Ship hull heave motion prediction and analysis method and system based on time series analysis

Abstract

This invention discloses a method and system for predicting and analyzing ship heave motion based on time series analysis, comprising: acquiring raw data of ship heave displacement; constructing a periodic autoregressive model based on the raw data and time series analysis; obtaining random error based on the periodic autoregressive model; constructing a harmonic steady-state mean regression model based on the random error; constructing a periodic-harmonic steady-state mean regression model based on the periodic autoregressive model and the harmonic steady-state mean regression model; and inputting the raw data into the periodic-harmonic steady-state mean regression model to obtain the predicted value of ship heave motion. This invention can more accurately characterize the characteristics of random distribution peaks, thick tails, bias, and wave clustering in ship heave motion.

Description

Technical Field

[0001] This invention belongs to the field of periodic-harmonic steady-state mean regression model analysis technology, and particularly relates to a method and system for predicting and analyzing the heave motion of ship hulls based on time series analysis. Background Technology

[0002] Predictive analysis of ship heave motion is crucial not only for the efficiency of maritime operations but also for the operational safety of shipping. Currently, ship heave compensation devices primarily use the real-time heave motion signal of the hull as the given signal and the real-time compensation motion signal of the actuators in the compensation system as the feedback signal, thereby controlling the actuators to track the hull's heave motion in real time. In practical heave compensation systems, the position compensation efficiency can generally reach 85%-90%. The main limiting factor for further improving compensation accuracy lies in the significant lag between the hull heave motion signal detected by the inertial attitude sensor and the actual hull motion. This severely affects the system performance and steady-state stability of the ship heave compensation device. Therefore, by accurately predicting the future value of the ship's heave motion in the short term to compensate for this lag, the control performance of the ship heave compensation device can be effectively improved.

[0003] Currently, the prediction and analysis of ship heave motion is mostly based on autoregressive models in time series analysis. However, with the continuous expansion of the sea areas where ships operate and the increasing distance from the shore, the complexity of ship motion under the influence of sea wind and wave changes also increases, making it difficult for traditional autoregressive models to meet prediction requirements. In order to meet the needs of actual ship operations and ensure the accuracy of ship heave compensation, a real-time and accurate ship motion prediction method is crucial. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for predicting and analyzing the heave motion of ships based on time series analysis, thereby resolving the issues existing in the prior art.

[0005] This invention provides a method for predicting and analyzing the heave motion of ships based on time series analysis, including:

[0006] Obtain raw data on the ship's heave and displacement;

[0007] Based on the original data, and combined with time series analysis, a periodic autoregressive model is constructed.

[0008] Based on the aforementioned periodic autoregressive model, the random error is obtained;

[0009] Based on the aforementioned random error, a harmonic steady-state mean regression model is constructed;

[0010] Based on the aforementioned periodic autoregressive model and harmonic steady-state mean regression model, a periodic-harmonic steady-state mean regression model is constructed.

[0011] The raw data is input into a periodic-harmonic steady-state mean regression model to obtain the predicted value of the ship's deep-sinking motion.

[0012] Optionally, based on the original data, and combined with time series analysis, a periodic autoregressive model can be constructed, including:

[0013] Using ship lifting and lowering displacement as the dependent variable, and combining time series analysis, an initial periodic autoregressive model is constructed.

[0014] Based on the original data, and using the least squares method in time series analysis, the model parameters in the initial periodic autoregressive model are estimated to obtain the periodic autoregressive model.

[0015] Optionally, the method for constructing the initial periodic autoregressive model is as follows:

[0016] Y(t) = Λ(t) + T(t)

[0017] Where Y(t) represents the ship's heave displacement data, Λ(t) is a periodic or trend function, and T(t) is the random error.

[0018] Optionally, the method for obtaining a periodic autoregressive model is as follows:

[0019]

[0020] in, This is an estimate of the ship's heave displacement data. and These are the estimated values ​​of the model parameters.

[0021] Optionally, based on the random error, constructing a harmonic steady-state mean regression model includes:

[0022] Using random error as the dependent variable and combining time series analysis, an initial harmonic steady-state mean regression model is constructed.

[0023] Based on the random error, the least squares method in time series analysis is used to estimate the model parameters in the initial harmonic steady-state mean regression model, and the harmonic steady-state mean regression model is obtained.

[0024] Optionally, the method for obtaining the harmonic steady-state mean regression model is as follows:

[0025]

[0026] in, This is an estimate of the volatility function. and These are the estimated values ​​of the model parameters.

[0027] Optionally, the method for constructing the periodic-harmonic steady-state mean regression model is as follows:

[0028]

[0029] The present invention also provides a ship hull heave motion prediction and analysis system based on time series analysis, characterized in that it includes: a data acquisition module, a model building module and a data prediction module;

[0030] The data acquisition module is used to acquire raw data on the ship's heave and displacement.

[0031] The model building module is used to construct a periodic-harmonic steady-state mean regression model based on the periodic autoregressive model and the harmonic steady-state mean regression model.

[0032] The data prediction module is used to input the raw data into a periodic-harmonic steady-state mean regression model to obtain the predicted value of the ship's deep motion.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] This invention is based on periodic mean regression and harmonic steady-state mean regression models, which can more accurately characterize the heave motion of ships, improve the prediction accuracy of ship heave motion, and optimize the system performance of ship heave compensation devices. Attached Figure Description

[0035] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1 This is a flowchart of a ship hull heave motion prediction and analysis method based on time series analysis, according to an embodiment of the present invention. Detailed Implementation

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0039] This invention proposes a method for predicting and analyzing the heave motion of ship hulls based on time series analysis, such as... Figure 1As shown, the specific steps include:

[0040] Obtain raw data on the ship's heave and displacement;

[0041] Based on the original data, and combined with time series analysis, a periodic autoregressive model is constructed.

[0042] Based on the periodic autoregressive model, obtain the random error;

[0043] Based on random error, a harmonic steady-state mean regression model is constructed.

[0044] Based on the periodic autoregressive model and the harmonic steady-state mean regression model, a periodic-harmonic steady-state mean regression model is constructed.

[0045] The raw data is input into a periodic-harmonic steady-state mean regression model to obtain the predicted value of the ship's deep-sinking motion.

[0046] Specifically, this includes: S1, acquiring raw data of the ship's heave and displacement, and dividing the raw data into training data and validation data;

[0047] S2. Using ship heave displacement as the dependent variable, a periodic autoregressive model is constructed based on time series analysis.

[0048] S3. Based on the training data, use the least squares method in time series analysis to estimate the model parameters in the periodic autoregressive model, determine the form of the periodic autoregressive model, and estimate the random error.

[0049] S4. Construct a harmonic steady-state mean regression model with random error as the dependent variable;

[0050] S5. Based on the random error results, the least squares estimation method is used to estimate the model parameters in the harmonic steady-state mean regression model and determine the model form of the harmonic steady-state mean regression model.

[0051] S6. Based on the periodic autoregressive form and the mean regression model form of random error of the above training data, construct a periodic-harmonic steady-state mean regression model.

[0052] S7. Substitute the verification data into the constructed periodic-harmonic steady-state mean regression model to predict and analyze the ship's heave motion, and obtain the analysis results.

[0053] Furthermore, based on the original data, the construction of a periodic autoregressive model includes:

[0054] Using ship lifting and lowering displacement as the dependent variable, an initial periodic autoregressive model is constructed based on time series analysis.

[0055] Based on the original data, and combined with the least squares method in time series analysis, the model parameters in the initial periodic autoregressive model are estimated to obtain the periodic autoregressive model.

[0056] Specifically, in S2, the periodic mean regression model with ship heave displacement as the dependent variable Y(t) takes the following form:

[0057] Y(t)=Λ(t)+T(t) (1)

[0058] Where T(t) is the random error term of the model, and Λ(t) is a periodic or trend function with the following form:

[0059]

[0060] Here, a0, b0, a i b i T1, I1, and I2 are undetermined parameters describing the propagation trend of heave displacement, and T(t) represents the random error result.

[0061] Furthermore, based on the training data {Y(t) k )}, k=1,…,n, using the least squares method, the periodic autoregressive model formula (1) is estimated to obtain the parameters that minimize the random error term. The estimated value Right now:

[0062]

[0063] Parameter estimates Substituting into formula (2), we obtain the estimated result of Y(t). Furthermore, using relational expressions Obtain the estimation result of the random error term T(t).

[0064] Specifically, the estimation results of Y(t) for:

[0065]

[0066] in, This is an estimate of the ship's heave displacement data. and These are the estimated values ​​of the model parameters.

[0067] Furthermore, based on random error, the harmonic steady-state mean regression model is constructed as follows:

[0068] An initial harmonic steady-state mean regression model is constructed with random error as the dependent variable;

[0069] Based on random error, the least squares method is used to estimate the model parameters in the initial harmonic steady-state mean regression model, and the harmonic steady-state mean regression model is obtained.

[0070] Specifically, a mean regression model driven by harmonic steady-state noise is constructed using random error results as the dependent variable;

[0071] In S4, the mean regression process driven by harmonic steady-state noise, which the random error result T(t) follows, is as follows:

[0072]

[0073] Wherein, σ(t) has the following form:

[0074]

[0075] Among them, c0, c j d j T2, J1, and J2 are model parameters. v(t) = dL(t) = L(t) - L(t-1) follows a harmonic steady-state distribution TS. α (a,b,m), whose distribution function is:

[0076]

[0077] Where a∈(0,1), b>0, and m∈R are undetermined parameters in the distribution function.

[0078] Furthermore, the method for obtaining the harmonic steady-state mean regression model is as follows:

[0079]

[0080] in, This is an estimate of the volatility function. and These are the estimated values ​​of the model parameters.

[0081] The estimated value of v(t) is obtained by calculation:

[0082]

[0083] Estimation results based on v(t) Calculate its empirical characteristic function value:

[0084]

[0085] Among them, 1{v(t) j The function} ≤ x is an indicator function, meaning it takes the value 1 when v(tj) ≤ x, and 0 otherwise. Using the least squares method, we estimate the parameter θ = (α, a, b, m) that minimizes the difference between the empirical and theoretical distribution function values, i.e.:

[0086]

[0087] We obtain the estimated value of θ = (α, a, b, m). Determine the approximate distribution of v(t)

[0088] Furthermore, the method for constructing a periodic-harmonic steady-state mean regression model is as follows:

[0089]

[0090] Furthermore, v(t) = dL(t) follows a harmonic steady-state distribution.

[0091] S7. Substitute the verification data into the constructed periodic-harmonic steady-state mean regression model to predict and analyze the ship's heave motion, and obtain the analysis results.

[0092] In S8, based on the analysis results, as shown in Table 1:

[0093] Table 1

[0094] Analysis of periodic-harmonic steady-state mean regression model Classical Autoregressive Model Analysis <![CDATA[Goodness of fit R 2 0.95]]> <![CDATA[Goodness-of-fit R 2 0.75]]> Error 0.2 Error 2.2

[0095] The analysis of the data leads to the following conclusions. Using the periodic-harmonic steady-state mean regression model, the predicted heave motion of the ship is better than the original prediction. Therefore, the predictive analysis method based on the periodic-harmonic steady-state mean regression model can be considered more effective.

[0096] The present invention also provides a ship hull heave motion prediction and analysis system based on time series analysis, characterized in that it includes: a data acquisition module, a model building module and a data prediction module;

[0097] The data acquisition module is used to acquire raw data on the ship's heave and displacement.

[0098] The model building module is used to construct a periodic-harmonic steady-state mean regression model based on the periodic autoregressive model and the harmonic steady-state mean regression model.

[0099] The data prediction module is used to input raw data into a periodic-harmonic steady-state mean regression model to obtain predicted values ​​of ship deep motion.

[0100] This invention is a predictive analysis method that integrates periodic mean regression and harmonic steady-state mean regression models. It improves the accuracy of predictions for variables with a certain degree of randomness and periodicity, enabling professional experimental personnel to obtain more accurate prediction results. Taking the prediction and analysis of ship heave motion as an example, by integrating periodic mean regression and harmonic steady-state mean regression models, the method accurately characterizes the periodic characteristics of ship heave motion under the interference of random factors, thereby obtaining more accurate prediction results for ship heave motion and improving the system performance of ship compensation devices. Simultaneously, it lays a solid foundation for supporting future mission-oriented simulation experiments of complex systems / structures.

[0101] The above are merely preferred embodiments 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 predicting and analyzing the heave motion of a ship's hull based on time series analysis, characterized in that, include: Obtain raw data on the ship's heave and displacement; Based on the original data, and combined with time series analysis, a periodic autoregressive model is constructed. Based on the aforementioned raw data, and combined with time series analysis, a periodic autoregressive model is constructed, including: Using ship lifting and lowering displacement as the dependent variable, and combining time series analysis, an initial periodic autoregressive model is constructed. Based on the original data, and combined with the least squares method in time series analysis, the model parameters in the initial periodic autoregressive model are estimated to obtain the periodic autoregressive model. The method for constructing the initial periodic autoregressive model is as follows: Where Y(t) represents the ship's heave displacement data. Is it a periodic or trend function? This is random error; The method for obtaining a periodic autoregressive model is as follows: in, This is an estimate of the ship's heave displacement data. , , , , , and These are the estimated values ​​of the model parameters; Based on the aforementioned periodic autoregressive model, the random error is obtained; Based on the aforementioned random error, a harmonic steady-state mean regression model is constructed; Based on the aforementioned random error, the harmonic steady-state mean regression model is constructed as follows: Using random error as the dependent variable and combining time series analysis, an initial harmonic steady-state mean regression model is constructed. Based on the random error, the least squares method in time series analysis is used to estimate the model parameters in the initial harmonic steady-state mean regression model, and the harmonic steady-state mean regression model is obtained. The method for obtaining the harmonic steady-state mean regression model is as follows: in, This is an estimate of the volatility function. , , and These are the estimated values ​​for the model parameters; based on the aforementioned periodic autoregressive model and harmonic steady-state mean regression model, a periodic-harmonic steady-state mean regression model is constructed. The method for constructing the periodic-harmonic steady-state mean regression model is as follows: ； The raw data is input into a periodic-harmonic steady-state mean regression model to obtain the predicted value of the ship's deep-sinking motion.

2. The ship hull heave motion prediction and analysis system based on time series analysis implemented according to the method of claim 1, characterized in that, include: Data acquisition module, model building module, and data prediction module; The data acquisition module is used to acquire raw data on the ship's heave and displacement. The model building module is used to construct a periodic-harmonic steady-state mean regression model based on the periodic autoregressive model and the harmonic steady-state mean regression model. The data prediction module is used to input the raw data into a periodic-harmonic steady-state mean regression model to obtain the predicted value of the ship's deep motion.

Images