Mooring chain tension monitoring method and device, electronic equipment and storage medium

Abstract

The application provides a mooring anchor chain tension monitoring method and device, electronic equipment and storage medium; applied to the field of ocean engineering mooring monitoring, including: based on the set time interval, the strain data and the inclination data corresponding to the target anchor chain are monitored; the strain data is converted into the tension increment corresponding to the target anchor chain, and the inclination data is converted into the absolute tension corresponding to the target anchor chain; the tension increment is used to represent the tension change of the target anchor chain between the current monitoring moment and the last monitoring moment; the state space model corresponding to the anchor chain tension of the target anchor chain is constructed, and the state space model is used to determine the anchor chain tension estimation value of the target anchor chain at the current moment; based on the tension increment and the absolute tension, the Kalman filtering recursive calculation is carried out through the state space model, and the anchor chain tension estimation value of the target anchor chain at the current monitoring moment is obtained. In this way, the monitoring accuracy and stability of the mooring anchor chain tension can be improved.

Description

Technical Field

[0001] This application relates to the field of marine engineering mooring monitoring, and in particular to a method, device, electronic equipment and storage medium for monitoring the tension of mooring anchor chains. Background Technology

[0002] During the long-term service of deep-sea oil and gas floating platforms, the mooring anchor chain, as a critical load-bearing component, directly affects the platform's positioning stability and operational safety. Current methods for monitoring mooring anchor chain tension include direct measurement (using strain gauges or fiber optic gratings on the chain links or anchor chain connectors to acquire local strain data in real time and convert it into tension) and indirect calculation (monitoring the anchor chain attitude and calculating the force based on the catenary equation). However, direct measurement suffers from unpredictable initial tension and is only applicable to newly constructed structures. Indirect calculation is significantly affected by sea state disturbances, resulting in large fluctuations in observation accuracy and difficulty in ensuring long-term stability. Therefore, current methods for monitoring mooring anchor chain tension suffer from low accuracy and poor monitoring stability. Summary of the Invention

[0003] This application provides a method, device, electronic device, and storage medium for monitoring the tension of mooring anchor chains, in order to solve one or more problems existing in the related art.

[0004] This application provides a method for monitoring the tension of mooring anchor chains. The method includes: monitoring strain data and inclination data corresponding to a target anchor chain based on a set time interval; converting the strain data into a tension increment corresponding to the target anchor chain, and converting the inclination data into an absolute tension corresponding to the target anchor chain; the tension increment is used to characterize the tension change of the target anchor chain between the current monitoring time and the previous monitoring time; constructing a state-space model corresponding to the anchor chain tension of the target anchor chain, the state-space model being used to determine the estimated anchor chain tension of the target anchor chain at the current time; and performing Kalman filtering recursive calculation based on the tension increment and the absolute tension to obtain the estimated anchor chain tension of the target anchor chain at the current monitoring time.

[0005] According to one embodiment of this application, converting the strain data into a tension increment corresponding to the target anchor chain includes: the strain data including at least the strain value measured by a strain sensor installed on the target anchor chain; calculating the tension value at the current monitoring moment based on the cross-sectional area of ​​the chain links, Young's modulus, stress conversion coefficient, strain transfer coefficient, temperature correction coefficient, and the strain value of the target anchor chain; and determining the difference between the tension value at the current monitoring moment and the tension value at the previous monitoring moment as the tension increment.

[0006] According to one embodiment of this application, converting the inclination angle data into the absolute tension corresponding to the target anchor chain includes: the inclination angle data includes at least the inclination angle between the chain link and the horizontal plane measured by an inclination angle sensor installed on the chain link of the target anchor chain; if the target anchor chain does not have a bottoming portion, the inclination angle is converted into the absolute tension according to the catenary mechanics model; if the target anchor chain has a bottoming portion, the absolute tension is calculated using a hyperbolic function model based on the inclination angle, water depth parameters, and horizontal distance parameters.

[0007] According to one embodiment of this application, after constructing the state-space model corresponding to the anchor chain tension of the target anchor chain, the method further includes: setting initial values ​​corresponding to the state-space model; setting the initial values ​​corresponding to the state-space model includes: determining the initial state estimate and initial error covariance corresponding to the state-space model based on the inclination data and the strain data; determining the process noise variance based on the measurement accuracy of the strain data and the environmental noise parameter corresponding to the target anchor chain; and determining the observation noise variance based on the measurement accuracy of the inclination data and the environmental noise parameter corresponding to the target anchor chain.

[0008] According to one embodiment of this application, constructing a state-space model corresponding to the anchor chain tension of the target anchor chain includes: constructing the state equation and observation equation of the state-space model; the state equation is expressed by the following formula: The observation equation is expressed by the following formula: in, This represents the anchor chain tension at time k. This represents the anchor chain tension at time k-1. This indicates the increase in tensile force. Indicates process noise. This represents the absolute tension of the target anchor chain at time k. This indicates observation noise.

[0009] According to one embodiment of this application, the step of obtaining the anchor chain tension estimate at the current monitoring time by performing Kalman filtering recursive calculation based on the tension increment and the absolute tension through the state-space model includes: determining the prior state estimate and prior error covariance at the current monitoring time based on the posterior state estimate of the previous monitoring time, the tension increment, and the process noise variance; in response to the inclination data not meeting the set inclination monitoring conditions, determining the prior state estimate at the current monitoring time as the posterior state estimate at the current monitoring time, and setting the current... The prior error covariance at the current monitoring time is determined as the posterior error covariance at the current monitoring time. In response to the inclination data satisfying the set inclination monitoring conditions, a Kalman gain value is calculated based on the prior error covariance and the observation noise variance at the current monitoring time. Based on the Kalman gain value and the absolute tension, the prior state estimate and prior error covariance at the current monitoring time are updated to obtain the posterior state estimate and posterior error covariance at the current monitoring time. The posterior state estimate is determined as the anchor chain tension estimate for the target anchor chain at the current monitoring time.

[0010] According to one embodiment of this application, the method further includes: determining the prediction residual for the current monitoring time based on the absolute tensile force and the prior error covariance of the current monitoring time; determining the noise update value based on the prediction residual using a pre-constructed dynamic observation noise model; and smoothing the observation noise variance of the previous monitoring time based on the noise update value to obtain the observation noise variance for the current monitoring time.

[0011] This application also provides a mooring anchor chain tension monitoring device, comprising: a monitoring module for monitoring strain data and inclination data corresponding to a target anchor chain based on a set time interval; a conversion module for converting the strain data into a tension increment corresponding to the target anchor chain and converting the inclination data into an absolute tension corresponding to the target anchor chain; the tension increment is used to characterize the tension change of the target anchor chain between the current monitoring time and the previous monitoring time; a construction module for constructing a state space model corresponding to the anchor chain tension of the target anchor chain, the state space model being used to determine the estimated value of the anchor chain tension of the target anchor chain at the current time; and a prediction module for performing Kalman filtering recursive calculation based on the tension increment and the absolute tension through the state space model to obtain the estimated value of the anchor chain tension of the target anchor chain at the current monitoring time.

[0012] This application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the above-described embodiments.

[0013] This application also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.

[0014] The method of this application embodiment monitors strain and inclination data corresponding to a target anchor chain based on a set time interval; converts the strain data into a tension increment corresponding to the target anchor chain, and converts the inclination data into an absolute tension corresponding to the target anchor chain; the tension increment is used to characterize the tension change of the target anchor chain between the current monitoring time and the previous monitoring time; a state space model corresponding to the anchor chain tension of the target anchor chain is constructed, and the state space model is used to determine the estimated value of the anchor chain tension of the target anchor chain at the current time; based on the tension increment and the absolute tension, Kalman filtering recursive calculation is performed through the state space model to obtain the estimated value of the anchor chain tension of the target anchor chain at the current monitoring time. This improves the monitoring accuracy and stability of mooring anchor chain tension.

[0015] It should be understood that the teachings of this application are not required to achieve all the beneficial effects described above, but rather that a specific technical solution can achieve a specific technical effect, and other embodiments of this application can also achieve beneficial effects not mentioned above. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which:

[0017] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0018] Figure 1 This paper illustrates the processing flow of the mooring anchor chain tension monitoring method provided in an embodiment of this application. Figure 1 ;

[0019] Figure 2 This paper illustrates the processing flow of the mooring anchor chain tension monitoring method provided in an embodiment of this application. Figure 2 ;

[0020] Figure 3 This paper illustrates the processing flow of the mooring anchor chain tension monitoring method provided in an embodiment of this application. Figure 3 ;

[0021] Figure 4 This illustrates an application scenario of the mooring anchor chain tension monitoring method provided in this application embodiment. Figure 1 ;

[0022] Figure 5 This illustrates an application scenario of the mooring anchor chain tension monitoring method provided in this application embodiment. Figure 2 ;

[0023] Figure 6 This illustrates an application scenario of the mooring anchor chain tension monitoring method provided in this application embodiment. Figure 3 ;

[0024] Figure 7 A flowchart illustrating the tension calculation in the mooring anchor chain tension monitoring method provided in this application embodiment is shown;

[0025] Figure 8 This illustration shows an optional schematic diagram of the mooring chain tension monitoring device provided in an embodiment of this application;

[0026] Figure 9 A schematic diagram of the composition structure of the electronic device provided in the embodiments of this application is shown. Detailed Implementation

[0027] To make the objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0029] In the following description, the terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0031] The processing flow of the mooring anchor chain tension monitoring method provided in the embodiments of this application is described below. See also... Figure 1 , Figure 1This is a schematic diagram of the processing flow of the mooring anchor chain tension monitoring method provided in the embodiments of this application. Figure 1 , will combine Figure 1 Steps S101-S104 are explained below.

[0032] Step S101: Based on the set time interval, monitor and obtain the strain data and inclination data corresponding to the target anchor chain.

[0033] In some embodiments, the time interval may include the time period between two consecutive data acquisitions or status updates. The target anchor chain may include the mooring anchor chain selected for tension monitoring. Strain data can be measured by strain sensors attached to the surface of the target anchor chain. Specifically, key links near the upper end of the target anchor chain (close to the guide cable hole of the floating platform) are preferred as sensor installation points, where the chain mainly bears axial tension, with low bending stress, uniform stress distribution, and ease of installation and maintenance. Specifically, links within 0.5-1 meter of the guide cable hole are selected as measuring points. If the selected location is in the water area, links in the splash zone should be avoided. After cleaning the surface dirt and rust at the middle of the straight section of the selected link (the axial centerline of the link, avoiding curved sections to reduce local stress concentration), a surface-mounted strain sensor (including a waterproof foil strain gauge or fiber optic grating support) is installed. The specific bonding method involves placing two strain gauges facing each other on the straight section of each chain link, that is, arranging two strain gauges axially aligned in the direction of chain tension. A Wheatstone half-bridge circuit is used for connection to eliminate temperature and bending interference. The surface of the strain gauges is coated with a silicone or epoxy resin protective layer to prevent seawater corrosion.

[0034] Inclination data can include the angle of inclination of the anchor chain relative to the horizontal plane, measured by an inclination sensor. Specifically, a miniature MEMS inclination sensor is installed on a selected link, fixed to a specially designed waterproof clamp, with the sensing axis precisely aligned with the anchor chain axis and parallel to the chain centerline, to measure the inclination angle θ between the link and the horizontal plane.

[0035] Step S102: Convert the strain data into the tension increment corresponding to the target anchor chain, and convert the inclination angle data into the absolute tension corresponding to the target anchor chain; the tension increment is used to characterize the tension change of the target anchor chain between the current monitoring time and the previous monitoring time.

[0036] In some embodiments, the tension increment may include: the change in the difference between the strain-based tension of the target anchor chain between the current monitoring time and the previous monitoring time. Strain-based tension F s The relationship between strain data and strain data can be expressed by the following formula.

[0037] F s =k c k p EAε+kT (T-T0)

[0038] Where k c k is the stress transformation factor, which characterizes the amplification of local stress by the geometry of the chain links; p ε is the strain transfer coefficient, which characterizes the transmission efficiency at the bonding interface; E is the Young's modulus of the anchor chain material; A is the cross-sectional area at the strain bonding location; ε is the axial strain; k T is the temperature correction factor; T is the measured temperature, and T0 is the reference temperature. Where, k c and k p The force distribution of the chain links was determined by finite element simulation; E, A, and k were determined. T The data was obtained through actual material testing and thermal cycling experiments. The above formula ensures that the strain data is accurately converted into the longitudinal tension of the anchor chain, while compensating for temperature drift.

[0039] Absolute tension can include: the total tension value of the anchor chain at the current moment, obtained by combining inclination angle data with a catenary tension function or hyperbolic function model. Absolute tension F a The relationship between strain data and strain data can be expressed by the following formula.

[0040] F a = f(θ) = ml / sin(θ)

[0041] Where f represents the catenary tension function, θ represents the angle of inclination between the target chain link and the horizontal plane in the inclination data, m is the mass per unit length (wet weight in water, characterizing buoyancy weight loss, determined by immersion test); and l is the suspension length from the sensor installation chain link to the bottom of the anchor chain (anchor point).

[0042] If the target anchor chain has a bottoming portion, the absolute tension can be calculated using a more complex hyperbolic function model that incorporates water depth and horizontal distance parameters.

[0043] Step S103: Construct a state-space model corresponding to the anchor chain tension of the target anchor chain. The state-space model is used to determine the estimated value of the anchor chain tension of the target anchor chain at the current moment.

[0044] In some embodiments, the state-space model may include a mathematical model consisting of state equations and observation equations. The anchor chain tension estimate may include the calculated anchor chain tension output after optimal estimation of sensor measurement data using a Kalman filter algorithm.

[0045] Step S104: Based on the tension increment and absolute tension, Kalman filtering is used to recursively calculate the estimated tension of the target anchor chain at the current monitoring moment through a state-space model.

[0046] In some embodiments, Kalman filter recursive calculation may include an algorithmic process of iteratively processing sensor data in each time interval through state prediction and observation updates. Kalman filter recursive calculation can effectively suppress measurement noise from strain sensors and tilt sensors, improving the accuracy and stability of anchor chain tension monitoring.

[0047] The beneficial effects of the method in this application embodiment are as follows:

[0048] 1. Achieved accurate recovery of absolute tension in in-service anchor chains: By integrating the high-sensitivity incremental measurement of strain sensors with the absolute tension reference value provided by tilt sensors, this method overcomes the fundamental deficiency of traditional strain measurement methods in failing to detect initial tension. This method eliminates the need for pre-calibration of a zero-stress state during anchor chain manufacturing or installation, allowing direct application to existing mooring systems already in operation. This significantly reduces the difficulty and cost of engineering modifications, making it widely applicable.

[0049] 2. Effectively Handling Asynchronous Multi-Source Data Fusion: Addressing the sampling frequency differences between strain and dip angle data, a unified state-space model was established based on the Kalman filter framework. During periods lacking dip angle observations, the filter relies solely on strain increments for state prediction, maintaining the continuity of tensile force monitoring. When dip angle observations become available, an automatic observation update step is triggered to correct accumulated errors. This mechanism ensures the organic fusion of data from different time scales, avoiding systematic errors introduced by simple weighted averaging or data interpolation.

[0050] 3. Adaptive Adjustment of Fusion Weights Enhances Robustness: A dynamic observation noise model driven by predictive residuals is introduced, enabling the system to automatically adjust the Kalman gain based on real-time sea state conditions and data quality. When severe sea state leads to a decrease in inclination measurement accuracy, the filter automatically reduces the inclination observation weight, relying more on the stable incremental information of strain measurements; when sea state is stable, the absolute value information of inclination observations is fully utilized for state correction. This adaptive mechanism significantly improves the algorithm's adaptability to complex marine environments and avoids the performance degradation of fixed-weight methods under extreme conditions.

[0051] 4. Compatible with Multiple Sensor Types and Highly Expandable: This solution does not restrict the type of strain sensor, and is suitable for both resistance strain gauges and fiber Bragg grating (FBG) and other sensing technologies. Tilt measurement can be achieved through MEMS inclinometers, attitude sensors, and even vision measurement systems. The modular architecture design facilitates the selection of the optimal sensor combination according to specific engineering conditions, and has good technical scalability and upgrade potential.

[0052] In some embodiments, the step S102 of converting strain data into the tensile increment corresponding to the target anchor chain may include: the strain data including at least the strain value measured by the strain sensor installed on the target anchor chain; calculating the tensile value at the current monitoring time based on the cross-sectional area of ​​the chain link, Young's modulus, stress conversion coefficient, strain transfer coefficient, temperature correction coefficient, and strain value of the target anchor chain; and determining the difference between the tensile value at the current monitoring time and the tensile value at the previous monitoring time as the tensile increment.

[0053] As an example, the strain data processing procedure may include: converting the raw strain voltage into a strain value ε using a bridge calibration formula. k (Unit: με), k represents time k, and k-1 represents time k-1. If the current monitoring time is time k, then the previous monitoring time is time k-1. The cross-sectional area of ​​the target chain link is determined to be A = 2.83 × 10⁻⁶. 5 m 2 Young's modulus E = 1.93 × 10 11 Pa (304 stainless steel), stress conversion factor k c =1.18, strain transfer coefficient k p =0.89, temperature correction factor k T =1.15N / °C, substitute into formula F s =k c k p EAε+k T (T-T0) is used to calculate the tension value F at the current monitoring time. s,k Obtain the tension value F at the previous monitoring time. s,k 1. Calculate the tension increment ΔF at the current monitoring moment. s,k =F s,k F s,k 1. For two chain link measuring points, take the arithmetic mean of their tension increments to reduce local measurement errors.

[0054] To simulate situations where strain sensors cannot detect the initial tensile force in actual engineering practice, strain-based tensile force can be applied during data processing. An unknown systematic error is superimposed (simulating initial stress): ,in The bias is randomly generated by the system at the start of the experiment, and its value range is []. The bias is a uniformly distributed random number within the range of [30N, +30N] (approximately ±37.5% of the typical working tensile force of 80N). This bias remains constant throughout a single experiment, completely hidden from the Kalman filter algorithm, realistically simulating the engineering challenge of determining the zero-stress state when retrofitting sensors to in-service anchor chains. The experimental log file can simultaneously save... The actual value is used for later performance evaluation.

[0055] In some embodiments, the conversion of the inclination angle data into the absolute tension corresponding to the target anchor chain in step S102 includes: the inclination angle data includes at least the inclination angle between the chain link and the horizontal plane measured by the inclination angle sensor installed on the chain link of the target anchor chain; if the target anchor chain does not have a bottoming portion, the inclination angle is converted into absolute tension according to the catenary mechanical model; if the target anchor chain has a bottoming portion, the absolute tension is calculated using a hyperbolic function model based on the inclination angle, water depth parameters, and horizontal distance parameters.

[0056] As an example, the tilt angle data processing procedure may include: Tilt angle data processing: If the target anchor chain does not have a bottoming portion, the tilt angle sensor continuously collects 15 sets of high-frequency data, removes outliers that deviate from the mean by more than 3σ, and calculates the arithmetic mean of the remaining valid data points. and standard deviation Substituting the average inclination angle into the catenary mechanical model Calculate the absolute tensile force F a,k Only when σ θ,k <3° and 25°< At <75°, F a,k These observations are marked as valid. If the data quality does not meet the requirements, the inclination data processing is skipped.

[0057] If the target anchor chain has a section touching the seabed, a mathematical model is constructed based on hyperbolic functions to characterize the static equilibrium state of the anchor chain with the seabed contact section. Water depth parameters and horizontal distance parameters from the anchor chain suspension point to the anchoring point are collected in the current operating area. Using the hyperbolic function model, the inclination angle between the chain links and the horizontal plane, water depth parameters, and horizontal distance parameters are calculated to obtain the absolute tension.

[0058] In some embodiments, the state space model corresponding to the anchor chain tension of the target anchor chain in step S103 may include: the state equation and observation equation for constructing the state space model.

[0059] The state equation is expressed by the following formula:

[0060] The observation equation is expressed by the following formula:

[0061] in, This represents the anchor chain tension at time k. This represents the anchor chain tension at time k-1. Indicates the increase in tensile force. Indicates process noise, w k The value range of is (0, Q), which characterizes the strain measurement error. This represents the absolute tension of the target anchor chain at time k. Indicates observation noise, v k The value range of is (0, R), which represents the error in tilt angle estimation. If the current monitoring time is time k, then time k-1 represents the previous monitoring time, Q represents the process noise variance, and R represents the observation noise variance.

[0062] In some embodiments, after constructing a state-space model corresponding to the anchor chain tension of the target anchor chain, the method further includes setting initial values ​​corresponding to the state-space model.

[0063] As an example, after constructing the state-space model based on the mechanical properties of the target anchor chain, the mooring monitoring system, during its initialization phase, first checks whether the tilt sensor outputs valid chain link tilt angle data. If valid chain link tilt angle data exists at startup, the initial estimate of the anchor chain tension is directly calculated based on this tilt angle observation and the catenary's mechanical properties. A relatively small initial error covariance is calculated to characterize a high initial confidence level, taking into account the tilt sensor's measurement accuracy and the catenary tension's sensitivity to tilt angle changes. Conversely, if no valid tilt angle data is available at startup, the tension value calculated based on strain sensor data and material elastic parameters is used as a temporary initial estimate via strain measurement mode. Simultaneously, a larger initial error covariance is set to conservatively represent the anchor chain tension. The initial state setting is highly uncertain, awaiting subsequent effective tilt angle observations to trigger state correction. After completing the initial state setting, the mooring monitoring system determines the process noise variance based on the deployed strain sensors and environmental noise parameters. If a high-precision fiber optic sensor with fiber Bragg grating encapsulation is used, a smaller noise variance is set; if a traditional resistance strain gauge is used, the noise variance is increased accordingly to reflect its larger measurement drift and electrical noise level. Subsequently, the mooring monitoring system sets the upper and lower boundaries of the observation noise variance based on historical sea state statistics. The optimal measurement accuracy under ideal sea conditions is set as the lower boundary, and the maximum uncertainty under severe sea conditions or significant model deviations is set as the upper boundary. The upper boundary value is selected as the initial observation noise variance to ensure that the filter carefully selects values ​​for the absolute tension observation value in the early stage of startup. At this point, all initial value settings for the state space model are completed.

[0064] In some embodiments, the processing flow of the mooring anchor chain tension monitoring method is illustrated. Figure 2 ,like Figure 2 As shown, setting the initial values ​​corresponding to the state-space model can include:

[0065] Step S201: Based on the tilt angle data and strain data, determine the initial state estimate and initial error covariance corresponding to the state-space model.

[0066] As an example, the initial state estimate The determination may include: if the output status of the tilt sensor is detected when the monitoring system starts, and if it is determined that there is a valid tilt angle observation value (i.e., the tilt angle observation value is within the range of the tilt sensor), then the initial state estimate is directly calculated based on the valid tilt angle observation value using the catenary tension function. If no valid dip angle observations are available, the strain gauge measurement will be used as a temporary initial value. At this point, the system error δ is unknown, and we need to wait for the initial tilt angle observation to trigger a status update. Valid tilt angle observations can include tilt angle data obtained through a tilt angle sensor and validated for validity. Validation is performed because tilt angle sensors have a measurement range; exceeding this range will result in the output of saturation or error values.

[0067] Determining the initial error covariance P0 may include: when initializing using tilt angle observations, ,in The sensitivity of the catenary tension function to the tilt angle. This represents the standard deviation of the error in the tilt sensor's measurement of the tilt angle. Under typical operating conditions, the initial error covariance P0 = 10. 6 N 2 When using the strain method for initialization, a large initial error covariance P0 = 10 is set due to the unknown system error δ. 8 N 2 N represents the unit of P0, namely Newton.

[0068] Step S202: Based on the measurement accuracy of the strain data and the environmental noise parameters corresponding to the target anchor chain, determine the process noise variance.

[0069] As an example, the process of determining the process noise variance Q can be represented by the following formula.

[0070]

[0071] Where, the typical range of values ​​for Q is Q∈

[10] 3 10 5 ]N 2 , This represents the standard deviation of the strain measurement error. A smaller value can be used when measuring strain using a fiber Bragg grating (FBG) packaged sensor; for resistance strain gauges, a larger value is required.

[0072] Step S203: Based on the measurement accuracy of the tilt angle data and the environmental noise parameters corresponding to the target anchor chain, determine the observation noise variance.

[0073] As an example, the process of determining the observation noise variance R may include: setting upper and lower bounds for the observation noise variance R based on historical sea state statistics, with the lower bound R0... min This represents the optimal accuracy for inclination measurement under ideal sea conditions. Determined based on historical sea state statistics or theoretical calculations, the typical value is R. min =5×10 5 N 2 Upper bound R max This represents the maximum uncertainty in inclination measurement under adverse sea conditions or when model bias is significant; a typical value is R. max =1×10 8 N 2 ; Take the initial observation noise R0 = R max .

[0074] In some embodiments, the processing flow of the mooring anchor chain tension monitoring method is illustrated. Figure 3 , like Figure 3 As shown, step S104, based on the tension increment and absolute tension, uses a state-space model to perform a Kalman filter recursive calculation to obtain the estimated anchor chain tension at the current monitoring moment. Specifically, this may include:

[0075] Step S301: Based on the posterior state estimate, tension increment, and process noise variance of the previous monitoring time, determine the prior state estimate and prior error covariance of the current monitoring time.

[0076] As an example, calculating prior state estimates and prior error covariance .

[0077] Step S302a: In response to the tilt angle data not meeting the set tilt angle monitoring conditions, the prior state estimate at the current monitoring time is determined as the posterior state estimate at the current monitoring time, and the prior error covariance at the current monitoring time is determined as the posterior error covariance at the current monitoring time.

[0078] As an example, tilt monitoring conditions may include: preset monitoring conditions for determining whether tilt sensor data is valid and usable. Specifically, this could be whether the tilt data is within the tilt sensor's measurement range. If no valid tilt observation is detected, then... , .in, P represents the posterior state estimate at the current monitoring time. k This represents the posterior error covariance.

[0079] In step S302b, in response to the tilt angle data meeting the set tilt angle monitoring conditions, the Kalman gain value is calculated based on the prior error covariance and the observation noise variance at the current monitoring time.

[0080] As an example, if an effective tilt angle observation is detected, the Kalman gain value can be calculated using the following formula based on the prior error covariance and the observation noise variance at the current monitoring time.

[0081]

[0082] Among them, K k Indicates Kalman gain, This represents the variance of the observation noise at the current monitoring time.

[0083] Step S303b: Based on the Kalman gain and absolute tension, update the prior state estimate and prior error covariance at the current monitoring time to obtain the posterior state estimate and posterior error covariance at the current monitoring time.

[0084] As an example, based on the Kalman gain and absolute tension, the prior state estimate and prior error covariance at the current monitoring time are updated, and the posterior state estimate and posterior error covariance at the current monitoring time can be expressed by the following formula.

[0085]

[0086]

[0087] in, This represents the prior state estimate at the current monitoring time. P represents the posterior state estimate at the current monitoring time. k This represents the posterior error covariance.

[0088] Step S304: Determine the posterior state estimate as the anchor chain tension estimate at the current monitoring time of the target anchor chain.

[0089] As an example, Output the estimated anchor chain tension at the current monitoring moment of the target anchor chain.

[0090] In some embodiments, the mooring chain tension monitoring method may further include: determining the prediction residual for the current monitoring time based on the absolute tension and the prior error covariance at the current monitoring time; determining the noise update value based on the prediction residual using a pre-built dynamic observation noise model; and smoothing the observation noise variance at the previous monitoring time based on the noise update value to obtain the observation noise variance at the current monitoring time.

[0091] As an example, the prediction residual at the current monitoring time can be represented by the following formula, based on the absolute tensile force and the prior error covariance at the current monitoring time.

[0092]

[0093] in, This represents the prediction residual at the current monitoring time.

[0094] Based on the prediction residuals, the noise update value is determined through a pre-built dynamic observation noise model, which can be expressed by the following formula.

[0095]

[0096] in, Indicates the noise update value. R represents the desired residual threshold, λ represents the control response sensitivity, and R min To observe the lower bound of the noise variance, R max This is the upper bound of the observed noise variance.

[0097] Based on the noise update value, the observed noise variance at the previous monitoring time is smoothed to obtain the observed noise variance at the current monitoring time, which can be expressed by the following formula.

[0098]

[0099] in, Represents the smoothing coefficient. .

[0100] refer to Figure 4 Application scenarios of the mooring anchor chain tension monitoring method provided in this application embodiment Figure 1 This is applied to the implementation process of monitoring the tension of mooring anchor chains.

[0101] Figure 4 The diagram illustrates the deployment structure of a mooring monitoring system for a semi-submersible offshore platform. The semi-submersible platform 1 is a deep-sea operational platform (such as a drilling platform or a floating production storage and offloading unit) that provides stability through its pillars and lower floating structure. The mooring chain 2 is a combination of multiple steel anchor chains connecting the platform's cable guide hole to the seabed anchor point; the mooring chain 2 may include multiple chain links. The sensor mounting link 3 is the chain link in the mooring chain 2 where sensors are installed, providing a reference surface for sensor installation and ensuring the structural coupling reliability of strain and tilt measurements.

[0102] refer to Figure 5 Application scenarios of the mooring anchor chain tension monitoring method provided in this application embodiment Figure 2 It is applied to the sensor installation process.

[0103] The sensor mounting chain 3 includes: a chain body 41, a sensor bracket 42, a tilt sensor 51, and a strain gauge 52. The sensor bracket 42 is fixed to the outer surface of the straight section of the chain body 41 by means of clamps, welding, or bolts; the tilt sensor 51 is mounted on the horizontal mounting surface of the sensor bracket 42 by means of mechanical fastening or adhesive bonding, and the sensitive axis of the tilt sensor 51 is kept parallel to the longitudinal axis of the chain body 41; the strain gauge 52 is directly glued to the surface of the chain body 41 by adhesive.

[0104] refer to Figure 6 Application scenarios of the mooring anchor chain tension monitoring method provided in this application embodiment Figure 3 It is applied to the sensor installation process.

[0105] Figure 6 This is a schematic diagram of the cross-sectional arrangement of the chain link body 41 and the sensor bracket 42. Figure 6 The radial mounting layout of the sensor assembly on the anchor chain link is shown. The sensor bracket 42 is fixed to the outer peripheral surface of the chain link body 41 in a clamp-like structure; two strain gauges 52 are attached to the outer peripheral surface of the chain link body 41 in a 180-degree symmetrical arrangement.

[0106] refer to Figure 7 The flowchart of the tension calculation in the mooring anchor chain tension monitoring method provided in this application embodiment can be specifically explained in conjunction with the implementation process of the mooring anchor chain absolute tension monitoring system based on multi-source data fusion.

[0107] The implementation process of the mooring anchor chain absolute tension monitoring system based on multi-source data fusion includes the following steps:

[0108] 1. Construction of the scaled-down experimental platform:

[0109] A 1:50 scale floating platform mooring system experimental setup was constructed, with a pool size of 50m × 30m × 10m. The experimental platform was a triangular pontoon, symmetrically moored by three anchor chains, each 8 meters long, 6mm in diameter, and made of 304 stainless steel. One end of each anchor chain was connected to the pontoon's cable guide hole, and the other end was fixed to an anchoring point at the bottom of the pool. A wave generator was used to simulate different sea conditions. A high-precision tension sensor (range 0-500N, accuracy class 0.1%FS, sampling rate 50Hz) was installed in series at the connection between the anchor chain and the anchoring point. This sensor directly measured the actual tension F of the anchor chain. true,k This serves as a benchmark reference value for evaluating the performance of the fusion algorithm. The tension sensor is connected to the data acquisition system via a watertight connector, and the signal is transmitted to the shore-based industrial control computer via a shielded cable.

[0110] 2. Sensor installation:

[0111] At a distance of 1 meter from the guide cable hole on each anchor chain, a chain link was selected as the strain measurement location. The middle section of the straight segment of the chain link was chosen, and after cleaning the surface, foil strain gauges were attached. Two strain gauges were arranged on each chain link using the Wheatstone half-bridge method. The surface of the strain gauges was coated with a silicone protective layer. A miniature MEMS tilt sensor (range ±90°, resolution 0.1°) was installed at the guide cable hole. The tilt sensor was fixed on a special fixture, with the sensitive axis aligned with the anchor chain axis.

[0112] 3. Data acquisition system configuration:

[0113] A data acquisition industrial computer, equipped with a bridge-type circuit acquisition card, is deployed on the bank of the pool. The tilt sensor is connected to the industrial computer via an RS485 bus, using the Modbus communication protocol. All sensor signals are timestamped and stored on the local hard drive. Strain and tension sensor data are set to continuous sampling mode at a sampling rate of 20Hz; tilt data uses a triggered sampling mode.

[0114] The triggering event is determined based on the experimental progress and wave conditions, triggering tilt angle data acquisition at an appropriate time. Triggering conditions may include: ① waves are relatively stable (visually small wave surface undulations); ② the interval since the last acquisition is no less than 2 minutes; ③ the platform does not experience significant large-amplitude swaying. After each triggering of tilt angle data acquisition, the system automatically and continuously acquires 15 sets of tilt angle data. During the experiment, different sea states and load variations are simulated by changing the wave generator parameters and platform ballast, allowing for flexible adjustment of the triggering frequency according to changes in sea state.

[0115] 4. Data Preprocessing and Initialization

[0116] 1) Strain data processing: The raw strain voltage is converted into strain value ε using the bridge calibration formula. k (Unit: με), combined with the cross-sectional area of ​​the chain link A = 2.83 × 10 5 m 2 Young's modulus E = 1.93 × 10 11 Pa (304 stainless steel), stress conversion factor k c =1.18, strain transfer coefficient k p =0.89, temperature correction factor k T =1.15 N / °C, calculate the tensile force F. s,k Calculate the tension increment ΔF at each moment. s,k =F s,k F s,k 1. This is used as the input to the Kalman filter. For two chain link measurement points, the arithmetic mean of their tension increments is taken to reduce local measurement errors.

[0117] To simulate the situation where strain sensors cannot detect the initial tensile force in actual engineering practice, an unknown systematic error (simulating the initial stress) is superimposed on the strain method tensile force value in the data processing algorithm: ,in The bias is randomly generated by the system at the start of the experiment, and its value range is []. The bias is a uniformly distributed random number within the range of [30N, +30N] (approximately ±37.5% of the typical working tensile force of 80N). This bias remains constant throughout a single experiment, is completely hidden from the algorithm, and realistically simulates the engineering challenge of determining the zero-stress state when retrofitting sensors to in-service anchor chains. The experimental log file also saves... The actual value is used for later performance evaluation.

[0118] 2) Tilt data processing: After each manual trigger, the tilt sensor continuously collects 15 sets of high-frequency data, removes outliers that deviate from the mean by more than 3σ, and calculates the arithmetic mean of the remaining valid data points. and standard deviation Substituting the average inclination angle into the catenary mechanical model Calculate the absolute tensile force. Only when σ θ,k <3° and 25°< At <75°, F a,k These observations are marked as valid. If the data quality does not meet the requirements, the tilt observation is skipped.

[0119] 3) Kalman filter initialization: At the start of the experiment, the first tilt observation is triggered. If a valid tilt observation is obtained, the initial state is set. The process noise variance is set to Q=0.8N based on the strain gauge technical parameters. 2 The observation noise boundary is set to R. min =15N 2 R max =300N 2 Initial value R0 = R max The adaptive parameters are set as follows: desired residual threshold δ0 = 3.5N, sensitivity coefficient λ = 1.5 × 10⁻⁶. 4. EWMA smoothing coefficient ρ = 0.2.

[0120] 5. Kalman filter recursive execution

[0121] At each strain sampling time (0.05 seconds interval), the following recursive calculation is performed:

[0122] 1) Prediction steps: Calculate the prior state estimate and prior error covariance .

[0123] 2) Determine if tilt observation exists: If tilt acquisition is triggered at the current moment, and the validity flag of the tilt data is true after preprocessing, then execute the observation update step; otherwise, skip the update and directly set... , .

[0124] 3) Observation update step (executed only when tilt acquisition is triggered and the validity flag of the tilt data is true after preprocessing)

[0125] Calculate the predicted residuals:

[0126] Update observation noise:

[0127] Smoothing:

[0128] Calculate the Kalman gain:

[0129] Update post-verification status:

[0130] Updated post-test covariance:

[0131] 4) Output: The estimated tension value at the current moment. strain method tensile force Inclination angle farad Kalman gain Predicting residuals Observation noise Store in the database.

[0132] The exemplary structure of the software modules included in the mooring chain tension monitoring device 90 provided in this application embodiment will be further described below. In some embodiments, such as... Figure 8 As shown, the mooring anchor chain tension monitoring device 90 may include:

[0133] The monitoring module 901 is used to monitor and obtain strain data and inclination data corresponding to the target anchor chain based on a set time interval; the conversion module 902 is used to convert the strain data into the tension increment corresponding to the target anchor chain and convert the inclination data into the absolute tension corresponding to the target anchor chain; the tension increment is used to characterize the tension change of the target anchor chain between the current monitoring time and the previous monitoring time; the construction module 903 is used to construct a state space model corresponding to the anchor chain tension of the target anchor chain, and the state space model is used to determine the estimated value of the anchor chain tension of the target anchor chain at the current time; the prediction module 904 is used to obtain the estimated value of the anchor chain tension of the target anchor chain at the current monitoring time by performing Kalman filtering recursive calculation based on the tension increment and absolute tension through the state space model.

[0134] In some embodiments, the conversion module 902 is configured to: include at least the strain data measured by strain sensors installed on the target anchor chain; calculate the tension value at the current monitoring moment based on the cross-sectional area of ​​the chain links, Young's modulus, stress conversion coefficient, strain transfer coefficient, temperature correction coefficient, and strain value of the target anchor chain; and determine the difference between the tension value at the current monitoring moment and the tension value at the previous monitoring moment as the tension increment.

[0135] In some embodiments, the conversion module 902 is configured to: include at least the tilt angle data including the tilt angle between the chain link and the horizontal plane measured by the tilt angle sensor installed on the chain link of the target anchor chain; if the target anchor chain does not have a bottoming portion, convert the tilt angle into absolute tension according to the catenary mechanical model; if the target anchor chain has a bottoming portion, calculate the absolute tension based on the tilt angle, water depth parameters, and horizontal distance parameters using a hyperbolic function model.

[0136] In some embodiments, the prediction module 904 can be used to: set the initial values ​​corresponding to the state-space model; setting the initial values ​​corresponding to the state-space model includes: determining the initial state estimate and initial error covariance corresponding to the state-space model based on the tilt angle data and strain data; determining the process noise variance based on the measurement accuracy of the strain data and the environmental noise parameter corresponding to the target anchor chain; and determining the observation noise variance based on the measurement accuracy of the tilt angle data and the environmental noise parameter corresponding to the target anchor chain.

[0137] In some embodiments, the construction module 903 can be used to: construct the state equations and observation equations of the state-space model; the state equations are expressed by the following formula: The observation equation is expressed by the following formula: in, This represents the anchor chain tension at time k. This represents the anchor chain tension at time k-1. Indicates the increase in tensile force. Indicates process noise. This represents the absolute tension of the target anchor chain at time k. Indicates observation noise; These are the state variables of the state-space model.

[0138] In some embodiments, the prediction module 904 can be used to: determine the prior state estimate and prior error covariance at the current monitoring time based on the posterior state estimate, tension increment, and process noise variance at the previous monitoring time; in response to the dip angle data not meeting the set dip angle monitoring conditions, determine the prior state estimate at the current monitoring time as the posterior state estimate at the current monitoring time, and determine the prior error covariance at the current monitoring time as the posterior error covariance at the current monitoring time; in response to the dip angle data meeting the set dip angle monitoring conditions, calculate the Kalman gain value based on the prior error covariance and the observation noise variance at the current monitoring time; update the prior state estimate and prior error covariance at the current monitoring time based on the Kalman gain value and absolute tension to obtain the posterior state estimate and posterior error covariance at the current monitoring time; and determine the posterior state estimate as the anchor chain tension estimate of the target anchor chain at the current monitoring time.

[0139] In some embodiments, the prediction module 904 can be used to: determine the prediction residual for the current monitoring time based on the absolute tensile force and the prior error covariance at the current monitoring time; determine the noise update value based on the prediction residual using a pre-built dynamic observation noise model; and smooth the observation noise variance at the previous monitoring time based on the noise update value to obtain the observation noise variance at the current monitoring time.

[0140] It should be noted that the description of the device in this application embodiment is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment, therefore it will not be repeated. For any technical details not covered in the mooring chain tension monitoring device provided in this application embodiment, please refer to... Figures 1 to 7 The meaning is understood in accordance with the description of any of the accompanying drawings.

[0141] According to embodiments of this application, this application also provides an electronic device and a non-transitory computer-readable storage medium.

[0142] Figure 9 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0143] like Figure 9As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0144] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0145] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the mooring chain tension monitoring method. For example, in some embodiments, the mooring chain tension monitoring method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the mooring chain tension monitoring method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform a mooring chain tension monitoring method by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0151] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0152] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0153] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the tension of mooring anchor chains, characterized in that, The method includes: Based on the set time interval, the strain data and inclination angle data corresponding to the target anchor chain are monitored and obtained; Based on the cross-sectional area of ​​the chain links, Young's modulus, stress transfer coefficient, strain transfer coefficient, temperature correction coefficient, and strain value of the target anchor chain, the tensile force value at the current monitoring moment is calculated. The difference between the tensile force value at the current monitoring moment and the tensile force value at the previous monitoring moment is determined as the tensile force increment corresponding to the target anchor chain. The inclination angle of the chain links to the horizontal plane in the inclination angle data is converted into the absolute tensile force corresponding to the target anchor chain through a catenary mechanical model or a hyperbolic function model. The tensile force increment is used to characterize the tensile force change of the target anchor chain between the current monitoring moment and the previous monitoring moment. A state-space model corresponding to the anchor chain tension of the target anchor chain is constructed. The state-space model includes state equations and observation equations. The state-space model is used to determine the estimated value of the anchor chain tension of the target anchor chain at the current moment. The state equation is expressed by the following formula: The observation equation is expressed by the following formula: in, This represents the anchor chain tension at time k. This represents the anchor chain tension at time k-1. Indicates the increase in tensile force. Indicates process noise, w k The value range of is (0, Q), which characterizes the strain measurement error. This represents the absolute tension of the target anchor chain at time k. Indicates observation noise, v k The value range of is (0, R), which represents the tilt angle estimation error; if the current monitoring time is time k, then time k-1 represents the previous monitoring time, Q represents the process noise variance, and R represents the observation noise variance; Based on the posterior state estimate of the previous monitoring time, the tensile force increment, and the process noise variance, the prior state estimate and prior error covariance of the current monitoring time are determined. In response to the tilt angle data not meeting the set tilt angle monitoring conditions, the prior state estimate at the current monitoring time is determined as the posterior state estimate at the current monitoring time, and the prior error covariance at the current monitoring time is determined as the posterior error covariance at the current monitoring time; the tilt angle monitoring conditions include the tilt angle data being within the range of the tilt angle sensor. In response to the tilt angle data satisfying the set tilt angle monitoring conditions, based on the prior error covariance and the observation noise variance at the current monitoring time, according to... Calculate the Kalman gain; based on the Kalman gain and the absolute tension, according to... Update the prior state estimate at the current monitoring time, according to Update the prior error covariance at the current monitoring time to obtain the posterior state estimate and posterior error covariance at the current monitoring time; where K k Indicates Kalman gain, This represents the variance of the observation noise at the current monitoring time. This represents the prior state estimate at the current monitoring time. This represents the posterior state estimate at the current monitoring time. P represents the prior error covariance. k This represents the posterior error covariance; The posterior state estimate is determined as the anchor chain tension estimate at the current monitoring time of the target anchor chain.

2. The method according to claim 1, characterized in that, The strain data includes at least the strain values ​​measured by strain sensors installed on the target anchor chain.

3. The method according to claim 1, characterized in that, The tilt angle data includes at least the tilt angle between the chain link and the horizontal plane measured by tilt angle sensors installed on the chain link of the target anchor chain; The hyperbolic function model is used to characterize the static equilibrium state of the anchor chain with the seabed contact section. The water depth parameter used in the hyperbolic function model is the vertical water depth of the sensor installation point of the target anchor chain, and the horizontal distance parameter used in the hyperbolic function model is the horizontal distance from the sensor installation point to the anchoring point. The anchoring point characterizes the fixed connection position between the bottom end of the target anchor chain and the seabed.

4. The method according to claim 1, characterized in that, After constructing the state-space model corresponding to the anchor chain tension of the target anchor chain, the method further includes: Set the initial values ​​corresponding to the state-space model; Setting the initial values ​​corresponding to the state space model includes: In response to the existence of a valid tilt angle observation value at the monitoring start time, the initial state estimate value corresponding to the state space model is calculated based on the tilt angle observation value through the catenary mechanical model, and the initial error covariance is determined based on the sensitivity of the catenary tension function to the tilt angle and the standard deviation of the tilt angle sensor error. In response to the absence of a valid tilt angle observation value at the time of monitoring initiation, the tensile force value determined based on the strain data is used as the initial state estimate, and an initial error covariance greater than that when a valid tilt angle observation value exists is set. Based on the standard deviation of the strain measurement error, the cross-sectional area of ​​the chain link, the Young's modulus, the stress transfer coefficient, and the strain transfer coefficient, according to Determine the process noise variance; where A is the cross-sectional area of ​​the chain link, k c k is the stress transformation coefficient. p Here, E is the strain transfer coefficient, and E is Young's modulus. This represents the standard deviation of the strain measurement error. Based on historical sea state statistics, a lower and upper bound for the observation noise variance are set, and the upper bound is taken as the initial observation noise variance.

5. The method according to claim 1, characterized in that, The method further includes: Based on the absolute tensile force and the prior error covariance at the current monitoring time, through... Determine the prediction residual at the current monitoring time; where, This represents the prediction residual at the current monitoring time; Based on the predicted residual, the noise update value is determined through a pre-constructed dynamic observation noise model; The noise update value is determined by the following formula: in, Indicates the noise update value. R represents the desired residual threshold, λ represents the control response sensitivity, and R min To observe the lower bound of the noise variance, R max This serves as the upper bound for the observed noise variance; Based on the noise update value, the observation noise variance at the previous monitoring time is smoothed to obtain the observation noise variance at the current monitoring time. The smoothing process is expressed by the following formula: Where ρ represents the smoothing coefficient. This represents the variance of the observation noise at the previous monitoring time.

6. A mooring anchor chain tension monitoring device, characterized in that, include: The monitoring module is used to monitor and obtain the strain and inclination data of the target anchor chain based on a set time interval. The conversion module is used to calculate the tension value at the current monitoring moment based on the cross-sectional area of ​​the chain links, Young's modulus, stress conversion coefficient, strain transfer coefficient, temperature correction coefficient, and strain value of the target anchor chain. The difference between the tension value at the current monitoring time and the tension value at the previous monitoring time is determined as the tension increment corresponding to the target anchor chain. The inclination angle of the chain link to the horizontal plane in the inclination angle data is converted into the absolute tension corresponding to the target anchor chain through a catenary mechanical model or a hyperbolic function model. The tension increment is used to characterize the change in tension of the target anchor chain between the current monitoring time and the previous monitoring time. A construction module is used to construct a state space model corresponding to the anchor chain tension of the target anchor chain. The state space model includes state equations and observation equations. The state space model is used to determine the estimated value of the anchor chain tension of the target anchor chain at the current moment. The state equation is expressed by the following formula: The observation equation is expressed by the following formula: in, This represents the anchor chain tension at time k. This represents the anchor chain tension at time k-1. Indicates the increase in tensile force. Indicates process noise, w k The value range of is (0, Q), which characterizes the strain measurement error. This represents the absolute tension of the target anchor chain at time k. Indicates observation noise, v k The value range of is (0, R), which represents the tilt angle estimation error; if the current monitoring time is time k, then time k-1 represents the previous monitoring time, Q represents the process noise variance, and R represents the observation noise variance; The prediction module is used to determine the prior state estimate and prior error covariance at the current monitoring time based on the posterior state estimate of the previous monitoring time, the tension increment, and the process noise variance. In response to the tilt angle data not meeting the set tilt angle monitoring conditions, the prior state estimate at the current monitoring time is determined as the posterior state estimate at the current monitoring time, and the prior error covariance at the current monitoring time is determined as the posterior error covariance at the current monitoring time; the tilt angle monitoring conditions include the tilt angle data being within the range of the tilt angle sensor. In response to the tilt angle data satisfying the set tilt angle monitoring conditions, based on the prior error covariance and the observation noise variance at the current monitoring time, according to... Calculate the Kalman gain; based on the Kalman gain and the absolute tension, according to... Update the prior state estimate at the current monitoring time, according to Update the prior error covariance at the current monitoring time to obtain the posterior state estimate and posterior error covariance at the current monitoring time; where K k Indicates Kalman gain, This represents the variance of the observation noise at the current monitoring time. This represents the prior state estimate at the current monitoring time. This represents the posterior state estimate at the current monitoring time. P represents the prior error covariance. k This represents the posterior error covariance; The posterior state estimate is determined as the anchor chain tension estimate at the current monitoring time of the target anchor chain.

7. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

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