Buoy model-based buoy anomaly detection method

Abstract

The invention relates to the technical field of underwater equipment, in particular to a buoy model-based buoy anomaly detection method, which comprises the following steps of: constructing a buoy model when a profile drifting buoy is in a hovering stage, and performing parameter configuration on the buoy model by using multi-source data through a least square method; acquiring current sea test data returned by the profile drifting buoy; predicting a first outer oil bag volume under a preset drifting depth based on a buoy model, analyzing a second outer oil bag volume in the current sea test data, determining whether an absolute difference value between the first outer oil bag volume and the second outer oil bag volume is smaller than or equal to a preset detection threshold value, and if yes, not performing parameter updating; otherwise, judging whether the buoy model is updated, and if the model is updated, judging that the buoy is in an abnormal state; otherwise, executing a parameter updating step according to the statistical distribution condition of the current sea trial data. According to the invention, based on limited return data, accurate identification of the operation state and dynamic updating of the parameter model are realized.

Description

Technical Field

[0001] This application relates to the field of underwater equipment technology, and in particular to a buoy anomaly detection method based on a buoy model. Background Technology

[0002] In modern marine environmental monitoring, Argo buoys, as deep-sea profiling devices, have been widely used for long-term automated measurement of key ocean parameters such as temperature, salinity, and depth due to their low noise, low cost, long endurance, light weight, and ease of deployment. These buoys, through their diving and surfacing processes, enable vertical profiling observations from the surface to the deep sea, and are an important component of the Global Ocean Observing System (GOOS).

[0003] However, despite continuous advancements in structural design and measurement accuracy, Argo buoys, as single-use devices, are inherently limited by their inability to be recovered and maintained, making condition monitoring during operation particularly critical. Due to the complexity and variability of the marine environment, buoys can be affected by issues such as grounding, seal failure, or sensor malfunction, leading to decreased data acquisition accuracy or loss of observational functionality. Furthermore, Argo buoys typically rely on satellite communication for small-scale data transmission; under conditions of limited bandwidth and real-time performance, anomaly detection and condition diagnosis are challenging.

[0004] Current research has employed some methods to identify anomalies and update models using buoy operational data, but these methods still face significant challenges due to the dual challenges of drastic changes in the deep-sea environment and limited data availability. In particular, there is a lack of effective judgment criteria and quantitative analysis mechanisms for distinguishing between parameter drift and state anomalies.

[0005] Therefore, how to accurately identify the operational status of deep-sea profiling buoys and dynamically update their parameter models based on limited transmitted data, given the inability to physically recover and maintain them on-site, has become a key technical challenge that urgently needs to be overcome in the application of deep-sea profiling buoys. The aforementioned problems not only limit the accuracy of anomaly detection but also affect the reliability of deep-sea observation data and the depth of scientific research. Summary of the Invention

[0006] This application provides a buoy anomaly detection method based on a buoy model, including: A buoy model of a profile drifting buoy in the hovering phase is constructed, and the parameters of the buoy model are configured using the least squares method with multi-source data. The current sea trial data is periodically acquired via satellite communication from the profile drifting buoy. Based on the buoy model, the volume of the outer oil bladder is predicted at a preset drift depth, and this volume is used as the first outer oil bladder volume. The volume of the outer oil bladder in the current sea trial data was analyzed and used as the volume of the second outer oil bladder. Determine the volume of the first outer oil bladder. and the volume of the second outer oil bladder Whether the absolute difference between them is less than or equal to the preset detection threshold. If so, then no parameter update is required; Otherwise, determine whether the buoy model has been updated. If the model has been updated, then the buoy is determined to be in an abnormal state; Otherwise, perform the parameter update step based on the statistical distribution of the current sea trial data.

[0007] Based on the above steps, this application establishes a buoy model based on the stable behavior of the profile drifting buoy during the hovering phase, and uses multi-source data for fitting and parameter setting; it periodically receives the current sea trial data through satellite communication, calculates the volume of the first outer oil bladder in combination with the buoy model, and determines whether the difference between the currently measured volume of the second outer oil bladder and the volume of the first outer oil bladder exceeds the detection threshold to identify potential anomalies. If the absolute difference exceeds the detection threshold, it determines whether to judge it as an anomaly based on the model update flag, or continues to execute the parameter update step to update the model parameters based on statistical consistency.

[0008] In the above embodiments, the profile drifting buoy determines the drifting depth based on preset control commands. When the depth value of the buoy within a certain period of time is within the drifting depth range, it is determined that the buoy has entered the hovering stage.

[0009] In some embodiments, the buoy model is represented by the following computational model: , in, The mass of the profile drifting buoy, It is the acceleration due to gravity. For running depth The density of the water below, The volume of the glass sphere is... The volume of the outer oil sac. For the volume of other components of the buoy, The coefficient of compressibility of the glass sphere. The compression coefficient of the external oil bladder. The compression coefficient of other components of the buoy. This is the parameter error term, used to represent the errors related to the aforementioned parameters such as density, glass sphere volume, outer oil bladder volume, other component volumes, and corresponding compressibility coefficients.

[0010] In some embodiments, as depth increases, temperature decreases, leading to an increase in density. Similarly, salinity affects density, and as depth increases linearly, water compression also occurs, leading to an increase in density. Density can be simplified to an empirical function or fitted to a reference table based on historical sea trial data.

[0011] In another embodiment, in the buoy model, the density of water... As depth The function comprehensively considers the coupling effects of factors such as temperature gradient, salinity distribution, and hydrostatic pressure increase caused by depth changes. This is achieved by establishing... and The functional relationship between them can enable accurate modeling of buoyancy changes, enhance the accuracy of buoyancy prediction at different depths, and thus improve the overall response characteristics of the model.

[0012] Based on the above buoy model, the embodiments of this application are constructed based on the principle of buoyancy balance. The left side of the equation represents the gravity acting on the buoy, and the right side represents the buoy's position at depth. Buoyancy and error terms at the location. The buoyancy component corresponds to the buoyancy experienced by the volume of the glass bulb, external oil bladder, and other structural components in the buoy in the water. Due to the compressibility of materials underwater, their effective volume shrinks with increasing depth; therefore, a volume compressibility factor is introduced into the buoyancy component.

[0013] By establishing a buoyancy calculation model that incorporates the compressibility characteristics of multiple components and the effects of water density variations, accurate modeling of the mechanical state of a profile drifting buoy at different depths can be achieved. The model also considers the non-rigid response characteristics of the buoy's main components, making the calculation results closer to actual operating conditions. A parameter error term is introduced. It provides flexible model adjustment capabilities, which effectively reduce model prediction errors through fitting and correction methods such as least squares.

[0014] In some embodiments, the parameter error term is determined by the following calculation model: , in, , , These are the first coefficient, the second coefficient, and the third coefficient.

[0015] Based on the above calculation model, the embodiments of this application represent the parameter error term of the buoy model during the hovering phase as a depth-based composite exponential function structure, utilizing depth... As independent variables, through three coefficients , , To describe the trend of error with depth, the coefficient , , The method is determined by regression fitting based on historical sea trial data, and can be dynamically updated according to the current sea trial data during operation through parameter update steps to improve prediction accuracy.

[0016] In some embodiments, the multi-source data includes: simulation results, laboratory data, and historical sea trial data. Specific methods for configuring the parameters of the buoy model using multi-source data include: Based on the simulation results, the parameters of the buoy model are determined using laboratory data as initial values ​​(which may include the mass of the profile drifting buoy, the volume of the buoy's glass sphere, the volume of the buoy's external oil bladder and other components, the running speed, etc.). The parameters of the buoy model are updated using the least squares method based on historical sea trial data.

[0017] In some embodiments, the parameter update step further includes: Calculate the statistical distribution of the current sea trial data and determine whether its distribution follows the statistical distribution of historical sea trial data; If so, the parameter error term is updated using the current sea trial data and the least squares method, and the determination coefficient corresponding to the absolute difference is calculated. , Further determine the determination coefficient If the value is greater than 0.9, then the parameter error term is updated again using the least squares method with the current sea trial data; otherwise, the parameter error term is not updated. Otherwise, do not use the current sea trial data to update the parameter error terms.

[0018] Based on the above steps, this application embodiment dynamically updates the parameter error terms in the buoy model based on the dual constraints of determining the consistency of sea trial data distribution and evaluating the model fitting quality, so as to improve the fitting accuracy and environmental adaptability of the buoy model to actual ocean measurement data.

[0019] The least squares method used in this application can update the parameter error term under the condition of satisfying distribution consistency, and use the determination coefficient as a secondary update condition to enhance the model's ability to identify data quality and effectively suppress the influence of instantaneous anomalies or local noise on the parameter estimation of the parameter error term. Setting the threshold of the above determination coefficient helps to control the risk of overfitting while maintaining the model's accuracy, making the model more robust in long-term operation, and also providing a quantitative standard for multi-round iterative optimization.

[0020] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the buoy anomaly detection method according to an embodiment of this application; Figure 2 This is a schematic diagram of the buoy anomaly detection logic flow according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the mapping relationship between the absolute difference and pressure in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the change of the absolute difference over the buoy's operating timeline in an embodiment of this application. Figure 5 This is another mapping relationship between absolute difference and pressure in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the evolution of prediction error in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0023] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0024] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0025] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0026] Figure 1 This is a flowchart illustrating the buoy anomaly detection method according to an embodiment of this application. (Refer to...) Figure 1 As shown, a buoy anomaly detection method based on a buoy model includes: Step S1: Construct a buoy model of the profile drifting buoy in the hovering phase, and configure the parameters of the buoy model using the least squares method with multi-source data.

[0027] Step S2: Periodically acquire the current sea trial data transmitted back by the profile drifting buoy via satellite communication. The current sea trial data includes: real-time temperature, real-time salinity, real-time depth, historical oil discharge, etc. Step S3: Perform anomaly detection based on the buoy model and current sea trial data.

[0028] First, this application simulates the motion state of a buoy by constructing a buoy model according to the following formula (1). (1) in, The mass of the profile drifting buoy, The acceleration of the profile drifting buoy. It is the acceleration due to gravity. For running depth The density of the water below, The volume of the buoy's glass sphere is... The volume of the buoy's external oil bladder. For the volume of other components of the buoy, The coefficient of compressibility of the glass sphere. The compression coefficient of the external oil bladder. The compression coefficient is given by the value of other buoy components. These other components refer to all parts of the buoy other than the glass bulb and external oil bladder. Examples, but not limited to, include at least: protective shell, sensors, antenna, and counterweights. The drag coefficient of the profile drifting buoy. The speed at which the drifting buoy in the profile is traveled.

[0029] In practical applications, an effective method for detecting buoy state anomalies is to monitor the relationship between buoyancy and gravity. Therefore, this application mainly focuses on the changes in the motion state of the buoy during underwater hovering, when the buoy's velocity and acceleration are usually close to zero. In addition, the estimated volume and coefficients usually deviate from the true values. Considering these differences in the hovering phase, a buoy model with volume and parameter uncertainties can be expressed as follows (2):

[0030] in, , , , , , , The relevant errors are respectively for density, glass sphere volume, outer oil bladder volume, other buoy component volume, glass sphere compressibility coefficient, outer oil bladder compressibility coefficient, and other buoy component compressibility coefficient.

[0031] However, in practical applications, accurately measuring these individual errors is often impractical. Therefore, in this embodiment, the aforementioned related errors are expressed as parameter error terms, resulting in the buoy model shown in the following expression: , in, The mass of the profile drifting buoy, It is the acceleration due to gravity. For running depth The density of the water below, The volume of the glass sphere is... The volume of the outer oil sac. The volume refers to the volume of other components of the buoy. These other components are those excluding the glass bulb and external oil bladder. Examples, but not limited to, include at least: protective shell, sensors, antenna, and counterweights. The coefficient of compressibility of the glass sphere. The compression coefficient of the external oil bladder. The compression coefficient of other components of the buoy. This is the parameter error term, used to represent the errors related to the aforementioned parameters such as density, glass sphere volume, outer oil bladder volume, other component volumes, and corresponding compressibility coefficients.

[0032] In some embodiments, as depth increases, temperature decreases, leading to an increase in density. Similarly, salinity affects density, and as depth increases linearly, water compression also occurs, leading to an increase in density. Density can be simplified to an empirical function or fitted to a reference table based on historical sea trial data.

[0033] In another embodiment, in the buoy model, the density of water... As depth The function comprehensively considers the coupling effects of factors such as temperature gradient, salinity distribution, and hydrostatic pressure increase caused by depth changes. This is achieved by establishing... and The functional relationship between them can enable accurate modeling of buoyancy changes, enhance the accuracy of buoyancy prediction at different depths, and thus improve the overall response characteristics of the model.

[0034] Based on the above buoy model, the embodiments of this application are constructed based on the principle of buoyancy balance. The left side of the equation represents the gravity acting on the buoy, and the right side represents the buoy's position at depth. Buoyancy and error terms at the location. The buoyancy component corresponds to the buoyancy experienced by the volume of the glass bulb, external oil bladder, and other structural components in the buoy in the water. Due to the compressibility of materials underwater, their effective volume shrinks with increasing depth; therefore, a volume compressibility factor is introduced into the buoyancy component.

[0035] By establishing a buoyancy calculation model that incorporates the compressibility characteristics of multiple components and the effects of water density variations, accurate modeling of the mechanical state of a profile drifting buoy at different depths can be achieved. The model also considers the non-rigid response characteristics of the buoy's main components, making the calculation results closer to actual operating conditions. A parameter error term is introduced. It provides flexible model adjustment capabilities, which effectively reduce model prediction errors through fitting and correction methods such as least squares.

[0036] Specifically, the parameter error term is determined by the following calculation model: , in, , , These are the first coefficient, the second coefficient, and the third coefficient.

[0037] Based on the above calculation model, the embodiments of this application represent the parameter error term of the buoy model during the hovering phase as a depth-based composite exponential function structure, utilizing depth... As independent variables, through three coefficients , , To describe the trend of error with depth, the coefficient , , The method is determined by regression fitting based on historical sea trial data, and can be dynamically updated according to the current sea trial data during operation through parameter update steps to improve prediction accuracy.

[0038] In some embodiments, the multi-source data includes: simulation results, laboratory data, and historical sea trial data. Specific methods for configuring the parameters of the buoy model using multi-source data include: Based on the simulation results, the parameters of the buoy model are determined using laboratory data as initial values ​​(which may include the mass of the profile drifting buoy, the volume of the buoy's glass sphere, the volume of the buoy's external oil bladder and other components, the running speed, etc.). The parameters of the buoy model are updated using the least squares method based on historical sea trial data.

[0039] Figure 2 This is a schematic diagram of the buoy anomaly detection logic flow, for reference. Figure 2 As shown, in the above embodiment, step S3 specifically includes: S301: Predict the volume of the outer oil bladder at a preset drift depth based on the buoy model, and use this as the first outer oil bladder volume. The volume of the outer oil bladder in the current sea trial data was analyzed and used as the volume of the second outer oil bladder. ; S302: Determine the volume of the first outer oil bladder and the volume of the second outer oil bladder Whether the absolute difference between them is less than or equal to the preset detection threshold. If so, no parameter update is needed, and the process ends. Otherwise, that is, the volume of the first outer oil bladder and the volume of the second outer oil bladder If the absolute difference between the values ​​is greater than the detection threshold, proceed to step S303 to determine whether the buoy model has been updated based on the update flag. If the model has been updated, then the buoy is determined to be in an abnormal state; Otherwise, perform parameter update step S4 based on the statistical distribution of the current sea trial data.

[0040] In the above embodiments, the profile drifting buoy determines the drifting depth based on preset control commands. When the depth value of the buoy within a certain period of time is within the drifting depth range, it is determined that the buoy has entered the hovering stage.

[0041] Based on the above steps, this application establishes a buoy model based on the stable behavior of the profile drifting buoy during the hovering phase, and uses multi-source data for fitting and parameter setting; it periodically receives the current sea trial data through satellite communication, calculates the volume of the first outer oil bladder in combination with the buoy model, and determines whether the difference between the currently measured volume of the second outer oil bladder and the volume of the first outer oil bladder exceeds the detection threshold to identify potential anomalies. If the absolute difference exceeds the detection threshold, it determines whether to judge it as an anomaly based on the model update flag, or continues to execute the parameter update step to update the model parameters based on statistical consistency.

[0042] In some embodiments, the update flag can be set to a Boolean variable or a state enumeration value. Taking a Boolean variable as an example, its typical definition is as follows: flag_model_updated = True indicates that the model has been updated; `flag_model_updated = False` indicates that the model has not been updated.

[0043] After the buoy model is built and initialized, the update flag is set to False by default. When the current sea trial data returned meets the statistical consistency distribution and the determination coefficient is greater than 0.9, the update flag is configured to True. If the buoy is judged to be abnormal, the flag can be reset to False to wait for the next valid update. When the buoy is in a stable state for more than a set time period or data batch number, the flag is reset to improve long-term adaptability.

[0044] The updated flag can be stored in the local cache of the profile drifting buoy and uploaded to the shore-based control center as status data along with other parameters via satellite communication for remote judgment of the model status; or the shore-based system can dynamically generate and maintain the flag through algorithm logic during the model fitting and data processing stages without the buoy's involvement.

[0045] In another embodiment, the historical oil discharge volume of this application embodiment can be calculated based on the volume of the outer oil bladder and a fixed oil discharge rate and time, where the oil discharge time is the time it takes for hydraulic oil to be discharged from the inner oil tank to the outer oil bladder. The calculation method for the historical oil discharge volume can be adjusted according to the characteristics of the specific hydraulic system by adjusting the parameter model, or can be supplemented by direct sensing measurement.

[0046] In some embodiments, parameter update step S4 further includes: Calculate the statistical distribution of the current sea trial data and determine whether its distribution follows the statistical distribution of historical sea trial data; If so, the parameter error term is updated using the current sea trial data and the least squares method, and the determination coefficient corresponding to the absolute difference is calculated. , Further determine the determination coefficient If the value is greater than 0.9, it means that the volume of the first outer oil bladder predicted by the buoy model is very close to the volume of the second outer oil bladder, indicating that the data is reliable. Then, the parameter error term is updated again using the least squares method with the current sea trial data, and the update flag is configured. Otherwise, it means that the data residual is dominant and the data is abnormal. The parameter error term is not updated. Otherwise, do not update the parameter error term.

[0047] Based on the above steps, this application embodiment dynamically updates the parameter error terms in the buoy model based on the dual constraints of determining the consistency of sea trial data distribution and evaluating the model fitting quality, so as to improve the fitting accuracy and environmental adaptability of the buoy model to actual ocean measurement data.

[0048] The least squares method used in this application can update the parameter error term under the condition of satisfying distribution consistency, and use the determination coefficient as a secondary update condition to enhance the model's ability to identify data quality and effectively suppress the influence of instantaneous anomalies or local noise on the parameter estimation of the parameter error term. Setting the threshold of the above determination coefficient helps to control the risk of overfitting while maintaining the model's accuracy, making the model more robust in long-term operation, and also providing a quantitative standard for multi-round iterative optimization.

[0049] In another embodiment, the threshold of 0.9 for the determination coefficient can also be adapted and adjusted according to different environmental conditions or equipment performance requirements. For example, for deep-sea observation tasks with high detection accuracy requirements, the threshold can be increased to 0.95; for environments with limited resources or high noise levels, it can be appropriately reduced to 0.85 to improve model flexibility.

[0050] In the above embodiments, the determination coefficient It can be calculated using the following formula:

[0051] in, SSR It is the sum of squares of the absolute differences. SST It is the sum of squares of the differences between the volume of the second outer oil bladder and the average volume of multiple outer oil bladders within the current sea trial data window, which can be determined by a certain time range.

[0052] This application utilizes satellite communication to acquire and update the parameter error terms of the buoy model using the current sea trial data. A detection threshold is used as an auxiliary parameter to distinguish between parameter drift and abnormal status. The relationship between buoyancy and gravity is monitored by changes in the volume of the outer oil bladder. If the absolute difference between the volume of the outer oil bladder calculated based on the buoy model and the volume in the current sea trial data is greater than the detection threshold, and the buoy model has been updated, then the buoy is judged to be in an abnormal state. If the absolute difference is consistently lower than the detection threshold, the cause of the buoy's abnormal state is attributed to parameter drift, and it is not identified as an abnormal state.

[0053] Figure 3 It demonstrates the mapping relationship between absolute difference and pressure. Figure 3 The horizontal axis represents pressure, and the vertical axis represents the absolute difference. Pressure represents depth. The correlation function shows that underwater pressure increases with depth. The absolute difference (blue) is fitted using a power function (red solid line), and the shaded area represents the 99.7% confidence interval. The fitted curve effectively captures the nonlinear relationship between pressure and absolute difference, and most absolute differences fall within the confidence interval, indicating that the estimation of the parameter error term has a certain degree of accuracy. Accordingly, this application uses the parameter error term as a term in the buoy model, which helps to increase the volume of the first external oil bladder. To improve the accuracy of prediction results and reduce prediction errors.

[0054] Figure 4 The changes in absolute difference over the buoy's operational timeline are shown. In the initial phase (the first 30 data sampling points) of the current sea trial data, model updates cannot be triggered due to the limited number of valid measurements. Both the value and the detection threshold remain at their default settings (1 and 10, respectively). When the number of data sampling points returned in the sea trial data exceeds 30, the buoy model performs its first update (the update flag is configured to 1). The absolute difference decreased from a default value of 1 to 0.9909. As the buoy continued to operate, the absolute difference gradually increased from approximately 1 mL to 16 mL, eventually exceeding the detection threshold and triggering a second update (the update flag was configured to 2). During this phase, The absolute difference decreased from 0.9909 to 0.9876, after which it decreased significantly. In subsequent runs, the absolute difference accumulated again and exceeded the threshold, triggering a third update (with the update flag configured to 3). During this period, The absolute difference further decreased to 0.9807, showing another significant reduction. Overall, these results demonstrate that the proposed online update strategy can effectively adjust model parameters according to real-time operating conditions, keeping the absolute difference within an acceptable range, thereby enhancing the adaptability and robustness of the buoy model.

[0055] Based on another set of returned current sea trial data, the model is updated according to step S4 of this application. Figure 5 This demonstrates the mapping relationship between absolute difference and pressure. Figure 5 In the diagram, blue dots represent absolute differences, and red shaded bands represent the fitted curve and its confidence interval. Based on the proposed update strategy, the returned data are assigned different metrics: blue dots represent the absolute difference points (173 points) included in the model update; red "X" marks indicate factors that would affect the coefficient of determination. Absolute difference points below 0.9, thus reducing model quality and being discarded (7 points); yellow squares indicate absolute difference points exceeding the historical distribution “3-δ” ​​threshold, which are forcibly excluded as significant outliers (9 points).

[0056] Figure 6 This demonstrates the evolution of the prediction error under this detection mechanism. The blue curve represents the absolute difference, the dashed line represents the detection threshold, and the red dots represent detected anomalies. A total of 7 update events were identified. At approximately the 31st data sampling point (with the update flag set to 1), when the cumulative number of data sampling points exceeds 30, the model performs its first update. The value decreased from 1.000 to 0.9918. Similarly, when updating the flag to 2 to 7, the absolute difference exceeded the detection threshold, triggering subsequent parameter updates, and this was consistently maintained. It remained above 0.95. Outliers appeared between data sampling points 66 and 72, leading to... The error rate dropped below 0.9; therefore, these data were marked as rejected in step S4, the parameter error term was not updated, and they were deleted from the historical database used for updates. This confirmed that the rejected data were indeed anomalous. Subsequently, a large cluster of anomalous data appeared between data sampling points 141 and 149, significantly deviating from the historical distribution. These data sampling points were forcibly excluded to prevent further model updates, further confirming their anomalous nature. Overall, these results demonstrate that the proposed online update strategy not only enables dynamic model adaptation but also effectively detects and isolates anomalous states, thereby improving the reliability and robustness of the buoy model.

[0057] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0058] 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.

[0059] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A buoy anomaly detection method based on a buoy model, characterized in that, include: A buoy model of a profile drifting buoy in the hovering phase is constructed, and the parameters of the buoy model are configured using the least squares method with multi-source data. The current sea trial data transmitted back by the profile drifting buoy is obtained via satellite communication; Based on the buoy model, the volume of the outer oil bladder is predicted at a preset drift depth, and this volume is used as the first outer oil bladder volume. The actual volume of the outer oil bladder in the current sea trial data was analyzed and used as the volume of the second outer oil bladder. Determine the volume of the first outer oil bladder. and the volume of the second outer oil bladder Whether the absolute difference between them is less than or equal to the preset detection threshold. If so, then no parameter update is required; Otherwise, determine whether the buoy model has been updated. If the model has been updated, then the buoy is determined to be in an abnormal state; Otherwise, perform the parameter update step based on the statistical distribution of the current sea trial data.

2. The buoy anomaly detection method based on a buoy model according to claim 1, characterized in that, The buoy model is represented by the following calculation model: ， in, The mass of the profile drifting buoy, It is the acceleration due to gravity. For running depth The density of the water below, The volume of the glass sphere is... The volume of the outer oil sac. For the volume of other components of the buoy, The coefficient of compressibility of the glass sphere. The compression coefficient of the external oil bladder. The compression coefficient of other components of the buoy. This is the parameter error term.

3. The buoy anomaly detection method based on a buoy model according to claim 2, characterized in that, The parameter error term is determined by the following calculation model: ， in, , , These are the first coefficient, the second coefficient, and the third coefficient.

4. The buoy anomaly detection method based on a buoy model according to any one of claims 1 to 3, characterized in that, The parameter update step further includes: Calculate the statistical distribution of the current sea trial data and determine whether its distribution follows the statistical distribution of historical sea trial data; If so, the parameter error term is updated using the current sea trial data and the least squares method, and the determination coefficient corresponding to the absolute difference is calculated. , Further determine the determination coefficient If the value is greater than 0.9, then the parameter error term is updated again using the least squares method with the current sea trial data; otherwise, the parameter error term is not updated. Otherwise, do not use the current sea trial data to update the parameter error terms.

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