Underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion

Abstract

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

Description

A method for correcting node positioning errors in underwater acoustic sensing networks combined with sound velocity inversion Technical Field

[0001] The present invention relates to the technical field of sound velocity inversion, and more particularly to a method for correcting positioning errors of underwater acoustic sensor network nodes combined with sound velocity inversion. Background Art

[0002] In underwater environments, underwater acoustic sensing networks are a key communication and monitoring technology, widely used in fields such as ocean exploration, resource monitoring, and underwater navigation. However, underwater sound propagation is significantly affected by the water environment, including changes in water temperature, salinity, depth, and water flow. These factors will cause changes in sound speed, which in turn affects the positioning accuracy of underwater acoustic sensing nodes. Due to the dynamic characteristics of the underwater environment, positioning errors may gradually accumulate as the underwater environment changes. However, the system lacks an effective error assessment mechanism, resulting in serious errors that are difficult to detect and correct in a timely manner, easily leading to large positioning errors and causing system performance to degrade.

[0003] In order to solve the above-mentioned defects, a technical solution is now provided.

[0004] Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for correcting positioning errors of underwater acoustic sensor network nodes combined with sound velocity inversion to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion specifically comprises the following steps:

[0008] S1: Deploy fixed-position sound source points and underwater acoustic sensor nodes. The underwater acoustic sensor nodes collect sound velocity data emitted by the sound source points. Based on the sound velocity data at different underwater acoustic sensor nodes, a sound velocity field model is constructed using a sound velocity field inversion algorithm.

[0009] S2: Collecting real-time sound velocity data at different underwater acoustic sensing nodes to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes;

[0010] S3: Comprehensively analyze the sound velocity characteristic information and water characteristic information to evaluate the accuracy of the sound velocity field model in locating underwater acoustic sensor nodes;

[0011] S4: Compare the positioning accuracy results of the quantified underwater acoustic sensor nodes with the pre-set threshold and issue an early warning when a large error occurs in the positioning accuracy.

[0012] In a preferred embodiment, the sound velocity characteristic information at different underwater acoustic sensing nodes and the water characteristic information at different underwater acoustic sensing nodes include:

[0013] Real-time sound velocity data at different underwater acoustic sensing nodes are collected and analyzed to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes. The sound velocity characteristic information is represented by the sound velocity difference fluctuation variation coefficient and the noise concealment hazard coefficient, and the water characteristic information is represented by the total water body dynamic variation coefficient.

[0014] In a preferred embodiment, the sound speed difference fluctuation variation coefficient includes:

[0015] The logic for obtaining the sound speed difference fluctuation coefficient is as follows: the sound speed of the sound wave at each underwater acoustic sensor node is obtained by the sound wave emitted in real time by the sound source point, and the sound speed of the sound wave at each underwater acoustic sensor node is marked as: SQ n , where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of each underwater acoustic sensor node;

[0016] Obtain the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node, and mark the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node as: YC n ;

[0017] Obtain the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node, and mark the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node as: CY n , among which, CY n =|SQ n -YC n |;

[0018] Calculate the mean and standard deviation of the sound speed differences and label them as: CY avg and CY std ,in,

[0019] Calculate the coefficient of variation of the sound speed difference using the formula: Where BY is the coefficient of variation of the sound speed difference;

[0020] Calculate the coefficient of variation of the sound velocity difference fluctuation, the calculation formula is: QD by =CY avg ×ln(BY+1); where QD by is the coefficient of variation of the sound speed difference fluctuation.

[0021] In a preferred embodiment, the noise concealment risk factor includes:

[0022] The logic for obtaining the noise concealment risk coefficient is as follows: obtain the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data, and mark the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data as: LSZS n ;

[0023] Obtain the real-time background noise of the water body at each underwater acoustic sensor node, and mark the real-time background noise of the water body at each underwater acoustic sensor node as: SSZS n ;

[0024] Obtain the background noise deviation of the water body at each underwater acoustic sensor node, and mark the background noise deviation of the water body at each underwater acoustic sensor node as: PC n ,in,

[0025] Set the background noise threshold of the water body and mark it as: ZS yz ;

[0026] The real-time background noise of the water body at each underwater acoustic sensor node is compared with the background noise threshold, and the underwater acoustic sensor nodes with a noise level greater than the background noise threshold are obtained. The underwater acoustic sensor nodes with a noise level greater than the background noise threshold are re-labeled as: CX i , where i = 1, 2, 3, ..., I, i is the number of the underwater acoustic sensor node that is greater than the background noise threshold;

[0027] Calculate the noise concealment risk factor using the following formula: Among them, ZS wx is the noise concealment risk factor.

[0028] In a preferred embodiment, the total water body dynamic variation coefficient includes:

[0029] The acquisition logic of the total water body dynamic change coefficient is as follows: setting a monitoring interval, determining the flow velocity of each hydroacoustic sensor node in the monitoring interval through each hydroacoustic sensor node, and preprocessing the flow velocity data of each hydroacoustic sensor node in the monitoring interval, including removing outliers, filtering, and synchronizing timestamps;

[0030] Calculate the water dynamic change coefficient of a single underwater acoustic sensor node. The expression is: Among them, ST n is the water dynamic change coefficient of the nth underwater acoustic sensor node, t1~t2 is the time range of the monitoring interval, A is the amplitude, which indicates the amplitude of the flow velocity change, B is the frequency, which indicates the speed of the periodic change, C is the phase, LS avgis the average flow velocity of the hydroacoustic sensor node in the monitoring interval;

[0031] Calculate the dynamic change coefficient of the total water body using the following formula: Among them, ZST is the dynamic change coefficient of the total water body.

[0032] In a preferred embodiment, the sound velocity characteristic information and the water characteristic information are comprehensively analyzed, including:

[0033] By performing weighted analysis on the sound velocity difference fluctuation coefficient, noise concealment risk coefficient, and total water body dynamic change coefficient, an error assessment model is constructed to generate the error assessment coefficient. The expression of the error assessment coefficient is: Among them, pg yj is the error assessment coefficient, α is the proportional coefficient of the sound speed difference fluctuation coefficient, β is the proportional coefficient of the noise concealment hazard coefficient, and γ is the proportional coefficient of the total water body dynamic change coefficient. α, β, and γ are all greater than 0.

[0034] In a preferred embodiment, generating an error signal when a large error occurs in positioning accuracy includes:

[0035] Set an error assessment coefficient threshold and compare the error assessment coefficient with the error assessment coefficient threshold. If the error assessment coefficient is greater than the error assessment coefficient threshold, an error signal is generated to notify the staff to immediately use real-time sound speed data to rebuild the sound speed field model, or use real-time sound speed data to correct the error of the current sound speed field model. If the error assessment coefficient is less than the error assessment coefficient threshold, no error signal is generated.

[0036] The technical effects and advantages of the present invention are as follows:

[0037] The present invention deploys fixed-position sound source points and underwater acoustic sensor nodes, uses the sound velocity field inversion algorithm to construct a sound velocity field model based on the sound velocity data collected by the underwater acoustic sensor nodes, compares the sound velocity data predicted by the sound velocity field model with the actual sound velocity data, and constructs an error evaluation model based on the amplitude of water body changes, thereby helping to improve the accuracy of underwater sound wave propagation and the accuracy of underwater acoustic sensor network node positioning, and facilitating timely adjustment of the sound velocity field model to avoid errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0039] FIG1 is a flow chart of a method for correcting positioning errors of underwater acoustic sensor network nodes combined with sound velocity inversion according to the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1

[0042] FIG1 shows a flow chart of a method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to the present invention, which specifically includes the following steps:

[0043] S1: Deploy fixed-position sound source points and underwater acoustic sensor nodes. The underwater acoustic sensor nodes collect sound velocity data emitted by the sound source points. Based on the sound velocity data at different underwater acoustic sensor nodes, a sound velocity field model is constructed using a sound velocity field inversion algorithm.

[0044] S2: Collecting real-time sound velocity data at different underwater acoustic sensing nodes to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes;

[0045] S3: Comprehensively analyze the sound velocity characteristic information and water characteristic information to evaluate the accuracy of the sound velocity field model in locating underwater acoustic sensor nodes;

[0046] S4: Compare the positioning accuracy results of the quantified underwater acoustic sensor nodes with the pre-set threshold and issue an early warning when a large error occurs in the positioning accuracy.

[0047] In step 1, deploying fixed-position sound source points and underwater acoustic sensor nodes can effectively improve the performance and accuracy of the underwater acoustic sensor network. The deployment location is selected in an area with relatively stable water characteristics to reduce the impact of environmental changes on sound propagation and ensure that the deployment location can cover the monitoring area of ​​interest. The distance between the sound source point and the sensor node should be reasonably planned according to the sound propagation characteristics, and the location of the sound source point should ensure the emission frequency and transmission stability of the sound source point.

[0048] The sound velocity field model can provide more accurate sound velocity data for underwater acoustic sensor node positioning. Since the speed of sound waves propagating in water is affected by the dynamic characteristics of the water body, using an accurate sound velocity field model can reduce the error in the node positioning process. Constructing the sound velocity field model using the sound velocity field inversion algorithm includes the following steps:

[0049] By transmitting an acoustic signal and recording the time it takes for it to reach different receiving nodes, data on the propagation time of the acoustic signal is obtained, and environmental parameters during the propagation of the acoustic signal, including water temperature, salinity, depth, etc., are recorded;

[0050] Establishing a preliminary sound velocity profile based on existing hydrological data or empirical formulas, wherein the preliminary sound velocity profile is established by combining existing hydrological data or empirical sound velocity formulas, such as the Munk formula, the Chen-Millero formula, etc.;

[0051] Select a sound velocity field inversion algorithm, such as the empirical orthogonal function method or the matched field inversion method, to optimize the sound velocity value. Based on the known sound velocity profile data, establish a sound velocity field model, which is usually a multidimensional model that reflects the sound velocity distribution at different depths or positions in the area. Use a gridding method to divide the sound velocity field into multiple small units, and the sound velocity in each unit remains unchanged. Compare the collected sound velocity data with the preliminary sound velocity profile model, and use the inversion algorithm to optimize the sound velocity field model. According to the changes in the sound velocity data, it iteratively adjust the model to minimize the difference between the predicted sound velocity and the actual sound velocity.

[0052] Verify the accuracy of the inverted velocity field model using different data sets or independent measurement data, analyze the differences between the model predictions and the actual measurements, and identify potential sources of error;

[0053] The inverted sound velocity field model is used to correct the positioning error of nodes in the underwater acoustic sensing network and improve the positioning accuracy. According to the sound velocity field model, the changes in the water body are monitored in real time and the model is updated in time to adapt to environmental changes.

[0054] It should be noted that the sound velocity field inversion algorithm is a method of inferring the sound velocity distribution in the entire water body of the area through the sound velocity data of several known locations;

[0055] The inverted sound velocity field model is used to generate static sound velocity data. It is constructed based on historical measurement data or existing hydrological data. In the absence of new actual measurements, the inverted sound velocity field model can be regarded as static for a certain period of time and is suitable for analysis and prediction in the current waters.

[0056] In step 2, real-time sound velocity data at different underwater acoustic sensing nodes are collected and analyzed to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes. The sound velocity characteristic information is represented by the sound velocity difference fluctuation variation coefficient and the noise concealment hazard coefficient, and the water characteristic information is represented by the total water body dynamic variation coefficient.

[0057] By comparing the predicted values ​​of the sound velocity field model obtained by inversion with the actual measured values, the accuracy of the sound velocity field model is verified and possible deviations or errors in the model are identified. The fluctuation of the difference between the predicted values ​​and the actual measured values ​​at each underwater acoustic sensing node can fully reflect important information such as the accuracy of the sound velocity field model, the stability of the water environment, the performance of the sensor, the impact of noise, and the characteristics of sound wave propagation.

[0058] The logic for obtaining the sound speed difference fluctuation coefficient is as follows: the sound speed of the sound wave at each underwater acoustic sensor node is obtained by the sound wave emitted in real time by the sound source point, and the sound speed of the sound wave at each underwater acoustic sensor node is marked as: SQ n , where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of each underwater acoustic sensor node;

[0059] It should be noted that the sound velocity at the underwater acoustic sensing node is usually obtained by real-time monitoring of the sound wave signal, and the sound velocity emitted by the sound source point in real time is obtained by the root mean square value of the sound wave signal.

[0060] Obtain the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node, and mark the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node as: YC n ;

[0061] Obtain the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node, and mark the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node as: CY n , among which, CY n =|SQ n -YC n |;

[0062] Calculate the mean and standard deviation of the sound speed differences and label them as: CY avg and CY std ,in,

[0063] Calculate the coefficient of variation of the sound speed difference using the formula: Where BY is the coefficient of variation of the sound speed difference;

[0064] Calculate the coefficient of variation of the sound velocity difference fluctuation, the calculation formula is: QD by =CY avg ×ln(BY+1); where QD by is the coefficient of variation of the sound speed difference fluctuation.

[0065] It can be seen from the formula that the larger the coefficient of variation of the sound speed difference fluctuation, the greater the difference between the predicted value of the sound wave speed at each underwater acoustic sensor node by the sound speed field model and the actual value may be, and the greater the volatility of the difference at each underwater acoustic sensor node may be, indicating the instability of the water environment or the inconsistency of the sensor performance, and it may be necessary to correct the positioning error of the underwater acoustic sensor network node.

[0066] Noise changes in water can affect the prediction of the sound velocity field model. The specific impacts include the following aspects:

[0067] Masking valid signals: Noise may mask the acoustic signal, causing the model to be unable to correctly capture the characteristics of acoustic wave propagation, thus affecting prediction accuracy;

[0068] Environmental changes: The presence of noise may reflect dynamic changes in the water environment, such as water flow, bubbles, and ship activity. These factors can also affect the distribution of sound speed, resulting in the need for frequent adjustments to model parameters.

[0069] Calibration difficulties: In an environment with significant noise variations, model calibration and validation become more complicated, which may lead to inaccurate parameter estimates.

[0070] The logic for obtaining the noise concealment risk coefficient is as follows: obtain the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data, and mark the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data as: LSZS n ;

[0071] Obtain the real-time background noise of the water body at each underwater acoustic sensor node, and mark the real-time background noise of the water body at each underwater acoustic sensor node as: SSZS n ;

[0072] Obtain the background noise deviation of the water body at each underwater acoustic sensor node, and mark the background noise deviation of the water body at each underwater acoustic sensor node as: PC n ,in,

[0073] Set the background noise threshold of the water body and mark it as: ZS yz ;

[0074] It should be noted that background noise may absorb or scatter sound wave energy, weakening the received sound velocity, thereby affecting the accurate measurement of the sound velocity. If the noise of the water body exceeds the background noise threshold, it may have a significant impact on the sound velocity data monitored by each underwater acoustic sensor node. The background noise threshold is set by professional staff. Changes in background noise often reflect the dynamic characteristics of the water environment, such as flow rate changes, bubble movement, etc. These changes may cause the sound velocity field model to need to be frequently updated and corrected.

[0075] The real-time background noise of the water body at each underwater acoustic sensor node is compared with the background noise threshold, and the underwater acoustic sensor nodes with a noise level greater than the background noise threshold are obtained. The underwater acoustic sensor nodes with a noise level greater than the background noise threshold are re-labeled as: CX i, where i = 1, 2, 3, ..., I, i is the number of the underwater acoustic sensor node that is greater than the background noise threshold;

[0076] Calculate the noise concealment risk factor using the following formula: Among them, ZS wx is the noise concealment risk factor.

[0077] It can be seen from the formula that the greater the noise concealment risk coefficient, the more likely the background noise of the water body at each underwater acoustic sensing node has changed significantly.

[0078] The reasons for the dynamic changes of water bodies include a variety of natural and human factors, mainly including the following aspects:

[0079] Meteorological factors: Changes in air temperature affect water temperature, which in turn affects the physical properties of water bodies. Rainfall increases the volume of water bodies, while evaporation reduces it, both of which lead to changes in water levels and flows.

[0080] Water currents and tides: The fluidity of water causes the speed and direction of water currents to change over time. Oceans and offshore waters are affected by tides, resulting in periodic changes in water levels.

[0081] Geological and topographic changes: The flow of water bodies is affected by the topography. Topographic changes (such as soil erosion, mudslides, etc.) can change the path and speed of water flow. Earthquakes and landslides can cause changes in the structure of water bodies and affect the dynamics of water flow.

[0082] The acquisition logic of the total water body dynamic change coefficient is as follows: setting a monitoring interval, determining the flow velocity of each hydroacoustic sensor node in the monitoring interval through each hydroacoustic sensor node, and preprocessing the flow velocity data of each hydroacoustic sensor node in the monitoring interval, including removing outliers, filtering, and synchronizing timestamps;

[0083] Calculate the water dynamic change coefficient of a single underwater acoustic sensor node. The expression is: Among them, ST n is the water dynamic change coefficient of the nth underwater acoustic sensor node, t1~t2 is the time range of the monitoring interval, A is the amplitude, which indicates the amplitude of the flow velocity change, B is the frequency, which indicates the speed of the periodic change, C is the phase, LS avg is the average flow velocity of the hydroacoustic sensor node in the monitoring interval;

[0084] It should be noted that the frequency describes the speed of water body fluctuations, the phase angle describes the initial state or position of the fluctuation, and the monitoring interval is a specific length of time. The monitoring interval is set by professional staff.

[0085] Calculate the dynamic change coefficient of the total water body using the following formula: Among them, ZST is the dynamic change coefficient of the total water body.

[0086] It can be seen from the formula that the larger the total water body dynamic change coefficient, the more drastic the fluctuation or change of the water body. In other words, the water flow has undergone greater changes within this time period, indicating that the water body is more dynamic and the flow velocity fluctuation amplitude is larger, which may affect the measurement accuracy of the underwater acoustic sensing network and cause the sound velocity field model to need to be frequently updated and corrected.

[0087] In step 3, the sound velocity characteristic information and water characteristic information are comprehensively analyzed. A weighted analysis is performed using the sound velocity difference fluctuation coefficient, the noise concealment hazard coefficient, and the total water body dynamic change coefficient to construct an error assessment model and generate an error assessment coefficient. The error assessment coefficient is expressed as follows: Among them, pg yj is the error assessment coefficient, α is the proportional coefficient of the sound speed difference fluctuation coefficient, β is the proportional coefficient of the noise concealment hazard coefficient, and γ is the proportional coefficient of the total water body dynamic change coefficient. α, β, and γ are all greater than 0.

[0088] It can be seen from the formula that the larger the sound speed difference fluctuation coefficient, the noise concealment risk coefficient, and the total water body dynamic change coefficient, the larger the error assessment coefficient, indicating that the water body may change, and the accuracy of the sound speed field model constructed based on the historical sound speed data of each underwater acoustic sensor node may be poor. Conversely, the smaller the sound speed difference fluctuation coefficient, the noise concealment risk coefficient, and the total water body dynamic change coefficient, the smaller the error assessment coefficient, indicating that the water body may be relatively stable, and the accuracy of the sound speed field model constructed based on the historical sound speed data of each underwater acoustic sensor node may be high.

[0089] In step 4, an error assessment coefficient threshold is set, and the error assessment coefficient is compared with the error assessment coefficient threshold. If the error assessment coefficient is greater than the error assessment coefficient threshold, an error signal is generated to notify the staff to immediately use real-time sound speed data to rebuild the sound speed field model, or use real-time sound speed data to correct the error of the current sound speed field model. If the error assessment coefficient is less than the error assessment coefficient threshold, no error signal is generated.

[0090] The present invention deploys sound source points and underwater acoustic sensor nodes at fixed positions, uses a sound velocity field inversion algorithm to construct a sound velocity field model based on the sound velocity data collected by the underwater acoustic sensor nodes, compares the sound velocity data predicted by the sound velocity field model with the actual sound velocity data, and constructs an error assessment model based on the amplitude of water body changes, thereby helping to improve the accuracy of underwater sound wave propagation and the accuracy of underwater acoustic sensor network node positioning, and facilitating timely adjustment of the sound velocity field model to avoid errors.

[0091] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0093] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0094] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0096] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion, characterized in that: The specific steps include: S1: Deploy fixed-position sound source points and underwater acoustic sensor nodes. The underwater acoustic sensor nodes collect sound velocity data emitted by the sound source points. Based on the sound velocity data at different underwater acoustic sensor nodes, a sound velocity field model is constructed using a sound velocity field inversion algorithm. S2: Collecting real-time sound velocity data at different underwater acoustic sensing nodes to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes; S3: Comprehensively analyze the sound velocity characteristic information and water characteristic information to evaluate the accuracy of the sound velocity field model in locating underwater acoustic sensor nodes; S4: Compare the positioning accuracy result of the quantified underwater acoustic sensor node with a preset threshold, and generate an error signal when a large error occurs in the positioning accuracy.

2. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 1 is characterized in that: Sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes, including: Real-time sound velocity data at different underwater acoustic sensing nodes are collected and analyzed to obtain sound velocity characteristic information at different underwater acoustic sensing nodes and water characteristic information at different underwater acoustic sensing nodes. The sound velocity characteristic information is represented by the sound velocity difference fluctuation variation coefficient and the noise concealment hazard coefficient, and the water characteristic information is represented by the total water body dynamic variation coefficient.

3. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 2 is characterized in that: The coefficient of variation of the sound speed difference fluctuation includes: The logic for obtaining the sound speed difference fluctuation coefficient is as follows: the sound speed of the sound wave at each underwater acoustic sensor node is obtained by the sound wave emitted in real time by the sound source point, and the sound speed of the sound wave at each underwater acoustic sensor node is marked as: SQ n , where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of each underwater acoustic sensor node; Obtain the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node, and mark the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node as: YC n ; Obtain the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node, and mark the difference between the sound velocity value predicted by the sound velocity field model at each underwater acoustic sensor node and the actual sound velocity of the sound wave at each underwater acoustic sensor node as: CY n ,in, CY n =|SQ n -YC n |; Calculate the mean and standard deviation of the sound speed differences and label them as: CY avg and CY std ,in, Calculate the coefficient of variation of the sound speed difference using the formula: Where BY is the coefficient of variation of the sound speed difference; Calculate the coefficient of variation of the sound velocity difference fluctuation, the calculation formula is: QD by =CY avg ×ln(BY+1); where QD by is the coefficient of variation of the sound speed difference fluctuation.

4. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 3 is characterized in that: Noise hidden hazard factors, including: The logic for obtaining the noise concealment risk coefficient is as follows: obtain the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data, and mark the background noise of the water body at each underwater acoustic sensor node when the sound velocity field model is obtained according to historical data as: LSZS n ; Obtain the real-time background noise of the water body at each underwater acoustic sensor node, and mark the real-time background noise of the water body at each underwater acoustic sensor node as: SSZS n ; Obtain the background noise deviation of the water body at each underwater acoustic sensor node, and mark the background noise deviation of the water body at each underwater acoustic sensor node as: PC n ,in, Set the background noise threshold of the water body and mark it as: ZS yz ; The real-time background noise of the water body at each underwater acoustic sensor node is compared with the background noise threshold, and the underwater acoustic sensor nodes with a noise level greater than the background noise threshold are obtained. The underwater acoustic sensor nodes with a noise level greater than the background noise threshold are re-labeled as: CX i , where i = 1, 2, 3, ..., I, i is the number of the underwater acoustic sensor node that is greater than the background noise threshold; Calculate the noise concealment risk factor using the following formula: Among them, ZS wx is the noise concealment risk factor.

5. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 4 is characterized in that: Dynamic change coefficient of total water body, including: The acquisition logic of the total water body dynamic change coefficient is as follows: set the monitoring interval, determine the flow rate of each water acoustic sensor node in the monitoring interval through each water acoustic sensor node, and Preprocessing of velocity data in the monitoring interval, including outlier removal, filtering, and time stamp synchronization; Calculate the water dynamic change coefficient of a single underwater acoustic sensor node. The expression is: Among them, ST n is the water dynamic change coefficient of the nth underwater acoustic sensor node, t1~t2 is the time range of the monitoring interval, A is the amplitude, which indicates the amplitude of the flow velocity change, B is the frequency, which indicates the speed of the periodic change, C is the phase, LS avg is the average flow velocity of the hydroacoustic sensor node in the monitoring interval; Calculate the dynamic change coefficient of the total water body using the following formula: Among them, ZST is the dynamic change coefficient of the total water body.

6. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 5, characterized in that: Comprehensive analysis of sound velocity characteristics and water characteristics information, including: By performing weighted analysis on the sound speed difference fluctuation coefficient, noise concealment risk coefficient, and total water body dynamic change coefficient, an early warning assessment model is constructed to generate an error assessment coefficient. The expression of the error assessment coefficient is: Among them, pg yj is the error assessment coefficient, α is the proportional coefficient of the sound speed difference fluctuation coefficient, β is the proportional coefficient of the noise concealment hazard coefficient, and γ is the proportional coefficient of the total water body dynamic change coefficient. α, β, and γ are all greater than 0.

7. The method for correcting node positioning errors in an underwater acoustic sensor network combined with sound velocity inversion according to claim 6, characterized in that: When there is a large error in positioning accuracy, an error signal is generated, including: Set an error assessment coefficient threshold and compare the error assessment coefficient with the error assessment coefficient threshold. If the error assessment coefficient is greater than the error assessment coefficient threshold, an error signal is generated to notify the staff to immediately use real-time sound speed data to rebuild the sound speed field model, or use real-time sound speed data to correct the error of the current sound speed field model. If the error assessment coefficient is less than the error assessment coefficient threshold, no error signal is generated.

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