Distributed seabed observation network sampling control method and seabed monitoring system

Abstract

The present invention relates to the technical field of submarine emergency monitoring, and specifically provides a distributed submarine observation network sampling control method and a submarine monitoring system. The method includes: creating a global observation matrix based on the number of junction boxes and the number of optical pulse signals in the submarine observation network; creating an objective function associated with the global observation matrix; iteratively solving the objective function and outputting an intermediate solution result of the global observation matrix; judging whether the intermediate solution result meets the constraint conditions of the objective function; stopping the iteration when the constraint conditions are met, and outputting the intermediate solution result as the final solution result of the global observation matrix; and controlling the intermittent sampling of optical pulses by the junction box according to the final solution result. The present invention creates an alternative to continuous sampling through active intermittent sampling. While ensuring monitoring accuracy, it avoids long-term exposure of the equipment to environmental interference, reduces power consumption and improves system stability. It reduces the amount of collected and transmitted data and improves economic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of submarine emergency monitoring, and in particular to a distributed submarine observation network sampling control method and a submarine monitoring system. Background Art

[0002] In the prior art, long-term and continuous emergency monitoring of the marine environment is performed through a seabed observation network. The seabed observation network is a scientific observation platform deployed on the seabed and includes various sensors, instruments and communication systems.

[0003] Traditional submarine observation networks rely on continuous sampling, collecting and transmitting data non-stop. This results in excessively large data volumes and high storage and transmission costs. Long-term continuous sampling also consumes excessive power for underwater equipment, making it difficult to maintain long-term operation. Furthermore, sensor nodes operating over a long period of time are highly susceptible to environmental interference, leading to a high risk of equipment damage and failure, and insufficient system reliability and stability. Summary of the Invention

[0004] The distributed seabed observation network sampling control method and seabed monitoring system provided by the embodiments of the present invention at least solve the problems of excessive power consumption and insufficient stability of the seabed observation network during continuous operation.

[0005] In a first aspect, an embodiment of the present invention provides a distributed seabed observation network sampling control method, the method comprising:

[0006] Creating a global observation matrix based on the number of docking boxes and the number of optical pulse signals in the seabed observation network; wherein the docking boxes include optical sensors capable of sampling the optical pulse signals;

[0007] creating an objective function associated with the global observation matrix;

[0008] Iteratively solving the objective function and outputting an intermediate solution result of the global observation matrix;

[0009] Determine whether the intermediate solution meets the constraint conditions of the objective function; stop iteration when the constraint conditions are met, and output the intermediate solution as the final solution result of the global observation matrix;

[0010] The junction box is controlled to intermittently sample the optical pulse signal according to the final solution result.

[0011] The distributed seabed observation network sampling control method provided by the embodiment of the present invention creates an objective function associated with the global observation matrix, including:

[0012] Creating a spatial coverage function associated with the global observation matrix, and setting a spatial coverage weight corresponding to the spatial coverage function; wherein the spatial coverage function is used to characterize the spatial coverage of the sampling;

[0013] Creating a temporal randomness function associated with the global observation matrix, and setting a temporal randomness weight corresponding to the temporal randomness function; wherein the temporal randomness function is used to characterize the temporal randomness of sampling;

[0014] Taking a weighted sum of the spatial coverage function and the temporal randomness function;

[0015] The weighted summation result is added to the sparsity constraint of the global observation matrix to obtain the objective function.

[0016] The distributed seabed observation network sampling control method provided by an embodiment of the present invention further includes, before iteratively solving the objective function:

[0017] Creating an equivalent computation model; wherein the equivalent computation model is used to input an intermediate solution result of the global observation matrix, and output an equivalent observation sampling matrix corresponding to the intermediate solution result, and output an equivalent observation data matrix;

[0018] Create constraints for the objective function; wherein the constraints are set so that the equivalent observation data matrix satisfies restricted interval properties.

[0019] The distributed seabed observation network sampling control method provided by the present invention creates an equivalent calculation model, including:

[0020] Diagonalizing each row vector of the global observation matrix;

[0021] The diagonalization results of the row vectors are sequentially used as diagonal elements to perform block diagonalization to obtain the equivalent observation sampling matrix corresponding to the global observation matrix;

[0022] Acquire a sampling matrix of the junction box based on the number of optical pulses actually received by the junction box and the sampling length of the optical pulses;

[0023] Diagonalize the sampling matrices of all the junction boxes as diagonal elements to obtain a joint sampling matrix;

[0024] The equivalent observation sampling matrix is ​​multiplied by the transposed joint sampling matrix to obtain the equivalent observation data matrix, thereby completing the equivalent calculation model.

[0025] The distributed seabed observation network sampling control method provided by the embodiment of the present invention iteratively solves the objective function and outputs the intermediate solution result of the global observation matrix, including:

[0026] Initializing an iteration count value to generate an initialization result of the global observation matrix;

[0027] Performing gradient calculation on each part of the objective function to obtain the objective gradient and function;

[0028] Update the initialization result according to a preset learning rate and the target gradient and function;

[0029] Perform soft thresholding on the updated initialization result and output the intermediate solution result.

[0030] The distributed seabed observation network sampling control method provided by the embodiment of the present invention determines whether the intermediate solution meets the constraint conditions of the objective function, including:

[0031] Inputting the intermediate solution result into an equivalent calculation model and outputting an equivalent observation sampling matrix;

[0032] Calculating the observation error of the equivalent observation sampling matrix compared to the test signal set;

[0033] When the maximum value of the observation error is less than the observation error threshold, the constraint condition is satisfied;

[0034] When the maximum value of the observation error is greater than or equal to the observation error threshold, the constraint condition is not satisfied; the objective function is updated, and the iterative solution of the objective function is continued.

[0035] The distributed seabed observation network sampling control method provided by the embodiment of the present invention updates the objective function, including:

[0036] Inputting the intermediate solution result into a spatial coverage function to obtain spatial coverage, and inputting the intermediate solution result into a temporal randomness function to obtain temporal randomness;

[0037] comparing the spatial coverage with a spatial coverage threshold, and adjusting the spatial coverage weight of the objective function;

[0038] The temporal randomness is compared with a temporal randomness threshold, and the temporal randomness weight of the objective function is adjusted.

[0039] The distributed seabed observation network sampling control method provided by the embodiment of the present invention adjusts the spatial coverage weight of the objective function, including:

[0040] When the spatial coverage is greater than the spatial coverage threshold, increasing the spatial coverage weight of the objective function;

[0041] When the spatial coverage is less than or equal to the spatial coverage threshold, reducing the spatial coverage weight of the objective function;

[0042] Adjusting the temporal randomness weight of the objective function comprises:

[0043] When the temporal randomness is greater than the temporal randomness threshold, increasing the temporal randomness weight of the objective function;

[0044] When the temporal randomness is less than or equal to the temporal randomness threshold, the temporal randomness weight of the objective function is reduced.

[0045] The distributed seabed observation network sampling control method provided by the embodiment of the present invention controls the junction box to intermittently sample the optical pulse signal according to the final solution result, including: in the final solution result of the global observation matrix,

[0046] When a matrix element is 0, the junction box corresponding to the row of the matrix element is controlled to not sample the optical pulse corresponding to the column of the matrix element;

[0047] When a matrix element is 1, the junction box corresponding to the row of the matrix element is controlled to sample the optical pulse corresponding to the column of the matrix element.

[0048] In a second aspect, the present invention also provides a seabed monitoring system, including a sampling control module, an optical fiber transmission module, a data reconstruction module, and a data analysis module:

[0049] The sampling control module is used to control the intermittent sampling of the optical sensor in the docking box according to the distributed seabed observation network sampling control method described in any of the above embodiments;

[0050] The optical fiber transmission module is used to transmit the sampling signal of the optical sensor to the data reconstruction module;

[0051] The data reconstruction module is used to reconstruct the sampled signal into the original signal;

[0052] The data analysis module is used to extract abnormal information from the original signal and identify emergencies.

[0053] The distributed seafloor observation network sampling control method and seafloor monitoring system provided by the present invention employ an active intermittent sampling strategy to replace the traditional long-term continuous sampling method. While ensuring the accuracy of emergency monitoring, this method protects equipment from prolonged exposure to environmental interference, reduces power consumption, and improves system stability. It also reduces the amount of data collected and transmitted, improving the system's economic efficiency. This reduces the burden on equipment in the seafloor observation network operating in complex seafloor environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without inventive effort.

[0055] Figure 1 It is a flow chart of a distributed seabed observation network sampling control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following describes embodiments of the present invention in more detail with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0057] In the marine environment, sudden events such as submarine earthquakes, seabed landslides, and volcanic eruptions not only severely damage the marine ecosystem but also pose a significant threat to the safe and stable operation of submarine communication networks. As a vital component of communications infrastructure, damage to submarine communication networks can lead to communication disruptions, impacting numerous sectors, including the economy and scientific research.

[0058] To monitor the above-mentioned emergencies, multiple junction boxes are usually deployed in the seabed observation network. Each junction box serves as a sensor node in the seabed observation network, transmitting the observed optical pulse signal to the monitoring center on shore via optical fiber.

[0059] For example, the intensity, frequency, and phase characteristics of optical pulse signals are relatively stable under normal communication conditions. However, during emergencies, external shocks, temperature fluctuations, or other environmental factors can cause variations in optical pulse signal intensity, frequency, and / or phase. Therefore, existing technologies analyze optical pulse signals to determine whether there are potential risks or environmental impacts on the communication link, and thus determine whether a corresponding underwater emergency has occurred.

[0060] Based on the existing monitoring methods of submarine observation networks, existing technologies often incorporate several optical sensors within docking boxes. These sensors collect signals in the form of light pulses. These sensors, acting as sensor nodes, continuously sample these light pulse signals, collecting and transmitting data uninterruptedly. For submarine observation network equipment, long-term continuous operation results in extremely high power consumption, making it difficult to maintain system operation. Furthermore, long-term sampling makes sensor nodes susceptible to interference and influences from the marine environment, increasing the risk of equipment damage and failure, and thus impacting the stability and reliability of the submarine monitoring system. Furthermore, the massive amount of data collected and transmitted by continuous sampling significantly increases storage, transmission, and analysis costs.

[0061] Accordingly, an embodiment of the present invention provides a sampling control method for a distributed seabed observation network, which controls the sampling process of the junction box through the final solution after global observation matrix optimization. Changing the traditional long-term continuous sampling method to active intermittent sampling can not only effectively reduce the amount of data collected and transmitted, and reduce storage and transmission costs; it can also shut down sensor nodes during non-essential periods, significantly reducing the overall system power consumption. The timely shutdown of sensor nodes can also prevent the equipment from being continuously exposed to the external environment for a long time, reduce the possibility of interference and damage to the equipment, enhance the anti-interference and anti-destruction capabilities of the seabed monitoring system, and improve system stability.

[0062] Furthermore, the distributed seafloor observation network sampling control method provided by the present invention can also combine compressed sensing theory with a reduced sampling volume to achieve efficient reconstruction of seafloor signals. This reduces the impact of sampling volume control on the accuracy and precision of emergency monitoring, maintaining system reliability.

[0063] Specifically, refer to Figure 1 As shown, the method provided in this embodiment includes the following steps:

[0064] Step S100: Create a global observation matrix based on the number of docking boxes and the number of light pulses in the seabed observation network. .

[0065] In this embodiment, the global observation matrix Used to control sampling. As shown in the following formula, the global observation matrix The matrix elements in are set to Φ m(i, j); global observation matrix Set as a matrix with M rows and P columns, where M is set to the total number of docking boxes. Therefore, the global observation matrix The rows of correspond to different junction boxes. P is set to the total number of light pulses, so the global observation matrix In some embodiments, the junction boxes can be sorted based on dimensions such as spatial position, and the light pulses can be divided based on time or other dimensions based on the light pulse signal.

[0066] ;

[0067] Each junction box acts as a monitoring unit corresponding to the optical pulse, and the junction box includes an optical sensor that can sample the optical pulse signal. m (i, j) is used to define the sampling of the i-th junction box for the j-th optical pulse, for example, the matrix element Φ m (1, 2) is used to define the sampling situation of the first junction box for the second light pulse.

[0068] In this embodiment, the global observation matrix After optimization, each matrix element Φ m The value of (i, j) specifically controls the intermittent sampling of the junction box. When the global observation matrix When the matrix element is 1, it indicates sampling, and when the matrix element is 0, it indicates no sampling.

[0069] Step S200, create a global observation matrix The associated objective function.

[0070] As an implementable method, step S200 includes the following steps:

[0071] Step S210, create a global observation matrix Associated spatial coverage function , corresponding to the spatial coverage function Setting spatial coverage weights .

[0072] Among them, the spatial coverage function is the global observation matrix Perform spatial coverage constraints to measure the spatial coverage of the sampling process. Spatial coverage function Refer to the following formula for calculation:

[0073] ;

[0074] Where, Φ m (i, j) represents the global observation matrix The matrix elements in , that is, the sampling situation of the i-th junction box for the j-th light pulse. During intermittent sampling, some junction boxes are closed and the remaining junction boxes are opened. The global observation matrix The matrix elements Φ in m When (i, j) is 1, it means that the i-th junction box samples the j-th light pulse, which is used as a sampling point. Optimizing spatial coverage allows sampling points to spatially cover different parts of the seafloor observation network area, avoiding excessive concentration in a single area.

[0075] Step S220, create a global observation matrix Associated temporal randomness function , corresponding to the temporal randomness function Set the temporal randomness weight .

[0076] Among them, the time randomness function is the global observation matrix Temporal randomness constraints are used to measure the temporal randomness of the sampling process. Temporal randomness function Refer to the following formula for calculation:

[0077] ;

[0078] In matrix indexing, use commas to separate and colons as special index symbols to access elements. That is, Indicates taking the global observation matrix All elements of the i-th row of , the result of taking out the elements is to get the global observation matrix The row vector of the i-th row of . Autocorr(·) represents the calculation of the autocorrelation function for the objects within the brackets. In this embodiment, it represents the calculation of the autocorrelation of the sampling process in the time dimension. Due to the difference in the time dimension between intermittent sampling and continuous sampling, optimizing temporal randomness can make the sampling points random in the time domain, avoiding excessive concentration at a certain time period or moment. In this embodiment, the autocorrelation function is used to evaluate the randomness of the sampling time. A smaller autocorrelation value indicates better randomness.

[0079] Step S230: space coverage function and temporal randomness function Weighted sum.

[0080] Step S240, the weighted summation result in step S230 is added to the global observation matrix The sparsity constraint Add and get the objective function reference formula:

[0081] ;

[0082] in, is the corresponding spatial coverage function in step S210 Set spatial coverage weights; is the time randomness function corresponding to step S220 Set the time randomness weight. Sparsity constraint Set the global observation matrix of Norm, which represents the number of non-zero elements in the matrix. Since the global observation matrix When the matrix element in is 1, it means sampling, so the sparsity constraint Used to control the number of sampling points during intermittent sampling.

[0083] It should be noted that while intermittent sampling can address cost and power consumption issues associated with existing sampling processes by controlling sampling points, it also introduces threats and impacts on sampling accuracy. The inventors have discovered that to avoid missed detection or misjudgment of submarine emergencies, which could lead to greater losses, this embodiment incorporates compressed sensing theory to achieve efficient signal reconstruction even with fewer sampling points, mitigating the impact of reduced sampling points on monitoring accuracy.

[0084] Therefore, before solving the objective function, this embodiment also creates an equivalent calculation model and establishes constraints on the objective function to iteratively solve the objective function and simultaneously address monitoring accuracy. The equivalent calculation model participates in the iterative solution process and outputs an equivalent observation data matrix, which is used to establish the constraints of the objective function and test whether the solution satisfies the constraints.

[0085] Specifically, creating an equivalent calculation model includes:

[0086] The global observation matrix Diagonalize the row vectors of .

[0087] The diagonalization results of the row vectors are used as diagonal elements to perform block diagonalization, and the global observation matrix is ​​obtained. The corresponding equivalent observation sampling matrix , the equivalent observation sampling matrix Refer to the following formula for calculation:

[0088] ;

[0089] in, is the diagonalization function; Represents a diagonalization function that takes matrices as diagonal elements and is used to combine the input matrices into a block diagonal matrix. Indicates taking the global observation matrix All elements of the i-th row of , the result of taking out the elements is to get the global observation matrix The row vector of the i-th row of . Correspondingly, the value of i can be 1, 2, 3...M, Φ m (1,:) represents the global observation matrix The first row vector of Φ m (2,:) represents the global observation matrix The row vector of the second row of ; and so on, Φ m (M,:) represents the global observation matrix The row vector of the Mth row of .

[0090] Based on the number of optical pulses actually received by the junction box and the sampling length of the optical pulses, a sampling matrix of the junction box is obtained. For example, for the mth junction box, the matrix of the original optical pulse signal actually received is expressed as L is the sampling length of each optical pulse, and P is set to the total number of optical pulses.

[0091] The sampling matrices R1, R2, R3…R of all junction boxes M As diagonal elements, they are divided into blocks and diagonalized to obtain the joint sampling matrix , joint sampling matrix Refer to the following formula for calculation:

[0092] .

[0093] Equivalent observation sampling matrix With the joint sampling matrix The transposed matrix of Multiply to get the equivalent observation data matrix , complete the equivalent calculation model. Among them, the equivalent observation data matrix The combined sampling data for each equivalent junction box is calculated using the following formula:

[0094] .

[0095] In this embodiment, for the equivalent observation data matrix Constraints were also established, specifically the Restricted Isometry Property (RIP) from compressed sensing theory. These constraints ensure efficient signal recovery under intermittent sampling conditions, while ensuring that the sampled data approximately maintains the original signal's information structure. The RIP condition is described in the following formula:

[0096] ;

[0097] in, is a sparse isometric constant.

[0098] After establishing the constraints, the objective function is solved as a joint optimization problem as shown in the following formula in this embodiment, requiring the solution of the objective function to satisfy the corresponding constraints:

[0099] The joint optimization problem is: ;

[0100] The constraints are: .

[0101] In this embodiment, for the convenience of calculation, the above joint optimization problem is also relaxed before solving the objective function. Specifically, the sparsity constraint of the objective function is relaxed, using Norm approximation norm. The resulting relaxed joint optimization problem is:

[0102] ;

[0103] The constraints are: .

[0104] in, The relaxation process makes the objective function differentiable, which facilitates gradient solution.

[0105] Step S300, iteratively solve the objective function and output the global observation matrix The intermediate solution result of .

[0106] As an implementable method, step S300 includes the following steps:

[0107] Step S310, initialize the iteration count value q and generate the global observation matrix The initialization result.

[0108] Specifically, the iteration count value q is initialized to 0. Randomly generate an initial observation matrix . Corresponding to the initialization observation matrix , the objective function to be solved is as follows:

[0109] ;

[0110] Where, α （q） , β （q） They represent the spatial coverage weight and temporal randomness weight when the iteration count is q. Other superscripts in the formula also represent the case when the iteration count is q.

[0111] Step S320 , performing gradient calculation on each part of the objective function to obtain the target gradient and function.

[0112] For the relaxed sparsity constraint , the gradient calculation refers to the following formula:

[0113] ;

[0114] in, Represents the initialization of the observation matrix Each matrix element in takes a sign, and non-zero elements are +1 or -1. ▽ represents the gradient operator.

[0115] For the spatial coverage part , which is used to measure the coverage of the matrix in the spatial dimension. If it is defined as the coverage balance of each sensor, its gradient calculation refers to the following formula:

[0116] ;

[0117] Where, Represents the global observation matrix When the iteration count value is q, the sampling situation of the jth optical pulse by the i-th junction box is: Represents the global observation matrix The sampling situation of the jth optical pulse by the kth junction box when the iteration count value is q. The value of k can also be 1, 2, 3...M.

[0118] For the temporal randomness , which is used to measure the randomness of the sampling time. If it is defined as reducing autocorrelation, its gradient calculation refers to the following formula:

[0119] .

[0120] Based on this, the target gradient and function is the sum of the gradient calculations of each part, refer to the following formula:

[0121] .

[0122] Step S330: According to the preset learning rate And the target gradient and function update initialization result obtained in step S320 are referred to the following formula:

[0123] .

[0124] Step S340: perform soft thresholding on the updated initialization result and output an intermediate solution result.

[0125] Each time the gradient is updated, the matrix needs to be soft-thresholded, and the preset threshold is , the soft thresholding process refers to the following formula:

[0126] ;

[0127] when > When Subtract the preset threshold Complete soft thresholding;

[0128] when < When Add preset threshold Complete soft thresholding;

[0129] when ≤ ≤ hour, Updated to 0.

[0130] After the soft thresholding process is completed, the is the intermediate solution result.

[0131] Step S400: Determine the intermediate solution result Whether the constraints of the objective function are met; when the constraints are met, the iteration is stopped and the intermediate solution results are The output is the global observation matrix The final solution result.

[0132] In step S400, the intermediate solution result is determined Whether the constraints of the objective function are satisfied includes the following steps:

[0133] The intermediate solution results Input the equivalent calculation model, diagonalize each row vector, and then use the diagonalization results as diagonal elements to perform block diagonalization, and output the intermediate solution results The corresponding equivalent observation sampling matrix . Check the intermediate solution results The corresponding equivalent observation sampling matrix Whether the constraints are met. The checking process includes:

[0134] Generate a sparse test signal set As the sample data in the actual observation process, calculate the equivalent observation sampling matrix Compared with the sparse test signal set Test joint sampling signal Observation error, 1≤s≤S; observation error E S Refer to the following formula for calculation:

[0135] .

[0136] Among them, the sample data The common joint sampling matrix in the corresponding equivalent computation model In the equivalent observation sampling matrix Compared with the sparse test signal set After the observation errors of each test joint sampling signal are calculated, the maximum observation error is taken .

[0137] when When the maximum observation error is less than the observation error threshold, the constraint condition is met; the iteration is stopped and the intermediate solution result is The output is the global observation matrix The final solution result.

[0138] when When , the maximum observation error is greater than or equal to the observation error threshold, and the constraint condition is not met; update the objective function and continue to iterate and solve the objective function.

[0139] When the iteration cannot output the final solution and the objective function needs to be updated, the update process includes:

[0140] The intermediate solution results Input spatial coverage function to get spatial coverage , the intermediate solution result Input the time randomness function to get time randomness .

[0141] Comparing spatial coverage and spatial coverage thresholds , adjust the spatial coverage weight of the objective function;

[0142] Comparing temporal randomness and temporal randomness threshold , adjust the temporal randomness weight of the objective function.

[0143] The process of adjusting the weights is as follows:

[0144] ;

[0145] Among them, adjusting the spatial coverage weight of the objective function includes:

[0146] When the spatial coverage Greater than the spatial coverage threshold When the spatial coverage weight is increased, is the growth factor.

[0147] When the spatial coverage Less than or equal to the spatial coverage threshold When the spatial coverage weight is reduced, is the reduction factor.

[0148] Adjust the temporal randomness weight of the objective function, including:

[0149] When time randomness Greater than the temporal randomness threshold When , increase the time randomness weight, is the growth factor.

[0150] When time randomness Less than or equal to the time randomness threshold When , reduce the time randomness weight, is the reduction factor.

[0151] The updated objective function is as follows:

[0152] .

[0153] After the update, the iteration count value q is adjusted to set q = q + 1. Return to step S320 and repeatedly solve the objective function until the final solution is output and the iteration is stopped.

[0154] Step S500 , controlling the junction box to intermittently sample the optical pulse signal according to the final solution result in step S400 .

[0155] In the global observation matrix In the final solution, when a matrix element is 0, the junction box corresponding to the row of the matrix element is controlled not to sample the light pulse corresponding to the column of the matrix element. If the sensor in the junction box is dormant or off, the sensor is controlled to remain dormant or off. If the sensor in the junction box is on, the sensor is controlled to be off and not participate in the sampling of the corresponding light pulse.

[0156] When a matrix element is 1, the junction box corresponding to the row of the matrix element is controlled to sample the light pulse corresponding to the column of the matrix element. If the sensor in the junction box is dormant or off, the sensor is controlled to turn on and perform sampling; if the sensor in the junction box is on, the sensor is controlled to remain in the active state and perform sampling.

[0157] .

[0158] For example, When it is 0, the first junction box is controlled not to sample the second light pulse; When is 1, the first junction box is controlled to sample the second light pulse. In a specific implementation, the optical sensor in the junction box is specifically controlled by the sensor sampling optimization module.

[0159] Global observation matrix after optimization This reduces both the sampling and transmission volume, allowing the docking box to remain dormant until necessary, alleviating the burden on the device in complex submarine environments and avoiding the high power consumption caused by long-term continuous operation. Intermittent sampling also reduces the system's exposure to the outside world, lowering the risk of interference and damage to the device, making it particularly suitable for complex submarine environments. Furthermore, this embodiment incorporates compressed sensing theory to meet the requirements for efficient signal reconstruction and monitoring accuracy. Accurate signal reconstruction is achieved through a small number of sampling points, allowing the system to maintain high detection accuracy while reducing the sampling frequency.

[0160] An embodiment of the present invention further provides a seabed monitoring system, which includes a sampling control module.

[0161] The sampling control module is used to control the intermittent sampling of the optical sensor in the docking box according to the distributed seabed observation network sampling control method provided in the above embodiment, specifically according to the optimized global observation matrix The matrix elements in control the sampling process and control the sampling status of each junction box. When the i-th junction box is set to the sampling state during the j-th received light pulse, otherwise the sampling is turned off. Since the technical effects of the distributed seabed observation network sampling control method have been described in the above embodiment, the seabed monitoring system also has the corresponding technical effects based on the inclusion of the sampling control module that executes the method, which will not be repeated here.

[0162] The seabed monitoring system also includes an optical fiber transmission module, a data reconstruction module and a data analysis module.

[0163] Specifically, the optical fiber transmission module is used to transmit the signal obtained by intermittent sampling of the optical sensor in the junction box to the data reconstruction module.

[0164] The data reconstruction module is located in the monitoring center on the shore. The data reconstruction module is based on the global observation matrix , calculate the equivalent sampling observation matrix ,The compressed sensing algorithm is used to reconstruct the data and restore it to the original signal.

[0165] The data analysis module is used to extract abnormal information from the reconstructed original signal and identify sudden events. In some embodiments, the data analysis module can effectively extract abnormal components from the reconstructed signal through low-rank sparse decomposition methods for detecting submarine sudden events.

[0166] The seabed monitoring system replaces traditional long-term continuous sampling with an active intermittent sampling strategy. Utilizing a low-power control circuit, the docking box activates its sensors only at specific times, remaining dormant at other times, significantly reducing overall energy consumption. This enables efficient monitoring of seabed emergencies while reducing both data volume and power consumption. The optimized design of the observation matrix feeds back into the specific implementation of the sampling control module. This not only protects the equipment from prolonged exposure to environmental interference, reducing power consumption and improving system stability, but also reduces the amount of data collected and transmitted, improving the system's economic efficiency. This reduces the burden on equipment in the seabed observation network operating in complex seabed environments, enabling the docking box to flexibly adapt to a variety of complex seabed conditions, which is of great significance for ensuring coastal safety.

[0167] An embodiment of the present invention further provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer program to execute the distributed seabed observation network sampling control method of the embodiment.

[0168] An embodiment of the present invention further provides a computer program product, comprising a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the distributed seafloor observation network sampling control method in an embodiment of the present invention.

[0169] An embodiment of the present invention further provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and when executed by the at least one processor, the computer program causes the electronic device to perform the distributed seafloor observation network sampling control method according to an embodiment of the present invention.

[0170] Electronic devices are intended to refer to various forms of digital electronic computing devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also refer to various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the inventions described and / or claimed herein.

[0171] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0172] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, they should be understood as "one or more".

[0174] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and corresponding operation entrances are provided for the user to choose to authorize or reject.

[0175] The various steps described in the method implementation methods provided by the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0176] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referenced to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0177] The above-described embodiments merely represent several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art would be able to make various modifications and improvements without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A distributed seabed observation network sampling control method, characterized in that: The method comprises: Creating a global observation matrix based on the number of docking boxes and the number of optical pulse signals in the seabed observation network; wherein the docking boxes include optical sensors capable of sampling the optical pulse signals; creating an objective function associated with the global observation matrix; Iteratively solving the objective function and outputting an intermediate solution result of the global observation matrix; Determine whether the intermediate solution meets the constraint conditions of the objective function; stop iteration when the constraint conditions are met, and output the intermediate solution as the final solution result of the global observation matrix; Controlling the junction box to intermittently sample the optical pulse signal according to the final solution result; Creating an objective function associated with the global observation matrix includes: Creating a spatial coverage function associated with the global observation matrix, and setting a spatial coverage weight corresponding to the spatial coverage function; wherein the spatial coverage function is used to characterize the spatial coverage of the sampling; Creating a temporal randomness function associated with the global observation matrix, and setting a temporal randomness weight corresponding to the temporal randomness function; wherein the temporal randomness function is used to characterize the temporal randomness of sampling; Taking a weighted sum of the spatial coverage function and the temporal randomness function; The weighted summation result is added to the sparsity constraint of the global observation matrix to obtain the objective function.

2. The distributed seabed observation network sampling control method according to claim 1, characterized in that: Before iteratively solving the objective function, the method further includes: Creating an equivalent computation model; wherein the equivalent computation model is used to input an intermediate solution result of the global observation matrix, and output an equivalent observation sampling matrix corresponding to the intermediate solution result, and output an equivalent observation data matrix; Create constraints for the objective function; wherein the constraints are set so that the equivalent observation data matrix satisfies restricted interval properties.

3. The distributed seabed observation network sampling control method according to claim 2, characterized in that: Create an equivalent calculation model, including: Diagonalizing each row vector of the global observation matrix; The diagonalization results of the row vectors are sequentially used as diagonal elements to perform block diagonalization to obtain the equivalent observation sampling matrix corresponding to the global observation matrix; Acquire a sampling matrix of the junction box based on the number of optical pulses actually received by the junction box and the sampling length of the optical pulses; Diagonalize the sampling matrices of all the junction boxes as diagonal elements to obtain a joint sampling matrix; The equivalent observation sampling matrix is ​​multiplied by the transposed joint sampling matrix to obtain the equivalent observation data matrix, thereby completing the equivalent calculation model.

4. The distributed seabed observation network sampling control method according to claim 1, characterized in that: Iteratively solving the objective function and outputting an intermediate solution result of the global observation matrix include: Initializing an iteration count value to generate an initialization result of the global observation matrix; Performing gradient calculation on each part of the objective function to obtain the objective gradient and function; Update the initialization result according to a preset learning rate and the target gradient and function; Perform soft thresholding on the updated initialization result and output the intermediate solution result.

5. The distributed seabed observation network sampling control method according to claim 1, characterized in that: Determining whether the intermediate solution satisfies the constraint conditions of the objective function includes: Inputting the intermediate solution result into an equivalent calculation model and outputting an equivalent observation sampling matrix; Calculating the observation error of the equivalent observation sampling matrix compared to the test signal set; When the maximum value of the observation error is less than the observation error threshold, the constraint condition is satisfied; When the maximum value of the observation error is greater than or equal to the observation error threshold, the constraint condition is not satisfied; the objective function is updated, and the iterative solution of the objective function is continued.

6. The distributed seabed observation network sampling control method according to claim 5, characterized in that: Updating the objective function includes: Inputting the intermediate solution result into a spatial coverage function to obtain spatial coverage, and inputting the intermediate solution result into a temporal randomness function to obtain temporal randomness; comparing the spatial coverage with a spatial coverage threshold, and adjusting the spatial coverage weight of the objective function; The temporal randomness is compared with a temporal randomness threshold, and the temporal randomness weight of the objective function is adjusted.

7. The distributed seabed observation network sampling control method according to claim 6, characterized in that: Adjusting the spatial coverage weight of the objective function includes: When the spatial coverage is greater than the spatial coverage threshold, increasing the spatial coverage weight of the objective function; When the spatial coverage is less than or equal to the spatial coverage threshold, reducing the spatial coverage weight of the objective function; Adjusting the temporal randomness weight of the objective function comprises: When the temporal randomness is greater than the temporal randomness threshold, increasing the temporal randomness weight of the objective function; When the temporal randomness is less than or equal to the temporal randomness threshold, the temporal randomness weight of the objective function is reduced.

8. The distributed seabed observation network sampling control method according to claim 1, characterized in that: According to the final solution result, the junction box is controlled to intermittently sample the optical pulse signal, including: in the final solution result of the global observation matrix, When a matrix element is 0, the junction box corresponding to the row of the matrix element is controlled to not sample the optical pulse corresponding to the column of the matrix element; When a matrix element is 1, the junction box corresponding to the row of the matrix element is controlled to sample the optical pulse corresponding to the column of the matrix element.

9. A seabed monitoring system, characterized in that: Including sampling control module, optical fiber transmission module, data reconstruction module and data analysis module: The sampling control module is used to control the intermittent sampling of the optical sensor in the junction box according to the distributed seabed observation network sampling control method according to any one of claims 1 to 8; The optical fiber transmission module is used to transmit the sampling signal of the optical sensor to the data reconstruction module; The data reconstruction module is used to reconstruct the sampled signal into the original signal; The data analysis module is used to extract abnormal information from the original signal and identify emergencies.

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