An underwater fusion network system architecture and heterogeneous sensor data synchronization method

Abstract

The present invention discloses an underwater fusion network system architecture and a heterogeneous sensor data synchronization method. The underwater fusion network system architecture includes: a submarine optical cable, a fusion sensor node, a fixed-point sensor and a data fusion transmission device. The submarine optical cable is provided with a fusion signal optical fiber and multiple sensor optical fibers. The fusion sensor nodes are spaced apart along the length direction of the submarine optical cable. The fusion sensor node is provided with a first optical fiber sensor data acquisition device, which is connected to multiple sensor optical fibers in a section of the submarine optical cable before the fusion sensor node to perform optical fiber sensor data acquisition. The fusion sensor node is provided with a fixed-point sensor data acquisition device, which is connected to the fixed-point sensor to perform fixed-point sensor data acquisition, and a time-space joint calibration is performed on the collected data of each fixed-point sensor, thereby realizing comprehensive, efficient and accurate monitoring of the underwater environment.

Description

Technical Field

[0001] The present invention relates to the field of underwater monitoring technology, and in particular to an underwater fusion network system architecture and a heterogeneous sensor data synchronization method. Background Art

[0002] There are currently two main methods for monitoring the ocean:

[0003] The first approach utilizes traditional fixed-point underwater monitoring systems. These typically employ fixed-point sensors, such as pressure, temperature, or hydroacoustic sensors, deployed at specific locations on the seabed or underwater to monitor the underwater environment. These sensors collect real-time ocean environmental parameters such as water temperature, salinity, and vibration, and transmit the data via wired or wireless means to onshore data centers for analysis and processing.

[0004] The second is to use a fiber optic sensing underwater monitoring system. The fiber optic sensing system uses optical fiber as a transmission medium and realizes continuous and long-distance monitoring of the underwater environment through distributed fiber optic sensing technology or quasi-distributed fiber optic sensing technology. The fiber optic sensing system has the advantages of wide monitoring range, high sensitivity, and strong anti-electromagnetic interference ability. It can realize real-time monitoring of multiple parameters such as underwater temperature, pressure, and strain.

[0005] The two underwater monitoring system architectures described above both have different drawbacks, including:

[0006] Traditional fixed-point sensing underwater monitoring systems have problems such as limited monitoring range, difficult data transmission, limited data transmission rate, and high system maintenance costs. They are unable to meet the needs of large-scale, long-term, and efficient underwater environmental monitoring.

[0007] Fiber optic sensing underwater monitoring systems also have some limitations. For example, the sensing signal may still be subject to a certain degree of interference in the underwater environment, resulting in reduced data accuracy. In addition, it is limited by factors such as fiber optic attenuation and connection problems, resulting in problems such as short sensing distance.

[0008] Therefore, neither of the above two methods can fully solve all the problems of underwater environment monitoring and needs to be improved. Summary of the Invention

[0009] The main technical problem solved by the present invention is to provide an underwater fusion network system architecture and a heterogeneous sensor data synchronization method to achieve comprehensive, efficient and accurate monitoring of the underwater environment, and solve the problems of limited data transmission rate and high system maintenance cost in traditional systems.

[0010] In order to solve the above technical problems, a technical solution adopted by the present invention is: to provide an underwater fusion network system architecture, including: a submarine optical cable, a fusion sensor node, a fixed-point sensor and a data fusion transmission device, wherein the submarine optical cable is provided with a fusion signal optical fiber and a plurality of sensing optical fibers, the fusion sensor nodes are spaced apart along the length direction of the submarine optical cable, the fusion sensor node is provided with a first optical fiber sensing data acquisition device, the first optical fiber sensing data acquisition device is connected to a plurality of sensing optical fibers in a section of the submarine optical cable before the fusion sensor node, and performs optical fiber sensing data acquisition, the fixed-point sensors are respectively provided in the fusion sensor node or the fusion sensor node. On the outside of the node, a fixed-point sensor data acquisition device is provided in the fusion sensor node, and the fixed-point sensor data acquisition device is connected to the fixed-point sensor to perform fixed-point sensor data acquisition. The data fusion transmission device is provided in the fusion sensor node, and the input end of the data fusion transmission device is connected to the first optical fiber sensing data acquisition device and the fixed-point sensor data acquisition device in the fusion sensor node and the fusion signal optical fiber in a section of submarine optical cable before the fusion sensor node to aggregate data. The output end of the data fusion transmission device is connected to the fusion signal optical fiber in a section of submarine optical cable after the fusion sensor node to transmit signals.

[0011] In a preferred embodiment of the present invention, it also includes a shore base station located at the end of the submarine optical cable, wherein the shore base station is provided with a data processing terminal and a second optical fiber sensing data acquisition device, the sensing optical fiber in a section of the submarine optical cable between the shore base station and its adjacent fusion sensor node is connected to the second optical fiber sensing data acquisition device, and the data processing terminal is connected to the second optical fiber sensing data acquisition device and a section of fusion signal optical fiber at the end of the submarine optical cable.

[0012] In a preferred embodiment of the present invention, the data processing terminal aggregates and fuses the collected optical fiber sensing data and fixed-point sensing data, and compares and analyzes them with a large database to obtain more accurate detection data.

[0013] In a preferred embodiment of the present invention, the first fiber optic sensing data acquisition device and the second fiber optic sensing data acquisition device are integrated with a light source and a data acquisition board.

[0014] In a preferred embodiment of the present invention, the fixed-point sensor is one or more of a pressure sensor, a temperature sensor, a salinity sensor, a speedometer, an accelerometer, and an underwater acoustic sensor.

[0015] To solve the above technical problems, another technical solution adopted by the present invention is to provide a heterogeneous sensor data synchronization method based on spatiotemporal joint calibration, comprising the following steps:

[0016] Step 1: Initialization of spatiotemporal parameters. Assuming that there are M fusion sensor nodes deployed in the current system, and each fusion sensor node is connected to N fixed-point sensors, the spatiotemporal parameter initialization steps for the above structure are as follows:

[0017] Step 1.1: For the mth fusion sensor node, calculate its corresponding basic transmission delay

[0018]

[0019] Among them, n eff is the effective refractive index of the sensing fiber, L m is the effective transmission distance of the mth fusion sensor node, and c is the speed of light;

[0020] Step 1.2: Calculate the environmental dynamic compensation term of the mth fusion sensor node

[0021]

[0022] in, and They are the temperature-delay correlation coefficient, salinity-delay correlation coefficient, temperature change, and salinity change of the n-th fixed-point sensor under the m-th fusion sensor node;

[0023] Step 2: Define the spatiotemporal coordinate system of the nth fixed-point sensor under the mth fusion sensor node, including the global coordinate system and the local coordinate system.

[0024] The global coordinate system is

[0025]

[0026] in, is the three-dimensional coordinate of the fixed-point sensor;

[0027] The local coordinate system is

[0028]

[0029] in, is the pitch angle Roll angle Yaw angle The rotation matrix of is the coordinate translation of the mth fusion sensor node;

[0030] Step 3: Dynamically calibrate the spatial position change of the fixed-point sensor to compensate for the spatial position drift of the fixed-point sensor caused by ocean activities such as tides. Specifically:

[0031] Step 3.1: Set the radius around the fixed-point sensor under the mth fusion sensor node to r(m) The area is defined as the corresponding dynamic drift space position set Ω of the current subordinate sensor group (m) , with a radius of r (m) The definition of

[0032]

[0033] Among them, v (m) with a (m) are the velocity and acceleration values returned by the speedometer and accelerometer in the mth fusion sensor node, respectively. s is the signal sampling interval;

[0034] Step 3.2: Convert the speed returned by the speedometer in the mth fusion sensor node to the global coordinate system speed

[0035]

[0036] Step 3.3: Predict the next moment position of the nth fixed-point sensor under the mth fusion sensor node

[0037]

[0038] Step 3.4: Return the position drift delay caused by spatial position drift

[0039]

[0040] Step 4: Calculate the total delay compensation

[0041]

[0042] Compensate the signal timestamp:

[0043]

[0044] Compensate for the sampling period:

[0045]

[0046] Step 5: The aforementioned environmental influences and sensor spatial position drift can lead to non-uniform sampling time distribution (i.e., unequal sampling intervals or misaligned timestamps). A fractional delay filter (such as the Farrow structure) is used to interpolate or resample the non-uniformly sampled signal to align it to a unified time base and eliminate time axis deviation. Specifically:

[0047]

[0048] in, is the input sampling period, is the sampling signal of the nth fixed-point sensor under the mth fusion sensor node, l is the sampling point index value, is the kernel function of the Q-order Farrow structure filter, specifically

[0049]

[0050] Among them, c q are the polynomial coefficients, Determine and set the delay parameters by interpolation conditions or optimization objectives (such as minimizing approximation error)

[0051] In a preferred embodiment of the present invention, the performance improvement of sensor data synchronization is demonstrated by the following steps. For the mth node, assuming that the uncalibrated total system delay error is:

[0052]

[0053] After calibration, the total delay error is compensated as the residual:

[0054]

[0055] in, Represents the estimation error of each component. If the calibration algorithm is accurate, then

[0056] The beneficial effects of the present invention are as follows: an underwater fusion network system architecture and a heterogeneous sensor data synchronization method pointed out by the present invention, by cleverly combining the advantages of fixed-point sensors and optical fiber sensing technology, taking advantage of their strengths and overcoming their weaknesses, realize comprehensive and efficient monitoring of the underwater environment. It not only inherits the advantages of high-precision and real-time data acquisition of fixed-point sensors, but also fully utilizes the advantages of long-distance and continuous monitoring of optical fiber sensing technology. Through innovative network system architecture design, it solves the problems of limited data transmission rate and high system maintenance cost existing in traditional systems, provides a new solution for the field of underwater environment monitoring, and performs spatiotemporal joint calibration on the collected data of each fixed-point sensor, which can significantly improve the stability, accuracy and real-time performance of the underwater monitoring system, enhance environmental adaptability, improve flexibility, and more comprehensively solve the problems faced by underwater environment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0058] Figure 1 It is a structural diagram of a preferred embodiment of an underwater fusion network system architecture of the present invention;

[0059] Figure 2 This is a comparison diagram of the CDF curves of event positioning errors before and after data calibration in a heterogeneous sensor data synchronization method based on spatiotemporal joint calibration of the present invention. DETAILED DESCRIPTION

[0060] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] In practical applications, it is necessary to select a suitable sensing system based on specific needs and environmental conditions, and consider combining and complementing multiple technical means to achieve more comprehensive and accurate underwater environment monitoring.

[0062] See also Figures 1 and 2 , embodiments of the present invention include:

[0063] like Figure 1 The underwater fusion network system architecture shown includes: submarine optical cables, fusion sensor nodes, fixed-point sensors, data fusion transmission equipment, and a shore-end base station at the end of the submarine optical cable. The submarine optical cable is equipped with a fusion signal optical fiber and multiple sensor optical fibers, including but not limited to Figure 1 The first sensing optical fiber, the second sensing optical fiber and the third sensing optical fiber.

[0064] The fusion sensor nodes are distributed at intervals along the length direction of the submarine optical cable. In this embodiment, a first optical fiber sensing data acquisition device is provided in the fusion sensor node. The first optical fiber sensing data acquisition device is connected to multiple sensing optical fibers in a section of submarine optical cable before the fusion sensor node to perform optical fiber sensing data acquisition. Real-time monitoring of multiple parameters such as underwater temperature, pressure, strain, etc. can be performed through multiple sensing optical fibers. It has the advantages of high sensitivity and strong anti-electromagnetic interference ability.

[0065] The fixed-point sensors are respectively set in the fusion sensor node or outside the fusion sensor node, such as Figure 1The first fixed-point sensor, the second fixed-point sensor, the third fixed-point sensor, and so on. The fused sensor node is equipped with a fixed-point sensor data acquisition device, which is connected to the fixed-point sensors to collect fixed-point sensor data. In this embodiment, the fixed-point sensors are various types of pressure sensors, temperature sensors, salinity sensors, speedometers, accelerometers, and underwater acoustic sensors. Sensors with other functions can also be selected. Fusion sensor nodes are deployed at specific locations on the seabed or underwater to monitor the underwater environment and collect marine environmental parameters such as water temperature, salinity, and vibration.

[0066] like Figure 1 As shown, the data fusion transmission device is set in the fusion sensor node, and the input end of the data fusion transmission device is connected to the first optical fiber sensing data acquisition device and the fixed-point sensor data acquisition device in the fusion sensor node and the fusion signal optical fiber in a section of submarine optical cable before the fusion sensor node, so as to aggregate the data of this node and the previous nodes, which is beneficial to reducing the length of a single fusion signal optical fiber and improving the stability and accuracy of data transmission.

[0067] The output end of the data fusion transmission equipment is connected to the fusion signal optical fiber in a section of submarine optical cable after the fusion sensor node to transmit the signal and transmit the aggregated data to the next fusion sensor node for relay. It combines the advantages of fixed-point sensors and optical fiber sensing technology, taking advantage of their strengths and making up for their weaknesses, and realizes comprehensive and efficient monitoring of the underwater environment.

[0068] Furthermore, the shore-based base station is equipped with a data processing terminal and a second fiber-optic sensing data acquisition device. The sensing fiber in a section of submarine optical cable between the shore-based base station and its adjacent fusion sensor node is connected to the second fiber-optic sensing data acquisition device, ensuring data collection from the sensing fiber in this section of submarine optical cable. In this embodiment, the first and second fiber-optic sensing data acquisition devices are integrated with a light source and a data acquisition board. The light source emits light to the sensing fiber, and the data acquisition board receives the optical signal.

[0069] The data processing terminal is connected to the second fiber-optic sensor data acquisition device and a fusion signal fiber at the end of the submarine cable, integrating all fiber-optic sensor data and fixed-point sensor data. The data processing terminal aggregates and integrates the collected fiber-optic sensor data with the fixed-point sensor data, and compares and analyzes it with the intelligent AI database to obtain more accurate detection data, improving data accuracy.

[0070] A heterogeneous sensor data synchronization method based on spatiotemporal joint calibration performs spatiotemporal joint calibration on the collected data of each fixed-point sensor, including the following steps:

[0071] Step 1: Initialization of spatiotemporal parameters. Assuming that there are M fusion sensor nodes deployed in the current system, and each fusion sensor node is connected to N fixed-point sensors, the spatiotemporal parameter initialization steps for the above structure are as follows:

[0072] Step 1.1: For the mth fusion sensor node, calculate its corresponding basic transmission delay

[0073]

[0074] Among them, n eff is the effective refractive index of the sensing fiber, L m is the effective transmission distance of the mth fusion sensor node, and c is the speed of light;

[0075] Step 1.2: Calculate the environmental dynamic compensation term of the mth fusion sensor node

[0076]

[0077] in, and They are the temperature-delay correlation coefficient, salinity-delay correlation coefficient, temperature change, and salinity change of the n-th fixed-point sensor under the m-th fusion sensor node;

[0078] Step 2: Define the spatiotemporal coordinate system of the nth fixed-point sensor under the mth fusion sensor node, including the global coordinate system and the local coordinate system.

[0079] The global coordinate system is

[0080]

[0081] in, is the three-dimensional coordinate of the fixed-point sensor;

[0082] The local coordinate system is

[0083]

[0084] in, is the pitch angle Roll angle Yaw angle The rotation matrix of is the coordinate translation of the mth fusion sensor node;

[0085] Step 3: Dynamically calibrate the spatial position change of the fixed-point sensor to compensate for the spatial position drift of the fixed-point sensor caused by ocean activities such as tides. Specifically:

[0086] Step 3.1: Set the radius around the fixed-point sensor under the mth fusion sensor node to r(m) The area is defined as the corresponding dynamic drift space position set Ω of the current subordinate sensor group (m) , with a radius of r (m) The definition of

[0087]

[0088] Among them, v (m) with a (m) are the velocity and acceleration values returned by the speedometer and accelerometer in the mth fusion sensor node, respectively. s is the signal sampling interval;

[0089] Step 3.2: Convert the speed returned by the speedometer in the mth fusion sensor node to the global coordinate system speed

[0090]

[0091] Step 3.3: Predict the next moment position of the nth fixed-point sensor under the mth fusion sensor node

[0092]

[0093] Step 3.4: Return the position drift delay caused by spatial position drift

[0094]

[0095] Step 4: Calculate the total delay compensation

[0096]

[0097] Compensate the signal timestamp:

[0098]

[0099] Compensate for the sampling period:

[0100]

[0101] Step 5: The aforementioned environmental influences and sensor spatial position drift can lead to non-uniform sampling time distribution (i.e., unequal sampling intervals or misaligned timestamps). A fractional delay filter (such as the Farrow structure) is used to interpolate or resample the non-uniformly sampled signal to align it to a unified time base and eliminate time axis deviation. Specifically:

[0102]

[0103] in, is the input sampling period, is the sampling signal of the nth fixed-point sensor under the mth fusion sensor node, l is the sampling point index value, is the kernel function of the Q-order Farrow structure filter, specifically

[0104]

[0105] Among them, c q are the polynomial coefficients, Determine and set the delay parameters by interpolation conditions or optimization objectives (such as minimizing approximation error)

[0106] The performance improvement of sensor data synchronization is demonstrated by the following steps. For the mth node, the total uncalibrated system delay error is assumed to be:

[0107]

[0108] After calibration, the total delay error is compensated as the residual:

[0109]

[0110] in, Represents the estimation error of each component. If the calibration algorithm is accurate, then

[0111] The reduction of residuals after calibration directly improves the spatiotemporal accuracy of event estimation. The following error propagation model intuitively illustrates how residuals affect event estimation accuracy:

[0112] Assuming that events are detected collaboratively by multiple sensors (e.g., seismic wave location, target tracking), the estimation of spatiotemporal parameters depends on the synchronization and spatial consistency of sensor data. Reducing calibration residuals improves accuracy through the following mechanisms:

[0113] For the estimation of the time dimension of an event, the time of event occurrence t event Through sensor timestamp alignment estimation, let the time synchronization variance of the kth sensor be The estimated error is:

[0114]

[0115] like Independent and identically distributed Then the time synchronization variance is

[0116]

[0117] Residual variance The smaller the time synchronization variance Var(Δt event) is smaller, and the more accurate the estimated time of the event is;

[0118] For event location estimation in the spatial dimension, based on the time difference of arrival (TDOA) positioning, it is assumed that the synchronization residual of the kth sensor and the jth sensor is Then the TDOA error is

[0119]

[0120] Position estimation error ΔP event and The relationship is

[0121]

[0122] The synchronization residual is reduced by the calibration method of this scheme and The event position estimation error can be significantly reduced.

[0123] The above-mentioned sensor data synchronization method can effectively reduce the alignment error of the data sampling time of each fusion sensor node caused by environmental changes and position drift, thereby reducing the error of the seabed observation network in detecting and identifying seabed events.

[0124] Conduct simulation experiments, simulation experiment results:

[0125]

[0126] The experiment was repeated a large number of independent experiments through Monte Carlo experiments to verify the robustness of the spatiotemporal joint calibration method in a dynamic ocean environment. Figure 2 The CDF curves of event location errors before and after data calibration are compared. The following analysis can be obtained from the experimental results:

[0127] index Uncalibrated system Calibrated system Improvement ratio Median error 19.3m 0.18m 99.10％ 90% quantile error 28.5m 0.35m 98.80％ Maximum error 52.1m 1.2m 97.70％

[0128] After calibration, the system's positioning error was less than 0.35 meters in 90% of the experiments, providing reliable support for applications such as high-precision ocean monitoring and target tracking.

[0129] Furthermore, the key performance indicators before and after the spatiotemporal joint calibration verified by simulation experiments are compared as shown in the following table:

[0130]

[0131] By performing spatiotemporal joint calibration of the collected data from each fixed-point sensor, the stability, accuracy and real-time performance of the underwater monitoring system can be significantly improved.

[0132] In summary, the underwater fusion network system architecture and heterogeneous sensor data synchronization method pointed out in the present invention integrate fixed-point sensors and fiber optic sensors. It can simultaneously perform data analysis on the collected fixed-point sensor signals and fiber optic sensor signals, and compare and process the data through an intelligent AI big database to obtain the required accurate detection data, which can comprehensively solve the problem of underwater environmental monitoring.

[0133] The above are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A heterogeneous sensor data synchronization method based on spatiotemporal joint calibration, which performs spatiotemporal joint calibration on the collected data of each fixed-point sensor, characterized in that: The following steps are involved: Step 1: Initialization of spatiotemporal parameters. Assuming that there are M fusion sensor nodes deployed in the current system, and each fusion sensor node is connected to N fixed-point sensors, the specific steps for initializing the spatiotemporal parameters of the system are as follows: Step 1.1: For the mth fusion sensor node, calculate its corresponding basic transmission delay Among them, n eff is the effective refractive index of the sensing fiber, L m is the effective transmission distance of the mth fusion sensor node, and c is the speed of light; Step 1.2: Calculate the environmental dynamic compensation term of the mth fusion sensor node in, and They are the temperature-delay correlation coefficient, salinity-delay correlation coefficient, temperature change, and salinity change of the n-th fixed-point sensor under the m-th fusion sensor node; Step 2: Define the spatiotemporal coordinate system of the nth fixed-point sensor under the mth fusion sensor node, including the global coordinate system and the local coordinate system. The global coordinate system is in, is the three-dimensional coordinate of the fixed-point sensor; The local coordinate system is in, is the pitch angle Roll angle Yaw angle The rotation matrix of is the coordinate translation of the mth fusion sensor node; Step 3: Dynamically calibrate the spatial position change of the fixed-point sensor to compensate for the spatial position drift of the fixed-point sensor caused by ocean activities. Specifically: Step 3.1: Set the radius around the fixed-point sensor under the mth fusion sensor node to r (m) The area is defined as the corresponding dynamic drift space position set Ω of the current subordinate sensor group (m) , with a radius of r (m) The definition of Among them, v (m) with a (m) are the velocity and acceleration values returned by the speedometer and accelerometer in the mth fusion sensor node, respectively. s is the signal sampling interval; Step 3.2: Convert the speed returned by the speedometer in the mth fusion sensor node to the global coordinate system speed Step 3.3: Predict the next moment position of the nth fixed-point sensor under the mth fusion sensor node Step 3.4: Return the position drift delay caused by spatial position drift Step 4: Calculate the total delay compensation Compensate the signal timestamp: Compensate for the sampling period: Step 5: Interpolate or resample the non-uniformly sampled signal through a fractional delay filter to align it to a uniform time base and eliminate time axis deviation. Specifically: in, is the input sampling period, is the sampling signal of the nth fixed-point sensor under the mth fusion sensor node, l is the sampling point index value, is the kernel function of the Q-order Farrow structure filter, specifically Among them, c q are the polynomial coefficients, Determine the delay parameters by interpolation conditions or optimization objectives 2. An underwater fusion network system for implementing the heterogeneous sensor data synchronization method based on spatiotemporal joint calibration according to claim 1, characterized in that: include: A submarine optical cable, wherein the submarine optical cable is provided with a fused signal optical fiber and a plurality of sensing optical fibers; Fusion sensor nodes, the fusion sensor nodes are spaced apart along the length of the submarine optical cable, the fusion sensor nodes are provided with a first optical fiber sensing data acquisition device, the first optical fiber sensing data acquisition device is connected to multiple sensing optical fibers in a section of the submarine optical cable before the fusion sensor node, and performs optical fiber sensing data acquisition; Fixed-point sensors, each of which is disposed in a fusion sensor node or outside the fusion sensor node. The fusion sensor node is provided with a fixed-point sensor data acquisition device, which is connected to the fixed-point sensor to perform fixed-point sensor data acquisition; A data fusion transmission device is provided in a fusion sensor node. An input end of the data fusion transmission device is connected to a first optical fiber sensing data acquisition device and a fixed-point sensor data acquisition device in the fusion sensor node, as well as a fusion signal optical fiber in a section of submarine optical cable before the fusion sensor node, for data aggregation. An output end of the data fusion transmission device is connected to a fusion signal optical fiber in a section of submarine optical cable after the fusion sensor node for signal transmission.

3. The underwater fusion network system according to claim 2, characterized in that: It also includes a shore base station located at the end of the submarine optical cable, in which a data processing terminal and a second optical fiber sensing data acquisition device are provided. The sensing optical fiber in a section of the submarine optical cable between the shore base station and its adjacent fusion sensor node is connected to the second optical fiber sensing data acquisition device, and the data processing terminal is connected to the second optical fiber sensing data acquisition device and a section of fusion signal optical fiber at the end of the submarine optical cable.

4. The underwater fusion network system according to claim 3, characterized in that: The data processing terminal aggregates and integrates the collected optical fiber sensing data and fixed-point sensing data, and compares and analyzes them with a large database to obtain more accurate detection data.

5. The underwater fusion network system according to claim 3, characterized in that: The first optical fiber sensing data acquisition device and the second optical fiber sensing data acquisition device are integrated with a light source and a data acquisition board.

6. The underwater fusion network system according to claim 2, characterized in that: The fixed-point sensor is one or more of a pressure sensor, a temperature sensor, a salinity sensor, a speedometer, an accelerometer and an underwater acoustic sensor.

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