Argo salinity profile observation data drift correction method

Abstract

The invention discloses an Argo salinity profile observation data drift correction method, and relates to the technical field of ocean observation data processing and correction, and the method specifically comprises the following steps: obtaining historical time sequence salinity profile observation data of Argo buoys with corresponding numbers, and constructing a climate state salinity estimation model; respectively introducing a large-scale space parameter and a small-scale space parameter into the climate state salinity estimation model to obtain a large-scale salinity variation and a small-scale salinity variation; calculating the weight of the large-scale salinity variation and the weight of the small-scale salinity variation, and adding the weights to obtain a climate state salinity objective estimation value; collecting a salinity section observation value in real time, comparing the salinity section observation value with the climate state salinity objective estimation value for analysis, and judging whether the salinity section observation value has a drift error or not; if the drift error exists, fitting the climatic state potential conductivity value to obtain a corrected climatic state potential conductivity value; and converting the corrected climate state potential conductivity value into a corresponding corrected salinity value.

Description

Technical Field

[0001] This invention relates to the field of marine observation data processing and correction technology, specifically a method for correcting drift in Argo salinity profile observation data. Background Technology

[0002] Argo is an international ocean observation program that primarily collects data such as temperature, salinity, and pressure in the global oceans using floating observation buoys (called "Argo buoys"). Argo buoys can automatically float between the ocean surface and the deep sea, continuously collecting and transmitting ocean profile data, especially salinity and temperature data. This is crucial for studying ocean circulation, climate change, marine ecosystems, and weather forecasting.

[0003] Chinese invention application CN120314529A discloses an error correction system and method for marine environmental observation data, comprising modules for data acquisition, compensation modeling, error analysis, data compensation, data fusion, and feedback optimization. The data acquisition module acquires ADCP current measurement, GPS, IMU, temperature, pressure, and water quality monitoring data. The error analysis module uses Kalman filtering and Hidden Markov Models to detect drift errors and analyzes environmental errors based on Long Short-Term Memory Networks. The data compensation module optimizes the compensation model to improve data accuracy. The data fusion module uses particle filtering and Bayesian optimization algorithms to fuse multi-sensor data. The feedback optimization module dynamically adjusts the weights of the compensation model based on the error correction data, improving system adaptability. This invention improves the accuracy of ocean current monitoring data, providing more reliable data support for underwater environmental research.

[0004] Existing technologies suffer from the following problems: the extreme complexity of the marine environment, with phenomena existing at various spatiotemporal scales, from large-scale ocean circulation to small-scale eddy motions, inevitably leads to drift in Argo buoy salinity data during long-term observations. Furthermore, relying on human understanding of the oceanographic characteristics of the area and past data experience to identify simple anomalies and then remove outliers has limitations, such as low efficiency and significant susceptibility to subjective factors. Therefore, there is an urgent need for a method and system for correcting drift in Argo salinity profile observation data to address the problems of existing technologies. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a method for correcting drift in Argo salinity profile observation data, the method comprising: Multiple Argo buoys in the target sea area were numbered, historical time-series salinity profile observation data of the corresponding numbered Argo buoys were obtained, and a climatological salinity estimation model was constructed. Large-scale and small-scale spatial parameters are introduced into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes. The weights of the large-scale and small-scale salinity changes are calculated and summed to obtain an objective estimate of climatological salinity. A pre-set salinity profile observation data acquisition device is used to collect salinity profile observation values ​​in real time and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values. If drift error exists, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. The corrected climatological potential conductivity values ​​are converted into corresponding corrected salinity values. Based on the corrected salinity values, the real-time collected salinity profile observations are corrected to complete the drift correction of the salinity profile observation data, and the corrected salinity profile observations are output.

[0006] In a preferred embodiment, the time-series salinity profile observation data includes: salinity profile observation data, temperature profile data, pressure profile data, observation time, buoy location coordinates, and buoy number.

[0007] In a preferred embodiment, the large-scale spatial parameters include: ocean circulation characteristic parameters, large-scale topographic and geomorphological parameters, and global climate system parameters.

[0008] In a preferred embodiment, the small-scale spatial parameters include: local water mixing parameters, small-scale eddy parameters, and local topographic parameters.

[0009] In a preferred embodiment, obtaining an objective estimate of climatological salinity includes: The climatological salinity estimation model introduces both large-scale and small-scale spatial parameters to obtain large-scale and small-scale salinity changes. A dynamic allocation mechanism based on Bayesian optimization is used to calculate the weights of large-scale and small-scale salinity changes; including, We construct objective functions for the contribution of large-scale parameters m and the contribution of small-scale parameters 1-m, with the goal of minimizing the root mean square error of the climatological salinity estimation model. By iteratively adjusting the value of m, we determine the optimal weight combination when the root mean square error reaches its minimum value. To obtain the weights of large-scale and small-scale salinity changes.

[0010] In a preferred embodiment, real-time acquisition of salinity profile observations is compared and analyzed with climatological salinity objective estimates to determine whether there is a drift error in the salinity profile observations, including: The pre-set comparison time window divides the real-time collected salinity profile observations and the climatological salinity objective estimates into continuous pre-set comparison time windows according to the time series. The deviation between the real-time salinity profile observations and the climatological salinity objective estimates for each preset comparison time window is calculated, and the allowable range threshold for the climatological salinity objective estimates is preset. If the real-time collected salinity profile observations are within the threshold of the preset allowable variation range of the objective estimate of climatological salinity, then it is determined that the salinity profile observations do not have drift error. If the real-time collected salinity profile observations are outside the preset threshold range of allowable variation for the objective estimate of climatological salinity, then the salinity profile observations are determined to have drift errors.

[0011] In a preferred embodiment, the corrected climatological potential conductivity value is obtained by: If drift error exists, obtain the salinity value deviation sequence corresponding to the buoy salinity profile observations; Calculate the climatological potential conductivity value of the salinity value deviation sequence corresponding to the observed salinity profile of the buoy, and the climatological potential conductivity value of the objective estimate of climatological salinity; For the climatological potential conductivity values ​​of the salinity deviation sequence corresponding to the buoy salinity profile observations and the climatological potential conductivity values ​​of the objective estimates of climatological salinity, corresponding fitting functions are constructed respectively. The climatological potential conductivity value fitted by the objective estimate of climatological salinity is used as the benchmark function; The climatological potential conductivity values ​​are corrected based on the weighted least squares method for the fitting function of the climatological potential conductivity values ​​of the salinity value deviation sequence corresponding to the buoy salinity profile observation values, with the benchmark function as the objective. The climatological potential conductivity value is obtained by iteratively calculating and adjusting the salinity value deviation sequence corresponding to the buoy salinity profile observations until the weighted residual sum of squares converges to the preset convergence threshold.

[0012] Another aspect of the present invention discloses an Argo salinity profile observation data drift correction system, which includes the following modules: numbering and data acquisition module, generation module one, judgment and analysis module, generation module two, and salinity value correction module; Numbering and data acquisition module: Number multiple Argo buoys in the target sea area, acquire historical time series salinity profile observation data of the corresponding numbered Argo buoys, and construct a climatological salinity estimation model; Module 1: Introduce large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes; calculate the weights of the large-scale and small-scale salinity changes and add them together to obtain an objective estimate of climatological salinity. Judgment and analysis module; a preset salinity profile observation data acquisition device is used to collect salinity profile observation values ​​in real time and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values; Module 2 is generated; if there is a drift error, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. The salinity correction module converts the corrected climatological potential conductivity value into the corresponding corrected salinity value, corrects the real-time collected salinity profile observation value based on the corrected salinity value, completes the drift correction of the salinity profile observation data, and outputs the corrected salinity profile observation value.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains historical time-series salinity profile observation data of the corresponding numbered Argo buoys in the target sea area by numbering them, and constructs a climatological salinity estimation model; it introduces large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes; it calculates the weights of the large-scale and small-scale salinity changes and adds them together to obtain an objective estimate of climatological salinity; and it presets salinity profile observation data... The data acquisition device collects real-time salinity profile observations and compares them with climatological salinity estimates to determine if there is a drift error in the salinity profile observations. If a drift error exists, the climatological potential conductivity value is fitted using the weighted least squares method to obtain a corrected climatological potential conductivity value. The corrected climatological potential conductivity value is then converted into a corresponding corrected salinity value. Based on the corrected salinity value, the real-time salinity profile observations are corrected to complete the drift correction of the salinity profile observation data, and the corrected salinity profile observations are output. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart illustrating the steps of the Argo salinity profile observation data drift correction method according to an embodiment of this application.

[0015] Figure 2 This is a schematic diagram showing the connections of the various modules in the Argo salinity profile observation data drift correction system according to an embodiment of this application. Detailed Implementation

[0016] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example 1 Please see Figure 1 As shown in the embodiments of this application, a method for correcting drift in Argo salinity profile observation data is provided. This method includes the following steps: Step 1: Number multiple Argo buoys in the target sea area, obtain historical time series salinity profile observation data of the corresponding numbered Argo buoys, and construct a climatological salinity estimation model; Based on the above embodiments, the time-series salinity profile observation data specifically includes, but is not limited to: salinity profile observation data, temperature profile data, pressure profile data, observation time, buoy position coordinates, and buoy number; Specifically, numbering multiple Argo buoys in the target sea area aims to allow buoys to collect data simultaneously at different locations. Numbering enables precise positioning and tracking of each buoy, facilitating the accurate extraction of specific buoy observation data from a large volume of data. For example, when studying salinity changes in a specific area, data from buoys within that area can be quickly filtered based on their numbers, without confusing information from buoys at different locations, greatly improving the efficiency of data management and use. Specifically, the construction of the climatological salinity estimation model includes: building models such as linear regression, random forest, and multilayer perceptron; dividing historical time series salinity profile observation data into training, validation, and test sets for training, validation, and testing; updating model parameters with optimizers such as Adam to minimize the loss function; monitoring the validation set loss during training to prevent overfitting; and evaluating model performance using the test set after training. Step 2: Introduce large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes; calculate the weights of the large-scale and small-scale salinity changes and add them together to obtain an objective estimate of climatological salinity. Based on the above embodiments, the large-scale spatial parameters specifically include: ocean circulation characteristic parameters (such as circulation patterns, current velocities, and eddy scales), large-scale topographic and geomorphological parameters (such as ocean basin shape and depth, and continental shelf distribution), and global climate system parameters (such as atmospheric circulation patterns and global wind fields). Small-scale spatial parameters specifically include: local water mixing parameters (such as turbulent mixing coefficient, temperature and salinity dual diffusion parameters), small-scale eddy parameters (such as eddy intensity and eddy scale), and local topographic parameters (such as seamounts and trenches, coral reefs and other shallow sea landforms). Specifically, major global ocean currents, such as the Atlantic Meridional Overturning Circulation (AMOC) and the Pacific Subtropical Circulation, determine the direction and speed of large-scale seawater flow, which has a crucial impact on the large-scale distribution of salinity. For example, the AMOC transports high-salinity seawater from low latitudes to high latitudes, affecting the salinity distribution in the North Atlantic. The velocity of the current directly affects the efficiency of salinity transport; a faster velocity can carry the salinity characteristics of one area to other areas more quickly, causing large-scale changes in salinity. For example, the Kuroshio Current's high-speed flow causes significant differences in the salinity distribution of the sea areas it passes through compared to the surrounding sea areas. Small-scale eddies are a common phenomenon in the ocean. Their intensity determines the motion of the water inside the eddy and the degree of exchange with the surrounding water. Strong eddies can draw water with different salinities into or out of the eddy region, leading to significant changes in salinity. The scale of the eddy (such as diameter and height) determines the spatial range of its influence on salinity. Smaller-scale eddies may only affect the salinity of a very small local area, while larger-scale eddies may change the salinity distribution over a larger area. The interaction of eddies of different scales can also lead to complex patterns of salinity variation at small scales. It should be noted that the purpose of introducing large-scale spatial parameters is to comprehensively grasp the impact of large-scale processes in the ocean on salinity from a macroscopic perspective. Its significance lies in revealing the overall distribution pattern and long-term trend of salinity in vast sea areas, helping to understand how large-scale factors such as ocean circulation and the global climate system drive salinity changes, and providing key evidence for global ocean salinity research and objective estimates of climatological salinity. The purpose of introducing small-scale spatial parameters is to accurately capture the effect of local, subtle processes in the ocean on salinity. Its significance lies in explaining complex changes in salinity within a smaller range, such as salinity anomalies caused by local water mixing and small-scale eddies, improving the understanding of the microstructure and dynamic changes of ocean salinity, and providing detailed information for marine ecosystem research and regional oceanographic analysis. The combination of the two can provide a more comprehensive, in-depth, and accurate understanding and simulation of the true state of ocean salinity. The specific process for obtaining an objective estimate of climatological salinity includes: introducing large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes; calculating the weights of large-scale and small-scale salinity changes using a Bayesian optimization-based dynamic allocation mechanism; specifically, constructing objective functions for the contribution of large-scale parameters *m* and the contribution of small-scale parameters *1-m*, with the optimization objective being to minimize the root mean square error (RMSE) of the climatological salinity estimation model; iteratively adjusting the value of *m*, and determining the optimal weight combination when the RMSE reaches its minimum value; thus obtaining the weights of large-scale and small-scale salinity changes. Step 3: Set up a salinity profile observation data acquisition device, collect salinity profile observation values ​​in real time, and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values; Based on the above embodiments, the specific process of real-time acquisition of salinity profile observations and comparison with climatological salinity objective estimates to determine whether there is a drift error in the salinity profile observations includes: The pre-set comparison time window divides the real-time collected salinity profile observations and the climatological salinity objective estimates into continuous pre-set comparison time windows according to the time series. The deviation between the real-time salinity profile observations and the climatological salinity objective estimates for each preset comparison time window is calculated, and the allowable range threshold for the climatological salinity objective estimates is preset. If the real-time collected salinity profile observations are within the threshold of the preset allowable variation range of the objective estimate of climatological salinity, then it is determined that the salinity profile observations do not have drift error. If the real-time collected salinity profile observation values ​​are not within the threshold range of the preset climatological salinity objective estimate, then the salinity profile observation values ​​are determined to have drift errors. Specifically, the pre-set salinity profile observation data acquisition device includes: various sensor devices (depth sensor, temperature sensor, pressure sensor); a data acquisition unit for receiving real-time data on salinity, temperature, pressure, depth, etc. collected by the sensors; and a communication unit (equipped with wireless communication such as satellite communication, Wi-Fi, Bluetooth, etc.) for transmitting the collected salinity profile observation data to the onshore monitoring station or cloud platform, supporting real-time data upload and remote monitoring. Step 4: If drift error exists, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. Based on the above embodiments, the specific process for obtaining the corrected climatological potential conductivity value includes: If drift error exists, obtain the salinity value deviation sequence corresponding to the buoy salinity profile observations; Calculate the climatological potential conductivity value of the salinity value deviation sequence corresponding to the observed salinity profile of the buoy, and the climatological potential conductivity value of the objective estimate of climatological salinity; For the climatological potential conductivity values ​​of the salinity deviation sequence corresponding to the buoy salinity profile observations and the climatological potential conductivity values ​​of the objective estimates of climatological salinity, corresponding fitting functions are constructed respectively. The climatological potential conductivity value fitted by the objective estimate of climatological salinity is used as the benchmark function; The climatological potential conductivity values ​​are corrected based on the weighted least squares method for the fitting function of the climatological potential conductivity values ​​of the salinity value deviation sequence corresponding to the buoy salinity profile observation values, with the benchmark function as the objective. The climatological potential conductivity value is obtained by iteratively calculating and adjusting the salinity value deviation sequence corresponding to the buoy salinity profile observations until the weighted residual sum of squares converges to a preset convergence threshold. Specifically, conductivity is a physical quantity that measures the concentration of dissolved ions in a body of water and is usually used to estimate the salinity of the water. In ocean observation, ocean observation equipment such as Argo buoys measure the conductivity, temperature and pressure of seawater to calculate the salinity of the seawater. "Climate state" refers to the long-term average state of a specific area under normal climatic conditions. Step 5: Convert the corrected climatological potential conductivity value into the corresponding corrected salinity value, correct the real-time collected salinity profile observation value based on the corrected salinity value, complete the drift correction of the salinity profile observation data, and output the corrected salinity profile observation value. Based on the above embodiments, the corrected climatological potential conductivity value is converted into the corresponding corrected salinity value through a preset conversion formula. This preset conversion formula is based on the TEOS-10 (Thermodynamic Equation of Seawater-2010) ocean thermodynamic equation system commonly used in international oceanography, and specifically adopts the practical salinity calculation formula therein.

[0019] Example 2 Please see Figure 2 As shown, in another embodiment of the present invention, the present invention also discloses an Argo salinity profile observation data drift correction system, which includes the following modules: numbering and data acquisition module, generation module one, judgment and analysis module, generation module two, and salinity value correction module; The above modules are connected via wired and / or wireless means to enable data transmission between the modules; Numbering and data acquisition module: Number multiple Argo buoys in the target sea area, acquire historical time series salinity profile observation data of the corresponding numbered Argo buoys, and construct a climatological salinity estimation model; Module 1: Introduce large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes; calculate the weights of the large-scale and small-scale salinity changes and add them together to obtain an objective estimate of climatological salinity. Judgment and analysis module; a preset salinity profile observation data acquisition device is used to collect salinity profile observation values ​​in real time and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values; Module 2 is generated; if there is a drift error, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. The salinity correction module converts the corrected climatological potential conductivity value into the corresponding corrected salinity value, corrects the real-time collected salinity profile observation value based on the corrected salinity value, completes the drift correction of the salinity profile observation data, and outputs the corrected salinity profile observation value.

[0020] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0021] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for correcting drift in Argo salinity profile observation data, characterized in that, The method includes: Multiple Argo buoys in the target sea area were numbered, historical time-series salinity profile observation data of the corresponding numbered Argo buoys were obtained, and a climatological salinity estimation model was constructed. Large-scale and small-scale spatial parameters are introduced into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes. The weights of the large-scale and small-scale salinity changes are calculated and summed to obtain an objective estimate of climatological salinity. A pre-set salinity profile observation data acquisition device is used to collect salinity profile observation values ​​in real time and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values. If drift error exists, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. The corrected climatological potential conductivity values ​​are converted into corresponding corrected salinity values. Based on the corrected salinity values, the real-time collected salinity profile observations are corrected to complete the drift correction of the salinity profile observation data, and the corrected salinity profile observations are output.

2. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, The time-series salinity profile observation data includes: salinity profile observation data, temperature profile data, pressure profile data, observation time, buoy location coordinates, and buoy number.

3. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, Large-scale spatial parameters include: ocean circulation characteristic parameters, large-scale topographic and geomorphological parameters, and global climate system parameters.

4. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, Small-scale spatial parameters include: local water mixing parameters, small-scale eddy parameters, and local topographic parameters.

5. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, The objective estimates of climatological salinity include: The climatological salinity estimation model introduces both large-scale and small-scale spatial parameters to obtain large-scale and small-scale salinity changes. A dynamic allocation mechanism based on Bayesian optimization is used to calculate the weights of large-scale and small-scale salinity changes; including, We construct objective functions for the contribution of large-scale parameters m and the contribution of small-scale parameters 1-m, with the goal of minimizing the root mean square error of the climatological salinity estimation model. By iteratively adjusting the value of m, we determine the optimal weight combination when the root mean square error reaches its minimum value. To obtain the weights of large-scale and small-scale salinity changes.

6. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, Real-time acquisition of salinity profile observations and comparison with climatological salinity objective estimates are used to determine whether there is drift error in the salinity profile observations, including: The pre-set comparison time window divides the real-time collected salinity profile observations and the climatological salinity objective estimates into continuous pre-set comparison time windows according to the time series. The deviation between the real-time salinity profile observations and the climatological salinity objective estimates for each preset comparison time window is calculated, and the allowable range threshold for the climatological salinity objective estimates is preset. If the real-time collected salinity profile observations are within the threshold of the preset allowable variation range of the objective estimate of climatological salinity, then it is determined that the salinity profile observations do not have drift error. If the real-time collected salinity profile observations are outside the preset threshold range of allowable variation for the objective estimate of climatological salinity, then the salinity profile observations are determined to have drift errors.

7. The Argo salinity profile observation data drift correction method according to claim 1, characterized in that, The corrected climatological potential conductivity values ​​include: If drift error exists, obtain the salinity value deviation sequence corresponding to the buoy salinity profile observations; Calculate the climatological potential conductivity value of the salinity value deviation sequence corresponding to the observed salinity profile of the buoy, and the climatological potential conductivity value of the objective estimate of climatological salinity; For the climatological potential conductivity values ​​of the salinity deviation sequence corresponding to the buoy salinity profile observations and the climatological potential conductivity values ​​of the objective estimates of climatological salinity, corresponding fitting functions are constructed respectively. The climatological potential conductivity value fitted by the objective estimate of climatological salinity is used as the benchmark function; The climatological potential conductivity values ​​are corrected based on the weighted least squares method for the fitting function of the climatological potential conductivity values ​​of the salinity value deviation sequence corresponding to the buoy salinity profile observation values, with the benchmark function as the objective. The climatological potential conductivity value is obtained by iteratively calculating and adjusting the salinity value deviation sequence corresponding to the buoy salinity profile observations until the weighted residual sum of squares converges to the preset convergence threshold.

8. An Argo salinity profile observation data drift correction system, employing the Argo salinity profile observation data drift correction method as described in any one of claims 1-7, characterized in that, The system includes the following modules: numbering and data acquisition module, generation module one, judgment and analysis module, generation module two, and salinity value correction module; Numbering and data acquisition module: Number multiple Argo buoys in the target sea area, acquire historical time series salinity profile observation data of the corresponding numbered Argo buoys, and construct a climatological salinity estimation model; Module 1: Introduce large-scale and small-scale spatial parameters into the climatological salinity estimation model to obtain large-scale and small-scale salinity changes. The weights of large-scale and small-scale salinity changes are calculated and summed to obtain an objective estimate of climatological salinity. Judgment and analysis module; A pre-set salinity profile observation data acquisition device is used to collect salinity profile observation values ​​in real time and compare and analyze them with the climatological salinity objective estimate values ​​to determine whether there is a drift error in the salinity profile observation values. Module 2 is generated; if there is a drift error, the climatological potential conductivity value is fitted based on the weighted least squares method to obtain the corrected climatological potential conductivity value. The salinity correction module converts the corrected climatological potential conductivity value into the corresponding corrected salinity value, corrects the real-time collected salinity profile observation value based on the corrected salinity value, completes the drift correction of the salinity profile observation data, and outputs the corrected salinity profile observation value.

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