A three-dimensional positioning method and system for submarine wireless sensor network

Abstract

The present invention provides a three-dimensional positioning method and system for a submarine wireless sensor network, which relates to the field of data processing technology. The method includes: deploying wireless sensor nodes with a ranging function in a submarine area according to a predetermined layout strategy, and the wireless sensor nodes are used to send and receive sound wave signals to measure the distance between nodes; acquiring underwater environmental parameters, and modeling the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain inter-node distance data; calculating the position coordinates of unknown nodes according to the inter-node distance data; and performing three-dimensional positioning of nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain positioning results, and the positioning results include determining the specific coordinate position of each node in three-dimensional space. The present invention effectively improves the accuracy of the inter-node distance data, thereby improving the positioning accuracy of the unknown nodes.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a three-dimensional positioning method and system for a submarine wireless sensor network. Background Art

[0002] Traditional multilateral positioning methods are often based on a simplified assumption when calculating position, that is, sound waves propagate along a straight line.

[0003] However, in the actual underwater environment, due to the influence of water flow, medium inhomogeneity, temperature gradient, salinity change and other complex factors, the propagation path of sound waves often deviates from the straight line. If this bending effect of sound wave propagation is not properly considered and compensated in the positioning algorithm, it will lead to deviations in the positioning results, thus affecting the accuracy and reliability of positioning. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a three-dimensional positioning method and system for a submarine wireless sensor network, which effectively improves the accuracy of distance data between nodes and further improves the positioning accuracy of unknown nodes.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a three-dimensional positioning method for a submarine wireless sensor network is provided, the method comprising:

[0007] Wireless sensor nodes with ranging function are deployed in the seabed area according to a predetermined layout strategy. The wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes.

[0008] Acquire underwater environmental parameters, and model the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain the distance data between nodes. The underwater environmental parameters include medium density, temperature and salinity.

[0009] Calculate the position coordinates of unknown nodes based on the distance data between nodes;

[0010] According to the position coordinates of the unknown nodes, the nodes in the submarine wireless sensor network are three-dimensionally positioned to obtain positioning results, and the positioning results include determining the specific coordinate position of each node in the three-dimensional space;

[0011] Monitor the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; trigger the repositioning process when a change in the network status is detected or a predetermined repositioning cycle is reached;

[0012] Update the node's location information and broadcast it to the entire network so that the remaining nodes in the network can obtain the latest location information.

[0013] Furthermore, wireless sensor nodes with ranging functions are deployed in the seabed area according to a predetermined layout strategy, including:

[0014] Determine the scope of the seafloor monitoring area, number of nodes, communications and ranging requirements;

[0015] Randomly generate node positions that meet the basic layout requirements as the initial layout, and define an energy function to evaluate the pros and cons of the current layout solution ;

[0016] For the current temperature ,conduct iterations, in each iteration, a node is randomly selected and its position is changed to generate a new layout solution;

[0017] Calculate the energy of the new layout solution and the energy of the original layout plan ,if < , the new layout is accepted; if ≥ , then with probability Accept the new layout, where Represents the base of natural logarithm; updates the current temperature for ,in, is the temperature drop rate, if Below the end temperature , then stop the annealing process to obtain the final layout solution;

[0018] According to the final layout plan, wireless sensor nodes with ranging function are actually deployed in the seabed area.

[0019] Furthermore, the energy function The calculation formula is:

[0020] ;

[0021] in, Indicates the area of ​​the monitoring area; Indicates the area of ​​the covered region; Indicates the total number of nodes; It is i The communication success rate of each node; Indicates the minimum distance between nodes; Represents the distance between nodes; iIndicates the index of the node currently being evaluated, ranging from 1 to , each i Represents a specific sensor node; Representation and Node i The index of the other node to compare, ranging from arrive ; , and is the weight coefficient.

[0022] Furthermore, underwater environmental parameters are obtained, and the sound wave propagation path between underwater nodes is modeled according to the underwater environmental parameters and a preset curved sound line model to obtain inter-node distance data, including:

[0023] Determine the underwater environmental parameters to be measured based on the deployment area of ​​the submarine wireless sensor network;

[0024] Conduct field measurements at key locations within the deployment area to obtain underwater environmental parameter data representative of the entire deployment area;

[0025] According to the characteristics of the underwater environment, a curved sound line model is constructed;

[0026] According to the curved sound line model and underwater environmental parameter data, the expected propagation path of the sound wave from the sending node to the receiving node is calculated;

[0027] Based on the expected propagation path and the speed of sound waves underwater, calculate the time required for the sound wave to propagate from the sending node to the receiving node;

[0028] The time required for the sound wave to propagate from the sending node to the receiving node is multiplied by the propagation speed of the sound wave to obtain the distance data between the nodes.

[0029] Furthermore, according to the characteristics of the underwater environment, a curved sound line model is constructed, including:

[0030] Obtain seafloor topography data, including seafloor undulations, slope, and seafloor sediment type information;

[0031] Based on the water temperature data at different depths underwater, calculate the temperature difference between different depths to determine the temperature gradient; analyze the salinity of the water body, including the spatial distribution and variation of salinity; use an echo sounder to measure the water depth to obtain a water depth distribution map;

[0032] Calculate the sound velocity distribution in the water column based on the temperature gradient, salinity of the water column, and the water depth distribution map;

[0033] According to the sound speed distribution and seabed topography data, the propagation path of sound waves underwater is simulated to form a curved sound line model.

[0034] Furthermore, the position coordinates of the unknown nodes are calculated based on the distance data between the nodes, including:

[0035] Get the distance data between all known location nodes and unknown nodes;

[0036] From the nodes with known positions, select at least four non-coplanar nodes as reference nodes;

[0037] For each reference node, a distance equation is listed based on the distance data between the known position node and the unknown node;

[0038] Set an initial estimate for the coordinates of the unknown nodes. Near the initial estimate, expand the nonlinear equations through Taylor series, and use Gaussian elimination to solve the linearized equations to obtain an approximate solution for the coordinates of the unknown nodes.

[0039] The approximate solution is used as the new initial estimate, and the linearization and solution steps are repeated for iterative optimization. Through multiple iterations, the actual unknown node coordinates are gradually approached. After the iteration is completed, the final position coordinates of the unknown nodes are obtained.

[0040] Furthermore, according to the position coordinates of the unknown nodes, the nodes in the submarine wireless sensor network are three-dimensionally positioned to obtain positioning results, including:

[0041] According to the position coordinate data of unknown nodes, a three-dimensional coordinate system is established to represent the spatial structure of the submarine wireless sensor network; nodes with known positions are used as reference points and marked in the three-dimensional coordinate system;

[0042] Map the position coordinate data of the unknown nodes into a three-dimensional coordinate system, where each unknown node has a corresponding position in the three-dimensional space;

[0043] According to the positions of known nodes and unknown nodes, the topological structure of the entire wireless sensor network is constructed in three-dimensional space, including the relative position relationship between known nodes and unknown nodes;

[0044] The topological structure of the wireless sensor network and the locations of known nodes and unknown nodes are displayed in a visual form to obtain the positioning results.

[0045] In a second aspect, a three-dimensional positioning system for a submarine wireless sensor network includes:

[0046] A layout module is used to deploy wireless sensor nodes with ranging function in the seabed area according to a predetermined layout strategy. The wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes.

[0047] An acquisition module is used to acquire underwater environmental parameters and model the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain the distance data between nodes. The underwater environmental parameters include medium density, temperature and salinity.

[0048] A calculation module is used to calculate the position coordinates of unknown nodes based on the distance data between nodes;

[0049] A positioning module is used to perform three-dimensional positioning of nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain positioning results, wherein the positioning results include determining the specific coordinate position of each node in the three-dimensional space;

[0050] The monitoring module is used to monitor the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; when a change in the network status is detected or a predetermined repositioning cycle is reached, the repositioning process is triggered; the node location information is updated and broadcast to the entire network so that the remaining nodes in the network can obtain the latest location information.

[0051] According to a third aspect, a computing device includes:

[0052] one or more processors;

[0053] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0054] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0055] The above scheme of the present invention includes at least the following beneficial effects.

[0056] This method obtains underwater environmental parameters (such as medium density, temperature and salinity) and combines them with a preset curved sound line model to accurately model the sound wave propagation path between underwater nodes. This modeling method can more realistically reflect the propagation of sound waves in the underwater environment, thereby effectively improving the accuracy of the distance data between nodes and the positioning accuracy of unknown nodes. Since multiple factors of the underwater environment are taken into account, this method has stronger environmental adaptability. Regardless of complex conditions such as changes in medium density, temperature gradients, or salinity differences, this method can maintain high positioning stability and reliability.

[0057] This method can determine the specific coordinate position of each node in three-dimensional space, providing richer spatial information for the submarine wireless sensor network. By monitoring the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data, this method can promptly detect changes in the network status. When a change is detected or a predetermined repositioning cycle is reached, the repositioning process is triggered to ensure the real-time and accuracy of the node location information. This dynamic update mechanism enables the positioning system to better cope with the dynamic changes of the submarine environment. The precise location information obtained by this method helps to optimize the configuration of network resources, improve data transmission efficiency, and reduce energy consumption, thereby comprehensively improving the overall performance of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The present invention is a flowchart of a three-dimensional positioning method for a submarine wireless sensor network provided by an embodiment of the present invention.

[0059] Figure 2 It is a schematic diagram of a three-dimensional positioning system of a submarine wireless sensor network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a three-dimensional positioning method for a submarine wireless sensor network, the method comprising the following steps:

[0062] Step 11, deploying wireless sensor nodes with ranging function in the seabed area according to a predetermined layout strategy, the wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes;

[0063] Step 12, acquiring underwater environmental parameters, and modeling the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain inter-node distance data, wherein the underwater environmental parameters include medium density, temperature, and salinity;

[0064] Step 13, calculating the position coordinates of the unknown nodes according to the distance data between the nodes;

[0065] Step 14, performing three-dimensional positioning of the nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain positioning results, wherein the positioning results include determining the specific coordinate position of each node in the three-dimensional space;

[0066] Step 15, monitoring the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; when a change in the network status is detected or a predetermined repositioning period is reached, the repositioning process is triggered;

[0067] Step 16: Update the location information of the node and broadcast it to the entire network so that the remaining nodes in the network can obtain the latest location information.

[0068] In the embodiment of the present invention, step 11, by reasonably arranging wireless sensor nodes in the seabed area, it is possible to ensure wide coverage of the network and effectively collect various environmental data on the seabed. Step 12, considering the influence of underwater environmental parameters (such as medium density, temperature and salinity) on the propagation of sound waves, it is possible to more accurately simulate the propagation path of sound waves underwater, thereby improving the accuracy of ranging between nodes. Through the preset curved sound line model, it is possible to better adapt to the complex and changeable underwater environment and ensure the stable transmission of sound wave signals. Step 13, by calculating the position coordinates of unknown nodes, each node can be accurately located in three-dimensional space. Accurate three-dimensional positioning helps the reasonable allocation of network resources, such as the planning of data transmission paths, the effective use of energy, etc. By continuously monitoring the network status, including the communication quality of the nodes and the stability of the ranging data, problems in the network can be discovered and handled in a timely manner to ensure the stable operation of the network; when the network status changes or reaches a predetermined repositioning cycle, triggering the repositioning process can ensure the real-time and accuracy of the network configuration. By regularly updating and broadcasting the location information of the nodes, it is possible to ensure that each node in the network has the latest location data; the latest location information helps to optimize the data transmission path, reduce transmission delays, and improve the overall work efficiency of the network. At the same time, it also helps to detect and handle faulty nodes in a timely manner to ensure the robustness of the network.

[0069] In a preferred embodiment of the present invention, the above step 11, deploying wireless sensor nodes with ranging function in the seabed area according to a predetermined layout strategy, may include:

[0070] Step 111, determine the scope of the seabed monitoring area, the number of nodes, and the communication and ranging requirements, including: using maps and geographic information systems (GIS) to determine the specific boundaries of the seabed area to be monitored. Consider the monitoring target, such as a specific marine ecological zone, a seabed resource development zone, or a potential pollution area, to determine the precise coordinates of the monitoring range. Analyze the monitoring purpose, such as environmental monitoring, resource exploration, disaster prevention, etc., to determine the required monitoring accuracy and data collection frequency. Determine the required sensor type according to the monitoring target, such as a temperature sensor, a pressure sensor, a chemical oxygen demand (COD) sensor, etc. Estimate the required number of wireless sensor nodes based on the size and shape of the monitoring area and the required monitoring accuracy. Consider the communication range and coverage capability of the nodes to ensure that there are no dead spots in the monitoring area and that there is sufficient redundancy to ensure the robustness of the network. Determine the communication protocol between nodes, such as which wireless communication technology (such as acoustic wave communication) to use, and set key communication parameters such as communication rate, transmission distance, and bit error rate to meet the requirements of real-time data transmission and reliability. Determine the ranging accuracy according to application requirements, such as whether it is necessary to achieve centimeter-level, meter-level, or ten-meter-level accuracy.

[0071] Step 112, randomly generating node positions that meet the basic layout requirements as the initial layout, specifically including: using a random number generator, randomly selecting positions in the monitoring area as the initial placement points of the wireless sensor nodes, so that the position of each node meets the basic layout requirements, for example, the nodes should not be too close or too scattered, if there are positions that do not meet the requirements, such as the nodes are too dense or there are uncovered areas, then adjustments are made, and once the initial layout that meets the requirements is obtained, the specific coordinate position of each node is recorded; define an energy function for evaluating the pros and cons of the current layout scheme ;

[0072] Step 113, for the current temperature ,conduct In each iteration, a node is randomly selected and its position is changed to generate a new layout scheme, which includes: setting an initial temperature , set the number of iterations , that is, at each temperature The number of layout adjustments to be made next, from 1 to , and iterate in a loop; in each iteration, a random number generator is used to randomly select a node from the current layout solution. This randomness ensures that the algorithm will not be biased towards a specific node or area during the search process, which helps to find the global optimal solution; the position of the selected node is adjusted, and the adjustment method can be to randomly move a distance; the moving distance and direction are determined according to the specific needs and constraints of the problem to ensure that the new position still meets the basic layout requirements, and the node position after the move is combined with the remaining node positions that have not been moved to form a new layout solution;

[0073] Step 114, calculate the energy of the new layout solution and the energy of the original layout plan ,if < , the new layout is accepted; if ≥ , then with probability Accept the new layout, where Represents the base of natural logarithm; updates the current temperature for ,in, is the temperature drop rate, if Below the end temperature , then stop the annealing process to obtain the final layout solution;

[0074] Step 115, according to the final layout plan, actually deploying wireless sensor nodes with ranging functions in the seabed area, specifically including: according to the final layout plan, formulating a deployment plan, including the specific placement positions of the nodes, the deployment order, and the required resources and time; according to the deployment plan, placing the wireless sensor nodes with ranging functions one by one at the predetermined positions in the seabed area, during the placement process, it is necessary to ensure the stability and fixity of the nodes to prevent them from moving due to water flow or other external factors; for each placed node, recording its actual location information, and comparing it with the final layout plan to ensure the accuracy of the deployment.

[0075] In the embodiment of the present invention, step 111, by clarifying the scope of the monitoring area and the number of nodes, can ensure that network resources are reasonably allocated while meeting specific communication and ranging requirements. Step 112, by randomly generating the initial layout, the optimization process can be quickly started to reduce manual intervention; steps 113 to 114, through the iterative optimization process, the node positions can be continuously explored and adjusted to find a more flexible and efficient layout solution, and the introduction of a probabilistic acceptance mechanism (such as the acceptance criterion in the simulated annealing algorithm) helps the algorithm to jump out of the local optimal solution and find the global optimal layout. Step 115, after the optimization of the previous steps, it can ensure that the expected monitoring and communication effects are achieved when deployed in an actual submarine environment, and the optimized layout solution helps to improve the overall performance and resource utilization efficiency of the wireless sensor network.

[0076] In a preferred embodiment of the present invention, the energy function The calculation formula is:

[0077] ;

[0078] in, Indicates the area of ​​the monitoring area; Indicates the area of ​​the covered region; Indicates the total number of nodes; It is i The communication success rate of each node; Indicates the minimum distance between nodes; Represents the distance between nodes; i Indicates the index of the node currently being evaluated, ranging from 1 to , each i Represents a specific sensor node; Representation and Node i The index of the other node to compare, ranging from arrive ; , and is the weight coefficient.

[0079] In this embodiment of the present invention, the first term in the energy function Pay attention to the coverage of the monitoring area, and ensure that the wireless sensor nodes can cover the entire monitoring area as comprehensively as possible by minimizing the ratio of uncovered areas to the total monitoring area, reducing blind areas and improving the integrity and accuracy of monitoring data. Taking into account the communication success rate of nodes, the energy function helps to build a more stable and reliable communication network by improving the communication success rate of each node, reducing the loss and errors in the data transmission process and ensuring the effective transmission of information.

[0080] Item 3 Pay attention to the distance between nodes and set the minimum distance limit between nodes to avoid nodes being too densely deployed in a certain area, causing resource waste and communication interference. At the same time, this also helps to ensure the uniform distribution of the network and improve the perception ability of the entire monitoring area.

[0081] In a preferred embodiment of the present invention, the above step 12, obtaining underwater environmental parameters, and modeling the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain inter-node distance data, may include:

[0082] Step 121, according to the deployment area of ​​the submarine wireless sensor network, determine the underwater environmental parameters to be measured, specifically including: analyzing the specific deployment area of ​​the submarine wireless sensor network, including understanding the geographical location, marine climate conditions, submarine topography and possible hydrological characteristics of the area; based on the analysis of regional characteristics, determine a set of key underwater environmental parameters, including water temperature, salinity, pressure (depth), flow rate and possible bottom type; after determining the key environmental parameters, it is necessary to set the measurement priority according to the actual situation. For example, some parameters may vary greatly over time and space, so more frequent measurements are required; while other parameters may be relatively stable and can be measured less frequently;

[0083] Step 122, conduct field measurements at each key location in the deployment area to obtain underwater environmental parameter data representing the entire deployment area, specifically including: using the geographic information data of the seabed topographic map and the nautical chart to identify the key terrain features in the deployment area, such as seabed mountains, canyons, plains, etc.; based on the terrain feature analysis, plan a measurement route that can cover the entire deployment area and cross different terrain features as much as possible; select a series of key measurement points on the measurement route to ensure that these points can represent the main environmental features in the area, and the selection of measurement points should take into account factors such as water flow conditions, depth changes, and bottom type; select appropriate measurement equipment according to the underwater environmental parameters to be measured (such as water temperature, salinity, pressure, flow rate, etc.), such as CTD (temperature, salinity, depth) instrument, current profiler, etc.; according to the planned measurement route and measurement points, deploy measurement equipment one by one for data collection; stay at each measurement point for a sufficient time to allow the measurement equipment to fully collect data. At the same time, record the location information (such as longitude and latitude, depth) and environmental characteristics (such as geological type, water flow conditions) of each measurement point in detail; after completing data collection, recover the measurement equipment one by one and ensure that the data is safely stored. For parameters that require long-term continuous monitoring, consider using equipment with automatic recording and data transmission functions;

[0084] Step 123, constructing a curved sound line model according to the characteristics of the underwater environment;

[0085] Step 124, calculating the expected propagation path of the sound wave from the sending node to the receiving node according to the curved sound line model and the underwater environmental parameter data, specifically comprising: inputting the underwater environmental parameter data (such as water temperature, salinity, pressure distribution, velocity profile, etc.) obtained by field measurement in step 122 into the curved sound line model, and these data are used to define the environmental field in the model, so as to accurately simulate the propagation behavior of the sound wave in the actual underwater environment; determining and setting the position coordinates of the sending node and the receiving node in the curved sound line model, including their horizontal positions and depths; running the curved sound line model, simulating the propagation process of the sound wave from the sending node to the receiving node according to the input environmental parameters and node positions, and the curved sound line model will output one or more expected propagation paths, which take into account the bending, refraction and other effects of the sound wave in the underwater environment;

[0086] Step 125, based on the expected propagation path and the underwater propagation speed of the sound wave, calculate the time required for the sound wave to propagate from the sending node to the receiving node, specifically including: using the underwater environmental parameter data input in step 124, combined with the relationship between the sound wave propagation speed and the environmental parameters (such as the sound speed profile), determine the propagation speed of the sound wave at each point along the expected propagation path; based on the expected propagation path obtained in step 124, calculate the total length of the path, if the path is curved, use an appropriate mathematical method (such as integration) to accurately calculate the path length; divide the total length of the expected propagation path by the average propagation speed of the sound wave on the path (or integrate by considering the speed change section by section), to obtain the time required for the sound wave to propagate from the sending node to the receiving node;

[0087] Step 126, multiplying the time required for the sound wave to propagate from the sending node to the receiving node by the propagation speed of the sound wave to obtain the distance data between the nodes, specifically including: obtaining the time data required for the sound wave to propagate from the sending node to the receiving node from step 125; next, determining the propagation speed of the sound wave in a specific underwater environment; with the propagation time and the speed of the sound wave, the formula "distance = speed × time" can be used to calculate the distance between the sending node and the receiving node.

[0088] In an embodiment of the present invention, by measuring underwater environmental parameters on the spot and combining with a curved sound line model, the propagation path of sound waves underwater can be simulated more accurately, which helps to more accurately calculate the distance between nodes, thereby improving the positioning accuracy and data transmission efficiency of the wireless sensor network. The underwater environment is usually complex and changeable, including multiple influencing factors such as water temperature, salinity, pressure, and flow rate. By constructing a curved sound line model, the process can better adapt to these complex environments and provide more reliable predictions of sound wave propagation paths. Accurate distance data between nodes is the key to optimizing the layout and performance of wireless sensor networks. Through this process, more accurate distance information can be obtained to guide the optimal deployment of the network and improve the coverage and communication quality of the network. Accurate modeling of sound wave propagation paths helps to reduce unnecessary signal retransmissions and energy consumption, thereby extending the service life of sensor nodes. At the same time, this also reduces the cost of network maintenance and upgrades. Through real-time monitoring and modeling of underwater environmental parameters, the system can respond more quickly to environmental changes, such as changes in water flow velocity or fluctuations in water temperature. This rapid response capability enhances the robustness of the system, allowing it to maintain stable performance in various underwater environments.

[0089] In a preferred embodiment of the present invention, the above step 123, constructing a curved sound line model according to the characteristics of the underwater environment, includes:

[0090] Step 1231, obtaining seafloor topography data, including seafloor undulation, slope, and seafloor sediment type information, specifically including: using a multi-beam bathymetric system, side-scan sonar, or seafloor topography mapping instrument to measure the seafloor topography, which can emit sound waves and receive echoes, and determine the depth and shape of the seafloor by analyzing the echo signals; importing the collected raw data into geographic information system (GIS) software or special seafloor topography processing software, and generating a smooth and continuous seafloor topography model through processing steps such as data cleaning, filtering, and interpolation; extracting key features from the processed seafloor topography model, such as seafloor undulation, slope changes, and sediment type information;

[0091] The fluctuation degree analysis specifically includes the following steps:

[0092] Calculate the elevation difference, traverse each grid point of the model, and calculate the elevation difference between adjacent points. This can be done by taking the difference in the elevation values ​​of adjacent points; perform statistical analysis on the calculated elevation difference, such as calculating the mean value, standard deviation, etc., to quantify the degree of undulation of the seabed. A large range of elevation difference changes means that the seabed is relatively undulating, and vice versa. Use geographic information system (GIS) software to generate an undulation map to intuitively display the undulation of the seabed through color or contour lines.

[0093] Slope change analysis specifically includes the following steps:

[0094] Calculate the slope. For each grid point, use the elevation data of its surrounding points to calculate the slope of the point. The slope is defined as the ratio between the elevation change and the horizontal distance. Slope classification. According to the calculated slope value, the seabed terrain is divided into different slope categories, such as gentle, medium slope, steep, etc. The slope classification results are presented in a graphical way to help identify the slope change pattern of the seabed terrain and potential risk areas (such as steep slopes).

[0095] Sediment type information extraction specifically includes the following steps:

[0096] Applying bottom classification algorithms, such as acoustic-based classification methods, to analyze data in the seafloor terrain model, the type of sediment can be inferred based on parameters such as echo intensity and texture characteristics; using sample data of known sediment types to train the classifier, and evaluating its accuracy through methods such as cross-validation; applying the classification algorithm to the entire seafloor terrain model to generate a sediment type distribution map. This map can clearly show the spatial distribution of different sediment types.

[0097] Step 1232, based on the water temperature data at different depths underwater, calculate the temperature difference between different depths to determine the temperature gradient; analyze the salinity of the water body, including the spatial distribution and variation of the salinity; and use an echo sounder to measure the water depth to obtain a water depth distribution map, specifically including:

[0098] Use underwater detection equipment equipped with temperature sensors to measure water temperature at different depths, and record the depth and corresponding water temperature value of each measurement point; calculate the temperature difference between adjacent depths based on the water temperature data at different depths, and determine the temperature gradient distribution in the entire water body by fitting these data points; collect water samples and use a salinity meter or conductivity meter to measure the salinity value of the water body, and perform multiple measurements at different locations and depths to obtain the spatial distribution of salinity and its variation with depth. Use an echo sounder (such as a single-beam or multi-beam echo sounder) to measure water depth. The echo sounder determines the water depth by emitting sound waves and receiving echoes reflected from the seabed. Continuous measurements are performed along a predetermined route or grid to generate a water depth distribution map;

[0099] Step 1233, calculating the sound velocity distribution in the water body according to the temperature gradient, the salinity of the water body and the water depth distribution map, specifically includes:

[0100] According to the principle of hydroacoustics, an appropriate sound velocity calculation formula (such as the Mackenzie formula) is selected; the temperature gradient, salinity distribution and water depth data obtained in step 1232 are used as input parameters; the sound velocity value is calculated at each depth point using the selected sound velocity formula and input parameters; the calculated sound velocity values ​​are sorted and interpolated by depth to generate a continuous sound velocity distribution diagram, which helps to intuitively display the change of sound velocity with depth;

[0101] Step 1234, simulating the propagation path of the sound wave underwater according to the sound velocity distribution and the seabed topography data to form a curved sound line model, specifically includes:

[0102] Define the calculation domain of the curved sound line model, including the horizontal range and the depth range; import the seabed topography data obtained in step 1231 and the sound velocity distribution data calculated in step 1233 into the curved sound line model, and set the initial conditions and boundary conditions of the curved sound line model, such as the emission position, frequency and power of the sound source.

[0103] Determine the size of each grid cell and generate a series of fine grid cells in the computational domain. These grid cells can be regular (such as rectangular or cubic) or irregular to adapt to the complex seabed topography.

[0104] In each grid cell, variables such as the pressure and velocity of the sound wave are represented by a bilinear interpolation function. For example, in a two-dimensional rectangular grid cell, a bilinear interpolation function can be selected to represent the pressure distribution of the sound wave. This interpolation function uses the pressure values ​​on the four grid nodes to calculate the pressure at any point in the cell.

[0105] At each mesh node, define the value of the acoustic wave variables, such as pressure, velocity, etc., which will serve as the unknowns in the discrete algebraic equations. For example, in a two-dimensional problem, each mesh node may have a pressure value associated with it, and in a three-dimensional problem, each node may have multiple values ​​corresponding to different acoustic wave variables.

[0106] Within each grid cell, the distribution of the acoustic variable is approximated using a selected interpolation function, which involves combining the interpolation function with the acoustic variable values ​​at the grid nodes. For example, in a two-dimensional rectangular grid cell, the pressure value at any point in the cell can be calculated using a bilinear interpolation function, which calculates the pressure at other points in the cell based on the pressure values ​​at the four grid nodes.

[0107] Using bilinear interpolation functions and acoustic wave variable values ​​at the mesh nodes, the continuous acoustic wave propagation equations (such as the wave equation) are approximated as discrete algebraic equations, which involves replacing the derivatives in the equations with differences or finite difference approximations. For example, in the wave equation, the time and space derivatives can be replaced by finite difference approximations, so that the continuous wave equation is discretized into a set of algebraic equations, where each equation corresponds to a mesh node. The discrete equations of all mesh elements are combined into a global set of algebraic equations that describes the propagation behavior of the acoustic waves in the entire computational domain. This global set of equations is a sparse matrix because each mesh node is directly connected only to the nodes around it.

[0108] According to the actual situation of the problem, boundary conditions are imposed on the corresponding parts of the equations, such as the emission conditions of the sound source, the reflection or absorption conditions of the seabed and the sea surface, etc.; numerical solution methods (such as direct method or iterative method) are used to solve the global algebraic equations; when applied specifically, it can include:

[0109] Define spatial grid points and ,in, and is the grid index; defines the time step ,in, is the time index. The formula of the continuous wave equation is:

[0110] ;

[0111] in, is the wave function, is the speed of sound waves in the medium, is the sound source function;

[0112] The derivative is approximated using finite differences, where the time derivative is approximated using the difference formula:

[0113] ;

[0114] The difference approximation formula of the spatial derivative is:

[0115] ;

[0116] Apply the above discretized equation to each grid node and each time step ,get:

[0117] ;

[0118] This equation is for each grid point and time step A linear equation is provided.

[0119] in, Indicates at time and grid points The value of the acoustic wave function; represents the time step; and represents the spatial step length; Indicates at time and location The sound source function value represents the external force; and is a spatial coordinate representing the position in the medium where the sound wave propagates. In two-dimensional space, and Specify horizontal and vertical positions respectively; is a time variable, representing the state at a specific moment; is the symbol for partial derivative, which is used to express the local rate of change of a function with respect to one or more of its variables; Represents the position ( , ) and time The wave field quantities at the location, such as pressure, displacement, etc. Indicates that at the same spatial position ( , ), but at an earlier time The wave field quantity (one time step ago); Indicates that at the same spatial position ( , ), but at a later time The wave field quantity (after one time step); Indicates a fixed spatial position On, in time (i.e. current time The value of the wave field at the next time step); In the same spatial position On, in time (i.e. current time The value of the wave field at the previous time step); is the wave field at the grid point In time (Right now moment); Represents the time index, and Represents a spatial index; is the wave field at the grid point In time (Right now moment); is the wave field at the grid point (Right now direction) at time The value of is the wave field at the grid point ( , )(Right now The adjacent point on the left side of the direction) at time The value of is the wave field at the grid point (Right now The neighboring point above the direction) at time The value of is the wave field at the grid point (Right now direction) at time The value of .

[0120] At the beginning of the simulation, the location and initial waveform of the sound source can be specified by setting The value is realized, for example, using a Gaussian pulse or a sine wave as the sound source signal.

[0121] Define the spatial grid and time step of the entire computational domain. The spatial grid can be uniform or non-uniform as required, and the time step should be small enough to meet stability conditions (such as CFL conditions).

[0122] At each time step , according to the previous time step and the current time step The solution to calculate the next time step The solution is calculated at each grid point using the finite difference equation ( , ) ,in:

[0123] ;

[0124] Monitoring TRON When the difference between several consecutive time steps is less than a certain threshold, the numerical solution is considered stable and the iteration can be stopped; output wave field data At each time step for subsequent analysis or visualization, the propagation process of sound waves can be visualized using software tools such as Matplotlib (Python); by calculating the energy or amplitude of sound waves at different time and space positions, the energy attenuation of sound waves can be analyzed. Energy can be obtained by To approximate, it represents the wave field energy density.

[0125] In an embodiment of the present invention, by obtaining detailed seabed topography data (step 1231), including information such as seabed undulations, slopes, and sediment types, the model can more accurately reflect the complexity and diversity of the actual underwater environment. In step 1232, by analyzing the water temperature data at different depths underwater, calculating the temperature difference and temperature gradient, and examining the spatial distribution and variation of the salinity of the water body, as well as the water depth distribution, the model can comprehensively consider the impact of these key environmental parameters on the propagation of sound waves. This comprehensive analysis helps to improve the accuracy and reliability of model predictions. In step 1233, the sound velocity distribution in the water body is calculated based on the temperature gradient, salinity, and water depth distribution map, providing a key input for the simulation of the sound wave propagation path. Step 1234 uses the finite element method, combined with the sound velocity distribution and seabed topography data, to simulate the propagation path of sound waves underwater, which can efficiently and accurately simulate the physical process in a complex environment.

[0126] In a preferred embodiment of the present invention, the above step 13, calculating the position coordinates of the unknown node according to the distance data between nodes, includes:

[0127] Step 131, obtaining the distance data between all known position nodes and unknown nodes, specifically includes: creating an empty data set for storing the distance data between all known position nodes and unknown nodes; for each node with a known position in the network (the coordinates of these nodes are predetermined or measured), performing the following operations; using appropriate ranging technology (such as RSSI based on signal strength, TOA or TDOA based on time difference, RTT based on signal round-trip time, etc.) to measure the distance between the current known position node and the unknown node, ensuring that possible sources of error are considered in the measurement process, such as signal attenuation, multipath effect, non-line-of-sight (NLOS) conditions, etc.; storing the measured distance data together with the corresponding known position node and unknown node identifiers in the initialized distance data set, and ending this step when all known position nodes have been traversed and the distances have been measured.

[0128] Step 132, select at least four non-coplanar nodes from the nodes with known positions as reference nodes, specifically including: creating an empty set for storing the selected reference nodes; including at least four nodes with known positions in the distance data set, because at least four non-coplanar points are required in three-dimensional space to determine the position of an unknown point; randomly selecting three nodes with known positions from the distance data set or selecting them according to a certain strategy (such as the best signal quality, the closest distance, etc.) as initial reference nodes; verifying whether the three initial reference nodes are non-coplanar in the three-dimensional space, and if so, reselecting; selecting at least one additional node from the remaining nodes with known positions to ensure that this node is not coplanar with the previous three nodes, and verifying whether they are non-coplanar by calculating the volume of the tetrahedron formed by the four nodes (if the volume is non-zero, they are not coplanar), adding the selected non-coplanar nodes to the reference node set, and ending this step when the reference node set contains at least four non-coplanar nodes.

[0129] Step 133, for each reference node, a distance equation is listed according to the distance data between the known position node and the unknown node, specifically including: creating an empty set or list for storing the distance equation corresponding to each reference node; for each reference node selected in step 132, performing the following operations; retrieving the distance value between the current reference node and the unknown node from the distance data set obtained in step 131, assuming that the coordinates of the unknown node are ( x , y , z ), the known coordinates of the current reference node are ( x i , y i , z i ),in, i Represents the index of the reference node. According to the above coordinates, calculate the distance in three-dimensional space. Add the listed distance equations to the initialized equation set and associate them with the corresponding reference nodes. When all reference nodes have been traversed and each node corresponds to a distance equation, end this step.

[0130] Step 134, setting an initial estimate for the coordinates of the unknown node, expanding the nonlinear equations by Taylor series near the initial estimate, solving the linearized equations by Gaussian elimination method, and obtaining an approximate solution for the coordinates of the unknown node, specifically including:

[0131] For each distance equation of the reference node listed in step 133, perform the following.

[0132] Add the distance equation of the current reference node to the system of equations, and the equation form is:

[0133] ;

[0134] in, are the unknown node coordinates, are the reference node coordinates, It is to measure the distance. When all distance equations are added to the system of equations, the traversal ends.

[0135] Assume that the initial estimated coordinates of the unknown node are ; For each equation in the system, compute its first-order derivative (that is, the element of the Jacobian matrix) at the initial guess.

[0136] The derivative form is: , , ,in, It is Equations.

[0137] Using the calculated first-order derivatives and initial guesses, construct the linearized system of equations Ax = b , A is a matrix of first-order derivatives, each row corresponds to the first-order derivative of an equation, x is a small change in the coordinates of the unknown node, in the form of △ x ,△ y ,△ z ; b is a vector consisting of the constant term on the right side of the equation and the initial estimate, that is:

[0138] .

[0139] The linear equations Ax = b The augmented matrix Perform Gaussian elimination; through row transformation, A Transform into upper triangular matrix U and update vector b ; For each column, starting from the second row, divide the elements of the current row by the elements of the corresponding column of the previous row to obtain the multiplier, and use the multiplier to update all the elements and b The corresponding elements in , starting from the last row, are solved row by row x The value of (△ x ,△ y ,△ z ), for each row, use the solved x The value of is used to calculate the unknown quantity of the current row;

[0140] The solved x (△ x ,△ y,△ z ) is added to the initial estimate On the above, we get the updated unknown node coordinate estimate ( , , ); Updated unknown node coordinate estimates ( , , ) is of the form:

[0141] .

[0142] Step 135, using the approximate solution as a new initial estimate, repeating the linearization and solution steps, performing iterative optimization, gradually approaching the true unknown node coordinates through multiple iterations, and obtaining the final unknown node position coordinates after the iterations are completed, specifically including:

[0143] Set the iteration counter iter_count to 0; define the maximum number of iterations max_iter (for example, 10 times); set the convergence threshold convergence_threshold (a small positive number, such as 1×10 -6 , used to determine whether the coordinate change is small enough); use the approximate solution obtained in step 134 as the initial estimate initial_estimate; enter the iterative loop and repeat the following steps until the termination condition is met. Use the current initial_estimate as the center point of the Taylor series expansion, perform Taylor series expansion on each distance equation, retain the first-order terms, form a linearized system of equations, construct the Jacobian matrix A composed of the first-order derivatives and the constant term vector b; apply Gaussian elimination to solve the linear system of equations Ax = b , and obtain the small change delta_x of the unknown node coordinates.

[0144] Add the small change delta_x of the unknown node coordinates to the current initial_estimate to obtain a new coordinate estimate, update initial_estimate to the new coordinate estimate, and calculate the change between the new and old coordinate estimates (for example, the Euclidean distance); if the change is less than the convergence threshold convergence_threshold, it is determined to have converged and the iteration loop is exited; increase iter_count by 1. If iter_count reaches the maximum number of iterations max_iter, the iteration is forced to terminate and the iteration loop is exited. After the iteration loop ends, the final initial_estimate is used as the position coordinate of the unknown node.

[0145] In an embodiment of the present invention, by selecting at least four non-coplanar known position nodes as references and constructing a nonlinear set of equations using distance data, the method can provide high-precision positioning results. The iterative optimization process further improves the positioning accuracy and reduces the error by gradually approaching the true coordinates of the unknown nodes. The method does not rely on a specific network layout or node configuration, and as long as there are a sufficient number (at least four) of known position nodes with reasonable distribution, the unknown node can be effectively located. Due to the use of iterative optimization and linearization techniques (such as Taylor series expansion and Gaussian elimination), the method has a low dependence on the initial estimate and can resist noise and errors in distance measurement to a certain extent, thereby providing a robust positioning result. By reasonably selecting reference nodes and effectively managing the iterative process, the method can achieve efficient positioning in resource-constrained environments (such as low-power wireless sensor networks).

[0146] In a preferred embodiment of the present invention, the above step 14, performing three-dimensional positioning of nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain positioning results, includes:

[0147] Step 141, based on the position coordinate data of the unknown nodes, establish a three-dimensional coordinate system for representing the spatial structure of the submarine wireless sensor network; use the nodes with known positions as reference points and mark them in the three-dimensional coordinate system, specifically including: obtaining the position coordinate data of all unknown nodes, ensuring that the data format is unified, and performing necessary preprocessing on the data, such as removing outliers and smoothing; selecting an origin as the starting point of the three-dimensional coordinate system according to the deployment characteristics and requirements of the submarine wireless sensor network; determining the directions of the three orthogonal axes, the direction of the geographic North Pole can be selected as the positive direction of the Z axis (indicating the depth direction), and the X axis and the Y axis are perpendicular to each other in the ground plane, respectively representing the direction of change of longitude and latitude or the horizontal and vertical directions of the network; using mathematical modeling software Software or programming environment (such as MATLAB, Python, etc.) is used to establish a three-dimensional coordinate system model according to the determined origin and axis. In the collected data, nodes with known positions (such as anchor nodes or pre-calibrated nodes) are identified, and the coordinate data of these known nodes are input into the three-dimensional coordinate system, and marked in the model. The mark can be a point or an icon for easy distinction and identification. The accuracy of the three-dimensional coordinate system is verified by comparing the actual position of the known nodes with the marked position in the model. If a large deviation or inconsistency is found, the parameters of the coordinate system (such as the origin position, axis, etc.) need to be adjusted until satisfactory accuracy is achieved. After completing the establishment of the three-dimensional coordinate system and marking of the known nodes, all relevant parameters and settings are recorded.

[0148] Step 142, mapping the position coordinate data of the unknown node into a three-dimensional coordinate system, where each unknown node's coordinate has a corresponding position in the three-dimensional space, specifically including: if the collected coordinate data is not directly in the three-dimensional coordinate system, coordinate conversion is required. For example, if the original data is based on spherical coordinates (longitude, latitude, depth), it needs to be converted into coordinates (x, y, z) in a three-dimensional rectangular coordinate system; input the converted coordinate data into the previously established three-dimensional coordinate system to ensure that each unknown node has an accurate position in the system;

[0149] Step 143, constructing the topology of the entire wireless sensor network in three-dimensional space according to the positions of the known nodes and the unknown nodes, including the relative position relationship between the known nodes and the unknown nodes, specifically including: determining which nodes are directly connected based on information such as the communication range or signal strength of the nodes; connecting the directly communicating nodes using line segments or arcs in the three-dimensional coordinate system according to the determined connection relationship, thereby constructing a topology map of the entire network; analyzing the constructed topology structure to check whether there are isolated nodes, potential communication bottlenecks or critical paths, and optimizing the network topology according to the analysis results, such as adjusting the node positions or adding relay nodes, etc., to improve the connectivity and stability of the network;

[0150] Step 144, the topological structure of the wireless sensor network, the positions of known nodes and unknown nodes are displayed in a visualized form to obtain the positioning result, which specifically includes: selecting a suitable 3D graphics software or network visualization platform, such as MATLAB, Unity3D, Blender, etc.; importing the node position and topological structure data in the 3D coordinate system into the selected visualization tool; creating a blank 3D scene in the visualization tool; setting appropriate scene parameters, such as background color, lighting effect, observation angle, etc., to enhance the visualization effect; drawing a graphical representation of each node in the 3D scene according to the coordinate information of the node , which can be a sphere, cube or other shapes; use different colors or marks to distinguish known nodes from unknown nodes for easy identification by users; draw connection lines or arrows according to the connection relationship between nodes to indicate the communication link or relative position relationship between nodes; add interactive functions such as zooming, rotating, and translating to the three-dimensional scene, so that users can view the network topology from different angles and scales; by observing the node distribution and connection status in the three-dimensional scene, users can intuitively understand the topological structure and node position relationship of the wireless sensor network, analyze the relative position between known nodes and unknown nodes, and evaluate the positioning accuracy and reliability of unknown nodes.

[0151] In an embodiment of the present invention, by establishing a three-dimensional coordinate system, the spatial structure of the submarine wireless sensor network can be intuitively displayed, so that the network layout is clear at a glance. Using nodes with known locations as reference points to locate unknown nodes in three-dimensional space can improve the accuracy of positioning. Constructing the topological structure of the entire wireless sensor network helps to understand the mutual connection and influence between nodes, which is of great significance for the optimization and maintenance of the network. At the same time, a clear topological structure also helps to predict network failure points and improve the stability and reliability of the network. By displaying the network topology and node locations in a visual form, data analysis can be performed more conveniently. In the event of an emergency, such as a submarine equipment failure or an environmental pollution incident, quickly understanding the specific location of the sensor nodes and the network layout can speed up the response and improve processing efficiency.

[0152] The implementation process of step 15 includes monitoring the communication quality of the node and the stability of the ranging data, and triggering the relocation process under certain conditions. The following are the specific implementation steps:

[0153] Send test signals or heartbeat packets regularly to check whether the communication link between nodes is normal, record key indicators such as signal strength and packet loss rate of each node to evaluate the communication quality; if the communication quality is found to be degraded (such as the packet loss rate exceeds the preset threshold), mark the link as unstable; collect and analyze the ranging data between nodes, such as signal propagation time, signal strength, etc.; use statistical methods (such as standard deviation, variance) to evaluate the stability of ranging data; if there are large fluctuations or outliers in the ranging data, it may indicate that the network environment has changed or the node position has moved; when a significant change in the network status is detected (such as multiple node communication failures, ranging data abnormalities, etc.), the repositioning process is triggered immediately. In addition, a predetermined repositioning cycle (such as hourly, daily or weekly) can be set. Regardless of whether the network status changes, the node is repositioned on time to ensure the real-time and accuracy of the location information.

[0154] The implementation process of step 16 involves updating the location information of the node and broadcasting it to the entire network so that other nodes can obtain the latest location information. The specific steps are as follows:

[0155] After the relocation process is completed, each node will obtain the updated location coordinates, the node will store the new location information in the local memory and prepare for broadcasting; use the broadcast mechanism in the wireless sensor network to send the updated location information to all other nodes in the network; during the broadcast process, multi-hop communication can be used to ensure that the information can cover the entire network. After receiving the broadcast information, other nodes will update their locally stored adjacent node location information tables to maintain the real-time and consistency of the data; after the broadcast is completed, the node can send a confirmation message to verify whether other nodes have successfully received the updated location information. If a node has not received the update or there is data inconsistency, it can be reissued or synchronized to ensure that the location information of all nodes in the network remains synchronized and accurate.

[0156] like Figure 2 As shown, an embodiment of the present invention further provides a three-dimensional positioning system 20 for a submarine wireless sensor network, comprising:

[0157] A layout module 21 is used to deploy wireless sensor nodes with ranging function in the seabed area according to a predetermined layout strategy, and the wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes;

[0158] An acquisition module 22 is used to acquire underwater environmental parameters and model the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain distance data between nodes. The underwater environmental parameters include medium density, temperature and salinity.

[0159] A calculation module 23, used to calculate the position coordinates of unknown nodes according to the distance data between nodes;

[0160] The positioning module 24 is used to perform three-dimensional positioning of the nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain a positioning result, wherein the positioning result includes determining the specific coordinate position of each node in the three-dimensional space;

[0161] The monitoring module 25 is used to monitor the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; when a change in the network status is detected or a predetermined repositioning cycle is reached, the repositioning process is triggered; the location information of the node is updated and broadcasted to the entire network so that the remaining nodes in the network can obtain the latest location information.

[0162] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0163] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0164] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0165] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A three-dimensional positioning method for a submarine wireless sensor network, characterized in that: The method comprises: Wireless sensor nodes with ranging function are deployed in the seabed area according to a predetermined layout strategy. The wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes. Acquire underwater environmental parameters, and model the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain the distance data between nodes. The underwater environmental parameters include medium density, temperature and salinity. Calculate the position coordinates of unknown nodes based on the distance data between nodes; According to the position coordinates of the unknown nodes, the nodes in the submarine wireless sensor network are three-dimensionally positioned to obtain positioning results, and the positioning results include determining the specific coordinate position of each node in the three-dimensional space; Monitor the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; trigger the repositioning process when a change in the network status is detected or a predetermined repositioning cycle is reached; Update the node's location information and broadcast it to the entire network so that the remaining nodes in the network can obtain the latest location information; Wireless sensor nodes with ranging functions are deployed in the seabed area according to a predetermined layout strategy, including: Determine the scope of the seafloor monitoring area, number of nodes, communications and ranging requirements; Randomly generate node positions that meet the basic layout requirements as the initial layout, and define an energy function to evaluate the pros and cons of the current layout solution ; For the current temperature ,conduct iterations, in each iteration, a node is randomly selected and its position is changed to generate a new layout solution; Calculate the energy of the new layout solution and the energy of the original layout plan ,if , the new layout is accepted; if , then with probability Accept the new layout, where Represents the base of natural logarithm; updates the current temperature for ,in, is the temperature drop rate, if Below the end temperature , then stop the annealing process to obtain the final layout solution; According to the final layout plan, wireless sensor nodes with ranging function are actually deployed in the seabed area; Energy function The calculation formula is: ； in, Indicates the area of ​​the monitoring area; Indicates the area of ​​the covered region; Indicates the total number of nodes; It is i The communication success rate of each node; Indicates the minimum distance between nodes; Represents the distance between nodes; i Indicates the index of the node currently being evaluated, ranging from 1 to Every i Represents a specific sensor node; Representation and Node i The index of the other node to compare, ranging from arrive ; is the weight coefficient; Underwater environment parameters are obtained, and the sound wave propagation path between underwater nodes is modeled according to the underwater environment parameters and the preset curved sound line model to obtain the distance data between nodes, including: Determine the underwater environmental parameters to be measured based on the deployment area of ​​the submarine wireless sensor network; Conduct field measurements at key locations within the deployment area to obtain underwater environmental parameter data representative of the entire deployment area; According to the characteristics of the underwater environment, a curved sound line model is constructed; According to the curved sound line model and underwater environmental parameter data, the expected propagation path of the sound wave from the sending node to the receiving node is calculated; Based on the expected propagation path and the speed of sound waves underwater, calculate the time required for the sound wave to propagate from the sending node to the receiving node; The time required for the sound wave to propagate from the sending node to the receiving node is multiplied by the propagation speed of the sound wave to obtain the distance data between the nodes; According to the characteristics of the underwater environment, a curved sound line model is constructed, including: Obtain seafloor topography data, including seafloor undulations, slope, and seafloor sediment type information; Based on the water temperature data at different depths underwater, calculate the temperature difference between different depths to determine the temperature gradient; analyze the salinity of the water body, including the spatial distribution and variation of salinity; use an echo sounder to measure the water depth to obtain a water depth distribution map; Calculate the sound velocity distribution in the water column based on the temperature gradient, salinity of the water column, and the water depth distribution map; According to the sound speed distribution and seabed topography data, the propagation path of sound waves underwater is simulated to form a curved sound line model; According to the distance data between nodes, the position coordinates of unknown nodes are calculated, including: Get the distance data between all known location nodes and unknown nodes; From the nodes with known positions, select at least four non-coplanar nodes as reference nodes; For each reference node, a distance equation is listed based on the distance data between the known position node and the unknown node; Set an initial estimate for the coordinates of the unknown nodes. Near the initial estimate, expand the nonlinear equations through Taylor series, and use Gaussian elimination to solve the linearized equations to obtain an approximate solution for the coordinates of the unknown nodes. The approximate solution is used as a new initial estimate, and the linearization and solution steps are repeated to perform iterative optimization. Through multiple iterations, the actual unknown node coordinates are gradually approached. After the iteration is completed, the final unknown node position coordinates are obtained. According to the position coordinates of the unknown nodes, the nodes in the submarine wireless sensor network are three-dimensionally positioned to obtain positioning results, including: According to the position coordinate data of unknown nodes, a three-dimensional coordinate system is established to represent the spatial structure of the submarine wireless sensor network; nodes with known positions are used as reference points and marked in the three-dimensional coordinate system; Map the position coordinate data of the unknown nodes into a three-dimensional coordinate system, where each unknown node has a corresponding position in the three-dimensional space; According to the positions of known nodes and unknown nodes, the topological structure of the entire wireless sensor network is constructed in three-dimensional space, including the relative position relationship between known nodes and unknown nodes; The topological structure of the wireless sensor network and the locations of known nodes and unknown nodes are displayed in a visual form to obtain the positioning results.

2. A three-dimensional positioning system for a submarine wireless sensor network, characterized in that: Applied in the method as claimed in claim 1, comprising: A layout module is used to deploy wireless sensor nodes with ranging function in the seabed area according to a predetermined layout strategy. The wireless sensor nodes are used to send and receive acoustic wave signals to measure the distance between nodes. An acquisition module is used to acquire underwater environmental parameters and model the sound wave propagation path between underwater nodes according to the underwater environmental parameters and a preset curved sound line model to obtain the distance data between nodes. The underwater environmental parameters include medium density, temperature and salinity. A calculation module is used to calculate the position coordinates of unknown nodes based on the distance data between nodes; A positioning module is used to perform three-dimensional positioning of nodes in the submarine wireless sensor network according to the position coordinates of the unknown nodes to obtain positioning results, wherein the positioning results include determining the specific coordinate position of each node in the three-dimensional space; The monitoring module is used to monitor the status of the submarine wireless sensor network, including the communication quality of the nodes and the stability of the ranging data; when a change in the network status is detected or a predetermined repositioning cycle is reached, the repositioning process is triggered; the node location information is updated and broadcast to the entire network so that the remaining nodes in the network can obtain the latest location information.

3. A computing device, characterized in that: include: one or more processors; A storage device, used for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 1 when executed by a processor.

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