A marine instrument dynamic calibration coefficient generation method, device, equipment and medium

Abstract

The application discloses a marine instrument dynamic calibration coefficient generation method and device, equipment and medium, and relates to the field of marine instrument calibration. The method comprises the following steps: substituting the real bottom layer original electric signal of many years into the benchmark coefficient, combining the standard test reference value to calculate the pure physical aging amount of each year, and constructing a three-dimensional topological curved surface network; according to the user input sea observation date and the three-dimensional topological curved surface network, the theoretical pure physical aging amount on the date is calculated by using a smoothing algorithm, the virtual standard reference value, the benchmark coefficient and the inverse residual error equation are combined to inversely deduce the theoretical bottom layer electric signal sequence on the date, and the configuration coefficient belonging to the date is generated; whether to update the three-dimensional topological curved surface network and the current configuration coefficient is determined according to the degree of deviation of the theoretical pure physical aging amount from the three-dimensional topological curved surface network. The application can provide the user with the best theoretical configuration coefficient at any time, improve the accuracy of the observation data, provide internal quality control, and ensure the accuracy of the calibration process.

Description

Technical Field

[0001] This application relates to the field of marine instrument calibration, and in particular to a method, apparatus, equipment and medium for generating dynamic calibration coefficients for marine instruments. Background Technology

[0002] The Conductivity-Temperature-Depth (CTD) instrument is a fundamental piece of equipment for ocean observation and a cornerstone of physical oceanography and global climate models. T (Temperature) is a high-accuracy temperature sensor that can capture minute temperature changes in the ocean; C (Conductivity) is a high-accuracy conductivity sensor that can capture minute changes in ocean conductivity (salinity); and D (Depth) is a depth sensor that captures the pressure field through a pressure sensor, and then performs a series of complex calculations to obtain an accurate depth value in the ocean. Because the ocean is a complex environmental system, the various ocean measurement parameters, such as C, T, and D, actually interact internally, and the final measurement result is obtained by combining these parameters.

[0003] With technological advancements and expanded applications, CTDs have found practical use in various scenarios. However, from the perspective of broad application paradigms, they can be basically divided into two modes: fixed (moored) and mobile (shipborne, profile) types. Moored CTDs, represented by the SBE37 manufactured by Seabird Corporation in the United States, are generally fixed to specific buoys, moored platforms, etc. They store measurement information such as temperature, conductivity (salinity), and depth (pressure) of specific water layers in their memory through methods such as cables (magnetic induction). They are retrieved after a specific period (e.g., every six months or a year). This type of CTD generally has a relatively low sampling frequency (e.g., once every 10 to 60 minutes). Shipborne profiling CTDs, exemplified by the SBE19plus, typically involve a survey vessel (or measurement vessel) carrying the CTD to a specific latitude and longitude area. After the vessel stops, the equipment is lowered to a specific water layer (or multiple water layers) at a specific speed (e.g., 1 m / s) to perform real-time and rapid measurements of the profile conditions at various ocean stations. The measurement data is then uploaded in real-time to the host computer on the ship via data cables hundreds or even thousands of meters long. These CTDs generally have a relatively high sampling frequency (e.g., 4Hz, 8Hz, or even higher sampling frequencies can be set).

[0004] The currently valid metrological technical specification is JJG 763-2019, Verification Procedure for CTD (Conductivity, Temperature, and Depth) Instruments. This specification is used for the periodic verification and qualification determination of CTDs. To ensure data quality, the metrological verification procedure JJG 763-2019 requires instruments to undergo metrological calibration periodically (once a year) and recommends verification or calibration before and after deployment, with evaluation based on the Maximum Permissible Error (MPE). However, in actual long-term (e.g., months to years) marine deployment missions, the traditional static calibration mode has revealed insurmountable operational bottlenecks: static constants cannot cope with real dynamic nonlinear decay; apparent errors mask the true health trajectory of the sensor; and the calibration process lacks a quality assurance (QA) mechanism.

[0005] In summary, providing users with the optimal theoretical configuration coefficients at any given time and improving the accuracy of observation data and calibration processes are pressing issues that need to be addressed. Summary of the Invention

[0006] The purpose of this application is to provide a method, apparatus, equipment, and medium for generating dynamic calibration coefficients for marine instruments, which can provide users with the optimal theoretical configuration coefficients at any time, improve the accuracy of observation data, and provide internal quality control to ensure the accuracy of the calibration process.

[0007] To achieve the above objectives, this application provides the following solution.

[0008] In a first aspect, this application provides a method for generating dynamic calibration coefficients for marine instruments, comprising the following steps.

[0009] Obtain the multimodal historical calibration profile of the target instrument, and extract the apparent readings, corresponding standard test reference values, and built-in current configuration coefficients of the target instrument based on the multimodal historical calibration profile.

[0010] Determine the original, underlying electrical signals of the target instrument over many years.

[0011] Substitute the underlying raw electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging amount based on the absolute reference physical quantity and the standard test reference value.

[0012] A three-dimensional topological surface network is constructed based on the pure physical aging amount over many consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument.

[0013] Based on the user-input date of the sea observation and the three-dimensional topological surface network, a smoothing algorithm is used to calculate the theoretical pure physical aging amount of the target instrument on the sea observation date.

[0014] Based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation, the theoretical underlying electrical signal sequence on the sea departure observation date is back-reasoned. The theoretical underlying electrical signal sequence and the corresponding virtual standard reference value are fitted to generate configuration coefficients belonging to the sea departure observation date. The inverse residual equation characterizes the relationship between the apparent value, the built-in current configuration coefficient, and the underlying original electrical signal.

[0015] If the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than the set threshold, then the configuration coefficient calibration process is manually judged to be incorrect.

[0016] If there are no errors, the target instrument is manually assessed for malfunction. If a malfunction is found, feedback information is sent to the user terminal. If no malfunction is found, the calibration data is used to update the three-dimensional topological surface network, and the generated configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a request is received from the user terminal, the dynamic compensation golden coefficients are sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date. The calibration data includes: the sea observation date, the corresponding virtual standard reference value, and the corresponding theoretical pure physical aging amount.

[0017] If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a user request is received, the dynamic compensation golden coefficients are sent to the user terminal.

[0018] Secondly, this application provides a dynamic calibration coefficient generation device for marine instruments, including: a data acquisition and extraction module, used to acquire the multimodal historical calibration archive of the target instrument, and extract the apparent value of the target instrument, the corresponding standard test reference value, and the built-in current configuration coefficient based on the multimodal historical calibration archive.

[0019] The underlying signal determination module is used to determine the original underlying electrical signals of the target instrument over many years.

[0020] The pure physical aging calculation module is used to substitute the underlying original electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging quantity based on the absolute reference physical quantity and the standard test reference value.

[0021] A three-dimensional topological surface network construction module is used to construct a three-dimensional topological surface network based on the pure physical aging amount over many consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument.

[0022] The theoretical physical aging calculation module is used to calculate the theoretical physical aging of the target instrument on the date of the sea observation, based on the user-input sea observation date and the three-dimensional topological surface network, using a smoothing algorithm.

[0023] The dynamic configuration coefficient generation module is used to reverse-engineer the theoretical underlying electrical signal sequence on the sea departure observation date based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation. It then fits the theoretical underlying electrical signal sequence with the corresponding virtual standard reference value to generate configuration coefficients belonging to the sea departure observation date. The inverse residual equation represents the relationship between the apparent value, the built-in current configuration coefficient, and the underlying original electrical signal.

[0024] The quality control and defense module is used to manually determine whether the configuration coefficient calibration process is incorrect if the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than a set threshold.

[0025] If there are no errors, the target instrument is manually assessed for malfunction. If a malfunction is found, feedback information is sent to the user terminal. If no malfunction is found, the calibration data is used to update the three-dimensional topological surface network, and the generated configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a request is received from the user terminal, the dynamic compensation golden coefficients are sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date. The calibration data includes: the sea observation date, the corresponding virtual standard reference value, and the corresponding theoretical pure physical aging amount.

[0026] If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a user request is received, the dynamic compensation golden coefficients are sent to the user terminal.

[0027] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the marine instrument dynamic calibration coefficient generation method described above.

[0028] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for generating dynamic calibration coefficients for marine instruments as described above.

[0029] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, apparatus, equipment, and medium for generating dynamic calibration coefficients for marine instruments. It substitutes the real, underlying raw electrical signals from multiple years into the reference coefficients, calculates the annual pure physical aging amount by combining standard test reference values, and constructs a three-dimensional topological surface network. Based on the user-input sea observation date and the three-dimensional topological surface network, a smoothing algorithm is used to calculate the theoretical pure physical aging amount for that date. Combining virtual standard reference values, reference coefficients, and inverse residual equations, the theoretical underlying electrical signal sequence for that date is inversely derived, generating configuration coefficients for that date. The degree to which the theoretical pure physical aging amount deviates from the three-dimensional topological surface network determines whether to update the three-dimensional topological surface network and the current configuration coefficients. This provides users with the optimal theoretical configuration coefficients for any given time, improving the accuracy of observation data. Furthermore, it provides internal quality control, enabling metrology institutions to provide high-quality metrology technical services, controlling the quality of calibration data, ensuring the accuracy of the calibration process, and guaranteeing the accuracy of user instrument measurement data from a metrological perspective. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating a method for generating dynamic calibration coefficients for marine instruments, provided in an embodiment of this application.

[0032] Figure 2 This is a schematic diagram illustrating the logical principle of reverse root tracing and bidirectional error decoupling provided in the embodiments of this application.

[0033] Figure 3 A schematic diagram illustrating the dynamic golden coefficient generation principle based on a three-dimensional topological surface network, provided for an embodiment of this application.

[0034] Figure 4This is a schematic diagram of the topology used in an embodiment of this application to perform reverse auditing of laboratory test anomalies using a three-dimensional topological surface network.

[0035] Figure 5 The evolution trajectory of nonlinear hysteresis deep V fatigue generated by the pressure sensor provided in the embodiments of this application during multiple calibration cycles is shown in the figure.

[0036] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] Traditional static calibration methods have the following drawbacks.

[0039] Static coefficients cannot handle real dynamic nonlinear decay: Traditional metrology institutions only issue one set of static coefficients based on the conditions of the day of calibration (such as the configuration coefficients of pressure sensors) for each calibration. , and However, due to physical wear or mechanical fatigue of the strain gauges under alternating high pressure in the deep sea, their physical state undergoes continuous and nonlinear drift every day. If users consistently use the static coefficients given by the laboratory on the first day throughout the 300-day observation period, systematic deviations will occur in the later stages of the observation, making it difficult to meet the high accuracy requirements for marine environmental monitoring, detection, and measurement.

[0040] Apparent errors mask the true health trajectory of sensors: errors recorded in user historical data often mix errors caused by the instrument's own physical aging with human management errors caused by users entering incorrect or forgotten update coefficients in the host computer software. Traditional static calibration methods cannot mathematically isolate human interference, and therefore cannot establish a true model of the instrument's natural lifespan degradation.

[0041] The calibration process lacks a quality assurance mechanism: traditional calibration unconditionally trusts the standard instrument. However, in practice, if the constant-temperature seawater bath is not fully thermally balanced, or if the operation is improper (such as residual microbubbles on the electrodes), or if the person in charge of the metrology fails to input coefficients in a timely manner or inputs incorrect coefficients, or if the calibration personnel do not pay attention to the details of their operation, sporadic test anomalies may occur in the calibration data. Related technologies lack a quality control and defense mechanism that utilizes the historical physical laws of the instrument being calibrated to monitor and intercept such sporadic laboratory errors.

[0042] Based on the above-mentioned deficiencies, the purpose of this application is to provide a method, apparatus, equipment and medium for generating dynamic calibration coefficients for marine instruments, to provide users with the best theoretical configuration coefficients at any time, to improve the accuracy of observation data, and to provide internal quality control to ensure the accuracy of the calibration process.

[0043] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] In one exemplary embodiment, such as Figure 1 As shown, a method for generating dynamic calibration coefficients for marine instruments is provided, including: Step 101: Obtain the multimodal historical calibration file of the target instrument, and extract the apparent value of the target instrument, the corresponding standard test reference value, and the built-in current configuration coefficient based on the multimodal historical calibration file.

[0045] Step 102: Determine the original, underlying electrical signals of the target instrument over many years.

[0046] Step 103: Substitute the underlying original electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging amount based on the absolute reference physical quantity and the standard test reference value.

[0047] Step 104: Construct a three-dimensional topological surface network based on the pure physical aging amount over many consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument.

[0048] Step 105: Based on the user-input date of the sea observation and the three-dimensional topological surface network, a smoothing algorithm is used to calculate the theoretical pure physical aging amount of the target instrument on the sea observation date.

[0049] Step 106: Based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient and inverse residual equation, reversely deduce the theoretical underlying electrical signal sequence on the sea departure observation date, fit the theoretical underlying electrical signal sequence and the corresponding virtual standard reference value to generate configuration coefficients belonging to the sea departure observation date.

[0050] The inverse residual equation characterizes the relationship between the apparent value, the built-in current configuration coefficients, and the underlying raw electrical signal. The reference coefficients can be factory default coefficients or reset reference coefficients after maintenance or sensor replacement.

[0051] Step 107: If the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than the set threshold, then the configuration coefficient calibration process is manually judged to be incorrect.

[0052] Step 108: If there are no errors, manually determine whether the target instrument is faulty. If a fault exists, send feedback information to the user terminal. If no fault exists, update the three-dimensional topological surface network using calibration data, and determine the generated configuration coefficient belonging to the sea observation date as the dynamic compensation golden coefficient.

[0053] Step 109: If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as the dynamic compensation golden coefficients.

[0054] Upon receiving a request from the user terminal, the dynamic compensation golden coefficient is sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date; the calibration data includes: the sea observation date, the corresponding virtual standard reference value, and the corresponding theoretical pure physical aging amount.

[0055] In another exemplary embodiment of this application, step 102 specifically includes: based on the apparent values ​​of each year and the built-in current configuration coefficients, calling the numerical iterative root-finding algorithm to perform reverse calculation on the inverse residual equation to obtain the actual underlying original electrical signal of that year.

[0056] In another exemplary embodiment of this application, the inverse residual equation The expression is: ;in, Indicates the apparent value; Indicates the built-in current configuration coefficient; This represents the underlying raw electrical signal; This indicates auxiliary calibration conditions, such as temperature compensation terms or pressure compensation terms; This represents the forward calculus function.

[0057] In another exemplary embodiment of this application, if the target instrument is a pressure sensor, the complex internal temperature compensation black box is ignored, and the coefficients of the final fitted quadratic polynomial on the certificate are directly extracted. , , ) and apparent pressure readings Construct the inverse residual equation of the pressure sensor. : Then, the Brent iteration method is invoked, which combines the bisection method and the secant method into a high-speed algorithm to calculate the underlying original electrical signal within a safe range. .

[0058] Specifically, for strain gauge pressure sensors, the expression for the inverse residual equation is: ;in, This indicates the apparent pressure value; , , This indicates the configuration coefficient of the strain gauge pressure sensor; This represents the underlying raw electrical signal.

[0059] The reverse root-finding process involves using known apparent pressure values. and configuration coefficient , , Constructing a quadratic equation to solve the underlying original electrical signal .

[0060] In another exemplary embodiment of this application, if the target instrument is a temperature sensor (taking a device with analog-to-digital conversion as an example), its internal count value (i.e., temperature value) is used as the underlying raw electrical signal. When solving for the underlying raw electrical signal, the internal count value is first mapped to voltage, then the voltage is substituted into the resistance calculation formula, and finally the resistance is substituted into the Steinhart-Hart thermodynamic polynomial. The Steinhart-Hart thermodynamic polynomial is used as the inverse residual equation, and through inverse calculation, the internal count value, which serves as the underlying raw electrical signal, is obtained.

[0061] Specifically, for the profile-type SBE19plus temperature sensor, the internal count value of the profile-type SBE19plus temperature sensor... Mapped to voltage Then the voltage Substitute the resistance calculation formula Finally, the resistor Substituting into the Steinhart-Hart thermodynamic polynomial , expressed as apparent temperature To reverse-engineer the internal count value of the target . , , , This is the configuration factor for the temperature sensor.

[0062] For anchor-mounted self-contained SBE37 temperature sensors: directly input the internal count value of the anchor-mounted self-contained SBE37 temperature sensor. Substituting the Steinhart-Hart thermodynamic polynomial By performing reverse calculations, its internal count value can be deduced. .

[0063] In another exemplary embodiment of this application, if the target instrument is a conductivity sensor, for the profiled SBE19plus conductivity sensor (without thermal inertia), the measured value of the profiled SBE19plus conductivity sensor... As the underlying, raw electrical signal. Substitute into the frequency calculation formula Then Substitute into the inverse residual equation Reverse reasoning .in, The apparent conductivity value is expressed in mS / cm. , , and For the configuration coefficient of the conductivity sensor; This is the pressure constant, typically taken as -9.5700e-008; This is a temperature constant, typically taken as 3.250000e-06; (dbar) represents the pressure during calibration (when the equipment is working). During calibration, the instrument is usually placed in air or just above the water surface, which can be considered as being under normal pressure (gauge pressure is 0), so it is generally taken as 0. This indicates the temperature sensed by the device during calibration (operation) (the temperature sensor reading is used as the input during calibration).

[0064] For bulky mooring equipment without a water pump, the glass tube exhibits significant thermal conduction delay. The system forces a simultaneous solution of a broadband ocean temperature correction formula in a polynomial to deduce the original frequency, thus identifying laboratory operational errors. This is specifically addressed for the mooring self-contained SBE37 conductivity sensor (including core error-proofing thermal conduction inertial compensation). Measurement values ​​of the anchor-mounted self-contained SBE37 conductivity sensor As the underlying raw electrical signal, Substitute into the modified frequency response formula The calculated frequency Substitute into the inverse residual equation Reverse reasoning .

[0065] In another exemplary embodiment of this application, the formula for calculating the historical pure physical aging amount is: ;in, This represents the amount of physical aging over a historical period. Represents an absolute reference physical quantity; This indicates the standard test reference value.

[0066] In another exemplary embodiment of this application, step 104 specifically includes: constructing a three-dimensional topological surface network using a conformal interpolation algorithm with the time series as the X-axis, the standard test reference value as the Y-axis, and the historical pure physical aging amount over many consecutive years as the Z-axis.

[0067] In another exemplary embodiment of this application, step 105 specifically includes: Based on the physical nominal full-scale range of the target instrument and the test point distribution rules required by the metrological verification procedure, a virtual standard test grid is constructed; each ideal standard test point in the virtual standard test grid corresponds to a virtual standard reference value. The user-inputted sea observation date is obtained and converted into a bottom-level time anchor point; the bottom-level time anchor point is a continuous floating-point time variable to ensure the highest resolution of the spatiotemporal surface interpolation. For each ideal standard test point in the virtual standard test grid, cross-sectional interpolation is performed on the three-dimensional topological surface based on the bottom-level time anchor point to obtain the theoretical pure physical aging amount of each ideal standard test point corresponding to the sea observation date.

[0068] In another exemplary embodiment of this application, step 106 specifically includes: superimposing the theoretical pure physical aging amount and the virtual standard reference value to obtain a superimposed value. Based on the superimposed value, the benchmark coefficient, and the inverse residual equation, the theoretical underlying electrical signal sequence for the sea departure observation date is inversely derived. The theoretical underlying electrical signal sequence and the corresponding virtual standard reference value are fitted using the polynomial least squares method to obtain the configuration coefficients belonging to the sea departure observation date.

[0069] In another exemplary embodiment of this application, step 107 specifically includes: if the theoretical pure physical aging amount of multiple target instruments deviates from the three-dimensional topological surface network by a greater than a set amplitude value and there is a jump in the same direction and magnitude, then manually determine whether the configuration coefficient calibration process is incorrect.

[0070] In practical applications, a more specific implementation process of the above-mentioned method for generating dynamic calibration coefficients for marine instruments is as follows.

[0071] Step 1: Initialization and dynamic closed-loop update of digital twin archives.

[0072] Obtain the multimodal historical calibration records of the target instrument since it entered service, and extract its apparent values. With built-in current configuration coefficients The data is structured and stored in a local database. During the long-term operation of the system, each latest calibration data that is determined to be free of operational anomalies through reverse auditing (step 6) will be automatically imported into the instrument's digital twin archive, and the boundary nodes of the multidimensional spatiotemporal baseline network will be refreshed in real time to ensure the timeliness and high accuracy of the prediction algorithm.

[0073] Step 2: Multimodal file reading and low-level electrical signal inverse reconstruction.

[0074] Extract apparent values ​​from the target instrument's calibration records. Corresponding standard test reference values and historical configuration coefficients Inverse residual equations are constructed based on different physical sensors. The numerical iterative root-finding algorithm (such as the Brent method) is invoked to reverse-engineer the original underlying electrical signal from that year.

[0075] Step 3: Bidirectional error decoupling and extraction of pure physical aging amount.

[0076] The extracted raw electrical signal from the bottom layer is substituted forward into the instrument's reference coefficient. In this process, an absolute reference physical quantity that is completely unaffected by human configuration is calculated. Then, decoupling is performed: pure physical aging amount. Management configuration deviation .

[0077] The logical principle of reverse root tracing and bidirectional error decoupling is as follows: Figure 2 As shown.

[0078] Step 4: Construct a three-dimensional topological surface network of "time-environmental standard test value-pure error".

[0079] Extracting from consecutive years Using a fractional time series accurate to the day (Epoch) as the X-axis and environmental standard test values ​​(standard values ​​of different temperatures, conductivity, and pressure) as the Y-axis, a three-dimensional topological surface network describing the aging trend of the sensor is constructed using a conformal interpolation algorithm (such as Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) to strictly eliminate mathematical overshoot during historical data gaps).

[0080] Step 5: Generation of dynamic golden ratio at specific time points.

[0081] (1) Virtual Ideal Mesh Construction: The virtual ideal standard test mesh is an absolutely ideal numerical sequence automatically generated in computer memory based on the physical nominal full scale of the target instrument and the test point distribution rules required by the metrological verification procedure. For example, for a CTD temperature sensor with a range of 0~35℃, the system generates an absolutely regular array (such as [0,5,10,15,20,25,30,35]℃) directly in memory, independent of any historical real calibration value, as a unified positioning anchor point for subsequent interpolation and refitting.

[0082] (2) Three-dimensional mesh slicing interpolation: The system extracts the physical aging evolution trajectory of each environmental test point in the three-dimensional topology surface mesh in step 4 as it changes over time; the user-input date is converted into a low-level continuous floating-point time variable t_{target} (for example, converting 2022-08-02 into 2022.58356); for each ideal standard test point in the virtual mesh generated in step (1), the PCHIP algorithm is used to perform interpolation calculation along the time axis to obtain the theoretical pure physical aging amount of the instrument for each ideal standard test point under the date t_{target}.

[0083] (3) Reverse calculation of theoretical signal: After superimposing the theoretical aging amount of each test point with the virtual ideal standard true value of the "virtual ideal standard test grid" in step (1), the theoretical underlying raw electrical signal sequence corresponding to that day is deduced through the reverse physical equation of the reference coefficient.

[0084] (4) Golden Coefficient Fitting and Verification: The electrical signal sequence and the virtual standard reference value are refitted using the polynomial least squares method of the sensor conforming to this instrument model to generate dynamic golden coefficients specific to this date. The system automatically substitutes this set of golden coefficients into the forward equation for closed-loop self-checking. After confirming that the fitting residual meets the requirements (generally its absolute value is not greater than one-third of the nominal maximum permissible error of this instrument), it is sent to the user. The system supports traversing the time axis back to provide the best coefficients for any date within the calibration period.

[0085] The overall description of steps (1) to (4) above is as follows: Obtain the specific date of the sea observation (e.g., YYYY-MM-DD) input by the user. The system converts this date into a bottom-level time anchor point, performs cross-sectional interpolation on the three-dimensional topological surface, and calculates the theoretical pure physical aging amount of the instrument on that date. After superimposing the theoretical aging amount with the standard true value, the theoretical bottom-level electrical signal sequence of that day is inversely derived through the benchmark coefficient. The sequence and the standard true value are refitted using the polynomial least squares method to generate a dynamic compensation golden coefficient specific to the observation date, replacing the traditional annual static coefficient.

[0086] Step 6: Intelligent reverse monitoring of systemic test anomalies in the laboratory (internal quality control defense).

[0087] In the current laboratory calibration, the amount of pure physical aging is extracted. If all three target instruments in the same batch of verification or calibration show abnormal deviations of the same direction and magnitude (such as deviation from the predicted trajectory and violation of the normal physical wear principle), it is determined that the laboratory metrology standard system (such as zero-point drift of the reference standard, loss of control of the constant temperature bath, human operation error, etc.) may have a systemic abnormality, the system triggers a fuse alarm, and the certificate issuance is blocked.

[0088] The method for generating dynamic calibration coefficients for marine instruments in this embodiment utilizes the instrument's historical calibration archives to remove human error through reverse tracing, and constructs a three-dimensional topological surface network that evolves with time and environment. This provides dynamic golden calibration coefficients for specific dates for long-term marine observations and also has a laboratory anomaly verification function, solving the problem that users cannot conduct long-term, high-precision observations. The details are as follows.

[0089] (1) Reverse restoration technology: By constructing a nonlinear residual equation with instrument hardware characteristics (such as thermal conduction compensation), the underlying original electrical signal is directly extracted using a numerical root-finding algorithm, completely removing human configuration deviations and restoring the pure physical aging trajectory.

[0090] (2) Collective intelligence reverse verification and dynamic closed-loop ecosystem: Establish a three-dimensional topological surface network library based on the historical pure physical performance of the calibrated instrument. This not only realizes intelligent "period verification" of the laboratory operating environment, but also automatically feeds new data generated by each legitimate calibration back to the database, dynamically iteratively updating the PCHIP interpolation parameters and realizing the self-evolution of the algorithm.

[0091] (3) Spatiotemporal dynamic interpolation and deduction based on "virtual ideal grid" (golden coefficient generation): Breaking through the traditional one-dimensional time series correction, a three-dimensional topological surface network based on "time-standard environmental parameters-pure physical error" is established. Among them, the standard environmental parameters include three independent standard parameters: temperature, conductivity, and pressure. According to any specified date, the system constructs a virtual ideal environmental grid covering the full range in memory, and through surface slice interpolation and inverse inversion, refits a set of dynamic golden coefficients (historical backtracking effect) specific to that date, and provides sensor calibration cycle optimization suggestions accordingly.

[0092] (4) Establish a "fuse-off defense" for laboratory anomalies: If all three instruments calibrated in the same batch deviate significantly from the three-dimensional prediction baseline during the current calibration, the system will determine that the laboratory conditions are out of control (such as abnormal temperature control, standard drift, incorrect standard settings, personnel operation errors, etc.) and automatically trigger an interception to ensure that the error coefficients are not sent to the customer. This will trigger a laboratory self-verification procedure to conduct comprehensive internal self-checks, including verification during the encryption period of the standard and verification of personnel operation.

[0093] Two specific examples are provided below.

[0094] Example 1: Dynamic derivation of the golden coefficient of a deep-sea profile pressure sensor.

[0095] A marine profiler (SN:P1) with a range of 2000 dbar underwent routine calibration in 2021 and 2023. Data decoupling revealed that its pressure strain gauges exhibited severe nonlinear hysteresis fatigue (deep V-shaped negative deviation) at a depth of 1500 dbar, and the degree of fatigue increased with each year.

[0096] A project requires the use of this instrument to process deep-sea diving data from August 2022 (between two calibrations). If the old static coefficients from 2021 are used, uncontrollable errors of up to 1 dbar will occur in deep water.

[0097] The operator inputs the target date, 2022-08-02, into the system. The system automatically performs precise surface slice interpolation on the constructed "time-pressure-error" three-dimensional topological surface using the PCHIP algorithm, such as... Figure 3 As shown. The system calculates the theoretical physical state of the day at each depth grid and refits a set of dynamic correction golden coefficients specific to that day ( Through closed-loop verification, this golden coefficient forcibly suppresses the nonlinear historical residual of 1 dbar to within ±0.11 dbar, meeting the requirements of high-precision ocean exploration.

[0098] Example 2: Intelligent fuse failure due to abnormal conductivity calibration environment in the same batch.

[0099] A batch of five instruments, including SN:P2, were subjected to annual conductivity calibration. During step 5 of the automatic system execution, a sudden and precipitous drop in the instrument's conductivity due to pure physical aging occurred. Since physical wear of electrodes is irreversible and extremely slow, this jump is illogical. Simultaneously, a cross-sectional comparison revealed that the baselines of two other instruments in the same tank also showed a negative deviation in the same direction. The system immediately determined: "Systematic conductivity test anomaly; please check the relevant processes." The system automatically blocked certificate issuance and prompted the experiment to be repeated, reducing the likelihood of errors.

[0100] The following is a sample of a running local verification interface.

[0101] python.exe golden_validator_pro.py ============================================================= Digital Twin [Golden Coefficient] Dynamic Generation and Closed-Loop Verification System ============================================================= [Task Initiation] Calculate the PRESSURE golden ratio for a specific departure date [2022-08-02] for SBE19plus (P3). System-level spatiotemporal anchor point transformation: 2022.58356 To simulate a gap in data, real data from 2022 was forcibly hidden. PCHIP predicts the amount of physical aging on this date: [-0.5908 -0.8036 -0.7361 -0.8225 -0.8035 -0.7615 -0.9203 -1.1266] The specific [golden ratio] for this date has been generated: PA0 = 2.08549412E+00 PA1 = 1.56205960E-02 PA2 = -6.30524464E-10 Closed-loop quality defense: The system inputs the golden ratio into the model for back-calculation, confirming that the mathematical residuals are suppressed to their maximum value: 1.17E-01. [Task Initiation] Calculate the TEMPERATURE golden ratio for a specific departure date of [2022-08-02] for SBE19plus (P3). System-level spatiotemporal anchor point transformation: 2022.58356 To simulate a gap in data, real data from 2022 was forcibly hidden. PCHIP predicts the amount of physical aging for this date: [-0.0009 -0.0011 -0.0015 -0.002 -0.0021 -0.0022 -0.0025 -0.0027] The specific [golden ratio] for this date has been generated: A0 = 1.23685315E-03 A1 = 2.78211715E-04 A2 = -1.43699050E-06 A3 = 1.91245667E-07 Closed-loop quality defense: The system inputs the golden ratio into the model for back-calculation, confirming that the mathematical residuals are suppressed to their maximum value: 1.530001E-04 [Task Initiation] Calculate the CONDUCTIVITY golden ratio for SBE37(M3) on a specific departure date of [2024-05-18]. System-level spatiotemporal anchor point transformation: 2024.37705 To simulate a gap in data, real data from 2024 was forcibly hidden. PCHIP predicts the amount of physical aging on this date: [-0.0147 -0.0174 -0.0215 -0.0246 -0.0274 -0.0311 -0.0353 -0.0389] The specific [golden ratio] for this date has been generated: G = -9.78783621E-01 H = 1.39218476E-01 I = -4.42910455E-04 J = 5.15104253E-05 CPCOR = -9.57000000E-08 CTCOR = 3.25000000E-06 WBOTC = -2.19466400E-07 Closed-loop quality defense: The system inputs the golden ratio into the model for back-calculation, confirming that the mathematical residuals are suppressed to their maximum value: 4.440001E-04. ============================================================= Verification complete. The output precision of all coefficients, the number of grid cells, and the interpolation logic for decimal years have met the usage standards.

[0102] To facilitate understanding of the basic coefficient structure of CTD SBE37, the baseline coefficient section from the YAML file is provided.

[0103] instrument_metadata: # Metadata Model: SBE 37SI sn: "M1 pressure_sensor_sn: "psn1" pressure_range_abs: 508 psia pressure_range_dbar: 350.0 firmware_version: V 3.0h firmware_coefficients_baseline: # Baseline data Comment: Original firmware from 2009. Updated on March 9, 2009. First applied on April 9, 2009, and used continuously for the next 16 years. Last verified as of August 21, 2025, this firmware version is still in use. temperature: TA0: 9.227968e-05 TA1: 2.629039e-04 TA2: -1.596799e-06 TA3: 1.288322e-07 conductivity: G: -1.018224e+00 H: 1.613618e-01 I: -2.268268e-04 J: 4.240152e-05 CPCOR: -9.570000e-08 CTCOR: 3.250000e-06 WBOTC: 3.976967e-07 pressure: PA0: 5.876814e-02 PA1: 1.719245e-03 PA2: 1.049182e-11 PTCA0: 5.249775e+05 PTCA1: 3.448506e+00 PTCA2: -8.795874e-02 PTCB0: 2.513763e+01 PTCB1: -6.750001e-04 PTCB2: 0.000000e+00 PTEMPA0: -6.945014e+01 PTEMPA1: 5.122358e-02 PTEMPA2: -6.137238e-07 POFFSET: 0.000000e+00.

[0104] To facilitate understanding of the baseline coefficient structure of SBE19plus, the baseline coefficient section from the YAML file is provided.

[0105] instrument_metadata: model: SBE 19plus V2 sn: "P1" firmware_version: V 3.1.8 baseline_date: "2018-12-02" pressure_sensor_sn: "psn3" pressure_range_psia: 5000 pressure_depth_rating_m: 3500 pressure_coeff_date: "2018-11-28" firmware_coefficients_baseline: Comment: Factory Calibration Coefficients temperature: Comment: Modified version of the Steinhart-Hart equation coefficients: TA0: 0.00123727142 TA1: 0.000278035418 TA2: -1.40995939e-06 TA3: 1.89847914e-07 conductivity: Comment: SBE conductivity equation coefficients coefficients: G: -0.989775235 H: 0.127928314 I: -0.000120394661 J: 2.5002692e-05 CPCOR: -9.57e-08 CTCOR: 3.25e-06 WBOTC: 0.0 pressure: Comment: SBE pressure polynomial coefficients coefficients: PA0: 1.09231456 PA1: 0.0156204332 PA2: -6.34795584e-10 PTCA0: 524120.201 PTCA1: 5.50047682 PTCA2: -0.107749552 PTCB0: 25.14425 PTCB1: -0.00035 PTCB2: 0.0 PTEMPA0: -51.8504223 PTEMPA1: 54.3037032 PTEMPA2: 0.0249781549 POFFSET: 0.0 Based on the same inventive concept, this application also provides a marine instrument dynamic calibration coefficient generation device for implementing the above-described method for generating marine instrument dynamic calibration coefficients. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the marine instrument dynamic calibration coefficient generation device provided below can be found in the limitations of the marine instrument dynamic calibration coefficient generation method described above, and will not be repeated here.

[0106] In one exemplary embodiment, a dynamic calibration coefficient generation device for marine instruments is provided, comprising: a data acquisition and extraction module, used to acquire the multimodal historical calibration archive of a target instrument, and extract the apparent value of the target instrument, the corresponding standard test reference value, and the built-in current configuration coefficient based on the multimodal historical calibration archive.

[0107] The underlying signal determination module is used to determine the original underlying electrical signals of the target instrument over many years.

[0108] The pure physical aging calculation module is used to substitute the underlying original electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging quantity based on the absolute reference physical quantity and the standard test reference value.

[0109] A three-dimensional topological surface network construction module is used to construct a three-dimensional topological surface network based on the pure physical aging amount over many consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument.

[0110] The theoretical physical aging calculation module is used to calculate the theoretical physical aging of the target instrument on the date of the sea observation, based on the user-input sea observation date and the three-dimensional topological surface network, using a smoothing algorithm.

[0111] The dynamic configuration coefficient generation module is used to reverse-engineer the theoretical underlying electrical signal sequence on the sea departure observation date based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation. It then fits the theoretical underlying electrical signal sequence with the corresponding virtual standard reference value to generate configuration coefficients belonging to the sea departure observation date. The inverse residual equation represents the relationship between the apparent value, the built-in current configuration coefficient, and the underlying original electrical signal.

[0112] The quality control and defense module is used to manually determine whether the configuration coefficient calibration process is incorrect if the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than a set threshold.

[0113] If there are no errors, the target instrument is manually assessed for malfunction. If a malfunction is found, feedback information is sent to the user terminal. If no malfunction is found, the calibration data is used to update the three-dimensional topological surface network, and the generated configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a request is received from the user terminal, the dynamic compensation golden coefficients are sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date. The calibration data includes: the sea observation date, the corresponding virtual standard reference value, and the corresponding theoretical pure physical aging amount.

[0114] If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a user request is received, the dynamic compensation golden coefficients are sent to the user terminal.

[0115] The key points of this embodiment are as follows.

[0116] ① Multimodal historical archive reading and reverse extraction of underlying electrical signals.

[0117] Since historical records often only document apparent values ​​(e.g., 15.002℃) and the formula coefficients used at the time, the system first needs to locate the sensor's underlying raw electrical signal. Based on hardware principles, the system dynamically matches a reverse engineering engine.

[0118] ② Bidirectional error decoupling.

[0119] Combination Figure 2 The system calculates the underlying raw electrical signal and substitutes it into the instrument's factory reference coefficients to calculate the theoretical reference physical quantity. Subsequently, the system calculates the pure physical aging quantity and management configuration deviation to distinguish whether it is wear and tear of the machine itself or an error in the user's coefficient input.

[0120] ③ Construct a three-dimensional topological surface network.

[0121] To prevent gaps between years from causing false "bulges" (Runge phenomenon) in the graph, the system strictly requires the use of the PCHIP algorithm, which stitches together the pure physical aging data from consecutive years to smoothly create a "time-environment-pure error" prediction network. This algorithm reduces interpolation overshoot and preserves local shape.

[0122] ④ Internal reverse quality audit and single-machine life assessment.

[0123] This embodiment implements a crowd-based verification mechanism, which handles two different scenarios.

[0124] Scenario 1: Abnormal reverse warning for conductivity calibration in the same batch, such as... Figure 4 As shown.

[0125] A batch of instruments, including SN:P2, were undergoing annual calibration. After the operator entered the data, the system's 3D audit module immediately issued a bright red alarm. Figure 4 The data shows that the instrument's purely physical aging process was a smooth "plateau" period between 2021 and 2022, but a significant plateau suddenly appeared in 2023. The pit.

[0126] The system's logic determined that the ablation of the conductivity electrode was irreversible and extremely slow, a sudden change that violated fundamental physical principles. Simultaneously, the system detected similar deviations in other instruments within the same bath. At this point, the system concluded that the instrument was not damaged; the problem likely lay with the laboratory thermostat not reaching thermal equilibrium or microbubbles adhering to the conductivity cell. The system automatically shut down, preventing the issuance of certificates for this batch. Until 2024, the instrument's data automatically rebounded to a healthy plateau, thus confirming that the system had successfully prevented a measurement error.

[0127] Scenario 2: End-of-life assessment of a single machine that exceeds traditional limits, such as... Figure 5 As shown.

[0128] A certain profiler (SN:P1) has been performing oceanographic observation missions at a depth of 2000 meters for an extended period. According to the regulations, its maximum permissible pressure error (MPE) is... (i.e., allowable error) ).

[0129] After the operator entered the 2014 data, the system generated the following: Figure 5 The diagram shows a panoramic view of nonlinear pressure hysteresis. Figure 5 The horizontal axis is arranged strictly in the order of first increasing pressure and then decreasing pressure. The system reveals that the instrument not only experienced zero-point drift but also suffered from severe "deep V-shaped mechanical fatigue (hysteresis effect)." In the deep-water zone in 2014, its pure physical aging amount actually reached... Since it exceeds the 2dbar MPE baseline, the system automatically generates the conclusion that "the sensor's physical fatigue life has ended and it is recommended to return it to the factory for replacement." Figure 5 After maintenance in 2017, the deep V-shape disappeared and the baseline was reset, proving the accuracy of the system assessment.

[0130] ⑤ Output the dynamic golden ratio at a specific time point.

[0131] When a user plans to deploy the instrument in the ocean for 300 days, traditional static coefficients will deviate over time, and the measurement accuracy may not meet the requirements for high-precision observation. The user can input a specified date into the system (such as the 150th day after deployment). The system uses a three-dimensional topological surface network to perform precise interpolation on the time axis, calculate the theoretical physical state of the instrument on that day, and refit it to obtain a set of golden correction coefficients specific to that date.

[0132] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores dynamic compensation gold coefficients. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for generating dynamic calibration coefficients for marine instruments.

[0133] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 6 The embodiments show more or fewer components, combinations of certain components, or different component arrangements. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, which the processor executes to implement the steps in the above-described method embodiments.

[0134] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0135] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0137] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for generating dynamic calibration coefficients for marine instruments, characterized in that, include: Obtain the multimodal historical calibration profile of the target instrument, and extract the apparent value of the target instrument, the corresponding standard test reference value, and the built-in current configuration coefficient based on the multimodal historical calibration profile; Determine the target instrument's true, underlying, original electrical signals over many years; Substitute the underlying raw electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging amount based on the absolute reference physical quantity and the standard test reference value. A three-dimensional topological surface network is constructed based on the purely physical aging data over several consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument. Based on the user-input date of the sea observation and the three-dimensional topological surface network, a smoothing algorithm is used to estimate the theoretical pure physical aging amount of the target instrument on the date of the sea observation. Based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation, the theoretical underlying electrical signal sequence on the sea departure observation date is back-reasoned. The theoretical underlying electrical signal sequence and the corresponding virtual standard reference value are fitted to generate configuration coefficients belonging to the sea departure observation date. The inverse residual equation characterizes the relationship between the apparent value, the built-in current configuration coefficient, and the underlying original electrical signal. If the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than the set threshold, then the configuration coefficient calibration process is manually judged to be incorrect. If there are no errors, the target instrument is manually checked for malfunction. If a malfunction is found, feedback information is sent to the user terminal. If no malfunction is found, the three-dimensional topology surface network is updated using calibration data, and the configuration coefficients generated for the sea observation date are determined as dynamic compensation golden coefficients. When a request is received from the user terminal, the dynamic compensation golden coefficients are sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date; the calibration data includes: sea observation date, corresponding virtual standard reference value, and corresponding theoretical pure physical aging amount; If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a user request is received, the dynamic compensation golden coefficients are sent to the user terminal.

2. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, Determine the target instrument's original, underlying electrical signals over many years, specifically including: Based on the apparent values ​​for each year and the built-in current configuration coefficients, the numerical iterative root-finding algorithm is invoked to perform reverse calculation on the inverse residual equation to obtain the actual underlying raw electrical signal for that year.

3. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, The expression for the inverse residual equation is: ； in, Indicates the apparent value; Indicates the built-in current configuration coefficient; This represents the underlying raw electrical signal; Indicates auxiliary calibration conditions; This represents the forward calculus function.

4. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, The formula for calculating the amount of purely physical aging is: ； in, This indicates the amount of aging purely physical; Represents an absolute reference physical quantity; This indicates the standard test reference value.

5. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, A three-dimensional topological surface network is constructed based on the purely physical aging data over several consecutive years, specifically including: Using time series as the X-axis, standard test reference values ​​as the Y-axis, and historical pure physical aging amounts over many consecutive years as the Z-axis, a three-dimensional topological surface network is constructed using a conformal interpolation algorithm.

6. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, Based on the user-input date of the sea observation and the three-dimensional topological surface network, a smoothing algorithm is used to estimate the theoretical pure physical aging of the target instrument on the sea observation date, specifically including: Based on the physical nominal full scale of the target instrument and the test point distribution rules required by the metrological verification procedure, a virtual standard test grid is constructed; each ideal standard test point in the virtual standard test grid corresponds to a virtual standard reference value. Obtain the sea observation date input by the user and convert the sea observation date into a low-level time anchor point; the low-level time anchor point is a low-level continuous floating-point time variable; For each ideal standard test point in the virtual standard test grid, cross-sectional interpolation is performed on the three-dimensional topological surface based on the underlying time anchor point to obtain the theoretical pure physical aging amount of each ideal standard test point corresponding to the sea observation date.

7. The method for generating dynamic calibration coefficients for marine instruments according to claim 1, characterized in that, Based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation, the theoretical underlying electrical signal sequence for the sea departure observation date is inversely calculated. The theoretical underlying electrical signal sequence and the corresponding virtual standard reference value are then fitted to generate configuration coefficients for the sea departure observation date, specifically including: The theoretical pure physical aging amount and the virtual standard reference value are superimposed to obtain the superimposed value; Based on the superposition value, the reference coefficient, and the inverse residual equation, the theoretical underlying electrical signal sequence on the date of the sea exit observation is inversely derived; The theoretical underlying electrical signal sequence and the corresponding virtual standard reference value are fitted using the polynomial least squares method to obtain the configuration coefficients belonging to the sea observation date.

8. A device for generating dynamic calibration coefficients for marine instruments, characterized in that, include: The data acquisition and extraction module is used to acquire the multimodal historical calibration archive of the target instrument, and extract the apparent value of the target instrument, the corresponding standard test reference value, and the built-in current configuration coefficient based on the multimodal historical calibration archive. The underlying signal determination module is used to determine the target instrument's true underlying raw electrical signals over many years; The pure physical aging calculation module is used to substitute the underlying original electrical signal into the reference coefficient of the target instrument to obtain the absolute reference physical quantity, and calculate the annual pure physical aging quantity based on the absolute reference physical quantity and the standard test reference value. A three-dimensional topological surface network construction module is used to construct a three-dimensional topological surface network based on the purely physical aging amount over many consecutive years; the three-dimensional topological surface network is used to characterize the aging trend of the target instrument; The theoretical pure physical aging calculation module is used to calculate the theoretical pure physical aging of the target instrument on the date of the sea observation based on the user-input sea observation date and the three-dimensional topological surface network, using a smoothing algorithm. The dynamic configuration coefficient generation module is used to reverse-engineer the theoretical underlying electrical signal sequence on the sea departure observation date based on the theoretical pure physical aging amount, virtual standard reference value, benchmark coefficient, and inverse residual equation. It then fits the theoretical underlying electrical signal sequence with the corresponding virtual standard reference value to generate configuration coefficients belonging to the sea departure observation date. The inverse residual equation represents the relationship between the apparent value, the built-in current configuration coefficient, and the underlying original electrical signal. The quality control and defense module is used to manually determine whether the configuration coefficient calibration process is incorrect if the deviation between the theoretical pure physical aging amount in the calibration data obtained by the target instrument during the current calibration and the pure physical aging amount of the three-dimensional topological surface network at the sea observation date and the corresponding standard test point is greater than a set threshold. If there are no errors, the target instrument is manually checked for malfunction. If a malfunction is found, feedback information is sent to the user terminal. If no malfunction is found, the three-dimensional topology surface network is updated using calibration data, and the configuration coefficients generated for the sea observation date are determined as dynamic compensation golden coefficients. When a request is received from the user terminal, the dynamic compensation golden coefficients are sent to the user terminal. The user terminal is used to update the current configuration coefficients built into the target instrument, thereby correcting the observation data for the sea observation date; the calibration data includes: sea observation date, corresponding virtual standard reference value, and corresponding theoretical pure physical aging amount; If there is an error, the coefficient calibration process is repaired, and the coefficient calibration process is re-executed to obtain new calibration data and new configuration coefficients belonging to the sea observation date. After manual judgment, the three-dimensional topological surface network is updated using the new calibration data, and the generated new configuration coefficients belonging to the sea observation date are determined as dynamic compensation golden coefficients. When a user request is received, the dynamic compensation golden coefficients are sent to the user terminal.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the method for generating dynamic calibration coefficients for marine instruments according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for generating dynamic calibration coefficients for marine instruments as described in any one of claims 1-7.

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