algorithm.

[0098] The gazelle adopts Brownian motion in the later stages of detecting the predator:

[0099] Ga i+1 =Ga i +S×λ×A×R B ×(Elite i -R L ×Ga i ); (1)

[0100] Where A = (1-iter / max iter) (2(iter / max_ter)) Cumulative effect for predators.

[0101] In addition, the introduction of dynamic weight coefficients during the development phase of Gazelle can improve Gazelle's global optimization capability. The dynamic weight expression is:

[0102] Where t represents the current iteration number and T represents the maximum number of iterations. In the early stages of the algorithm, E1 is large, which makes the gazelle explore the search space more to find a better area. When the elite gazelle is close to the optimal area, the weight of E2 gradually increases, which prompts the gazelle to focus more on the development of the optimal area, thereby improving the global development ability of the algorithm. Using the dynamic weight coefficient to join the global development stage, the improved formula is:

[0103] Ga i+1 =E1×Ga i +E2×s×R×R B ×(Elite i -R B ×Ga i );

[0104] Exemplarily, the reference formula when executing the development activity simulation is:

[0105] y n (t)=by n (t+1)(1-y n (t-1)) (2)

[0106] Among them, y n ∈[0, 1], t represents the current iteration number, the value range of parameter b is: 1≤b≤4, b is a control parameter.

[0107] Exemplarily, the reference formula for executing the Levy flight simulation is:

[0108]

[0109] Where x = Normal (0, μ 2 x ), y = Normal(0, μ 2 y ), μ y and β are constants, μ x Defined as: "It's the gamma function.

[0110] Exemplarily, the reference formula when performing Brownian motion simulation is:

[0111] Ga i+1 =Ga i +s×R×R B ×(Elite i -R B ×Ga i ) (4)

[0112] Among them, Elite is the elite gazelle matrix, Ga i+1 is the solution for the next iteration, s represents the speed of the gazelle, R B Represents a random vector based on Brownian motion, where R is a random number between 0 and 1.

[0113] In summary, the steps of IGOA by fusing Logistic mapping and dynamic weight coefficients are as follows:

[0114] Step 1: Use the Logistic chaos mapping formula (1) to initialize the parameters of the Gazelle algorithm;

[0115] Step 2: Calculate the fitness of gazelles, elite gazelles = adaptive gazelles, and construct elite gazelle matrix;

[0116] Step 3: If r is less than the preset limit threshold (here set to 0.5), execute the development activities simulated in formula (2), otherwise execute step 4;

[0117] Step 4: If r is greater than or equal to the preset limit threshold (here set to 0.5), enter the exploration phase: If iter is less than Ga / 2 (preset iteration condition), execute the exploration activities simulated in formula (3). Otherwise, execute the exploration activities simulated in formula (4);

[0118] Step 5: Update the fitness and elite gazelle matrix, apply PSRs, and update the gazelle position based on the following formula (5);

[0119] Ga i+1 =Ga i +S×λ×R×R L ×(Elite i -R L ×GGa i ) (5)

[0120] Among them, λ is -1 or 1, indicating two directions of movement, S represents the maximum speed, and RL is a random number vector of Levy distribution.

[0121] Step 6: Determine whether the number of iterations is the maximum. If so, end the loop and output the optimal solution. Otherwise, return to step 2.

[0122] Compared with the prior art, the embodiment of the present invention provides a three-dimensional positioning and precise identification method based on an optimization algorithm. By uniformly sampling water sample information of the target water area, the target water area is clustered in combination with the task information to obtain multiple underwater target sub-areas. Each underwater target sub-area is a space with similar environment and similar monitoring requirements. An anchor node (fixed node) is placed in each underwater target sub-area, and other unknown nodes (mobile nodes) are arranged as needed. The ratio of the total number of nodes and anchor nodes is adjusted according to the clustering results to ensure positioning accuracy while saving operation and maintenance costs. Then, the dual communication radius method is used to count the minimum number of hops from all unknown nodes to each anchor node, optimize the node communication radius, weaken the positioning error caused by the node hop number and hop distance calculation, and finally use the optimized algorithm to locate and identify the object of the positioning task. Mobile nodes are deployed in the network to build a dynamic UWSN, and the influence of water flow velocity on positioning accuracy is studied to improve positioning accuracy.

[0123] An embodiment of the present application provides a three-dimensional positioning and precise identification system based on an optimization algorithm, including: a clustering module, a statistical module, a first calculation module, a second calculation module and a positioning and identification module.

[0124] A clustering module, used for spatially clustering the underwater target area of ​​the ocean according to the area information of the underwater target area and the task information of the positioning task, to obtain a plurality of underwater target sub-areas;

[0125] The statistical module is used to set an anchor node at the center of each underwater target sub-area, and count the minimum number of hops from all unknown nodes to each anchor node using a dual communication radius method according to the three-dimensional spatial coordinates of each anchor node;

[0126] The first calculation module is used to calculate the hop distance between each unknown node and each anchor node after correcting the average hop distance of each unknown node according to the weight coefficient and combining the minimum hop number from the unknown node to each anchor node;

[0127] The second calculation module is used to determine the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node;

[0128] The positioning and identification module is used to move each unknown node according to the three-dimensional coordinates of each unknown node, and then locate and identify the object of the positioning task according to each anchor node and each unknown node.

[0129] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the identification system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0130] Compared with the prior art, the embodiment of the present invention provides a three-dimensional positioning and precise identification system based on an optimization algorithm. By uniformly sampling water sample information of the target water area, the target water area is clustered in combination with the task information to obtain multiple underwater target sub-areas. Each underwater target sub-area is a space with similar environment and similar monitoring requirements. An anchor node (fixed node) is placed in each underwater target sub-area, and other unknown nodes (mobile nodes) are arranged as needed. The ratio of the total number of nodes and anchor nodes is adjusted according to the clustering results to ensure positioning accuracy while saving operation and maintenance costs. Then, the dual communication radius method is used to count the minimum number of hops from all unknown nodes to each anchor node, optimize the node communication radius, weaken the positioning error caused by the node hop number and hop distance calculation, and finally use the optimized algorithm to locate and identify the object of the positioning task. Mobile nodes are deployed in the network to build a dynamic UWSN, and the influence of water flow velocity on positioning accuracy is studied to improve positioning accuracy.

[0131] In summary, in order to solve the problem of large three-dimensional positioning error of the traditional DV-hop algorithm, this application proposes a dynamic UWSN three-dimensional positioning algorithm based on the improved gazelle optimization algorithm. The number of hops between nodes is corrected by dual communication radius, and then the error between anchor nodes is weighted and corrected to optimize the hop distance. Finally, the gazelle optimization algorithm that introduces Logistic mapping and dynamic weight coefficients is used to replace the least squares method for position calculation. Compared with the traditional DV-Hop algorithm and other swarm intelligence optimization algorithms, the proposed IGOADV-Hop algorithm has reduced positioning errors. Combined with the actual three-dimensional engineering environment, it further improves positioning accuracy and optimizes the algorithm computing power.

[0132] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A three-dimensional positioning accurate recognition method based on optimization algorithm, characterized in that: include: According to the area information of the underwater target area of ​​the ocean and the task information of the positioning task, spatially clustering the underwater target area to obtain a plurality of underwater target sub-areas; An anchor node is set at the center of each underwater target sub-area. According to the three-dimensional spatial coordinates of each anchor node, the minimum number of hops from all unknown nodes to each anchor node is counted using a dual communication radius method. After correcting the average hop distance of each unknown node according to the weight coefficient, the hop distance between each unknown node and each anchor node is calculated in combination with the minimum hop number from the unknown node to each anchor node; According to the hop distance between each unknown node and each anchor node, the three-dimensional coordinates of each unknown node are determined using the improved gazelle optimization algorithm; After moving each unknown node according to its three-dimensional coordinates, the object of the positioning task is positioned and identified according to each anchor node and each unknown node; The method of spatially clustering the underwater target area according to the area information of the underwater target area in the ocean and the task information of the positioning task to obtain a plurality of underwater target sub-areas specifically includes: According to the water volume of the underwater target area of ​​the ocean, the underwater target area is divided into a plurality of initial sub-areas according to the same preset water volume, and a plurality of cluster centers having the same number of initial sub-areas are established; Perform environmental sampling at the location of each cluster center, and obtain multiple center vectors based on the sampling results and the task information of the positioning task; Multiple sampling points are sequentially set in each initial sub-region for multi-point environmental sampling, and multiple sub-region feature vectors are obtained in each initial sub-region according to the sampling results and the task information of the positioning task; Cluster all sub-region feature vectors to obtain multiple new cluster centers as the centers of the underwater target sub-regions; during the clustering process, if the distance between two cluster centers is less than the maximum communication radius of the anchor node, select one of the cluster centers for deletion; The sampling result includes the water flow velocity, illumination value and DOI value of the sampling point; the task information of the positioning task includes the importance index value of the location of the sampling point to the task target of the positioning task; The method of counting the minimum number of hops from all unknown nodes to each anchor node in a dual communication radius manner according to the three-dimensional spatial coordinates of each anchor node specifically includes: According to the three-dimensional spatial coordinates of each anchor node, the distances between multiple anchor nodes are calculated; Select the smallest distance between multiple anchor nodes as the reference distance; Determine a first communication radius according to a ratio of a square value of a maximum communication radius of the anchor node to the reference distance; Using the maximum communication radius as the second communication radius; According to the first communication radius and the second communication radius, counting the minimum number of hops from all unknown nodes to each anchor node; The method of determining the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node specifically includes: Use Logistic chaotic mapping to initialize the gazelle population; Within a preset maximum number of iterations, the fitness of all gazelles is calculated, and an elite gazelle matrix is ​​constructed according to the fitness of all gazelles; In each iteration, if the generated random number is greater than a preset stage threshold, a development activity simulation is performed on the elite gazelle matrix; In each iteration, if the generated random number is less than or equal to the preset stage threshold and the current iteration number meets the preset iteration condition, the elite gazelle matrix performs Levy flight simulation; if the generated random number is less than or equal to the preset stage threshold and the current iteration number does not meet the preset iteration condition, the elite gazelle matrix performs Brownian motion simulation; In each iteration, the fitness of the position is evaluated after each simulation, the elite gazelle matrix is ​​updated and the elite gazelle is updated; The reference formula when performing development activity simulation is: y n (t)=by n (t+1)(1-y n (t-1)), where y n ∈[0, 1], t represents the current iteration number, the value range of parameter b is: 1≤b≤4, b is a control parameter; The reference formula for executing the Levy flight simulation is: Where x = Normal (0, μ 2 x ),y=Normal(0,μ 2 y ), μ y and β are constants, μ x Defined as: Γ is the gamma function; The reference formula for performing Brownian motion simulation is: Ga i+1 =Ga i +s×R×R B ×(Elite i -R B ×Ga i ), where Elite is the elite gazelle matrix, Ga i+1 is the solution for the next iteration, s represents the speed of the gazelle, R B Represents a random vector based on Brownian motion, where R is a random number between 0 and 1.

2. The three-dimensional positioning accurate recognition method based on the optimization algorithm as claimed in claim 1, characterized in that: The average hop distance of each unknown node is corrected according to the weight coefficient, including: According to the distance error between every two anchor nodes, multiple propagation distance weight coefficients are obtained; According to multiple propagation distance weight coefficients, the average hop distance of all unknown nodes is calculated.

3. A three-dimensional positioning and accurate recognition system based on an optimization algorithm, characterized in that: include: A clustering module, used for spatially clustering the underwater target area of ​​the ocean according to the area information of the underwater target area and the task information of the positioning task, to obtain a plurality of underwater target sub-areas; The statistical module is used to set an anchor node at the center of each underwater target sub-area, and count the minimum number of hops from all unknown nodes to each anchor node using a dual communication radius method according to the three-dimensional spatial coordinates of each anchor node; The first calculation module is used to calculate the hop distance between each unknown node and each anchor node after correcting the average hop distance of each unknown node according to the weight coefficient and combining the minimum hop number from the unknown node to each anchor node; The second calculation module is used to determine the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node; A positioning and identification module is used to move each unknown node according to the three-dimensional coordinates of each unknown node, and then locate and identify the object of the positioning task according to each anchor node and each unknown node; The method of spatially clustering the underwater target area according to the area information of the underwater target area in the ocean and the task information of the positioning task to obtain a plurality of underwater target sub-areas specifically includes: According to the water volume of the underwater target area of ​​the ocean, the underwater target area is divided into a plurality of initial sub-areas according to the same preset water volume, and a plurality of cluster centers having the same number of initial sub-areas are established; Perform environmental sampling at the location of each cluster center, and obtain multiple center vectors based on the sampling results and the task information of the positioning task; Multiple sampling points are sequentially set in each initial sub-region for multi-point environmental sampling, and multiple sub-region feature vectors are obtained in each initial sub-region according to the sampling results and the task information of the positioning task; Cluster all sub-region feature vectors to obtain multiple new cluster centers as the centers of the underwater target sub-regions; during the clustering process, if the distance between two cluster centers is less than the maximum communication radius of the anchor node, select one of the cluster centers for deletion; The sampling result includes the water flow velocity, illumination value and DOI value of the sampling point; the task information of the positioning task includes the importance index value of the location of the sampling point to the task target of the positioning task; The method of counting the minimum number of hops from all unknown nodes to each anchor node in a dual communication radius manner according to the three-dimensional spatial coordinates of each anchor node specifically includes: According to the three-dimensional spatial coordinates of each anchor node, the distances between multiple anchor nodes are calculated; Select the smallest distance between multiple anchor nodes as the reference distance; Determine a first communication radius according to a ratio of a square value of a maximum communication radius of the anchor node to the reference distance; Using the maximum communication radius as the second communication radius; According to the first communication radius and the second communication radius, counting the minimum number of hops from all unknown nodes to each anchor node; The method of determining the three-dimensional coordinates of each unknown node by using the improved gazelle optimization algorithm according to the hop distance between each unknown node and each anchor node specifically includes: Use Logistic chaotic mapping to initialize the gazelle population; Within a preset maximum number of iterations, the fitness of all gazelles is calculated, and an elite gazelle matrix is ​​constructed according to the fitness of all gazelles; In each iteration, if the generated random number is greater than a preset stage threshold, a development activity simulation is performed on the elite gazelle matrix; In each iteration, if the generated random number is less than or equal to the preset stage threshold and the current iteration number meets the preset iteration condition, the elite gazelle matrix performs Levy flight simulation; if the generated random number is less than or equal to the preset stage threshold and the current iteration number does not meet the preset iteration condition, the elite gazelle matrix performs Brownian motion simulation; In each iteration, the fitness of the position is evaluated after each simulation, the elite gazelle matrix is ​​updated and the elite gazelle is updated; The reference formula when performing development activity simulation is: y n (t)=by n (t+1)(1-y n (t-1)), where y n ∈[0, 1], t represents the current iteration number, the value range of parameter b is: 1≤b≤4, b is a control parameter; The reference formula for executing the Levy flight simulation is: Where x = Normal (0, μ 2 x ),y=Normal(0,μ 2 y ), μ y and β are constants, μ x Defined as: Γ is the gamma function; The reference formula for performing Brownian motion simulation is: Ga i+1 =Ga i +s×R×R B ×(Elite i -R B ×Ga i ), where Elite is the elite gazelle matrix, Ga i+1 is the solution for the next iteration, s represents the speed of the gazelle, R B Represents a random vector based on Brownian motion, where R is a random number between 0 and 1.

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