A multi-profiling instrument cooperative velocity profile measurement system and method based on sound velocity benchmark calibration

Abstract

This invention discloses a multi-profiler collaborative flow velocity profile measurement system and method based on sound velocity reference calibration. The system adopts a master-slave distributed chain architecture, including one ADCP master unit and multiple vertically deployed sound velocity profiler units. Each sound velocity profiler unit independently integrates a sound velocity probe and temperature, conductivity, and pressure probes. The data aggregation and fusion processing unit executes a bidirectional closed-loop mutual calibration method: by jointly determining the faulty and normal nodes of the sound velocity probe through self-check status, time series jumps, and neighbor point consistency; for normal nodes, the conductivity sensor is calibrated online using the sound velocity reference value to invert the equivalent conductivity; for faulty nodes, the sound velocity value is reconstructed using temperature, pressure, and conductivity data; a high-precision sound velocity profile is constructed using the direct sound velocity measurement value of the normal node and the reconstructed sound velocity value of the faulty node as discrete control points, and then substituted into the ADCP flow velocity calculation formula to correct the flow velocity profile. This invention fundamentally cuts off the cascade amplification path of sound velocity error to flow velocity, realizes in-situ bidirectional mutual calibration of sensors, and significantly improves the accuracy of flow velocity measurement and the reliability of long-term observation.

Description

Technical Field

[0001] This invention belongs to the field of marine environmental three-dimensional monitoring and underwater acoustic detection technology, and specifically relates to a multi-profiler collaborative current velocity profile measurement system and method based on sound velocity reference calibration. Background Technology

[0002] Accurate acquisition of water temperature, salinity, depth, current velocity, and sound velocity profiles is crucial for physical oceanography, underwater acoustics research, and marine engineering construction. Current technologies mainly face the following bottlenecks: In traditional observations, Acoustic Doppler Current Profiler (ADCP) and Temperature-Salinity-Depth (CTD) profiler often operate independently, resulting in spatiotemporal asynchrony and measurement isolation. Even when the CTD is integrated into the ADCP, it is mostly a single-point measurement, failing to reflect the cross-sectional structure. This leads to a cascading amplification problem of sound velocity errors: the temperature, conductivity, and pressure data measured by the CTD are used to indirectly calculate the sound velocity through empirical formulas, which are then substituted into the ADCP to calculate the flow velocity. In this process, contamination and drift errors of the conductivity sensor, as well as model errors in the empirical sound velocity formulas, are proportionally transmitted to the final flow velocity error, becoming the root cause of ADCP accuracy limitations.

[0003] More critically, existing technologies lack effective in-situ, real-time verification and calibration methods for both conductivity sensors, which are susceptible to contamination, and sound velocity sensors, which may be subject to deviations due to biofouling and electron drift. Data quality relies on expensive, periodic laboratory calibration, and the sensors within the system are independent of each other, making cross-validation and redundant backup impossible, resulting in insufficient reliability of long-term observation data.

[0004] Therefore, there is an urgent need for a highly reliable collaborative measurement system and method that can break the sound velocity error transmission chain and achieve in-situ mutual calibration of sensors. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a multi-profiler collaborative flow velocity profile measurement system and method based on sound velocity reference calibration, aiming to cut off the cascade amplification path of sound velocity errors, achieve in-situ bidirectional mutual calibration of sensors, and systematically improve the accuracy of flow velocity measurement and the reliability of long-term observation.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A multi-profiler collaborative flow velocity profile measurement system based on sound velocity reference calibration, characterized in that it includes: An acoustic Doppler current profiler main unit, abbreviated as ADCP main unit; Multiple sound velocity profiler units, referred to as SVP units, are distributed vertically around the ADCP main unit. Each SVP unit independently integrates a sound velocity probe for directly measuring the sound velocity at its depth, as well as temperature, conductivity, and pressure probes for measuring the temperature, conductivity, and pressure parameters at its depth. A data aggregation and fusion processing unit is communicatively connected to the ADCP host unit and all SVP units; the data aggregation and fusion processing unit is configured to perform the following functions: Simultaneously acquire direct sound velocity measurements from each SVP unit. Temperature measurement value Conductivity measurement value Pressure measurement value and the self-test status of the sound velocity probe; Fault diagnosis is performed on the sound velocity probe of each SVP unit. Based on one or more criteria, such as self-test status, time series stability of direct sound velocity measurement value, and spatial consistency with adjacent nodes, it is determined to be a normal node or a faulty node. Using direct sound velocity measurements from all normal nodes The reconstructed sound velocity value generated by reverse reconstruction of the faulty node As discrete control points, a continuous sound velocity profile covering the entire water layer measured by ADCP is constructed. The output velocity profile is then substituted into the velocity calculation algorithm of the ADCP host unit to correct its output velocity profile. ; For each normal node, use its direct sound velocity measurement value The conductivity measurement value of this node is calibrated online to generate calibrated conductivity data; For each faulty node, the sound velocity value of that node is reconstructed using its own or adjacent normal node temperature, pressure, and conductivity data, combined with the standard sound velocity formula. It is used to replace the direct sound velocity measurement value of the faulty node in subsequent processing.

[0007] In the above scheme, the fault diagnosis function specifically includes: determining whether any of the following situations occur: Scenario 1: The sound velocity probe's self-test status is abnormal; Scenario 2: Direct sound speed measurement The time series changes exceeded the preset physical mutation threshold; Scenario 3: Direct sound speed measurement The spatial gradient of the sound velocity value with respect to the adjacent normal node exceeds the preset hydrological gradient threshold. If any of the above conditions occur, the sound velocity probe of the corresponding SVP unit is determined to be a faulty node; if none of the above conditions are met, it is determined to be a normal node.

[0008] In the above scheme, the online calibration function specifically includes: For each normal node, utilize the temperature it measures. Electrical conductivity ,pressure The calculated value of the speed of sound is obtained by using the empirical formula for the speed of sound. ; Calculate the direct sound velocity measurement at this node. and deviation ; when When the conductivity exceeds a preset threshold, it is determined that there is an error in the conductivity measurement of that node, and it is then... and the temperature at the same point ,pressure As a constraint, the equivalent calibration conductivity value is solved by inverse function inversion of the empirical sound velocity formula. Used to replace the original conductivity measurement. The state is marked; otherwise, the original conductivity value is retained.

[0009] In the above scheme, the reconstruction of the sound speed value of the node specifically includes: Determine if the temperature, conductivity, and pressure probes at the faulty node are usable; If available, the original temperature, pressure, and conductivity data of the faulty node are obtained; if not available, spatial interpolation is performed on the temperature, pressure, and calibrated conductivity data of adjacent normal nodes to obtain the temperature, pressure, and conductivity values ​​at that depth. The acquired temperature, conductivity, and pressure data are substituted into the standard sound velocity formula to calculate the reconstructed sound velocity value. ; Output the reconstructed sound speed value And mark its reconstruction status.

[0010] In the above scheme, the construction of a continuous sound velocity profile covering the entire water layer measured by ADCP is described. Further features include: In the sparse region of SVP nodes, the estimated sound velocity is supplemented by calculating the temperature and pressure data of normal nodes and the calibrated conductivity data using the standard sound velocity formula. The supplementary sound velocity estimate, together with the discrete control points, is used to generate a continuous sound velocity profile using a physical constraint interpolation algorithm that considers the vertical gradient of the sound velocity. .

[0011] A measurement method based on the system described above includes the following steps: Step 1: Synchronously trigger the ADCP host unit and all SVP units to perform measurements; Step 2: Obtain the direct measurement value of the sound velocity of each SVP unit. Temperature measurement value Conductivity measurement value Pressure measurement value and the self-test status of the sound velocity probe; Step 3: Based on the fault diagnosis rules, divide the sound velocity probes of each SVP unit into normal nodes and faulty nodes; Step 4: For normal nodes, compare their direct sound velocity measurements. Compared with the sound velocity calculated based on temperature, conductivity, and pressure data The conductivity sensor is calibrated online to generate calibrated conductivity data. Step 5: For the faulty node, reconstruct the velocity of sound value using its own or adjacent normal node temperature, pressure, and conductivity data. ; Step 6: Collect direct measurements of sound velocity from normal nodes Reconstructed sound velocity values ​​of faulty nodes Constructing a high-precision sound velocity profile And use this to correct the flow rate calculation of ADCP; Step 7: Output the corrected high-precision velocity profile The data includes the temperature, conductivity, pressure, and sound velocity of each node after bidirectional closed-loop calibration, as well as their status indicators.

[0012] In the above scheme, the fault diagnosis rules include any one or more of the following combinations: Rule 1: The sound velocity probe's self-test status is abnormal; Rule 2: Direct measurement of sound speed A threshold jump occurs in the time series; Rule 3: Direct measurement of sound speed The spatial gradient between the sound velocity value and that of adjacent normal nodes exceeds the preset hydrological gradient threshold.

[0013] In the above scheme, the online calibration further includes the following steps: Step 4.1: When the speed of sound is directly measured Compared with the sound velocity calculated based on temperature, conductivity, and pressure data Deviation between When the preset threshold is exceeded, Based on this, the equivalent conductivity value was inverted. ; Step 4.2: Use the aforementioned equivalent conductivity value Replace the original conductivity measurement This enables in-situ software compensation for conductivity sensors.

[0014] In the above scheme, the reconstructed sound velocity value further includes the following steps: Step 5.1: If the temperature, conductivity, and pressure probes of the faulty node are unavailable, then perform spatial interpolation based on the temperature, pressure data of the adjacent normal nodes and the calibrated conductivity data; Step 5.2: Substitute the interpolated temperature, pressure, and conductivity data into the standard sound velocity formula, and combine this with optional spatial gradient constraints to generate alternative sound velocity values. .

[0015] In the above scheme, the construction of a high-precision sound velocity profile Includes the following steps: Step 6.1: Measure the direct sound velocity at normal nodes. Reconstructed sound velocity value from the fault node As a high-confidence control point; Step 6.2: In sparse node regions, supplement the sound velocity estimate using the original temperature, pressure, and conductivity data; Step 6.3: Generate a continuous sound speed profile using a physical constraint interpolation algorithm. .

[0016] Through the above technical solution, the multi-profiler collaborative flow velocity profile measurement system and method based on sound velocity reference calibration provided by the present invention has the following beneficial effects: 1. Fundamental precision improvement This invention uses the sound velocity reference value measured directly on-site. By introducing the ADCP flow velocity calculation core, the transmission chain from the error of the empirical formula for sound velocity and the error of the CTD sensor to the flow velocity is broken from the physical principle, thus realizing a generational improvement in the accuracy of ADCP flow velocity profile measurement.

[0017] 2. Pioneering a new paradigm of in-situ two-way mutual verification Based on the speed of sound Calculated values ​​of temperature, conductivity, and pressure With its node-based closed-loop verification and clear fault diagnosis rules, this invention enables real-time status diagnosis and software compensation for conductivity sensors, effectively extending their maintenance-free cycle. Simultaneously, by reconstructing faulty sound velocity sensors from multi-node temperature, conductivity, and pressure data, it achieves, for the first time, mutual backup and verification between sound velocity and temperature, conductivity, and pressure sensors, greatly improving the robustness of long-term observations and data availability.

[0018] 3. High system reliability The "one master, multiple slaves" chain architecture adopted in this invention has multiple redundancy capabilities. When individual SVP nodes fail, the system can not only utilize the remaining nodes to work with high precision, but also ensure the continuity and integrity of the data of the failed node through a reverse reconstruction mechanism, avoiding data loss caused by single point of failure.

[0019] 4. The diagnostic rules are clear and easy to implement in engineering. This invention uses three criteria—self-check status, time series jump, and neighbor consistency—to jointly determine faults, avoiding the limitations of a single criterion and improving the accuracy of fault detection and the level of system automation. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0021] Figure 1 This is a schematic diagram of a deep-sea long-term current profile observation anchorage disclosed in an embodiment of the present invention; Figure 2 This is a flowchart of the flow profile measurement method disclosed in the embodiments of the present invention.

[0022] In the diagram, 1. Buoy; 2. ADCP main unit; 3. SVP unit; 4. Float; 5. Release device; 6. Weight; 7. Main cable. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] Example 1: Anchoring system for long-term deep-sea current profile observation like Figure 1 As shown, in this embodiment, the system described in this invention is deployed on a deep-sea mooring main cable at a depth of 1000 meters. A 300 kHz ADCP main unit 2 is installed at a depth of 500 meters. Above and below the ADCP main unit 2, 15 miniaturized SVP units 3 are distributed at 50-meter intervals, covering a depth range of 200 to 800 meters. Each SVP unit 3 integrates a sound velocity probe based on the time-of-flight method and a high-precision CTD probe. A buoy 4 and a release device 5 are connected between the main cable 7 and the weight 6.

[0025] like Figure 2 The measurement method shown involves the system synchronizing every 15 minutes. The data aggregation and fusion processing unit is located inside the surface buoy 1 and is connected to each SVP unit and ADCP main unit 2 via cables. During each measurement, each SVP unit 3 records its current depth. direct sound speed measurement ,temperature Electrical conductivity ,pressure The self-test status of the sound velocity probe is uploaded to the data aggregation and fusion processing unit. The data aggregation and fusion processing unit executes the following refinement algorithm: 1. Fault Diagnosis The processing unit performs real-time status assessment of the sound velocity probes at each node, and makes a joint judgment based on three criteria: Self-test status abnormal: If a node's sound velocity probe reports a fault, it will be marked directly.

[0026] Time series jump: Monitoring detected a node at a depth of 450 meters. The velocity jumped abruptly from 1500.2 m / s to 1520.5 m / s in three consecutive measurements, exceeding the preset physical abrupt change threshold of 1.0 m / s, thus triggering the time series jump rule.

[0027] Large neighbor deviation: If a certain node If the spatial gradient of the sound velocity value with respect to the adjacent normal node exceeds the hydrological gradient threshold of 5 m / s / km based on historical profile statistics, it is considered abnormal.

[0028] If any of the above conditions are met, the node is determined to be a faulty node of the sound velocity probe; otherwise, it is a normal node. In this embodiment, the 450-meter depth node was marked as a faulty node due to a jump in the time series; the 650-meter depth node was a normal node, but subsequent calculations revealed that the deviation exceeded the limit.

[0029] 2. Forward calibration: In-situ conductivity calibration based on sound velocity reference For normal nodes (such as the 650-meter node), the processing unit utilizes its measurements , , The speed of sound was calculated using the Chen-Milliero standard formula. : ; Then calculate the deviation. Set dynamic threshold For the 650-meter node, If the velocity consistently exceeds 0.5 m / s, the conductivity probe is considered contaminated or drifting. At this point... , , To constrain this, the equivalent calibration conductivity is solved by inverse function derivation from the sound velocity formula. : ; use Replace the original conductivity measurement And mark the node's data status as "conductivity compensation". If If the original conductivity value is retained, then the original conductivity value is retained.

[0030] 3. Reverse Reconstruction: Fault Sound Velocity Reconstruction Based on Multi-Node Temperature, Conductivity, and Pressure For the 450-meter node diagnosed as having a faulty sound velocity probe, the first step was to check the usability of its temperature, conductivity, and pressure probes. In this case, the temperature, conductivity, and pressure probes at this node were functioning normally and their data were reliable; therefore, the node's own probes were used directly. , , Substituting these data into the Chen-Milliero standard sound velocity formula, the reconstructed sound velocity value is calculated. : ; To further improve accuracy, direct sound velocity measurements were referenced from adjacent 400-meter and 500-meter normal nodes. and Perform linear spatial gradient fine-tuning to obtain the final result. This data is then labeled "reconstructed data". If the temperature, conductivity, and pressure probes for the faulty node are also unavailable, spatial interpolation is performed on the temperature, conductivity, and pressure data of adjacent normal nodes to obtain the data at that depth. , , The reconstructed sound velocity value is then calculated.

[0031] 4. Velocity Correction and Sound Velocity Profile Construction The data aggregation and fusion processing unit aggregates all valid sound velocity values: direct sound velocity measurements from normal nodes. and the reconstructed sound velocity value of the fault node These serve as high-precision discrete control points. In sparsely nodeed depth layers (e.g., regions between adjacent SVP nodes), supplementary sound velocity estimates are calculated using standard sound velocity formulas based on temperature, pressure, and conductivity data from each normal node. Then, second-order smooth spline interpolation (considering the physical constraints of the vertical sound velocity gradient) is used to construct a full-profile continuous sound velocity field. .Will Substituting into the ADCP Doppler frequency shift formula, we can solve for each depth cell. Radial velocity: ; in, For Doppler frequency shift, The transmission frequency is used. The output is a corrected, high-precision flow velocity profile. .

[0032] 5. Data Output The final result is a complete dataset with uniform timestamps, depth coordinates, and sensor status identifiers, including high-precision velocity profiles. The system also collected calibrated data on temperature, conductivity, pressure, and sound velocity at each node. During a maintenance voyage three months later, the 450-meter node sensor was cleaned, the sound velocity probe self-tested and returned to normal, the system automatically stopped reconstructing, and the direct measurement values ​​were resumed.

[0033] Example 2: Mobile Platform Observation Variation This embodiment integrates the system described in this invention onto an autonomous underwater vehicle (AUV). Multiple SVP units are distributed longitudinally at 5-meter intervals within the AUV hull, and an ADCP main unit is installed in the center of the AUV. All units are connected to the data aggregation and fusion processing unit via internal cables within the AUV.

[0034] When the AUV performs profile movements (such as surfacing or diving), the system synchronously triggers all SVP units and ADCP main unit to perform measurements. The processing unit performs fault diagnosis, bidirectional closed-loop mutual calibration, and flow velocity correction in real time. At a certain depth layer, a certain SVP node is affected by turbulence, and its direct sound velocity probe measurement value... A transient time series jump occurs (a single mutation exceeds a preset threshold). The system immediately identifies this node as a faulty sound velocity probe and initiates reverse reconstruction: since the node's temperature, conductivity, and pressure probes are still functioning normally, its real-time temperature, pressure, and conductivity data are substituted into the sound velocity formula to instantly generate a reconstructed sound velocity value. To maintain the continuity and stability of the sound velocity output. The flow velocity calculation steps are the same as in Example 1.

[0035] This embodiment demonstrates the excellent platform adaptability and anti-dynamic interference capability of the system of the present invention, which can process sensor anomalies in real time on a mobile platform and ensure data quality.

[0036] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-profiler collaborative flow velocity profile measurement system based on sound velocity reference calibration, characterized in that, include: An acoustic Doppler current profiler main unit, abbreviated as ADCP main unit; Multiple sound velocity profiler units, referred to as SVP units, are distributed vertically around the ADCP main unit. Each SVP unit independently integrates a sound velocity probe for directly measuring the sound velocity at its depth, as well as temperature, conductivity, and pressure probes for measuring the temperature, conductivity, and pressure parameters at its depth. A data aggregation and fusion processing unit is communicatively connected to the ADCP host unit and all SVP units; the data aggregation and fusion processing unit is configured to perform the following functions: Simultaneously acquire direct sound velocity measurements from each SVP unit. Temperature measurement value Conductivity measurement value Pressure measurement value and the self-test status of the sound velocity probe; Fault diagnosis is performed on the sound velocity probe of each SVP unit. Based on one or more criteria, such as self-test status, time series stability of direct sound velocity measurement value, and spatial consistency with adjacent nodes, it is determined to be a normal node or a faulty node. Using direct sound velocity measurements from all normal nodes The reconstructed sound velocity value generated by reverse reconstruction of the faulty node As discrete control points, a continuous sound velocity profile covering the entire water layer measured by ADCP is constructed. The output velocity profile is then substituted into the velocity calculation algorithm of the ADCP host unit to correct its output velocity profile. ; For each normal node, use its direct sound velocity measurement value The conductivity measurement value of this node is calibrated online to generate calibrated conductivity data; For each faulty node, the sound velocity value of that node is reconstructed using its own or adjacent normal node temperature, pressure, and conductivity data, combined with the standard sound velocity formula. It is used to replace the direct sound velocity measurement value of the faulty node in subsequent processing.

2. The system according to claim 1, characterized in that, The fault diagnosis function specifically includes: determining whether any of the following situations occur: Scenario 1: The sound velocity probe's self-test status is abnormal; Scenario 2: Direct sound speed measurement The time series changes exceeded the preset physical mutation threshold; Scenario 3: Direct sound speed measurement The spatial gradient of the sound velocity value with respect to the adjacent normal node exceeds the preset hydrological gradient threshold. If any of the above conditions occur, the sound velocity probe of the corresponding SVP unit is determined to be a faulty node; if none of the above conditions are met, it is determined to be a normal node.

3. The system according to claim 1, characterized in that, The online calibration function specifically includes: For each normal node, utilize the temperature it measures. Electrical conductivity ,pressure The calculated value of the speed of sound is obtained by using the empirical formula for the speed of sound. ; Calculate the direct sound velocity measurement at this node. and deviation ; when When the conductivity exceeds a preset threshold, it is determined that there is an error in the conductivity measurement of that node, and it is then... and the temperature at the same point ,pressure As a constraint, the equivalent calibration conductivity value is solved by inverse function inversion of the empirical sound velocity formula. Used to replace the original conductivity measurement. The state is marked; otherwise, the original conductivity value is retained.

4. The system according to claim 1, characterized in that, The reconstructing of the sound velocity value of the node specifically includes: Determine if the temperature, conductivity, and pressure probes at the faulty node are usable; If available, the original temperature, pressure, and conductivity data of the faulty node are obtained; if not available, spatial interpolation is performed on the temperature, pressure, and calibrated conductivity data of adjacent normal nodes to obtain the temperature, pressure, and conductivity values ​​at that depth. The acquired temperature, conductivity, and pressure data are substituted into the standard sound velocity formula to calculate the reconstructed sound velocity value. ; Output the reconstructed sound speed value And mark its reconstruction status.

5. The system according to claim 1, characterized in that, The construction of a continuous sound velocity profile covering the entire water layer measured by ADCP Further features include: In the sparse region of SVP nodes, the estimated sound velocity is supplemented by calculating the temperature and pressure data of normal nodes and the calibrated conductivity data using the standard sound velocity formula. The supplementary sound velocity estimate, together with the discrete control points, is used to generate a continuous sound velocity profile using a physical constraint interpolation algorithm that considers the vertical gradient of the sound velocity. .

6. A measurement method based on the system according to any one of claims 1 to 5, characterized in that, Includes the following steps: Step 1: Synchronously trigger the ADCP host unit and all SVP units to perform measurements; Step 2: Obtain the direct measurement value of the sound velocity of each SVP unit. Temperature measurement value Conductivity measurement value Pressure measurement value and the self-test status of the sound velocity probe; Step 3: Based on the fault diagnosis rules, divide the sound velocity probes of each SVP unit into normal nodes and faulty nodes; Step 4: For normal nodes, compare their direct sound velocity measurements. Compared with the sound velocity calculated based on temperature, conductivity, and pressure data The conductivity sensor is calibrated online to generate calibrated conductivity data. Step 5: For the faulty node, reconstruct the velocity of sound value using its own or adjacent normal node temperature, pressure, and conductivity data. ; Step 6: Collect direct measurements of sound velocity from normal nodes Reconstructed sound velocity values ​​of faulty nodes Constructing a high-precision sound velocity profile And use this to correct the flow rate calculation of ADCP; Step 7: Output the corrected high-precision velocity profile The data includes the temperature, conductivity, pressure, and sound velocity of each node after bidirectional closed-loop calibration, as well as their status indicators.

7. The method according to claim 6, characterized in that, The fault diagnosis rules include any one or more of the following combinations: Rule 1: The sound velocity probe's self-test status is abnormal; Rule 2: Direct measurement of sound speed A threshold jump occurs in the time series; Rule 3: Direct measurement of sound speed The spatial gradient between the sound velocity value and that of adjacent normal nodes exceeds the preset hydrological gradient threshold.

8. The method according to claim 6, characterized in that, The online calibration further includes the following steps: Step 4.1: When the speed of sound is directly measured Compared with the sound velocity calculated based on temperature, conductivity, and pressure data Deviation between When the preset threshold is exceeded, Based on this, the equivalent conductivity value was inverted. ; Step 4.2: Use the aforementioned equivalent conductivity value Replace the original conductivity measurement This enables in-situ software compensation for conductivity sensors.

9. The method according to claim 6, characterized in that, The reconstructed sound velocity value further includes the following steps: Step 5.1: If the temperature, conductivity, and pressure probes of the faulty node are unavailable, then perform spatial interpolation based on the temperature, pressure data of the adjacent normal nodes and the calibrated conductivity data; Step 5.2: Substitute the interpolated temperature, pressure, and conductivity data into the standard sound velocity formula, and combine this with optional spatial gradient constraints to generate alternative sound velocity values. .

10. The method according to claim 6, characterized in that, The construction of high-precision sound velocity profile Includes the following steps: Step 6.1: Measure the direct sound velocity at normal nodes. Reconstructed sound velocity value from the fault node As a high-confidence control point; Step 6.2: In sparse node regions, supplement the sound velocity estimate using the original temperature, pressure, and conductivity data; Step 6.3: Generate a continuous sound speed profile using a physical constraint interpolation algorithm. .

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