A method, system and application for online identification of characteristic parameters of ocean wave disturbance

Abstract

The present invention belongs to the technical field of ship heading and track control, and discloses an online identification method, system and application of characteristic parameters of sea wave interference. The online identification method of characteristic parameters of sea wave interference includes: collecting measurement signals of ship heading measurement equipment, and performing high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading; establishing a high-frequency sea wave interference component model, and discretizing the model to obtain a high-frequency sea wave interference component identification model based on the heading signal; according to the high-frequency sea wave interference component identification model, using a two-stage augmented random gradient algorithm to identify model parameters; according to the identified model parameters, calculating the discrete model characteristic roots, and converting them into continuous time domain characteristic roots, calculating the sea wave peak frequency according to the continuous time domain characteristic roots, and realizing online tracking of the sea wave peak frequency. The sea wave peak frequency identified by the present invention can update the observer and filter parameters in real time, and improve the filter's adaptability to complex sea conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship heading and track control, and in particular relates to an online identification method, system and application of sea wave interference characteristic parameters. Background Art

[0002] At present, with the development of economic globalization and trade liberalization, the shipping industry has developed and prospered unprecedentedly. Ships as marine transportation tools are developing towards larger and higher speeds. The density of maritime traffic is constantly increasing, and the safety of ship navigation is increasingly threatened. Therefore, higher requirements are placed on ship motion control.

[0003] Due to the presence of interference factors such as wind, waves, and currents in the marine environment, the ship's heading is affected by steering and interference from wind, waves, currents, etc., including low-frequency and high-frequency parts. Among them, the low-frequency part is caused by steering and wind, current and second-order wave forces, and the high-frequency part is caused by the first-order wave forces in the sea wave interference. Usually, the frequency band of high-frequency interference is higher than the response frequency band of the ship control system, so the high-frequency interference cannot be effectively controlled through the response of the actuator. The heading filter can effectively filter out the high-frequency motion component of the heading caused by high-frequency sea wave interference in the sensor measurement signal, reduce invalid steering, and thus reduce energy consumption and actuator wear.

[0004] The peak frequency of waves is the main parameter that describes the characteristics of wave interference, and it is also a key factor in the design of heading filters. The performance of the filter mainly depends on its adaptability to changes in sea conditions. As the sea conditions change, the peak frequency of waves will change, which requires the filter parameters to be able to follow the changes in the peak frequency of waves. Traditional filters use low-pass or notch filters, and their filter cutoff frequencies are designed as fixed values ​​according to the peak frequency of waves. However, the sea conditions and navigation status of ships change continuously during navigation, causing the peak frequency of waves to change continuously. Therefore, the filter's adaptability is weak, resulting in unsatisfactory filtering effects. Therefore, a new online identification method for characteristic parameters of wave interference is urgently needed.

[0005] Through the above analysis, the problems and defects of the existing technology are: the traditional filter adopts a low-pass or notch filter, and the sea conditions and navigation status of the ship are constantly changing during the voyage, causing the peak frequency of the waves to change continuously. Therefore, the adaptability of the filter is weak, resulting in unsatisfactory filtering effect.

[0006] The difficulty in solving the above problems and defects is: the unpredictability of the marine environment and the time-varying nature of the ship's operating status cause the wave peak frequency to change continuously, the wave peak frequency is difficult to determine, and the heading filter cannot effectively filter out high-frequency wave interference.

[0007] The significance of solving the above problems and defects is that the method proposed in the present invention can identify the peak frequency of waves online, and is used to update the filter and observer parameters in real time, thereby improving the adaptability of the heading filter to complex sea conditions and effectively filtering out high-frequency wave interference, thereby reducing ineffective steering and control energy consumption of the control system. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a method, system and application for online identification of characteristic parameters of ocean wave disturbance.

[0009] The present invention is implemented in this way: an online identification method for characteristic parameters of sea wave disturbance, the online identification method for characteristic parameters of sea wave disturbance comprising the following steps:

[0010] Step 1: collect the measurement signal of the ship heading measurement equipment, and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading; collect data for model parameter identification;

[0011] Step 2: Establish a high-frequency wave interference component model, and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal; construct a high-frequency wave interference component identification model;

[0012] Step 3: According to the high-frequency wave interference component identification model, a two-stage augmented stochastic gradient algorithm is used to identify the model parameters; the identification algorithm is executed to identify the model parameters;

[0013] Step 4: Calculate the discrete model characteristic roots according to the identified model parameters, convert them into continuous time domain characteristic roots, and calculate the wave peak frequency according to the continuous time domain characteristic roots; solve the continuous time domain wave peak frequency;

[0014] Step 5, implement steps 1 to 4 online to achieve online tracking of the peak frequency of the waves; and achieve online identification of the peak frequency of the waves.

[0015] Further, in step 1, the high-pass filtering process includes:

[0016] According to the characteristics of the ship motion model, a high-pass filter is designed to filter the heading sensor signal:

[0017]

[0018] Among them, T f are the high-pass filter parameters, is the approximate value of the high-frequency motion component of the ship heading, ψ(s) is the actual ship heading signal; the design parameters of the high-pass filter should meet the following conditions:

[0019] h HP (s)h ship(s)uδ(s)=1;

[0020] Among them, h ship (s) is the transfer function of the ship heading motion model, and uδ(s) is the steering control input signal.

[0021] Further, in step 2, the high-frequency component model of the sea waves is established, and the model is discretized to obtain a high-frequency sea wave interference component identification model based on the heading signal, including:

[0022] The mathematical model of the high-frequency motion component of the ship heading caused by the first-order wave disturbance force is established:

[0023]

[0024] Where ζ is the damping coefficient, K ω is the gain constant, ω0 is the peak frequency of high-frequency interference from ocean waves, v(s) is the white noise input, ψ HF (s) is the high-frequency motion component of the ship’s heading.

[0025] Discretize the continuous mathematical model of the high-frequency components of ocean waves.

[0026]

[0027] Where z represents the unit forward operator, A(z -1 )=1+a1z -1 +a2z -2 and B(z -1 )=1+b1z -1 is the parameter model to be identified; Substitute into A(z -1 ) and B(z -1 ), according to z -i y(t)=z(ti), i=1,2, we can get:

[0028] ψ HF (t)=-a1ψ HF (t-1)-a2ψ HF (t-2)+b1v(t-1)+v(t).

[0029] Due to the high-frequency motion component of the ship's heading ψ HF (s) cannot be directly obtained by sensor measurement, so its approximate value is used replace:

[0030]

[0031] make Writing the above formula in vector form, we can obtain the high-frequency wave interference component identification model based on heading measurement signal:

[0032]

[0033] Among them, θ s =[a1 a2] T ,θ n = b1 and θ = [a1 a2 b1] T is the parameter vector to be identified, and is the information vector.

[0034] Further, in step 3, the identification model of the high-frequency wave interference component is based on the two-stage augmented stochastic gradient algorithm to identify the model parameters, including:

[0035] The two-stage augmented stochastic gradient algorithm is used to identify the parameters a1, a2 and b1. The identification algorithm is as follows:

[0036] Define two intermediate variables:

[0037]

[0038]

[0039] Based on the hierarchical identification principle, the identification model is decomposed into two subsystems:

[0040]

[0041]

[0042] Define two quadratic index functions:

[0043]

[0044]

[0045] Let the quadratic index function J1(θ s ) and J2(θ n ) is minimized and negative gradient search is used to obtain the calculation and The recursive relationship is:

[0046]

[0047]

[0048] Among them, μ s (t) and μ n (t) is the convergence factor, defined as:

[0049]

[0050]

[0051] Among them, λ s and λ n is the forgetting factor; substituting the intermediate variable into the above formula, we can get the following recursive relationship:

[0052]

[0053]

[0054] Since the right side of the equation contains the unknown parameter term θ s ,θ n and unmeasurable items Therefore, the above formula cannot be directly calculated and Construct the following auxiliary model:

[0055]

[0056] Use estimated value and Substitute the unknown term θ s ,θ n and The following two-stage augmented stochastic gradient algorithm is obtained:

[0057]

[0058] Further, in step 4, the discrete model characteristic roots are calculated according to the model parameters obtained by identification, and converted into continuous time domain characteristic roots, and the wave peak frequency is calculated according to the continuous time domain characteristic roots, including:

[0059] By solving the equation A(z -1 )=1+a1z -1 +a2z -2 =0 to obtain two discrete roots z1 and z2, according to the conversion relationship between discrete domain and continuous time domain:

[0060]

[0061] Where, h is the sampling time;

[0062] The conjugate complex root s corresponding to the high-frequency wave interference model is obtained 1,2 =-α±jβ, so the estimated value of the wave peak frequency is:

[0063]

[0064] Another object of the present invention is to provide an online identification system for sea wave disturbance characteristic parameters using the online identification method for sea wave disturbance characteristic parameters, the online identification system for sea wave disturbance characteristic parameters comprising:

[0065] The high-pass filter module is used to collect the measurement signal of the ship heading measurement equipment and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading;

[0066] A model building module is used to establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal;

[0067] Model parameter identification module, used to identify the model according to the high-frequency wave disturbance component, and use the two-stage augmented stochastic gradient algorithm to identify the model parameters;

[0068] The wave peak frequency calculation module is used to calculate the discrete model characteristic roots according to the identified model parameters, convert them into continuous time domain characteristic roots, and calculate the wave peak frequency according to the continuous time domain characteristic roots;

[0069] The online tracking module is used to implement the high-pass filtering module, the model building module, the model parameter identification module and the wave peak frequency calculation module online to achieve online tracking of the wave peak frequency.

[0070] Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0071] (1) Collect the measurement signal of the ship heading measurement equipment and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading;

[0072] (2) Establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal;

[0073] (3) Based on the high-frequency wave interference component identification model, the two-stage augmented stochastic gradient algorithm is used to identify the model parameters;

[0074] (4) Calculating the discrete model characteristic roots based on the identified model parameters and converting them into continuous time domain characteristic roots, and calculating the wave peak frequency based on the continuous time domain characteristic roots;

[0075] (5) Implement steps (1) to (4) online to achieve online tracking of the peak frequency of the waves.

[0076] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0077] (1) Collect the measurement signal of the ship heading measurement equipment and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading;

[0078] (2) Establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal;

[0079] (3) Based on the high-frequency wave interference component identification model, the two-stage augmented stochastic gradient algorithm is used to identify the model parameters;

[0080] (4) Calculating the discrete model characteristic roots based on the identified model parameters and converting them into continuous time domain characteristic roots, and calculating the wave peak frequency based on the continuous time domain characteristic roots;

[0081] (5) Implement steps (1) to (4) online to achieve online tracking of the peak frequency of the waves.

[0082] Another object of the present invention is to provide an information data processing terminal, which is used to implement the online identification system of wave disturbance characteristic parameters.

[0083] Another object of the present invention is to provide an application of the above-mentioned sea wave interference characteristic parameter online identification system in the design of a heading filter.

[0084] Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows: the online identification method of wave interference characteristic parameters provided by the present invention can realize online tracking of wave peak frequency by only measuring the ship heading signal, without the need for other measurement means; the identified wave peak frequency can update the observer and filter parameters in real time, so that the observer and filter can achieve good observation and filtering effects, and improve the filter's adaptability to complex sea conditions.

[0085] The simulation test results of the ship heading control system show that the two-stage augmented stochastic gradient algorithm provided by the present invention can accurately track the changes in the peak frequency of waves, and the observer and filter based on the identification of the peak frequency of waves can effectively reduce the response frequency and response amplitude of the steering gear. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0087] Figure 1 It is a flow chart of a method for online identification of characteristic parameters of sea wave disturbance provided by an embodiment of the present invention.

[0088] Figure 2 It is a schematic diagram of a method for online identification of characteristic parameters of sea wave disturbance provided by an embodiment of the present invention.

[0089] Figure 3 It is a structural block diagram of an online identification system for characteristic parameters of sea wave disturbance provided by an embodiment of the present invention;

[0090] In the figure: 1. High-pass filtering module; 2. Model building module; 3. Model parameter identification module; 4. Wave peak frequency calculation module; 5. Online tracking module.

[0091] Figure 4 It is a schematic diagram of the composition of the actual measurement signal of the ship heading provided by an embodiment of the present invention.

[0092] Figure 5 It is a two-stage augmented stochastic gradient algorithm parameter update calculation flow chart provided by an embodiment of the present invention.

[0093] Figure 6 It is a structural diagram of a ship heading control system provided by an embodiment of the present invention.

[0094] Figure 7 It is a schematic diagram of the identification results when the wave peak frequency ω0 provided by an embodiment of the present invention changes from 0.5 to 0.8.

[0095] Figure 8 It is a schematic diagram of the identification results when the wave peak frequency ω0 changes from 0.7 to 1 provided by an embodiment of the present invention.

[0096] Fig. 9 It is a schematic diagram of an input rudder angle comparison curve when the wave peak frequency ω0=0.5 provided by an embodiment of the present invention.

[0097] Fig.10 It is a schematic diagram of the input rudder angle comparison curve when the wave peak frequency ω0=0.7 provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0099] In view of the problems existing in the prior art, the present invention provides a method, system and application for online identification of characteristic parameters of sea wave disturbance. The present invention is described in detail below in conjunction with the accompanying drawings.

[0100] like Figure 1 As shown, the method for online identification of characteristic parameters of sea wave disturbance provided by the embodiment of the present invention comprises the following steps:

[0101] S101, collecting measurement signals of a ship heading measurement device and performing high-pass filtering to obtain an approximate value of a high-frequency motion component of the ship heading;

[0102] S102, establishing a high-frequency sea wave interference component model, and discretizing the model to obtain a high-frequency sea wave interference component identification model based on the heading signal;

[0103] S103, according to the high-frequency wave interference component identification model, a two-stage augmented stochastic gradient algorithm is used to identify model parameters;

[0104] S104, calculating the discrete model characteristic roots according to the identified model parameters, converting them into continuous time domain characteristic roots, and calculating the wave peak frequency according to the continuous time domain characteristic roots;

[0105] S105, implementing S101 to S104 online to achieve online tracking of the peak frequency of the waves.

[0106] The principle diagram of the method for online identification of characteristic parameters of sea wave disturbance provided by the embodiment of the present invention is as follows: Figure 2 shown.

[0107] like Figure 3 As shown, the wave disturbance characteristic parameter online identification system provided by the embodiment of the present invention includes:

[0108] The high-pass filter module 1 is used to collect the measurement signal of the ship heading measurement equipment and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading;

[0109] Model building module 2 is used to establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal;

[0110] Model parameter identification module 3, used to identify the model according to the high-frequency wave interference component, and to identify the model parameters using a two-stage augmented stochastic gradient algorithm;

[0111] The wave peak frequency calculation module 4 is used to calculate the discrete model characteristic roots according to the identified model parameters, convert them into continuous time domain characteristic roots, and calculate the wave peak frequency according to the continuous time domain characteristic roots;

[0112] The online tracking module 5 is used to implement the high-pass filtering module, the model building module, the model parameter identification module and the wave peak frequency calculation module online to achieve online tracking of the wave peak frequency.

[0113] The schematic diagram of the actual measurement signal of the ship heading provided by the embodiment of the present invention is as follows: Figure 4 As shown, the two-stage augmented stochastic gradient algorithm parameter update calculation flow chart provided by the embodiment of the present invention is as follows Figure 5 shown.

[0114] The technical solution of the present invention is further described below in conjunction with embodiments.

[0115] In order to verify the effectiveness and effect of the method of the present invention, a ship heading control system was built to carry out simulation tests. Figure 6 This embodiment mainly includes the following steps:

[0116] (1) Experimental conditions

[0117] Select the following ship model as the simulation model:

[0118]

[0119] Among them, K and T are parameters describing the ship's maneuvering characteristics, ψ and r are the ship's heading and heading angular velocity. Considering the nonlinear characteristics of the steering gear, the rudder angle limit is set to ±35°, and the steering rate limit is set to ±5° / s.

[0120] The observer design model is:

[0121]

[0122]

[0123]

[0124]

[0125] y=ψ LF +ψ HF +v

[0126] Among them, w1,w r ,v is the noise term, is the wave state variable.

[0127] Let x = [ζ ω ψ HFψ LF r] T is the state vector, ignoring the noise term, and designing the following observer structure:

[0128]

[0129]

[0130] Among them, the observer gain vector k is:

[0131]

[0132] It can be seen from the above formula that the observer gain vector is closely related to the peak frequency of the waves. In order to achieve good observation and filtering effects for the system observer and filter, their gains need to be adjusted online according to the peak frequency of the wave interference.

[0133] (2) Initial value setting

[0134] Set the ship model parameters T = 107.3 (s), K = 0.185 (s -1 ), the reference heading is set to 30°;

[0135] Set the observer initial state x(0) = [0 0 0 0] T ;

[0136] Set the initial value of the two-stage augmented stochastic gradient algorithm parameters to r s (0) = 1, r n (0) = 1, λ s =0.98,λ n =0.98, p0=10 6 .

[0137] (3) Experimental results and analysis

[0138] according to Figure 7 , Figure 8 , Fig. 9 , Fig.10 The simulation results show that:

[0139] The two-stage augmented stochastic gradient algorithm can accurately track the changes in the wave peak frequency. The observer and filter based on wave peak frequency identification can effectively reduce the response frequency and response amplitude of the servo.

[0140] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0141] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for online identification of characteristic parameters of sea wave disturbance, characterized in that: The method for online identification of characteristic parameters of sea wave disturbance comprises the following steps: Step 1: collect the measurement signal of the ship heading measurement equipment, and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading; Step 2: Establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal; Step 3: Based on the high-frequency wave interference component identification model, a two-stage augmented stochastic gradient algorithm is used to identify the model parameters; Step 4: Calculate the discrete model characteristic roots according to the identified model parameters, convert them into continuous time domain characteristic roots, and calculate the wave peak frequency according to the continuous time domain characteristic roots; Step 5, implementing steps 1 to 4 online to achieve online tracking of the peak frequency of the waves; In step 1, the high-pass filtering process includes: According to the characteristics of the ship motion model, a high-pass filter is designed to filter the heading sensor signal: Where Tf is the high-pass filter parameter, is the approximate value of the high-frequency motion component of the ship heading, ψ(s) is the actual ship heading signal; the design parameters of the high-pass filter should meet the following conditions: hHP(s)hship(s)uδ(s)=1; Among them, hship(s) is the transfer function of the ship heading motion model, and uδ(s) is the steering control input signal; In step 2, the high-frequency wave interference component model is established, and the model is discretized to obtain a high-frequency wave interference component identification model based on the heading signal, including: The mathematical model of the high-frequency motion component of the ship heading caused by the first-order wave disturbance force is established: Among them, ζ is the damping coefficient, Kω is the gain constant, ω0 is the peak frequency of high-frequency interference of waves, v(s) is the white noise input, and ψHF(s) is the high-frequency motion component of the ship heading; Discretize the continuous mathematical model of the high-frequency components of ocean waves. Where z represents the unit forward shift operator, A(z-1)=1+a1z-1+a2z-2 and B(z-1)=1+b1z-1 are the parameter models to be identified; Substituting into A(z-1) and B(z-1), according to z-iy(t)=z(ti), i=1,2, we can get: ψHF(t)=-a1ψHF(t-1)-a2ψHF(t-2)+b1v(t-1)+v(t); Since the high-frequency motion component of the ship's heading ψHF(s) cannot be directly obtained by sensor measurement, its approximate value is used replace: make Writing the above formula in vector form, we can obtain the high-frequency wave interference component identification model based on heading measurement signal: Among them, θs=[a1 a2]T, θn=b1 and θ=[a1 a2 b1]T are the parameter vectors to be identified. and is the information vector.

2. The method for online identification of characteristic parameters of sea wave disturbance according to claim 1, characterized in that: In step 3, the high-frequency wave interference component identification model is used to identify model parameters using a two-stage augmented stochastic gradient algorithm, including: The two-stage augmented stochastic gradient algorithm is used to identify the parameters a1, a2 and b1. The identification algorithm is as follows: Define two intermediate variables: Based on the hierarchical identification principle, the identification model is decomposed into two subsystems: Define two quadratic index functions: Minimize the quadratic index functions J1(θs) and J2(θn) and use negative gradient search to calculate and The recursive relationship is: Among them, μs(t) and μn(t) are convergence factors, defined as: Among them, λs and λn are forgetting factors; substituting the intermediate variables into the above formula, we can get the following recursive relationship: Since the right side of the equation contains unknown parameters θs, θn and unmeasurable terms Therefore, the above formula cannot be directly calculated and Construct the following auxiliary model: Use estimated value and Substitute the unknown terms θs, θn and The following two-stage augmented stochastic gradient algorithm is obtained:

3. The method for online identification of characteristic parameters of sea wave disturbance according to claim 1, characterized in that: In step 4, the discrete model characteristic roots are calculated based on the model parameters obtained by identification, and converted into continuous time domain characteristic roots, and the wave peak frequency is calculated based on the continuous time domain characteristic roots, including: By solving the equation A(z-1)=1+a1z-1+a2z-2=0, we can obtain two discrete roots z1 and z2. According to the conversion relationship between the discrete domain and the continuous time domain: Where, h is the sampling time; The conjugate complex root s1,2 = -α ± jβ corresponding to the high-frequency wave interference model is obtained, so the estimated value of the wave peak frequency is:

4. An online identification system for characteristic parameters of sea wave disturbances for implementing the online identification method for characteristic parameters of sea wave disturbances according to any one of claims 1 to 3, characterized in that: The sea wave disturbance characteristic parameter online identification system comprises: The high-pass filter module is used to collect the measurement signal of the ship heading measurement equipment and perform high-pass filtering to obtain the approximate value of the high-frequency motion component of the ship heading; A model building module is used to establish a high-frequency wave interference component model and discretize the model to obtain a high-frequency wave interference component identification model based on the heading signal; Model parameter identification module, used to identify the model according to the high-frequency wave disturbance component, and use the two-stage augmented stochastic gradient algorithm to identify the model parameters; The wave peak frequency calculation module is used to calculate the discrete model characteristic roots according to the identified model parameters, convert them into continuous time domain characteristic roots, and calculate the wave peak frequency according to the continuous time domain characteristic roots; The online tracking module is used to implement the high-pass filtering module, the model building module, the model parameter identification module and the wave peak frequency calculation module online to achieve online tracking of the wave peak frequency.

5. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for online identification of characteristic parameters of sea disturbance according to any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for online identification of characteristic parameters of sea disturbance according to any one of claims 1 to 3.

7. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the online identification system for characteristic parameters of sea wave disturbance as described in claim 4.

8. Application of the on-line identification system for characteristic parameters of sea wave disturbance as claimed in claim 4 in the design of heading filter.

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