Sea floating type structure data processing method and system

Abstract

The invention relates to the technical field of sea floating type structure data processing, in particular to a sea floating type structure data processing method and system, and the method comprises the following steps: building a macroscopic hydrodynamic response reference of a sea floating type structure; acquiring local water flow data and an actual macroscopic attitude of the sea floating type structure in real time; according to the local water flow data, the expected macroscopic attitude of the sea floating type structure is calculated in combination with the macroscopic hydrodynamic response benchmark; comparing the expected macroscopic attitude with the actual macroscopic attitude to obtain an attitude difference; according to the attitude difference, diagnosing the abnormality of the local water flow data, and estimating the physical noise offset caused by the local physical disturbance; and separating and recovering a real water flow signal according to the physical noise offset and the actual macroscopic attitude. And the accuracy and reliability of sea floating type structure data processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sea floating structure data processing, and in particular to a sea floating structure data processing method and system. Background Art

[0002] Floating observation platforms are widely deployed in the ocean to monitor currents over long periods of time and in detail, collecting crucial information about the marine environment. These platforms are typically equipped with advanced sensors, such as acoustic Doppler current profilers. During initial deployment, their internal data processing systems are pre-programmed with a carefully calibrated data denoising program. These programs are based on extensive theoretical calculations and controlled laboratory data, and employ fixed parameter settings to ensure stability and predictability during long-term operation in remote ocean locations.

[0003] When floating platforms operate continuously in the ocean for months or even years, marine biofouling will inevitably form on their underwater components, particularly sensor probes and surrounding structures. This irregular biofouling layer can significantly impact the surrounding hydrodynamics when currents pass through it, causing localized water disturbances.

[0004] Existing systems are unable to effectively identify or filter out this interference caused by biofouling. Summary of the Invention

[0005] The purpose of the present invention is to address the above-mentioned shortcomings and provide a method and system for processing sea floating structure data.

[0006] The present invention adopts the following technical solutions: A method for processing data of a floating sea structure comprises the following steps: establishing a macroscopic hydrodynamic response benchmark of the floating sea structure; acquiring local water flow data and an actual macroscopic posture of the floating sea structure in real time; calculating an expected macroscopic posture of the floating sea structure based on the local water flow data and in combination with the macroscopic hydrodynamic response benchmark; comparing the expected macroscopic posture with the actual macroscopic posture to obtain a posture difference; diagnosing anomalies in the local water flow data based on the posture difference and estimating a physical noise offset caused by a local physical disturbance; and separating and restoring a real water flow signal based on the physical noise offset and the actual macroscopic posture.

[0007] Through this technical solution, the present application can effectively identify and quantify the physical noise offset caused by local physical disturbances, and combine the actual macro-attitude to separate and restore the real water flow signal from the contaminated local water flow data, thereby overcoming the problem that the fixed parameter denoising program in the existing technology cannot adapt to the complex noise environment, resulting in data deviation and miscalibration, and significantly improving the accuracy and reliability of sea floating structure data processing.

[0008] Furthermore, the step of estimating the physical noise offset caused by the local physical disturbance includes: identifying a quiet flow time window; recording composite static zero-point attitude data of the sea-floating structure within the quiet flow time window; activating an internal actuator of the sea-floating structure to apply an internal force; measuring an instantaneous attitude response of the sea-floating structure to the internal force; calibrating a structural dynamic response benchmark of the sea-floating structure based on the instantaneous attitude response; and separating the physical noise offset caused by the local physical disturbance based on the composite static zero-point attitude data and the structural dynamic response benchmark.

[0009] Furthermore, the step of identifying the quiet flow time window includes: continuously monitoring the water flow velocity data, and obtaining the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the instantaneous change rate of the water flow velocity; judging whether the absolute value of the water flow velocity is lower than a first threshold; judging whether the standard deviation of the water flow velocity is lower than a second threshold; judging whether the absolute value of the instantaneous change rate of the water flow velocity is lower than a third threshold; when the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the absolute value of the instantaneous change rate of the water flow velocity all meet the corresponding threshold conditions within a continuous time period, and the duration of the continuous time period reaches a preset duration threshold, the quiet flow time window is identified.

[0010] Furthermore, the step of establishing a macro-hydrodynamic response benchmark of the sea-floating structure includes: obtaining structural dynamic response information reflecting the current structural state of the sea-floating structure; adjusting the macro-hydrodynamic response benchmark of the sea-floating structure according to the structural dynamic response information, thereby establishing the macro-hydrodynamic response benchmark.

[0011] Furthermore, the step of separating and restoring the real water flow signal based on the physical noise offset and the actual macro-attitude includes: continuously acquiring environmental influencing factors that affect the manifestation of the physical noise offset in the local water flow data, the environmental influencing factors including water temperature, salinity, water depth or wave state parameters; dynamically adjusting the influence weight of the physical noise offset on the local water flow data based on the environmental influencing factors, the influence weight being used to reflect the real-time change in the contribution of the environmental influencing factors to the physical noise offset in the local water flow data; stripping the physical noise offset from the local water flow data based on the influence weight; identifying and separating the logical deviation introduced by the incorrect calibration in the local water flow data in combination with the actual macro-attitude, and restoring the real water flow signal based on the local water flow data after stripping the physical noise offset.

[0012] Furthermore, the step of dynamically adjusting the influence weight of the physical noise offset on the local water flow data according to the environmental influencing factors includes: continuously acquiring the physical noise offset and the local water flow data; calculating the corresponding noise component in the local water flow data according to the physical noise offset and the currently set influence weight; removing the noise component from the local water flow data to obtain a preliminary restored water flow signal; comparing the preliminary restored water flow signal with the actual macro-attitude to obtain a signal consistency deviation; evaluating the explanatory power of the physical noise offset on the local water flow data according to the signal consistency deviation; and dynamically correcting the influence weight according to the evaluation result and in combination with the environmental influencing factors.

[0013] Furthermore, based on the signal consistency deviation, the step of evaluating the explanatory power of the physical noise offset for the local water flow data includes: setting an amplitude threshold and a duration threshold of the signal consistency deviation; judging whether the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation; judging whether the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation; when the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation and the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation, it is assessed that the explanatory power of the physical noise offset for the local water flow data is insufficient.

[0014] Furthermore, based on the evaluation results and in combination with environmental influencing factors, the steps of dynamically correcting the impact weight include: continuously calculating the average value of the signal consistency deviation; determining whether the average value of the signal consistency deviation continues to exceed the preset drift determination threshold; when the average value of the signal consistency deviation continues to exceed the drift determination threshold, identifying the presence of long-term drift between the physical noise offset and the local water flow data; adjusting the update rate of the impact weight based on the identification result of the long-term drift; and correcting the impact weight based on the adjusted update rate and in combination with the environmental influencing factors.

[0015] Furthermore, the steps of identifying and separating logical deviations introduced in local water flow data due to incorrect calibration include: continuously acquiring platform depth or power supply voltage fluctuation information that affects changes in logical deviations; dynamically determining a compensation coefficient for the logical deviation based on the platform depth or power supply voltage fluctuation information; using the compensation coefficient to preliminarily correct the local water flow data after stripping off the physical noise offset; comparing the preliminarily corrected local water flow data with the actual macro posture, and further separating the logical deviation based on the comparison results.

[0016] The present application also discloses a sea-floating structure data processing system, which is applied to the sea-floating structure data processing method. The system includes: a benchmark establishment module, which is used to establish a macro-hydrodynamic response benchmark of the sea-floating structure; a data acquisition module, which is used to acquire local water flow data and actual macro-attitude of the sea-floating structure in real time; a calculation module, which calculates the expected macro-attitude of the sea-floating structure based on the local water flow data and the macro-hydrodynamic response benchmark; a comparison module, which compares the expected macro-attitude with the actual macro-attitude to obtain the attitude difference; an estimation module, which diagnoses anomalies in the local water flow data based on the attitude difference and estimates the physical noise offset caused by local physical disturbances; and a recovery module, which separates and recovers the real water flow signal based on the physical noise offset and the actual macro-attitude.

[0017] Through this system, the present application provides a specific device for implementing the above-mentioned data processing method. Through modular design, the various functional units can work together to efficiently complete tasks such as data acquisition, noise diagnosis, offset estimation and real signal recovery, providing hardware and software support for the accurate processing of sea-floating structure data, and has good practicality.

[0018] This application provides a data processing method for floating structures at sea. By establishing a macroscopic hydrodynamic response benchmark, the method acquires local water flow data and actual macroscopic posture in real time, and calculates the difference between the expected and actual macroscopic postures. This method effectively diagnoses anomalies in local water flow data. Furthermore, the method estimates the offset of physical noise caused by local physical disturbances (such as surface irregularities caused by marine biofouling) and, combined with the actual macroscopic posture, separates and recovers the actual water flow signal.

[0019] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a method for processing sea floating structure data according to the present invention; Figure 2 The figure is a structural diagram of a sea-floating structure data processing system according to the present invention. DETAILED DESCRIPTION

[0021] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only for simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0022] This embodiment provides a method and system for processing sea floating structure data. Figure 1 and Figure 2 shown.

[0023] refer to Figure 1 The present application proposes a method for processing data of a sea-floating structure, which includes the following steps: establishing a macro-hydrodynamic response benchmark of the sea-floating structure; acquiring local water flow data and actual macro-posture of the sea-floating structure in real time; calculating an expected macro-posture of the sea-floating structure based on the local water flow data and the macro-hydrodynamic response benchmark; comparing the expected macro-posture with the actual macro-posture to obtain a posture difference; diagnosing anomalies in the local water flow data based on the posture difference, and estimating the physical noise offset caused by the local physical disturbance; and separating and restoring the real water flow signal based on the physical noise offset and the actual macro-posture.

[0024] The sea-floating structure involved in this application generally refers to an observation platform deployed in the ocean for long-term monitoring of water flow and collection of marine environmental information, such as a buoy or submersible equipped with sensors such as an acoustic Doppler current profiler.

[0025] A macroscopic hydrodynamic response benchmark is a reference model or dataset that describes the relationship between the overall attitude (e.g., pitch, roll, yaw, etc.) of a floating marine structure and the forces acting on it under specific flow conditions. This benchmark can be established based on theoretical calculations, laboratory tank tests, or historical observational data, and is used to predict the structure's behavior under ideal flow conditions. Local flow data refers to data collected in real time by local sensors onboard a floating marine structure (e.g., acoustic Doppler current profilers) that reflects the local water motion surrounding the structure, typically including information such as water velocity and direction. The actual macroscopic attitude refers to the real-world spatial attitude of a floating marine structure at a given moment, and can be obtained in real time using devices such as an inertial measurement unit (IMU), attitude sensors, or a global positioning system (GPS). The expected macroscopic attitude is the ideal macroscopic attitude of a floating marine structure under the current local flow conditions, derived from the local flow data and the macroscopic hydrodynamic response benchmark. The attitude discrepancy is the deviation between the expected and actual macroscopic attitudes. This deviation can reflect possible anomalies in the local flow data or disturbances not fully accounted for by the macroscopic hydrodynamic response benchmark. Physical noise offset refers to non-real signal components with systematic biases introduced into local water flow data by local physical disturbances (such as water flow separation and turbulent vortices caused by marine biofouling). A true water flow signal is the original water flow information that has been processed to remove all noise and biases and accurately reflects the actual state of water movement. The implementation environment of this application is typically a marine environment, where floating structures operate for long periods of time and continuously collect water flow data.

[0026] In specific implementations, the method of the present application includes an expanded description of the following key features: Establishing a macroscopic hydrodynamic response benchmark for the sea-floating structure: This step is intended to provide an important reference for subsequent data processing. The macroscopic hydrodynamic response benchmark can be understood as the ideal behavior pattern of the sea-floating structure under different water flow conditions. As an implementation method, this benchmark can be established by pre-running a large number of numerical simulations. For example, computational fluid dynamics (CFD) software can be used to simulate the hydrodynamic forces acting on the sea-floating structure under different flow rates and directions, and its corresponding attitude response can be recorded. These simulation results can be organized into a lookup table or a set of empirical formulas to serve as the macroscopic hydrodynamic response benchmark.

[0027] Real-time acquisition of local current data and the actual macroscopic attitude of floating structures: This step is fundamental to data processing, ensuring the system obtains real-time information about the current environment and structural status. Specifically, local current data can be acquired in real time using current sensors such as acoustic Doppler current profilers (ADCPs) installed in the underwater portion of floating structures. These sensors periodically transmit sound waves and receive echoes, calculating the velocity and direction of the water flow at different depths around the sensors through the Doppler effect. Simultaneously, the actual macroscopic attitude can be acquired in real time using an inertial measurement unit (IMU) or attitude sensor integrated within the floating structure. An IMU typically includes an accelerometer, gyroscope, and magnetometer, providing information on the structure's attitude in three dimensions, including pitch, roll, and yaw. This data is continuously collected and transmitted to a data processing unit.

[0028] Based on local flow data and a macroscopic hydrodynamic response benchmark, the expected macroscopic attitude of the floating structure is calculated. This step utilizes an established benchmark to predict the theoretical attitude of the structure under current local flow conditions. For example, a pre-established macroscopic hydrodynamic response benchmark (such as a lookup table or empirical formula) can be queried or calculated based on real-time local flow data. If the benchmark is a lookup table, the corresponding expected attitude value is found based on the current flow velocity and direction. If the benchmark is a set of formulas, the current flow parameters are substituted into the formulas for calculation to obtain the expected macroscopic attitude of the floating structure under the current flow.

[0029] Comparing the expected macro-attitude with the actual macro-attitude to obtain the attitude difference: This step is critical for identifying anomalies. By comparing theoretical predictions with actual observations, the deviation between the two can be quantified. Specifically, the calculated expected macro-attitude can be compared with the actual macro-attitude acquired in real time, point by point or time period by time period. This comparison can be performed using various mathematical methods such as vector difference, Euclidean distance, or angular difference. For example, the difference between the expected pitch angle and the actual pitch angle, the difference between the expected roll angle and the actual roll angle, and the difference between the expected yaw angle and the actual yaw angle can be calculated to obtain an attitude difference vector containing multiple attitude components.

[0030] Based on the posture difference, diagnose the abnormalities in the local water flow data and estimate the physical noise offset caused by the local physical disturbance: This step aims to infer the abnormal components in the local water flow data from the posture difference.

[0031] As an implementation method, when the posture difference continues to exceed a preset threshold, it can be preliminarily determined that there is an anomaly in the local water flow data. For example, if the amplitude of the posture difference continues to be greater than a certain empirically set threshold for a period of time, it is considered that there is an anomaly. On this basis, a statistical analysis method can be used to estimate the physical noise offset. For example, the average value of the local water flow data within the abnormal time period can be calculated, and the average value can be used as a preliminary estimate of the physical noise offset. As another implementation method, a simple linear model can be established to associate the posture difference with the physical noise offset. For example, a model is trained through historical data so that when a specific posture difference is input, the model can output a corresponding physical noise offset. The model can be a linear equation based on least squares fitting, or a simple proportional coefficient.

[0032] Separating and recovering the true water flow signal based on the physical noise offset and the actual macro-attitude: This step is crucial for ultimately obtaining accurate water flow data. Specifically, after estimating the physical noise offset, it can be directly subtracted from the original local water flow data to remove the noise component introduced by local physical disturbances. For example, if the local water flow data is a velocity vector and the physical noise offset is a constant velocity deviation vector, the two are subtracted. Subsequently, the noise-stripped local water flow data is attitude-corrected based on the actual macro-attitude. Since local water flow data is typically measured in the native coordinate system of the floating structure, and the actual macro-attitude of the structure changes over time, the local water flow data needs to be converted to an Earth-fixed coordinate system to eliminate the influence of the structure's own motion on the water flow measurement. For example, the rotation matrix provided by the actual macro-attitude can be used to transform the local water flow data from the structure coordinate system to the geographic coordinate system, thereby recovering a water flow signal that is closer to the actual water flow signal and unaffected by the structure's own attitude.

[0033] First, by establishing a macroscopic hydrodynamic response benchmark for the floating structure, a theoretical behavioral model is provided for subsequent attitude prediction. Second, during the actual operation of the floating structure, the system acquires real-time local water flow data around it and its actual macroscopic attitude. This real-time data forms the basis for analysis and diagnosis. Next, using this real-time local water flow data and combining it with the pre-established macroscopic hydrodynamic response benchmark, the system calculates the expected macroscopic attitude of the floating structure under current flow conditions. This expected attitude represents the ideal response of the structure to the current flow. The calculated expected macroscopic attitude is then accurately compared with the actual macroscopic attitude measured in real time to determine the attitude difference. This attitude difference is a key diagnostic metric, intuitively reflecting the deviation between the actual situation and the ideal model. When local physical disturbances (such as turbulence caused by biofouling) are present, these disturbances affect the local water flow data, causing the actual macroscopic attitude to deviate from the expected macroscopic attitude, resulting in a significant attitude difference. Based on this attitude difference, the system can diagnose anomalies in the local water flow data and further estimate the physical noise offset caused by the local physical disturbance. Finally, after estimating the physical noise offset, the system separates and restores the original local flow data based on this offset and the actual macroscopic posture. By stripping the physical noise offset from the local flow data and applying necessary posture corrections based on the actual macroscopic posture, the system ultimately recovers the true flow signal, unaffected by local physical disturbances and the structure's own motion.

[0034] The steps of estimating the physical noise offset caused by the local physical disturbance may include the following: identifying a static flow time window; recording composite static zero-point attitude data of the sea-floating structure within the static flow time window; activating an internal actuator of the sea-floating structure to apply an internal force; measuring an instantaneous attitude response of the sea-floating structure to the internal force; calibrating a structural dynamic response benchmark of the sea-floating structure based on the instantaneous attitude response; and separating the physical noise offset caused by the local physical disturbance based on the composite static zero-point attitude data and the structural dynamic response benchmark.

[0035] Identifying a static flow time window refers to determining a time period when the water environment is relatively stable or without significant flow disturbances, allowing for subsequent attitude data recording and baseline calibration. Furthermore, within the static flow time window, composite static zero-point attitude data for the sea-floating structure is recorded. This data reflects the sea-floating structure's baseline attitude state in the absence of external water flow interference and internal active forces. This composite static zero-point attitude data may include, but is not limited to, the structure's pitch, roll, and yaw angles, as well as its position in space. Specifically, activating the sea-floating structure's internal actuators and applying internal forces refers to applying a controllable, preset internal force to the sea-floating structure through internal drive devices, such as propellers, counterweight adjustment systems, or attitude control surfaces. This is intended to induce a measurable attitude response from the structure under controlled conditions. Thus, the instantaneous attitude response of the sea-floating structure to the internal force is measured. This instantaneous attitude response refers to the attitude change exhibited by the sea-floating structure within a very short period of time after the internal force is applied. This response can be monitored and recorded in real time using an inertial measurement unit, attitude sensor, or other high-precision positioning system within the structure. Based on this instantaneous attitude response, a structural dynamic response benchmark for the offshore floating structure is calibrated. This structural dynamic response benchmark is a model or parameter set that describes how the offshore floating structure's attitude changes when subjected to internal forces. Establishing this benchmark helps distinguish attitude changes caused by the structure's inherent characteristics from those caused by external water flow disturbances. Ultimately, the physical noise offset caused by local physical disturbances is isolated based on the composite static zero-point attitude data and the structural dynamic response benchmark. Specifically, by comparing the real-time attitude data with the composite static zero-point attitude data recorded under static flow conditions, and using the structural dynamic response benchmark to compensate for the response caused by internal forces, the attitude offset caused by physical disturbances in the local water flow environment (such as eddies and local turbulence) is accurately identified and isolated, i.e., the physical noise offset.

[0036] The present application further proposes that the steps for identifying the static flow time window include: continuously monitoring the water flow velocity data, and obtaining the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the instantaneous change rate of the water flow velocity; judging whether the absolute value of the water flow velocity is lower than a first threshold; judging whether the standard deviation of the water flow velocity is lower than a second threshold; judging whether the absolute value of the instantaneous change rate of the water flow velocity is lower than a third threshold; when the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the absolute value of the instantaneous change rate of the water flow velocity all meet the corresponding threshold conditions within a continuous time period, and the duration of the continuous time period reaches a preset duration threshold, the static flow time window is identified.

[0037] Specifically, continuous monitoring of water flow velocity data is intended to comprehensively capture the dynamics of water flow in the environment of the offshore floating structure. By obtaining the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the instantaneous rate of change of the water flow velocity, the degree of stillness and stability of the water flow can be quantitatively evaluated from multiple dimensions. Among them, the absolute value of the water flow velocity is used to measure the overall intensity of the water flow. If it is lower than the first threshold, it indicates that the water flow is in a low speed or near-still state; the standard deviation of the water flow velocity is used to reflect the volatility or stability of the water flow. If it is lower than the second threshold, it indicates that the water flow fluctuation is small and is in a relatively stable state; the instantaneous rate of change of the water flow velocity is used to evaluate how fast the water flow velocity changes over time. If its absolute value is lower than the third threshold, it means that the water flow has not undergone drastic acceleration or deceleration. In addition, in order to ensure that the identified static flow time window is continuous and stable, this application requires that the above three conditions must be met simultaneously within a continuous time period, and the duration of the continuous time period must reach a preset time threshold. This multi-condition, continuous judgment mechanism is designed to exclude instantaneous or short periods of water calm and ensure that the identified quiet flow time window can truly reflect a sufficiently stable and near-flow-free environment.

[0038] The solution of this application effectively solves the challenge of accurately identifying the quiet flow time window in a complex marine environment by introducing multi-dimensional water flow parameter monitoring and strict duration judgment. When the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the absolute value of the instantaneous rate of change of the water flow velocity all meet the preset threshold conditions, and this state lasts for a preset time, it can ensure that the local water area where the sea-floating structure is located is truly in a state close to stillness and stability. It is precisely because of this precise quiet flow window identification that the composite static zero-point attitude data recorded in the window and the calibrated structural dynamic response benchmark can eliminate the influence of external water flow disturbances to the greatest extent, thereby providing a reliable basis for the subsequent accurate separation of the physical noise offset caused by local physical disturbances.

[0039] In some preferred embodiments, assuming a floating structure is collecting data, a quiet flow time window needs to be identified to accurately estimate the physical noise offset caused by local physical disturbances. The system continuously monitors the water velocity data around the structure. Specifically, the first threshold is set to 0.05 m / s, the second threshold is set to 0.01 m / s, and the third threshold is set to 0.005 m / s². The preset duration threshold is 5 minutes. When the system detects that the absolute value of the water velocity is less than 0.05 m / s for 5 consecutive minutes, the standard deviation of the water velocity is less than 0.01 m / s for 5 consecutive minutes, and the absolute value of the instantaneous rate of change of the water velocity is less than 0.005 m / s² for 5 consecutive minutes, the system identifies the current 5-minute period as a quiet flow time window. During this quiet flow time window, the composite static zero-point attitude data of the floating structure is recorded, and its internal actuators are activated to apply internal forces to measure the instantaneous attitude response and calibrate the structural dynamic response benchmark. This precise identification of the quiet flow window ensures that external water flow disturbances are minimized during these critical measurements and calibration operations, providing highly reliable input data for subsequent physical noise offset separation.

[0040] The present application further proposes that the above-mentioned step of establishing a macro-hydrodynamic response benchmark of the sea-floating structure includes: obtaining structural dynamic response information reflecting the current structural state of the sea-floating structure; adjusting the macro-hydrodynamic response benchmark of the sea-floating structure according to the structural dynamic response information, thereby establishing a macro-hydrodynamic response benchmark.

[0041] Specifically, obtaining structural dynamic response information reflecting the current structural state of a floating structure refers to the real-time or periodic acquisition of data characterizing the current physical properties and health of the floating structure through various sensors or monitoring methods. For example, this information may include, but is not limited to, the structure's vibration frequency, modal damping ratio, strain distribution, accumulated material fatigue, structural deformation, and information on possible damage or defects. This information can be collected using accelerometers, strain gauges, inclination sensors, acoustic emission sensors, or fiber optic sensors deployed on the structure. Adjusting the macroscopic hydrodynamic response benchmark of a floating structure based on the structural dynamic response information refers to using the acquired structural dynamic response information to modify or update a pre-established or initially set macroscopic hydrodynamic response benchmark. For example, when the structural dynamic response information indicates a change in structural stiffness, parameters related to structural stiffness in the macroscopic hydrodynamic model can be adjusted accordingly. When the mass or inertia of the structure changes (e.g., due to increased mass due to biofouling), the mass or inertia parameters in the model can be updated. This adjustment can be achieved through various methods such as parameter identification, model modification, machine learning algorithms or lookup tables to ensure that the macro-hydrodynamic response benchmark can dynamically adapt to the changing actual state of the sea floating structure.

[0042] The solution of this application introduces the acquisition and utilization of dynamic response information about the current structural state of a floating sea structure, making the macro-hydrodynamic response benchmark no longer static and unchanging, but instead capable of adaptive adjustment based on the structure's actual physical condition. It is precisely because the structural dynamic response information can reflect in real time changes in the structure's physical properties due to factors such as aging, damage, or environmental impacts that the macro-hydrodynamic response benchmark can always remain consistent with the structure's current actual state. As a result, when calculating the expected macro-attitude of a floating sea structure, the benchmark used is corrected in real time, ensuring the accuracy of the expected attitude, effectively avoiding benchmark deviations caused by changes in the structural state, and thus improving the accuracy of attitude difference comparisons.

[0043] In some preferred embodiments, a floating structure deployed in the ocean for an extended period may experience minor structural changes due to factors such as seawater corrosion, biofouling, or internal equipment adjustments. To dynamically establish a macroscopic hydrodynamic response benchmark, a series of miniature accelerometers and strain sensors can be deployed at key locations on the structure (e.g., the main buoy, connectors, or sensor mounting points). These sensors continuously collect vibration and strain data, which provide dynamic response information reflecting the current state of the floating structure. Specifically, spectral analysis of the collected vibration data can be used to extract the structure's natural frequency and modal damping ratio. A significant shift in a natural frequency or change in modal damping ratio may indicate a change in structural stiffness or mass. For example, a decrease in natural frequency may indicate a decrease in structural stiffness or an increase in attached mass. Based on this dynamic response information, the system adjusts the relevant parameters in the macroscopic hydrodynamic response benchmark using a pre-set mapping relationship or a machine learning model based on historical data. For example, if the model includes a structural stiffness coefficient, this coefficient will be reduced accordingly based on the changing trend of the natural frequency. In this way, even if the structure undergoes subtle changes during operation, its macroscopic hydrodynamic response benchmark can be updated in real time, ensuring that it always matches the actual physical state of the structure, making subsequent calculations of the expected macroscopic attitude more precise, thereby improving the accuracy and robustness of the entire data processing method.

[0044] The present application further proposes that the steps of separating and restoring the real water flow signal based on the physical noise offset and the actual macro-attitude include: continuously obtaining environmental influencing factors that affect the manifestation of the physical noise offset in the local water flow data, the environmental influencing factors include water temperature, salinity, water depth or wave state parameters; dynamically adjusting the influence weight of the physical noise offset on the local water flow data according to the environmental influencing factors, the influence weight is used to reflect the real-time change of the contribution degree of the environmental influencing factors to the physical noise offset in the local water flow data; based on the influence weight, stripping the physical noise offset from the local water flow data; combining with the actual macro-attitude, identifying and separating the logical deviation introduced by incorrect calibration in the local water flow data, and restoring the real water flow signal based on the local water flow data after stripping the physical noise offset.

[0045] Specifically, environmental factors refer to external environmental parameters that influence the stress state of floating structures in the water and the accuracy of sensor measurements. These factors, such as water temperature, salinity, water depth, or wave state parameters, can directly or indirectly alter the physical properties of the water (such as density and viscosity) and the interaction between the structure and the current, thereby affecting the specific manifestation and intensity of the physical noise offset in local current data. Continuously acquiring these environmental factors is intended to provide a real-time and accurate basis for subsequent dynamic adjustments.

[0046] Among them, dynamically adjusting the influence weight of the physical noise offset on the local water flow data refers to the real-time correction of the weight coefficient used to remove the physical noise offset from the local water flow data based on the environmental impact factors obtained in real time. This influence weight is designed to quantify and reflect the extent to which the physical noise offset contributes to the local water flow data under specific environmental conditions. For example, in the case of large waves, the wave state parameters may cause the influence weight of the physical noise offset on the local water flow data to increase; while in an environment with deep water and smooth water flow, the water depth parameters may cause the influence weight to decrease. Through dynamic adjustment, it can be ensured that the physical noise stripping process is more in line with the actual environment and the accuracy of the stripping is improved.

[0047] In practical applications, removing the physical noise offset from local flow data based on influence weights is typically achieved through mathematical models or algorithms. For example, local flow data can be viewed as a superposition of the true flow signal and the physical noise offset. By multiplying the physical noise offset by its corresponding influence weight and then subtracting this weighted noise component from the local flow data, preliminary flow data is obtained, free of the primary physical noise.

[0048] Furthermore, identifying and isolating logical deviations introduced by miscalibration in local flow data, combined with the actual macro-attitude, involves further refined data processing after stripping out the physical noise offset. Logical deviations can arise from factors such as inaccurate sensor calibration, data acquisition system failures, or long-term drift. These deviations can lead to inconsistencies between local flow data and the actual macro-attitude of the floating structure. By comparing the preliminarily processed local flow data with the actual macro-attitude, these inconsistencies can be discovered and quantified, allowing logical deviations to be identified. Once the logical deviations are identified, they can be separated or corrected using appropriate compensation algorithms or correction models to ensure the accuracy and reliability of the ultimately recovered true flow signal.

[0049] The solution of the present application solves the problem of traditional methods' inadequate processing of physical noise offsets in complex and changing ocean environments by introducing environmental influencing factors and dynamically adjusting the influence weights. Specifically, since the physical noise offset is not fixed, but dynamically changes its appearance in local water flow data with changes in environmental factors such as water temperature, salinity, water depth, or wave conditions. By continuously monitoring and utilizing these environmental influencing factors, the system can evaluate the actual contribution of the physical noise offset in the current environment in real time and dynamically correct its stripping weight accordingly. This adaptive adjustment mechanism makes the stripping of physical noise more accurate, avoiding insufficient or excessive stripping due to environmental changes. In addition, the present application further considers possible logical deviations introduced by incorrect calibration. By comparing the data after stripping the physical noise with the actual macroscopic posture, these systematic errors can be discovered and corrected, thereby ensuring that the final restored real water flow signal not only removes physical disturbances but also eliminates potential systematic deviations, greatly improving the accuracy and reliability of data processing.

[0050] Through the above-mentioned technical solution, the present application can significantly improve the accuracy and robustness of data processing of sea-floating structures. Specifically, by dynamically considering the effect of environmental influencing factors on the offset of physical noise, a more accurate stripping of physical noise can be achieved, avoiding signal distortion caused by environmental changes in complex ocean environments. At the same time, the identification and separation of logical deviations introduced by incorrect calibration further eliminates systematic errors in the data, ensuring the purity and reliability of the recovered real water flow signal. This multi-dimensional, adaptive signal recovery strategy enables sea-floating structures to more accurately perceive and respond to the water flow environment in which they are located, providing high-quality input for subsequent structural attitude control, task execution and data analysis, thereby improving the operational efficiency and data value of sea-floating structures in complex ocean environments.

[0051] In some preferred embodiments, it is assumed that a floating structure is collecting water current data. During a certain period of time, the water temperature drops from 20 degrees Celsius to 15 degrees Celsius, while the wave height increases from 0.5 meters to 1.5 meters.

[0052] First, the system continuously monitors environmental factors, such as water temperature and wave state parameters. As water temperature drops and waves increase, the system uses pre-set models or historical data analysis to determine how these environmental changes will affect the physical noise offset in the local water flow data. For example, lower water temperatures may cause slight changes in sensor response characteristics, while larger waves will significantly increase the structure's inherent sway and the resulting physical noise.

[0053] Based on this, the system dynamically adjusts the weighting of the physical noise offset on the local current data. For example, the weighting of wave-induced noise components may be increased to ensure that this noise is more effectively removed during the stripping process. The system then uses these dynamically adjusted weightings to strip the physical noise offset from the real-time local current data. After stripping the physical noise, the system further compares the preliminarily processed local current data with the actual macro-attitude of the floating structure. Suppose, during this comparison, it is discovered that despite stripping the physical noise, the local current data at a particular attitude still exhibits a slight deviation from the actual macro-attitude. For example, when the structure is tilted to the left, the current data consistently shows a slight rightward offset. This may indicate a logical bias introduced by improper sensor installation or initial calibration. The system identifies and quantifies this logical bias and corrects it using a compensation factor. For example, this may be achieved by introducing an inverse, attitude-dependent offset into the data to offset the bias. Ultimately, the local flow data, after physical noise removal and logical bias correction, is treated as the restored true flow signal for subsequent analysis and application. This approach allows for highly accurate true flow signals to be obtained even in the presence of dynamic environmental changes or systematic calibration errors.

[0054] The steps of dynamically adjusting the influence weight of the physical noise offset on the local water flow data according to the environmental influencing factors include: continuously acquiring the physical noise offset and the local water flow data; calculating the corresponding noise component in the local water flow data according to the physical noise offset and the currently set influence weight; removing the noise component from the local water flow data to obtain a preliminarily recovered water flow signal; comparing the preliminarily recovered water flow signal with the actual macroscopic posture to obtain a signal consistency deviation; evaluating the explanatory power of the physical noise offset on the local water flow data according to the signal consistency deviation; and dynamically revising the influence weight according to the evaluation result and in combination with the environmental influencing factors.

[0055] Continuously acquiring physical noise offset and local water flow data means the system continuously receives and processes local water flow data measured by sensors on floating structures, as well as the physical noise offset estimated through attitude difference diagnosis. This data forms the basis for subsequent calculations and evaluations. Calculating the corresponding noise component in the local water flow data based on the physical noise offset and currently set influence weights involves mapping the estimated physical noise offset onto the local water flow data using the currently determined influence weights to quantify its impact on the local water flow data. For example, this noise component can be calculated using a weighted summation or more complex model. This noise component represents the specific manifestation of the local physical disturbance in the local water flow data. Removing the noise component from the local water flow data to obtain a preliminary recovered water flow signal involves subtracting the calculated noise component from the original local water flow data or removing it through other mathematical operations to obtain a preliminary water flow signal free of physical noise interference. This preliminary recovered water flow signal is considered to be closer to the actual water flow state. Comparing the initially recovered water flow signal with the actual macroscopic attitude to obtain the signal consistency deviation refers to comparing the initially processed water flow signal with the actual measured macroscopic attitude of the floating structure. Due to the inherent physical correlation between the actual water flow signal and the structure's macroscopic attitude, this comparison can reveal the accuracy of the current water flow signal. Signal consistency deviation is a metric that measures the degree of this correlation and can be, for example, a residual error, correlation, or specific model prediction error. Based on the signal consistency deviation, the explanatory power of the physical noise offset for local water flow data is evaluated. Based on the signal consistency deviation obtained from this comparison, the current physical noise offset and its influence weight are determined to effectively explain and remove noise components from the local water flow data. A small and stable deviation indicates strong explanatory power; conversely, a large or fluctuating deviation may indicate insufficient explanatory power. Based on the evaluation results and incorporating environmental influencing factors, the influence weight is dynamically adjusted. If the evaluation indicates that the physical noise offset's explanatory power for local water flow data is insufficient, the system will adaptively adjust the influence weight based on the deviation's magnitude and trend, as well as current environmental influencing factors (such as water temperature, salinity, water depth, or wave state parameters). This correction can be based on preset correction rules, machine learning algorithms or optimization iterative processes, aiming to make the impact weight more accurately reflect the true contribution of the physical noise offset under different environmental conditions, thereby improving the accuracy of noise removal.

[0056] The solution of this application continuously acquires physical noise offsets and local water flow data, calculates and removes noise components based on the current influence weights, and obtains a preliminary restored water flow signal. The key is to compare this preliminary restored water flow signal with the actual macroscopic posture to generate a signal consistency deviation. This deviation serves as a feedback mechanism to quantify the degree to which the current physical noise offset model (and its influence weights) interprets the noise components in the local water flow data. When the explanatory power is insufficient (i.e., the deviation is large), it indicates that the current influence weight may be inaccurate and needs to be corrected. Incorporating environmental influence factors ensures that the correction process is not only based on internal consistency, but also considers the impact of external environmental changes on noise characteristics, thereby achieving dynamic and adaptive optimization of the influence weights.

[0057] In some preferred embodiments, for example, suppose that in a specific ocean area, slight changes in water temperature or salinity cause the characteristics of the physical noise offset to drift slightly, and the preset influence weights fail to fully capture this change. The system continuously acquires the physical noise offset and local water flow data, calculates and removes the noise component based on the current influence weights. When the initially recovered water flow signal is compared with the actual macroscopic posture, if the signal consistency deviation consistently exceeds a preset threshold, it indicates that the current influence weights are not effectively removing noise, that is, the physical noise offset is insufficiently interpreting the local water flow data. Based on this evaluation result and incorporating environmental factors such as current water temperature and salinity, the system dynamically adjusts the influence weights, for example, through an iterative optimization algorithm or a model trained on historical data. This adaptive adjustment ensures that the physical noise offset is accurately removed from the local water flow data, even when environmental conditions change slightly or system characteristics drift, thereby restoring a water flow signal that is closer to the truest.

[0058] The present application further proposes that the steps of evaluating the explanatory power of the physical noise offset for local water flow data based on the signal consistency deviation include: setting an amplitude threshold and a duration threshold of the signal consistency deviation; judging whether the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation; judging whether the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation; when the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation and the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation, it is assessed that the explanatory power of the physical noise offset for local water flow data is insufficient.

[0059] Specifically, setting the amplitude and duration thresholds for signal consistency deviations refers to predetermining two key parameters to define the degree of abnormality of signal consistency deviations. The amplitude threshold for signal consistency deviations can be understood as the maximum instantaneous amplitude of the signal deviation allowed, while the duration threshold for signal consistency deviations can be understood as the maximum duration of the signal deviation allowed. These thresholds can be set based on historical data analysis, model simulation results, or expert experience, and are intended to distinguish normal system fluctuations from persistent deviations caused by insufficient interpretation of physical noise offsets.

[0060] Furthermore, judging whether the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation refers to real-time monitoring of the instantaneous value of the signal consistency deviation and comparing it with the preset amplitude threshold. If the instantaneous amplitude of the signal consistency deviation exceeds the threshold, it indicates that there is a large instantaneous inconsistency between the current preliminary recovered water flow signal and the actual macro-attitude. At the same time, judging whether the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation refers to tracking the duration of the signal consistency deviation exceeding the amplitude threshold. If the duration reaches the preset duration threshold, it indicates that this large inconsistency is not an accidental instantaneous fluctuation, but a persistent systematic deviation.

[0061] The solution of the present application sets an amplitude threshold and a duration threshold of the signal consistency deviation, and uses this as the key basis for judging the explanatory power of the physical noise offset. When the amplitude of the signal consistency deviation exceeds the preset amplitude threshold, and the duration of this over-limit state reaches the preset duration threshold, the system can accurately identify that the physical noise offset has insufficient explanatory power for the local water flow data. This dual judgment mechanism, which considers both the "size" and "duration" of the deviation, can effectively avoid misjudgments caused by transient noise or accidental errors, and ensures that subsequent impact weight corrections are triggered only when the physical noise offset is indeed unable to effectively explain the deviation in the local water flow data. In this way, it can be ensured that the dynamic adjustment of the impact weight of the physical noise offset is based on real and continuous deviation conditions, thereby improving the accuracy and robustness of data processing.

[0062] The above technical solution provides a more accurate and reliable evaluation mechanism for determining the explanatory power of physical noise offsets for local water flow data. This judgment method, based on dual thresholds of amplitude and duration, effectively filters out transient interference and occasional fluctuations, ensuring that the physical noise offset is deemed insufficient only when there are persistent and significant deviations, thus avoiding unnecessary or erroneous weight adjustments. This significantly improves the accuracy of physical noise offset extraction, thereby enhancing the quality and stability of real water flow signal recovery, providing a more reliable foundation for data processing of offshore floating structures.

[0063] Based on the evaluation results and in combination with environmental influencing factors, the steps of dynamically correcting the impact weight include: continuously calculating the average value of the signal consistency deviation; determining whether the average value of the signal consistency deviation continues to exceed the preset drift judgment threshold; when the average value of the signal consistency deviation continues to exceed the drift judgment threshold, identifying the existence of long-term drift between the physical noise offset and the local water flow data; adjusting the update rate of the impact weight based on the identification result of the long-term drift; and correcting the impact weight based on the adjusted update rate and in combination with the environmental influencing factors.

[0064] "Continuously calculating the average value of signal consistency deviation" means the system continuously collects signal consistency deviation data over a period of time and calculates its rolling or cumulative average. This average is intended to smooth out instantaneous fluctuations, thereby more clearly revealing the long-term trend of the deviation. "Determining whether the average value of signal consistency deviation consistently exceeds a preset drift determination threshold" means comparing the calculated average value with a pre-set threshold and determining whether this exceeding condition persists within a certain time window. This drift determination threshold can be set based on historical data, system design requirements, or empirical values ​​to distinguish between random noise and systematic drift. "Identifying long-term drift between physical noise offset and local water flow data" means that when the average signal consistency deviation consistently exceeds the threshold, the system determines that the intrinsic relationship between the physical noise offset and local water flow data has undergone a sustained, non-instantaneous change. This may be caused by factors such as environmental changes, sensor aging, or changes in structural characteristics. "Adjusting the update rate of influence weights" means dynamically changing the frequency or step size of influence weight corrections based on the nature and extent of identified long-term drift. For example, when significant long-term drift is detected, the update rate can be increased to allow the system to adapt to the new state more quickly; conversely, in a stable state, the update rate can be reduced to avoid over-correction. "Combining environmental factors and modifying the impact weights" means that on top of adjusting the update rate, environmental factors such as water temperature, salinity, water depth, or wave state parameters are further considered to influence the contribution of physical noise offset to local water flow data, thereby making more accurate weight corrections.

[0065] The solution of the present application can effectively identify the long-term, systematic drift that may exist between the physical noise offset and the local water flow data by introducing a continuous monitoring of the average value of the signal consistency deviation and a drift judgment mechanism. Once such a long-term drift is identified, the system no longer makes weight corrections based solely on instantaneous or short-term evaluation results, but adjusts the update rate of the weights affected according to the nature of the drift. For example, when the drift is significant, the weight adjustment speed can be accelerated so that the system can converge to a new equilibrium state more quickly; when the drift is slow or not obvious, a more conservative update rate can be used to avoid unnecessary fluctuations. This adaptive update rate adjustment mechanism, combined with the consideration of environmental influencing factors, makes the correction process of the weights more intelligent and robust, thereby ensuring that the physical noise offset can be continuously and accurately stripped from the local water flow data, and the accuracy of data processing can be maintained even in long-term operation or when environmental conditions change slowly.

[0066] Through the above-mentioned technical solution, this application effectively solves the problem of drift in the relationship between physical noise offset and local water flow data that may occur in traditional methods during long-term operation, significantly improving the robustness and adaptability of the dynamic correction of influence weights. This ensures that the accuracy of the recovery of real water flow signals can be maintained over the long term in various complex and dynamically changing marine environments, thereby providing a more reliable data foundation for the attitude prediction and control of floating structures at sea, avoiding the cumulative errors caused by long-term drift, and improving the overall reliability and stability of the system.

[0067] In some preferred embodiments, assume that a floating structure operates continuously in the ocean for several months. Initially, the influence weight of the physical noise offset on local water flow data is dynamically adjusted using conventional methods, keeping the average value of the signal consistency deviation within a preset drift threshold. However, over time, due to the gradual accumulation of biological deposits on the structure's surface or slight aging of internal sensor components, the relationship between the physical noise offset and local water flow data undergoes subtle but persistent changes. At this point, the average value of the signal consistency deviation, continuously calculated by the system, begins to slowly but consistently exceed the preset drift threshold. This identifies long-term drift between the physical noise offset and local water flow data. To address this long-term drift, the system dynamically adjusts the update rate of the influence weight based on the identified results. For example, it adjusts the update frequency from daily to hourly, or appropriately increases the step size of each correction. Furthermore, the system incorporates current environmental factors such as water temperature and salinity to make more refined corrections to the influence weight. In this way, even if systematic drift occurs during long-term operation, the impact weights can be corrected in a timely and effective manner, thereby ensuring the accuracy of stripping physical noise offsets from local water flow data, and ultimately restoring a more realistic water flow signal that is not affected by long-term drift.

[0068] When processing data from floating structures, it's necessary to separate and recover the true water flow signal based on the physical noise offset and the actual macro-attitude. Identifying and separating logical deviations introduced by miscalibration in local water flow data is a key step. However, in practice, the sources of these logical deviations can be complex and variable. For example, they can be affected by changes in the platform's own state (such as platform depth or power supply voltage fluctuations), making them difficult to accurately identify and separate based solely on macro-attitude comparisons. Failure to consider these potential internal or external factors can result in incomplete or inaccurate separation of logical deviations, affecting the accuracy of the ultimately recovered true water flow signal.

[0069] The present application further proposes steps for identifying and separating logical deviations introduced in local water flow data due to incorrect calibration, including: continuously acquiring platform depth or power supply voltage fluctuation information that affects changes in logical deviations; dynamically determining a compensation coefficient for the logical deviation based on the platform depth or power supply voltage fluctuation information; using the compensation coefficient to perform preliminary correction on the local water flow data after stripping off the physical noise offset; comparing the preliminary corrected local water flow data with the actual macro posture, and further separating the logical deviation based on the comparison results.

[0070] Specifically, continuously acquiring information about platform depth or power supply voltage fluctuations that affect logical deviations refers to the system monitoring the depth of the floating structure and the voltage status of its internal power supply system in real time. Platform depth can be acquired by a depth sensor, while power supply voltage fluctuation information can be provided by a voltage sensor or power management module. This information is considered a key factor in causing logical deviations introduced by miscalibration in local water flow data, as it can directly affect the performance or calibration status of the water flow sensor. Dynamically determining a compensation coefficient for logical deviation based on platform depth or power supply voltage fluctuation information involves calculating a compensation coefficient in real time to correct the local water flow data based on a pre-established model or lookup table that reflects the relationship between platform depth or power supply voltage and logical deviation. This model or lookup table can be obtained through experimental calibration or historical data analysis to ensure that the compensation coefficient accurately reflects the impact of the current platform status on the logical deviation. In practical applications, the compensation coefficient is used to perform preliminary correction on the local water flow data after removing the physical noise offset. This refers to applying the dynamically determined compensation coefficient to the local water flow data after removing the physical noise offset. This preliminary correction aims to eliminate or reduce systematic deviations caused by platform depth or power supply voltage fluctuations, making the data closer to the true value. Furthermore, the local water flow data after preliminary correction is compared with the actual macro-attitude. Based on the comparison results, logical deviations are further separated. This means that on the basis of preliminary correction, the processed local water flow data is again compared with the actual macro-attitude of the floating structure. By analyzing the residual differences between the two, logical deviations that were not completely eliminated by preliminary correction can be identified and more refined separation can be performed. This comparison can use a variety of algorithms, such as least squares method and Kalman filtering, to optimize the identification and separation of deviations.

[0071] The solution of this application incorporates continuous acquisition of platform depth or power supply voltage fluctuation information and dynamically determines a compensation coefficient for logical deviation based on this information, thereby performing preliminary corrections in local water flow data after stripping out physical noise offsets. This mechanism is based on a deep understanding of how sensor calibration on floating structures can be affected by internal or external environmental factors under different operating conditions. For example, changes in water depth can cause zero-point drift in pressure sensors or flow sensors, while fluctuations in power supply voltage can affect sensor output stability. By quantifying these influencing factors as compensation coefficients, the system can proactively pre-process the logical deviations caused by these factors in the local water flow data, significantly reducing the difficulty of subsequent comparison and separation. Subsequently, by comparing the pre-corrected local water flow data with the actual macro-position, residual logical deviations can be more accurately identified and separated. Because most of the deviations caused by known factors have been pre-processed, the comparison results more accurately reflect the true uncalibrated deviations.

[0072] The above-mentioned technical solution can effectively solve the problem of insufficient accuracy caused by traditional methods when identifying and separating logical deviations introduced by incorrect calibration in local water flow data, which may be caused by not fully considering the changes in the platform's own state. This application makes the identification and separation process of logical deviations more adaptive and robust by dynamically introducing platform depth or power supply voltage fluctuation information as a correction basis. As a result, systematic deviations caused by incorrect calibration can be more accurately separated from local water flow data, thereby significantly improving the accuracy and reliability of the final recovered real water flow signal, providing a more reliable data foundation for the precise attitude control and hydrodynamic analysis of offshore floating structures.

[0073] In some preferred embodiments, assuming a floating structure operates at different depths, its onboard local current sensors may experience slight zero-point drift or sensitivity variations at different water depths. These variations, if uncalibrated, can manifest as logical deviations in the local current data. Similarly, if the structure's power supply system ages or experiences load fluctuations, resulting in unstable sensor supply voltage, similar logical deviations may also be introduced. According to the solution of this application, the system continuously monitors the current platform depth and power supply voltage of the floating structure. For example, when the platform depth changes from 10 meters to 50 meters, the system dynamically calculates a compensation coefficient for the current depth based on a pre-established depth-deviation model. Furthermore, if the power supply voltage fluctuates from the nominal 12V to 11.8V, the system also calculates another compensation coefficient based on the voltage-deviation model. These compensation coefficients are then combined and applied to the local current data after removing the physical noise offset to perform a preliminary correction. Specifically, if the local current data after removing the physical noise offset at a certain moment is X, and the total compensation coefficient determined based on the current platform depth and power supply voltage is C, then the preliminary corrected data X' = XC. This initially corrected X' is then compared with the actual macroscopic posture of the floating structure. If the comparison results show that small, random deviations still exist between the two, these residual logical deviations that are not fully covered by the compensation coefficients can be further identified and separated through statistical analysis or machine learning algorithms. For example, by analyzing the residual distribution between X' and the actual macroscopic posture, outliers or trends can be identified and attributed to logical deviations for elimination or correction. In this way, even in complex marine environments, logical deviations introduced by incorrect calibration can be effectively and accurately separated, ensuring the purity of the true water flow signal.

[0074] refer to Figure 2The present application proposes a sea-floating structure data processing system, which is applied to the above-mentioned sea-floating structure data processing method. The system includes: a benchmark establishment module, which is used to establish a macro-hydrodynamic response benchmark of the sea-floating structure; a data acquisition module, which is used to acquire local water flow data and actual macro-posture of the sea-floating structure in real time; a calculation module, which calculates the expected macro-posture of the sea-floating structure based on the local water flow data and the macro-hydrodynamic response benchmark; a comparison module, which compares the expected macro-posture with the actual macro-posture to obtain a posture difference; an estimation module, which diagnoses anomalies in the local water flow data based on the posture difference and estimates the physical noise offset caused by the local physical disturbance; and a recovery module, which separates and recovers the real water flow signal based on the physical noise offset and the actual macro-posture.

[0075] The benchmark establishment module can be configured to perform the steps of establishing a macroscopic hydrodynamic response benchmark for the floating structure. This module can include a storage unit for storing a preset macroscopic hydrodynamic response model or one learned from historical data, and can adjust it based on actual environmental conditions or the structure's state. The data acquisition module is designed to collect local water flow data within the floating structure in real time, for example, using devices such as water flow sensors and acoustic Doppler current profilers (ADCPs), and to obtain the actual macroscopic posture of the floating structure, for example, using posture sensors and inertial measurement units (IMUs). The calculation module is configured to receive the local water flow data from the data acquisition module and, in combination with the macroscopic hydrodynamic response benchmark provided by the benchmark establishment module, calculate the expected macroscopic posture of the floating structure under current water flow conditions using a preset algorithm or model. The comparison module is configured to receive the expected macroscopic posture output by the calculation module and the actual macroscopic posture provided by the data acquisition module, and compare the two to quantify the difference between the two postures. The estimation module is configured to diagnose possible anomalies in the local flow data based on the attitude differences output by the comparison module, and further estimate the physical noise offset caused by local physical disturbances (such as structural vibration and sensor noise). The recovery module receives the physical noise offset provided by the estimation module and the actual macro-attitude provided by the data acquisition module. Based on this information, it separates the physical noise offset from the original local flow data, ultimately recovering the true flow signal.

[0076] The solution of the present application realizes the automation and systematization of the data processing process for offshore floating structures by concretizing the various logical steps in the above-mentioned data processing method into independent, collaborative modules. Specifically, the benchmark establishment module provides a basic reference for subsequent attitude calculations, ensuring the accuracy of the expected attitude. The data acquisition module, as the input of the system, continuously provides real-time, accurate raw data. The calculation module and the comparison module work together to quickly and effectively identify potential anomalies in the data by comparing the expected and actual attitudes. The estimation module further analyzes these anomalies and quantifies the noise introduced by physical disturbances, which is difficult to accurately isolate using traditional methods. Finally, the recovery module uses the estimated noise offset, combined with the actual macro-attitude, to perform fine-grained processing on the raw data, thereby removing noise and potential logical deviations and ensuring the authenticity and reliability of the final output water flow signal. This modular design makes the entire data processing process clearer, more controllable, and easier to maintain and upgrade.

[0077] In some preferred embodiments, it is assumed that a sea-floating structure is deployed in a marine environment for long-term monitoring of water flow data, and the sea-floating structure data processing system of the present application is integrated on the structure.

[0078] First, during initial system deployment or scheduled maintenance, the benchmarking module establishes a macroscopic hydrodynamic response benchmark based on the specific design parameters, material properties, and expected hydrodynamic behavior of the floating structure. This may involve, for example, loading a precomputed hydrodynamic model or optimizing model parameters using initial calibration data. Subsequently, the data acquisition module continuously collects local flow data from flow sensors (such as ADCPs) mounted on the structure, as well as the structure's actual macroscopic pose data from attitude sensors (such as IMUs). This data is transmitted to the system in real time. Upon receiving the local flow data, the computation module immediately combines it with the macroscopic hydrodynamic response benchmark provided by the benchmarking module to calculate the expected macroscopic pose of the structure under the current flow conditions. For example, if the flow velocity and direction are X, the structure should assume attitude Y. The comparison module then accurately compares the calculated expected macroscopic pose with the actual macroscopic pose provided by the data acquisition module and outputs the difference between the two poses. Once posture differences are identified, the estimation module intervenes. Based on these differences, it diagnoses whether anomalies exist in the local flow data. It further analyzes whether these anomalies are caused by local physical disturbances (such as micro-vibrations of the structure itself or local eddies at the sensor installation location) and estimates the resulting physical noise offset. For example, if the structure vibrates at a specific frequency, the estimation module identifies the periodic offset in the flow data caused by this vibration. Finally, the recovery module uses this estimated physical noise offset, combined with the actual macro-pose, to perform refined processing on the raw local flow data. It removes these physical noise offsets from the raw data while also considering the impact of the actual macro-pose on the flow measurement, ultimately recovering a flow signal that is closest to the actual state. For example, by subtracting the offset caused by vibration and correcting the flow vector based on the structure's tilt angle, a more accurate flow velocity and direction can be obtained.

[0079] The contents disclosed above are only preferred feasible embodiments of the present invention and do not limit the scope of protection of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the scope of protection of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A method for processing sea floating structure data, characterized in that: The method comprises the following steps: Establish a benchmark for the macroscopic hydrodynamic response of offshore floating structures; Real-time acquisition of local water flow data and actual macroscopic posture of floating structures at sea; Based on local water flow data and macro-hydrodynamic response benchmarks, the expected macro-attitude of the sea-floating structure is calculated; Comparing the expected macro-posture with the actual macro-posture to obtain the posture difference; Based on the attitude differences, diagnose the anomalies in the local water flow data and estimate the physical noise offset caused by the local physical disturbance; Separate and restore the real water flow signal based on the physical noise offset and actual macro-attitude.

2. The method for processing sea floating structure data according to claim 1, wherein: The steps for estimating the physical noise offset caused by a local physical disturbance include: Identify quiet flow time windows; Record the composite static zero-point attitude data of the sea-floating structure within the static flow time window; Activate the internal actuator of the sea floating structure to apply internal force; Measuring the transient attitude response of sea-floating structures to internal forces; Calibrate the structural dynamic response benchmark of the sea-floating structure based on the instantaneous attitude response; The physical noise offset caused by local physical disturbance is separated based on the composite static zero-point attitude data and the structural dynamic response benchmark.

3. The method for processing sea floating structure data according to claim 2, wherein: The steps to identify the quiet flow time window include: Continuously monitor water flow velocity data and obtain the absolute value of water flow velocity, standard deviation of water flow velocity and instantaneous rate of change of water flow velocity; Determining whether the absolute value of the water flow velocity is lower than a first threshold; determining whether the standard deviation of the water flow velocity is lower than a second threshold; determining whether the absolute value of the instantaneous rate of change of the water flow velocity is lower than a third threshold; When the absolute value of the water flow velocity, the standard deviation of the water flow velocity, and the absolute value of the instantaneous rate of change of the water flow velocity all meet the corresponding threshold conditions within a continuous time period, and the duration of the continuous time period reaches a preset duration threshold, the static flow time window is identified.

4. The method for processing sea floating structure data according to claim 1, wherein: The steps to establish a benchmark for the macroscopic hydrodynamic response of a floating structure include: Acquiring structural dynamic response information reflecting the current structural state of the offshore floating structure; According to the structural dynamic response information, the macro-hydrodynamic response benchmark of the sea floating structure is adjusted to establish the macro-hydrodynamic response benchmark.

5. The method for processing sea floating structure data according to claim 1, wherein: The steps to separate and restore the real water flow signal based on the physical noise offset and the actual macro-attitude include: Continuously acquiring environmental factors that affect the manifestation of the physical noise offset in the local water flow data, the environmental factors including water temperature, salinity, water depth or wave state parameters; Dynamically adjust the influence weight of the physical noise offset on the local water flow data based on the environmental impact factors. The impact weight is used to reflect the real-time changes in the contribution of the environmental impact factors to the physical noise offset in the local water flow data. Based on the influence weight, the physical noise offset is removed from the local water flow data; Combined with the actual macro-posture, the logical deviation introduced by incorrect calibration in the local water flow data is identified and separated, and the real water flow signal is restored based on the local water flow data after stripping off the physical noise offset.

6. The method for processing sea floating structure data according to claim 5, wherein: The steps of dynamically adjusting the influence weight of the physical noise offset on the local water flow data according to the environmental influencing factors include: Continuously acquiring the physical noise offset and local water flow data; Calculate the corresponding noise component in the local water flow data based on the physical noise offset and the currently set influence weight; Remove the noise component from the local water flow data to obtain the preliminary restored water flow signal; Compare the initially recovered water flow signal with the actual macroscopic posture to obtain the signal consistency deviation; The ability of the physical noise offset to interpret local water flow data was evaluated based on the signal consistency deviation; Based on the evaluation results and combined with environmental impact factors, the impact weights are dynamically revised.

7. The method for processing sea floating structure data according to claim 6, wherein: The steps to evaluate the interpretability of the physical noise offset for local flow data based on signal consistency deviation include: Set the amplitude threshold and duration threshold of signal consistency deviation; Determining whether the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation; Determining whether the duration of the signal consistency deviation reaches a duration threshold of the signal consistency deviation; When the amplitude of the signal consistency deviation exceeds the amplitude threshold of the signal consistency deviation and the duration of the signal consistency deviation reaches the duration threshold of the signal consistency deviation, it is assessed that the physical noise offset is insufficient to explain the local water flow data.

8. The method for processing sea floating structure data according to claim 6, wherein: Based on the assessment results and in combination with environmental impact factors, the steps for dynamically revising the impact weights include: Continuously calculate the average value of signal consistency deviation; Determine whether the average value of the signal consistency deviation continues to exceed the preset drift determination threshold; When the average value of the signal consistency deviation continuously exceeds the drift judgment threshold, it is recognized that there is a long-term drift between the physical noise offset and the local water flow data; Adjust the update rate of the impact weights based on the identification results of long-term drift; The impact weight is modified based on the adjusted update rate and combined with environmental impact factors.

9. The method for processing sea floating structure data according to claim 5, wherein: The steps to identify and isolate logical biases introduced by miscalibration in local streamflow data include: Continuously obtain platform depth or power supply voltage fluctuation information that affects logic deviation changes; Dynamically determine the compensation coefficient of logic deviation based on platform depth or power supply voltage fluctuation information; Using the compensation coefficient, the local water flow data after removing the physical noise offset is preliminarily corrected; The preliminary corrected local water flow data is compared with the actual macroscopic posture, and the logical deviation is further separated based on the comparison results.

10. A sea floating structure data processing system, applied to the sea floating structure data processing method according to claim 1, characterized in that: The system includes: Benchmark establishment module, used to establish the macroscopic hydrodynamic response benchmark of offshore floating structures; A data acquisition module, used to obtain local water flow data and actual macroscopic posture of the sea floating structure in real time; The calculation module calculates the expected macro-attitude of the sea-floating structure based on the local water flow data and the macro-hydrodynamic response benchmark; The comparison module compares the expected macro-pose with the actual macro-pose to obtain the pose difference; The estimation module diagnoses anomalies in local water flow data based on attitude differences and estimates the physical noise offset caused by local physical disturbances; The restoration module separates and restores the real water flow signal based on the physical noise offset and the actual macro posture.

Images