A data processing method for an underwater glider for obtaining a current profile

Abstract

The application discloses a data processing method of an underwater glider for obtaining a sea current profile, and belongs to the technical field of ocean exploration. The underwater glider for obtaining the sea current profile comprises a pressure-resistant unit, a tail unit and a flow velocity profile obtaining unit. The flow velocity profile obtaining unit comprises an acoustic Doppler flow velocity profiler, a current meter and a temperature-salinity-depth sensor. The underwater glider can obtain the sea current information by integrating the flow velocity sensor, but the obtained sea current information is the relative velocity relative to the moving glider platform, and is not the absolute velocity of the sea current. The application obtains the accurate sea current profile based on the motion information of the underwater glider and the flow velocity profile observation data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ocean exploration, and particularly relates to a data processing method of an underwater glider for obtaining a current profile. BACKGROUND

[0002] Current is a common form of seawater movement, which refers to large-scale and relatively stable flow of seawater. Seawater moves in three dimensions in space, both horizontally and vertically. Current has an important influence on global climate change, ocean engineering, facility damage, coastal erosion, and biological migration.

[0003] With the development and maturity of underwater glider technology, underwater gliders, as a new type of sensor mounting platform, have been widely used in ocean field observation, environmental protection and scientific research. Underwater gliders generate propulsion by changing net buoyancy and adjusting attitude angle, and move by a pair of fixed wings. The energy consumption is extremely small, and the underwater glider can perform sawtooth profile motion in the ocean, with the characteristics of low power consumption and long endurance.

[0004] Underwater gliders can obtain current information by integrating flow rate sensors, but these information are relative to the speed of the glider platform, not the absolute speed of the current. How to obtain accurate absolute profile flow rate based on the motion information of the underwater glider and the flow rate profile observation data is a scientific problem to be further studied.

[0005] Therefore, it is of great significance to design and develop underwater gliders and data processing methods for obtaining accurate current profiles to obtain high-resolution, wide-coverage and multi-parameterized ocean flow field data for promoting ocean scientific research and obtaining ocean data. At present, there is still a lack of corresponding solutions in this field, and therefore it is urgent to supplement related technologies. SUMMARY

[0006] In view of the problems in the prior art, the application provides a data processing method of an underwater glider for obtaining a current profile, which aims to solve the problem that the absolute profile flow rate of the current cannot be accurately obtained by the underwater glider in the prior art.

[0007] The application is implemented as follows: an underwater glider for current profile acquisition, comprising a pressure-resistant unit, a tail unit and a flow rate profile acquisition unit, characterized in that the flow rate profile acquisition unit comprises an acoustic Doppler flow rate profiler, a current meter and a temperature-salinity-depth sensor.

[0008] In the above technical solution, preferably, the flow rate profile acquisition unit comprises a front fairing and an auxiliary fairing, and the Doppler flow rate profiler, the current meter and the temperature-salinity-depth sensor are installed in the front fairing.

[0009] In the above technical solution, preferably, a main connecting frame is fixed to the front of the pressure-resistant unit; the acoustic Doppler current profiler is installed on the main connecting frame through an acoustic Doppler current profiler compression plate and an acoustic Doppler current profiler connecting frame; the temperature-salinity-depth sensor is installed on the main connecting frame through a temperature-salinity-depth sensor fixing frame and a temperature-salinity-depth sensor connecting frame; the current meter is installed on the main connecting frame through a current meter fixing frame and a current meter connecting frame; and the temperature-salinity-depth sensor pump is installed on the main connecting frame through a temperature-salinity-depth sensor pump fixing frame and a temperature-salinity-depth sensor pump connecting frame.

[0010] The underwater glider of the present application integrates a flow velocity profile acquisition unit in the head, which includes an acoustic Doppler current profiler, a current meter, and a temperature-salinity-depth sensor, and can acquire flow velocity profile data for a long time and in a wide range. This integrated design enables the underwater glider to comprehensively and continuously collect marine environmental parameters. By integrating flow velocity sensors, the glider can acquire current information, but traditional methods can only measure the relative velocity with respect to the glider platform and cannot acquire the absolute velocity of the current. The present application realizes accurate current profile acquisition by combining the motion information of the glider with the flow velocity profile observation data, solving the problem in the prior art. In addition, the present application has the following specific advantages and effects, including:

[0011] 1. Multi-parameter integration: The acoustic Doppler current profiler, current meter, and temperature-salinity-depth sensor are integrated, enabling the acquisition of multiple key marine parameters and realizing comprehensive environmental monitoring.

[0012] 2. Long-term and wide-range observation: With long-term endurance capability, the present application can continuously observe in a wide sea area and acquire long-term and wide-range flow velocity profile data.

[0013] 3. Low-power design: By changing the net buoyancy and adjusting the attitude angle to generate propulsion, the energy consumption is extremely small, making it suitable for long-term marine observation tasks.

[0014] 4. High-resolution data acquisition: Through advanced sensors and data processing technology, high-resolution flow velocity profile data can be acquired, providing detailed current information.

[0015] 5. Absolute velocity acquisition: The present application solves the limitation of traditional technology that can only acquire relative velocity, and realizes accurate measurement of the absolute velocity of the current by comprehensively analyzing the motion information of the glider and the flow velocity profile observation data.

[0016] 6. Promoting marine scientific research: The present application can provide high-quality marine flow field data, which is of great significance to climate research, marine engineering, environmental protection, and other fields, and promotes the development of marine scientific research.

[0017] 7. Wide application: suitable for ocean field observation, environmental monitoring and scientific research in multiple fields, expanding the application range of underwater gliders.

[0018] 8. Reliability and accuracy: the present application improves the accuracy and reliability of flow rate data, reduces measurement error, and provides more accurate data support for scientific research and engineering application.

[0019] Overall, the underwater glider of the present application realizes efficient and accurate acquisition of the current profile through its integrated advanced sensors and innovative data processing method, has the advantages of low power consumption, long endurance, multi-parameterization, etc., and greatly promotes the ability of ocean scientific research and data acquisition.

[0020] The second object of the present application is to provide a data processing method for an underwater glider for acquiring a current profile, comprising the following steps:

[0021] The raw data of the flow rate profile acquisition unit is preprocessed to obtain processed data; during the process of acquiring the absolute flow rate profile by the flow rate profile acquisition unit, an error model is constructed according to the motion trajectory of the underwater glider and its external environment; the error model, external environmental factors and the characteristics of the underwater glider itself are added as additional constraints to the inversion matrix; through an adaptive weight adjustment strategy, the weights of the influence factors corresponding to each additional constraint are dynamically adjusted to optimize the error model; the processed data and the parameters of the optimized error model are brought into the inversion algorithm to acquire the accurate current profile.

[0022] In the above technical solution, preferably, the preprocessing includes gross error discrimination, threshold screening and data smoothing of the raw data; the threshold screening includes velocity screening, echo intensity screening and correlation screening; data smoothing uses a sliding filter to reduce random fluctuations and noise in the raw data.

[0023] In the above technical solution, preferably, the error model includes mathematical model error and system error, and the system error includes attitude error, flow rate calculation error and sound speed calculation error.

[0024] In the above technical solution, preferably, the additional constraints of the inversion matrix include depth average flow constraint, attitude error correction constraint, sound speed error constraint, data smoothing constraint and underwater glider axial velocity constraint, wherein each additional constraint has a corresponding influence factor and a specific weight.

[0025] In the above technical solution, preferably, the adaptive weight adjustment strategy includes initialization weight, iterative optimization, termination iteration judgment and output of optimized influence factor weight, wherein the iterative optimization method uses a particle swarm optimization algorithm.

[0026] In the above technical solution, preferably, the input value of the inversion algorithm is the processed data, additional constraints of the inversion matrix, and the optimized influence factor weight.

[0027] The absolute flow rate inversion method of the present application significantly improves the measurement accuracy of the absolute flow rate of the sea current by comprehensively considering possible error sources, performing detailed theoretical analysis and error processing. The following are all the advantages and effects of the present method:

[0028] 1. Comprehensive error analysis: The present application performs comprehensive theoretical analysis on possible error sources in the absolute flow rate inversion method. The influence of excessive constraints and insufficient parameters in model errors is discussed in detail, and methods to avoid these errors are proposed.

[0029] 2. Quantitative analysis of systematic errors: The sources and characteristics of attitude errors, flow errors and sound speed errors in systematic errors are quantitatively analyzed through error transfer formula. This can more accurately understand the influence of various errors on the inversion result, and help to improve the accuracy of the data.

[0030] 3. Combination of error model and external environmental factors: The error model, external environmental factors and the axial velocity of the underwater glider are added to the inversion matrix as part of the additional constraints. This method reduces error factors in the inversion algorithm process, making the solving process more controllable, thereby improving the accuracy of the inversion result.

[0031] 4. Adaptive weight adjustment strategy: An adaptive weight adjustment strategy is proposed, which adjusts the weight according to the influence factor of each constraint to optimize the inversion algorithm. This strategy can dynamically adjust the weight of each constraint, better reflecting the absolute flow rate profile in the real situation.

[0032] 5. Particle swarm optimization: Particle swarm optimization is used for actual optimization processing of weight adjustment. Particle swarm optimization is a high-efficiency optimization algorithm that can find the optimal solution under complex constraint conditions, thereby further improving the accuracy of the inversion result.

[0033] 6. Improve the accuracy and reliability of the inversion result: Through detailed error analysis and optimization processing, the present application can more accurately reflect the absolute flow rate profile in the real situation, reduce the influence of errors on the inversion result, and improve the reliability of the data.

[0034] 7. Adapt to complex marine environment: The present method considers the influence of external environmental factors and can provide high-precision flow rate measurement in complex and variable marine environments, with wide application range.

[0035] 8. Enhancing the value of scientific research and engineering applications: High-precision flow rate measurement data is of great significance in the fields of ocean science research, marine engineering, environmental protection, etc., and can provide more accurate and reliable data support.

[0036] 9. Improving data processing efficiency: Advanced algorithm optimization strategies are adopted to improve the efficiency of data processing, enabling fast and accurate processing and analysis of large-scale long-term ocean observation data.

[0037] The present application significantly improves the accuracy and reliability of the absolute flow rate profile obtained by the underwater glider through systematic error analysis and optimization processing, which plays an important role in promoting ocean science research and engineering applications. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a schematic diagram of the overall structure of the present application;

[0039] Figure 2 is a schematic diagram of the flow rate profile acquisition unit of the present application;

[0040] Figure 3 is a schematic diagram of the flow rate profile acquisition unit structure of the present application Figure 1 ;

[0041] Figure 4 is a schematic diagram of the flow rate profile acquisition unit structure of the present application Figure 2 ;

[0042] Figure 5 is a schematic diagram of the pressure-resistant shell structure of the present application Figure 1 ;

[0043] Figure 6 is a schematic diagram of the pressure-resistant shell structure of the present application Figure 2 ;

[0044] Figure 7 is a data processing flowchart of an embodiment of the present application;

[0045] Figure 8 is a case diagram of the inversion algorithm of the present application;

[0046] Figure 9 is an error model influence analysis diagram of an embodiment of the present application;

[0047] Figure 10 is a flowchart of the adaptive weight adjustment strategy of an embodiment of the present application;

[0048] Figure 11 is a curve diagram of the inversion result of an embodiment of the present application;

[0049] Figure 12 is a curve diagram of the inversion result error of an embodiment of the present application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] To address the problem in existing technologies where underwater gliders cannot accurately obtain the absolute profile velocity of ocean currents, this invention provides a data processing method for underwater gliders used to obtain ocean current profiles. To further illustrate the structure of this invention, a detailed description is provided below in conjunction with the accompanying drawings:

[0052] Example 1

[0053] An underwater glider for obtaining ocean current profiles includes a pressure-resistant unit, a tail unit, and a current velocity profile acquisition unit. The pressure-resistant unit comprises a front cover, a pressure-resistant chamber shell, ribs, and a rear cover. Internally, the pressure-resistant unit includes, from front to rear, an attitude adjustment unit, a main control unit, and a buoyancy drive unit. The tail unit includes a communication module and a ballast jettisoning unit. The pressure-resistant unit and tail unit are existing known structures for underwater gliders. In this embodiment, specifically, the pressure-resistant unit's exterior is composed of a front cover 201, a front pressure-resistant chamber shell 202, a middle pressure-resistant chamber shell 204, a rear pressure-resistant chamber shell 205, and ribs 203, forming a pressure-resistant sealed chamber capable of withstanding water pressure at a depth of 1100m. Inside the pressure-resistant unit, the attitude adjustment unit 207 is located within the front pressure-resistant chamber shell; the main control unit 208 is located within the middle pressure-resistant chamber shell; and the buoyancy drive unit 209 is located within the rear pressure-resistant chamber shell.

[0054] The current profile acquisition unit includes an acoustic Doppler current profiler 107, a current meter 109, and a temperature, salinity, and depth sensor 105. The acoustic Doppler current profiler (ADCP) is a high-precision instrument that uses the acoustic Doppler effect to measure the current velocity profile in water. Its working principle is based on the fact that when sound waves encounter tiny particles in the water, the frequency of the sound waves changes due to the movement of the particles. By measuring this frequency change (Doppler shift), the ADCP can calculate the velocity and direction of the current in each layer of the water body. The current meter is an instrument used to measure the velocity and direction of water currents in the ocean, and is widely used in oceanographic research, environmental monitoring, and marine engineering. A current meter typically consists of a sensor and a data recording system. The sensor can sense the velocity and direction of the water current through various methods (such as mechanical rotors, acoustic Doppler effect, and electronic magnetism). The temperature, salinity, and depth (pressure) sensor (CTD sensor) is a high-precision instrument used to measure temperature, salinity, and depth (pressure) in ocean or freshwater. CTD sensors provide detailed profile data on the physical properties of water bodies by detecting their electrical conductivity (used to calculate salinity), temperature, and pressure.

[0055] Specifically, the flow velocity profile acquisition unit is the main part of the underwater glider for observing the ocean flow field, which is fixed in a detachable manner in the front end cover 201 in the pressure-resistant unit. The flow velocity profile acquisition unit includes a front fairing 102 and a secondary fairing 101. The flow velocity profile acquisition unit adopts a two-part fairing configuration of the front fairing 102 and the secondary fairing 101, which can improve the efficiency of the underwater glider equipment debugging stage.

[0056] The flow velocity profile acquisition unit further includes a main connecting frame 103, a temperature-salinity-depth sensor pump 104, an acoustic Doppler current profiler compression plate 106, an acoustic Doppler current profiler connecting frame 108, a temperature-salinity-depth sensor pump fixing frame 110, a temperature-salinity-depth sensor pump connecting frame 111, a temperature-salinity-depth sensor fixing frame 112, a temperature-salinity-depth sensor connecting frame 113, a current meter fixing frame 114, and a current meter connecting frame 115.

[0057] In the present embodiment, the main connecting frame 103 is connected to the front end cover by M6*30mm fixing bolts. The temperature-salinity-depth sensor pump connecting frame 111, the temperature-salinity-depth sensor connecting frame 113, and the current meter connecting frame 115 are all provided with grooves and pre-installed mounting holes on the main connecting frame 103 to be connected by bolts, flat washers, and lock nuts.

[0058] The temperature-salinity-depth sensor pump fixing frame 110, the temperature-salinity-depth sensor fixing frame 112, and the current meter fixing frame 114 are all bolted to the corresponding sensors: the temperature-salinity-depth sensor pump 104, the temperature-salinity-depth sensor 105, and the current meter 109 by embracing clamping. The acoustic Doppler current profiler compression plate 106 is connected to the acoustic Doppler current profiler 107 by bolts through the lower connecting hole, and is connected to the main connecting frame and the acoustic Doppler current profiler connecting frame 108 by bolts, flat washers, and lock nuts through the upper connecting hole. At the same time, the main connecting frame 103 and the acoustic Doppler current profiler connecting frame 108 are fixed along the axial direction of the underwater glider, so as to ensure that the acoustic Doppler current profiler axis is parallel to the underwater glider axis. The above connecting frames, fixing frames, and compression plates are all made of 6061-T6 aluminum alloy.

[0059] By adjusting the relative positions among the current meter connecting frame 115, the current meter fixing frame 114, and the current meter 109, the projected diameter below the front fairing 102 is made the same as the detection hole of the outer shell of the current meter 109, so as to realize the functions of more accurate acquisition of sensor data and stabilization of the current meter by the detection hole as a second fulcrum.

[0060] The data processing method of the underwater glider for acquiring accurate ocean current profiles includes the following steps:

[0061] S1, the raw data of the flow velocity profile acquisition unit is preprocessed and the processed data is obtained. The preprocessing includes gross error discrimination, threshold screening and data smoothing of the raw data.

[0062] The gross error discrimination adopts the "3 principles", sets the statistical boundary, can effectively distinguish the extreme deviation from the general deviation, and thus subsequent processing is carried out, and the flow velocity data accuracy is increased; the threshold screening mainly includes velocity screening, echo intensity screening and correlation screening; the data smoothing adopts a sliding filter to reduce random fluctuations and noise in the data.

[0063] S2, in the process of absolute flow velocity profile acquisition by the flow velocity profile acquisition unit, an error model is constructed according to the unique motion trajectory of the underwater glider and the external environment in which it is located, the error model includes mathematical model error and system error, the system error includes attitude error, flow velocity calculation error and sound velocity calculation error, and the corresponding relationship between the attitude error and the acquired flow velocity deviation is shown in Figure 9 ;

[0064] S3, the error model, external environmental factors and the characteristics of the underwater glider itself are added as additional constraints to the inversion matrix, to reduce error factors in the inversion algorithm process and constrain the solving process. The additional constraints of the inversion matrix include depth average flow constraint, attitude error correction constraint, sound velocity error constraint, data smoothing constraint and underwater glider axial velocity constraint, wherein each additional constraint has a corresponding influence factor and a specific weight.

[0065] S4, through an adaptive weight adjustment strategy, the weight of the influence factor corresponding to each constraint is dynamically adjusted to optimize the accuracy and stability of the model, and the adaptive weight adjustment strategy flow chart is shown in Figure 10 . The adaptive weight adjustment strategy includes initialization weight, iterative optimization, termination iteration judgment and output of optimal influence factor weight, wherein the iterative optimization method adopts a particle swarm optimization algorithm.

[0066] S5, the preprocessed data and the optimized parameters are brought into the inversion algorithm to obtain the accurate ocean current profile eastward and northward ocean current profile is shown in Figure 11 , the error curve diagram is shown in Figure 12 , and it can be seen from Figure 12 that the accurate ocean current profile data inversion algorithm considering the error model and additional constraints can control the error between the inversion result and the actual result within 0.05 m / s, improve the accuracy of data inversion, effectively constrain the solving process, and realize high-resolution, wide-coverage, multi-parameterized ocean flow field observation data acquisition.

[0067] The principle of the inversion algorithm is:

[0068] The data observed by the ADCP is expressed in the form of linear equations, and then solved by the least square method; the sea current velocity observed by the ADCP carried by the underwater glider can be expressed as: , wherein, is the sea current velocity observed by the ADCP carried by the underwater glider; is the absolute flow rate of the sea current; is the motion velocity of the underwater glider in the geodetic coordinate system; the formula is expressed as a linear equation group, which can be expressed as . Wherein, d is the instantaneous sea current velocity profile matrix observed by the underwater glider during operation; G is the model matrix; m is the set of absolute velocity of the sea current and the motion velocity of the underwater glider; n is the noise in the observation process; the inversion algorithm is shown in the following formula: Figure 8 The ADCP records 4 instantaneous sea current velocity profiles from top to bottom, and each instantaneous flow velocity profile is composed of 3 water layer units, wherein , and the like represent the observed velocity of the corresponding water layer unit; therefore, each matrix in the formula can be expressed as:

[0069]

[0070]

[0071]

[0072] Wherein, the dimension of the G matrix is 24 14.

[0073] Therefore, it can be seen that in the above equation group, there are 24 equations and 14 unknowns, which belong to an overdetermined linear equation group, and the corresponding equation solution is solved by using the least square method:

[0074]

[0075] When there is noise interference, the robust solution is:

[0076]

[0077] Further, the error model comprises:

[0078] Mathematical model error and system error, the mathematical model error includes two types of problems of insufficient model parameters and excessive parameters; the system error includes attitude error, flow velocity calculation error and sound velocity calculation error. Among them, in the model parameter error, the error equation corresponding to the original function model is , the additional constraint, that is, the multiple selected parameter term is , and the corresponding error equation can be written as: When the additional parameters are introduced, the original parameters still have unbiased estimates, and the unit weight variance is unbiased, but the variance and precision change. Therefore, when additional constraints are introduced, appropriate factors need to be found to compensate for the precision. The flow error model in the system error is:

[0079] The sound speed error model is: The attitude error model is:

[0080]

[0081] Further, the inversion matrix additional constraint includes:

[0082] The depth average flow constraint, the attitude error correction constraint, the sound speed error constraint, the data smoothing constraint, and the underwater glider axial velocity constraint, wherein each constraint has a corresponding impact factor and has a specific weight; the expansion matrix is in turn:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] wherein, is the depth average flow impact factor; is the attitude error impact factor; is the data smoothing constraint impact factor; is the underwater glider axial velocity impact factor.

[0089] Further, the adaptive weight adjustment strategy includes:

[0090] Initialization weight, iterative optimization, termination iteration judgment, and optimal impact factor weight output, wherein the initialization weight is when the algorithm starts, an initial weight is assigned to each impact factor wherein , is the weight of the th constraint condition, and these initial weights are usually based on previous experience or equally initially assigned; the iterative optimization is based on the model performance evaluation total loss and weight adjustment, and the loss function is , For the model-based constraint condition, in each iteration, firstly, the model is evaluated using the current weight combination, and the performance index is calculated, and then the weight adjustment is performed, wherein the iterative optimization method adopts a particle swarm optimization algorithm; the iteration process continues, and when a maximum number of iterations is reached, the model performance improvement in the optimization process is less than a certain threshold, or the weight change is less than a certain threshold, the iteration is terminated. is a preset value for judging whether the weight change is small enough; finally, the final constraint weight combination is used as the optimal solution for the inversion process of the accurate current profile. is a preset value for judging whether the weight change is small enough; finally, the final constraint weight combination is used as the optimal solution for the inversion process of the accurate current profile.

[0091] The input values of the inversion algorithm are the processed data, the additional constraints of the inversion matrix, and the optimized influence factor weight.

[0092] The above merely describes preferred embodiments of the present application and is not intended to limit the present application, and any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for an underwater glider for acquiring a current profile, the underwater glider comprising a pressure-resistant unit, a tail unit, and a flow velocity profile acquisition unit, characterized in that: The flow velocity profile acquisition unit comprises an acoustic Doppler current profiler, a current meter and a temperature-salinity-depth sensor; the flow velocity profile acquisition unit comprises a front fairing and a sub fairing, and the Doppler flow velocity profiler, the current meter and the temperature-salinity-depth sensor are installed in the front fairing; The main connecting frame is fixed to the front part of the pressure-resistant unit; the acoustic Doppler flow velocity profiler is installed on the main connecting frame through an acoustic Doppler flow velocity profiler compression plate and an acoustic Doppler flow velocity profiler connecting frame; the temperature-salinity-depth sensor is installed on the main connecting frame through a temperature-salinity-depth sensor fixing frame and a temperature-salinity-depth sensor connecting frame; the current meter is installed on the main connecting frame through a current meter fixing frame and a current meter connecting frame; and the main connecting frame is installed with a temperature-salinity-depth sensor pump through a temperature-salinity-depth sensor pump fixing frame and a temperature-salinity-depth sensor pump connecting frame; The data processing method of the underwater glider comprises the following steps: The raw data of the flow velocity profile acquisition unit is preprocessed to obtain processed data; during the process of acquiring the absolute flow velocity profile by the flow velocity profile acquisition unit, an error model is constructed according to the motion trajectory of the underwater glider and the external environment thereof; the error model, external environmental factors and the characteristics of the underwater glider itself are added as additional constraints to the inversion matrix; the weights of the influence factors corresponding to each additional constraint are dynamically adjusted through an adaptive weight adjustment strategy to optimize the error model; and the processed data and the parameters of the optimized error model are brought into an inversion algorithm to acquire an accurate current profile.

2. The data processing method of an underwater glider for acquiring a current profile according to claim 1, characterized in that: The preprocessing comprises rough error discrimination, threshold screening and data smoothing of the raw data; the threshold screening comprises velocity screening, echo intensity screening and correlation screening; and the data smoothing adopts a sliding filter to reduce random fluctuations and noises in the raw data.

3. The data processing method of the underwater glider for acquiring the ocean current profile according to claim 1, characterized in that: The error model comprises mathematical model errors and system errors, and the system errors comprise attitude errors, flow velocity calculation errors and sound velocity calculation errors.

4. The data processing method of the underwater glider for acquiring the ocean current profile according to claim 1, characterized in that: The additional constraints of the inversion matrix comprise depth average flow constraints, attitude error correction constraints, sound velocity error constraints, data smoothing constraints and underwater glider axial velocity constraints, wherein each additional constraint has a corresponding influence factor and a specific weight.

5. The data processing method of the underwater glider for acquiring the ocean current profile according to claim 1, characterized in that: The adaptive weight adjustment strategy comprises initialization of weights, iterative optimization, termination iteration judgment and output of optimized influence factor weights, wherein the iterative optimization method adopts a particle swarm optimization algorithm.

6. The data processing method of an underwater glider for acquiring a current profile according to claim 1, characterized in that: The input values of the inversion algorithm are the processed data, the additional constraints of the inversion matrix and the optimized influence factor weights.

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