Multi-signal fusion real-time deduction method for onboard three-dimensional wave parameters

Abstract

The invention relates to a multi-signal fusion real-time deduction method for three-dimensional wave parameters along with a ship, and a deduction device consists of a state sensor, a one-way stress sensor, a vertical acceleration sensor, a shunt, a terminal junction box, a signal acquisition instrument, a host, analysis software, a signal transmission cable and the like. The deduction method comprises the processes of system state matrix discretization, transfer function calculation, Kalman filtering deduction model construction, convergence analysis, result comparison and the like, and three-dimensional time-frequency parameters of waves are deduced in real time through fusion analysis of ship motion and actual measurement data of a key position stress response sensor. Real-time information of surrounding waves can be provided for more sailing ships, more comprehensive reference basis can be provided for ship sailing and ship type optimization, and the method has practical significance and market value.

Description

Technical Field

[0001] The present invention relates to the technical field of indirect measurement and real-time deduction of the wave environment near a ship during navigation, and in particular to a multi-signal fusion real-time deduction method for three-dimensional wave parameters of a ship-borne Background Technique

[0002] Real-time measurement of the wave environment near a ship during navigation is of guiding significance for supporting marine scientific research, assisting ship operation, avoiding navigation risks, and optimizing ship type design. Different from the characteristics of large-range (kilometer-level), statistical, low-precision, and lagging wave parameter measurement, the measurement and deduction of ship-borne three-dimensional wave parameters focus on the time-frequency characteristics of waves within a range of 50 meters near a ship during navigation. The measurement and deduction targets include key wave parameters such as wave surface time history, three-dimensional direction spectrum, and significant wave height. At the same time, higher requirements for small range, high precision, and high real-time are put forward. The guiding role in ship design and operation and maintenance is difficult to achieve by wave measurement in other fields. There are two means for ship-borne wave measurement: direct measurement and indirect deduction. The direct measurement means directly measures through testing equipment, mainly including wave radar, buoy, visual wave measurement, etc. The indirect deduction means indirectly deduces wave parameters through ship motion, mainly including Bayesian estimation method, deep learning method, hybrid framework method, etc. Although both means have achieved certain development in the past decade, the actual measurement effect is poor, mainly manifested in low precision and poor real-time performance. According to relevant data, there are about 80,000 international navigation ships in the world. Ship state monitoring systems that can measure the multi-degree-of-freedom motion and longitudinal bending stress of ships in real time have become standard configurations for most ships. The accumulation of massive monitoring data and the development of intelligent technologies have brought new development opportunities for indirect deduction means. In the context of the development of green intelligent ships and shipping, further integrating more measurement information and seeking a low-power, highly reliable real-time deduction method and device for wave parameters to provide real-time information on the surrounding waves for more navigation ships has practical significance and market value.

[0003] Among the existing in - ship wave measurement methods, the direct measurement method has advantages in wave field measurement. However, the motion compensation of the equipment itself and the long - term reliability of the equipment under harsh sea conditions are the main problems restricting its development. In particular, the wave - measuring buoy has problems such as difficult deployment and recovery and inability to measure continuously, and visual wave measurement has poor measurement effects in the night environment. On the contrary, the indirect deduction method only requires appropriate modification of the existing motion and stress measurement equipment. Since its idea is to establish a mapping model between ship motion and wave parameters, the ship motion response is more obvious under harsh sea conditions, and better measurement effects are often obtained. In addition, its lightweight, fast, and stable characteristics make it have greater potential for popularization and application in commercial navigation. However, the existing indirect deduction methods for in - ship three - dimensional wave parameters only consider the heave, roll, and pitch at the center of gravity of the ship, and only establish the deduction relationship from a single motion signal to directional wave parameters, and there are three defects in it. First, the accelerations at the bow and stern of the ship and the total longitudinal bending stress of the hull are ignored as inputs to the model. In particular, the total longitudinal bending stress, which is the most direct structural response of the hull structure under wave loads in harsh sea conditions, should be considered. Second, the influence of multi - signal fusion on the deduction result is ignored. The monitoring system provides multi - source data of the hull motion and stress response, and the improvement effect of multi - source signal fusion on the deduction accuracy should be considered in the method model. Third, the wave direction estimation ability is poor. Generally, only the time - frequency deduction of directional waves can be realized, and the ability to estimate the amplitude and phase combination of the component waves in each wave direction in three - dimensional waves is not available. This application proposes a new deduction scheme that simultaneously considers two types of multi - source input signals, namely hull motion and stress response, in the deduction model, establishes the deduction relationship to the amplitude and phase information of the 360° component waves, and realizes the real - time deduction of three - dimensional waves. Summary of the Invention

[0004] In view of the above - mentioned disadvantages in the existing production technology, the applicant provides a multi - signal fusion real - time deduction method for in - ship three - dimensional wave parameters, thereby establishing a wave deduction Kalman filter model that fuses multi - source test signals of ship motion and stress response as inputs. Without adding additional test equipment, the existing ship motion and stress monitoring equipment is transformed and upgraded to form a new device, effectively solving the real - time deduction problems of the three - dimensional in - ship wave surface time history, directional spectrum, and significant wave height, and providing a more comprehensive reference basis for ship navigation and ship type optimization.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters, including a deduction device. The structure of the deduction device is as follows: It includes an attitude sensor arranged at the center of gravity of the ship, unidirectional stress sensors symmetrically arranged on the starboard and port sides at the midship section of the main deck, and two vertical acceleration sensors located at the bow and stern positions of the ship. The attitude sensor, unidirectional stress sensors, and vertical acceleration sensors are connected through a splitter to distribute the lines and signal transmission cables. The measurement signals converge at the terminal junction box and are connected to the signal acquisition instrument through the terminal junction box for demodulation and acquisition of the sensor measurement signals, and are displayed, analyzed, and stored in the host. The analysis software writes the wave parameter deduction method to realize the real-time deduction of multi-signal fusion of shipborne three-dimensional wave parameters;

[0007] The deduction method includes the following operation steps:

[0008] S1. Introduce the linear superposition hypothesis of irregular waves, discretize the state of wave parameters, and establish the system state matrix ξ of wave parameters k , which is also the deduction target;

[0009] S2. Define the system model and measurement model of wave parameters;

[0010] S3. Calculate the wave-induced hull response transfer function as the measurement matrix H;

[0011] S4. Establish a multi-signal fusion wave deduction model based on the Kalman filter, set the initial values of wave parameters, input the measured ship motion signals, and iterate the system state matrix ξ k in the time domain;

[0012] S5. When the iteration ends, obtain the inversion results of the directional spectrum, wave surface time history, and directional wave power spectrum.

[0013] The attitude sensor (2) measures the rigid body motions such as heave, roll, and pitch of the ship during navigation.

[0014] The unidirectional stress sensor (3) measures the total longitudinal bending stress of the hull.

[0015] The vertical acceleration sensor (4) is used to measure the acceleration motions at the bow and stern of the ship under the action of waves.

[0016] In S1, for the inversion target, that is, the wave parameters, a linear superposition hypothesis and discretization processing are carried out. The key points include:

[0017] S1.1 The wind waves on the actual sea surface are extremely irregular, and the wave height, wavelength, and period of each wave vary randomly. Therefore, it is difficult to express them using the fixed expression of regular waves. Therefore, based on the understanding of the stationary random process and ergodicity of the statistical characteristics of random waves, it is assumed that irregular waves are composed of many unit regular waves with different wavelengths, amplitudes, and random phases. Then, the wave surface of the irregular wave from the j direction at the center of gravity of the ship can be expressed as:

[0018]

[0019] In the formula, N represents the number of unit regular waves, generally corresponding to the number of discrete wave parameters in the frequency range; A i , ω i , φ i respectively represent the amplitude, frequency, and phase of the i-th unit regular wave;

[0020] S1.2 Discretize the wave parameters in the two dimensions of frequency and wave direction; for the waves from the j direction at the center of gravity of the ship, in the frequency range, the unknown wave spectrum is discretized into N groups, each group containing the amplitude a N and the phase parameter φ N . Use the above parameters to construct the system state matrix of the directional wave, which is expressed as:

[0021] ξ kj =[a1,φ1,a2,φ2,...,a N ,φ N 2N

[0022] Divide the 360° wave direction evenly into M parts in the wave state space, and add the dimension of the wave direction angle on the basis of ξ kj . The system state matrix ξ k of the wave parameters is further expressed as:

[0023]

[0024] In the formula, a NM , φ NM respectively represent the amplitude and phase of the N-th frequency regular wave from the M direction.

[0025] In S2, define the system model and measurement model of the wave parameters to facilitate the iterative calculation of the two in S4. The two models are defined as follows:

[0026] S2.1 The system model describes the evolution of the wave parameters from t k-1 to t k . The formula of the system model is:

[0027] ξ k =A·ξ​k-1 +w k

[0028] Where A is the state transfer matrix; w k is the process noise matrix, w k ~N(0,Q); Q is the system error matrix, which describes the covariance of the noise in the system model and reflects the error introduced by the inaccurate model or the inherent uncertainty of the system when the system state is updated at each step;

[0029] S2.2 Measurement model used to establish system state ξ k With system measurement z k The mathematical relationship between them is expressed as:

[0030] z k =Hξ k +v k

[0031] Where H is the ship motion measurement matrix; v k is the measurement noise, which means the error in sensor measurement. The measurement noise covariance matrix is expressed as R.

[0032] In S3, in order to establish the mapping relationship between the hull response and the wave parameters, it is necessary to calculate the wave-induced hull response transfer function as the measurement matrix H. The corresponding measurement matrix H can be expressed as a matrix with 2N*M columns and 7 rows:

[0033] H=[H ag H as H aw H r H p H s1 H s2 ] (2N*M)×7

[0034] Where H ag 、H as 、H aw Represent the vertical acceleration RAO of the ship's center of gravity and bow and stern respectively;

[0035] H r 、H p Respectively represent the roll and pitch RAO of the ship's center of gravity;

[0036] H s1 、H s2 They represent the total longitudinal bending stress RAO at the port and starboard positions of the mid-transverse section of the main deck respectively;

[0037] H ag 、H as 、H aw 、H r 、Hp , H s1 and H s2 have a matrix scale of 2N*M one-dimensional matrices. Taking H ag as an example, it can be expressed as:

[0038]

[0039] In the formula, A NM , respectively represent the amplitude and phase of the acceleration response of the ship's center of gravity position induced by the Nth frequency unit regular wave from the M direction; H as , H aw , H r , H p , H s1 and H s2 can be constructed in a similar way. A NM , respectively represent the amplitude and phase of different types of responses.

[0040] In S4, a multi-signal fusion wave deduction model based on the Kalman filter is established, and the system state matrix ξ k is iterated in the time domain.

[0041] (a) Perform a priori estimation of the system state. The a priori estimation state vector of the wave parameters is calculated by the following formula:

[0042]

[0043] In the formula, represents the a priori estimated value of the system state at the current k moment; represents the posterior estimated value of the system state at the previous k-1 moment; A is the state transition matrix, which describes the dynamic characteristics of the system itself. During the Kalman filter iteration process, it reflects the statistical change trend of the wave from the previous moment to this moment and is set as the identity matrix;

[0044] The a priori estimation error of the current k moment. The predicted value P k - of the covariance matrix is calculated as follows:

[0045]

[0046] In the formula, P k-1 represents the actual value of the covariance matrix of the posterior error at the k-1 moment; the system error matrix Q takes

[0047]

[0048] (b) Perform a posteriori estimation of the system state, and the wave parameter a posteriori estimation state vector Calculated by the following formula:

[0049]

[0050] Where z k The motion or stress signal measured by the sensor is input through the device proposed by the present invention; K k is the Kalman gain, which is used to balance the weights of the measurement model and the system model. It is calculated as follows:

[0051]

[0052] The posterior error at the current k moment The covariance matrix P k The calculation method is:

[0053]

[0054] Where I is the unit matrix;

[0055] (c) The deduction model is iterated in the time domain, since the measurement matrix H includes two types of signals: hull motion and stress;

[0056] In S5, the deduction model iteratively approximates the real wave parameter ξ in the time domain k , combined with the phase information to output the wave height h at time k k , the calculation formula is:

[0057] h k =[cos(ω1k),-sin(ω1k),...,cos(ω N k),-sin(ω N k)]ξ k T

[0058] In order to judge the convergence speed of the deduction model, a theoretical wave spectrum can be established according to the actual ship application environment. The simulated hull motion is generated according to the wave-induced hull motion and stress response RAO. The wave height time history is input into the deduction model to invert the wave height, and the significant wave height Hs and average zero-crossing period T0 at each moment are counted and compared with the theoretical spectrum.

[0059] The beneficial effects of the present invention are as follows:

[0060] The multi-signal fusion real-time deduction method for ship-borne three-dimensional wave parameters proposed in the present invention is based on the wave-ship response RAO physical model, and is naturally interpretable and universal. For any ship type, the deduction calculation can be completed by simply updating the RAO. At the same time, it effectively integrates the measured information of multiple sensors such as acceleration, stress, roll, and pitch, making the wave inversion results more accurate. The method also takes into account the influence of sensor measurement noise, making the wave inversion more stable. More importantly, this method can realize the simultaneous inversion of wave frequency domain parameters (wave spectrum power and three-dimensional directional spectrum) and time domain parameters (real-time wave surface). The ship-borne three-dimensional wave parameter deduction device proposed in the present invention does not require additional testing equipment. The existing ship motion and stress monitoring equipment can be modified and upgraded to directly have the wave parameter deduction function. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a structural diagram (main view) of the multi-signal fusion real-time deduction device for ship-borne three-dimensional wave parameters provided by the present invention.

[0062] Figure 2 This is a structural diagram (top view) of the multi-signal fusion real-time deduction device for ship-borne three-dimensional wave parameters provided by the present invention.

[0063] Figure 3 The system state matrix ξ provided by the present invention k Construction process diagram (I).

[0064] Figure 4 The system state matrix ξ provided by the present invention k Construction process diagram (2).

[0065] Figure 5 Figure 1 is the wave-induced hull response transfer function (RAO) provided by the present invention.

[0066] Figure 6 Figure 2 is the wave-induced hull response transfer function (RAO) provided by the present invention.

[0067] Figure 7 This is an iterative diagram of the deduction method provided by the present invention in the time domain.

[0068] Figure 8 This is a diagram of the characteristic parameter convergence process provided by the present invention.

[0069] Figure 9 This is a comparison chart of the wave height time history deduction provided by the present invention.

[0070] Figure 10 This is a comparison chart of the directional wave spectrum deduction provided by the present invention.

[0071] Figure 11The wave direction spectrum deduction comparison diagram (true spectrum) provided by the present invention.

[0072] Figure 12 The wave direction spectrum deduction comparison diagram (inversion spectrum) provided by the present invention. Detailed implementation manners

[0073] The following combines with the drawings to illustrate the detailed implementation manners of the present invention.

[0074] As Figures 1 - 12 A multi-signal fusion real-time deduction method for shipborne three-dimensional wave parameters provided in this embodiment, which includes a device for multi-signal fusion real-time deduction of shipborne three-dimensional wave parameters. The function of the device is to collect the rigid body motion and total longitudinal bending stress of the hull on the ship, and provide necessary parameter inputs for the multi-signal fusion real-time deduction method.

[0075] The specific structure diagram of the device for multi-signal fusion real-time deduction of shipborne three-dimensional wave parameters is as Figure 1 And Figure 2 shown, which mainly includes: an attitude sensor 2, a unidirectional stress sensor 3, a vertical acceleration sensor 4, a splitter 5, a terminal junction box 6, a signal collector 7, a host 8, an analysis software 9, and a signal transmission cable 10.

[0076] Among them, the attitude sensor 2 is generally located at the center of gravity of the ship, measuring the rigid body motions such as heave, roll, and pitch of the ship during navigation. The unidirectional stress sensor 3 is located at the midship section 1 of the main deck, symmetrically arranged on the port and starboard sides, measuring the total longitudinal bending stress of the hull. The two vertical acceleration sensors 4 are respectively located at the bow and stern positions of the ship, used to measure the acceleration motions of the bow and stern under the action of waves.

[0077] The above three types of sensors are connected through the splitter 5 to distribute the lines and the signal transmission cable 10. The measured signals converge at the terminal junction box 6, and are connected to the signal collector 7 through the terminal junction box 6 for demodulation and acquisition of the sensor measurement signals, and are displayed, analyzed, and stored in the host 8. The analysis software 9 writes the wave parameter deduction method to realize the multi-signal fusion real-time deduction of shipborne three-dimensional wave parameters.

[0078] The multi-signal fusion real-time deduction method for shipborne three-dimensional wave parameters specifically includes the following steps:

[0079] S1. Introduce the linear superposition hypothesis of irregular waves, discretize the state of wave parameters, and establish the system state matrix ξ k , that is, the deduction target.

[0080] S2. Define the system model and measurement model of wave parameters.

[0081] S3. Calculate the wave-induced hull response transfer function (RAO) as the measurement matrix H.

[0082] S4. Establish a multi-signal fusion wave deduction model based on the Kalman filter, set the initial values of the wave parameters, input the measured ship motion signals, and perform prior estimation and posterior estimation on the system state matrix ξ k Iterate in the time domain.

[0083] S5. After the iteration ends, obtain the directional spectrum, wave surface time history, and the inversion results of the directional wave power spectrum.

[0084] Next, taking a ship as an example, analyze the real-time deduction process of multi-signal fusion of wave parameters.

[0085] In S1, for the inversion target, that is, the wave parameters, perform linear superposition assumption and discretization processing. The key points include:

[0086] S1.1 The wind waves on the actual sea surface are extremely irregular, and the wave height, wavelength, and period of each wave are randomly changing. Therefore, it is difficult to express them using the fixed expression of regular waves. Therefore, based on the understanding of the stationary random process and ergodicity of the statistical characteristics of random waves, it is assumed that the irregular wave is composed of many unit regular waves with different wavelengths, different wave amplitudes, and random phases. Then, the wave surface of the irregular wave from the j direction at the ship's center of gravity can be expressed as:

[0087]

[0088] In the formula, N represents the number of unit regular waves, generally corresponding to the number of discrete wave parameters in the frequency range; A i , ω i , φ i respectively represent the amplitude, frequency, and phase of the i-th unit regular wave.

[0089] S1.2 Discretize the wave parameters in the two dimensions of frequency and wave direction. For the waves from the j direction at the ship's center of gravity, in the frequency range, the unknown wave spectrum is discretized into N groups, each group containing the amplitude a N and the phase parameter φ N . Use the above parameters to construct the system state matrix of the directional wave, expressed as:

[0090] ξ kj = [a1, φ1, a2, φ2,..., a N , φ N 2N

[0091] Divide the 360° wave direction evenly into M parts in the wave state space, and add the dimension of the wave direction angle on the basis of ξ kj . The system state matrix ξ k of the wave parameters is further expressed as:​

[0092]

[0093] Wherein, a NM , φ NM respectively represent the amplitude and phase of the Nth frequency regular wave from the M direction.

[0094] For ease of understanding, the system state matrix ξ k construction process Figure 2 is shown as follows.

[0095] In S2, a system model and a measurement model for defining wave parameters are required so that the two can complete iterative calculations in S4. The two models are defined as follows:

[0096] S2.1 The system model describes the evolution of wave parameters (system state ξ k ) from t k-1 to t k . The system model formula is

[0097] ξ k = A·ξ k-1 + w k

[0098] Wherein, A is the state transition matrix. Since the wave statistical parameters change slowly and the states at adjacent moments are almost unchanged, it is set as the identity matrix; w k is the process noise matrix, w k ~ N(0, Q); Q is the system error matrix, which describes the covariance of the noise in the system model and reflects the error introduced due to inaccurate models or inherent uncertainties of the system in each step of updating the system state.

[0099] S2.2 The measurement model is used to establish the mathematical relationship between the system state ξ k and the system measurement z k (ship motion, longitudinal bending stress and bow and stern vertical acceleration), expressed as:

[0100] z k = Hξ k + v k

[0101] Wherein, H is the ship motion measurement matrix, which is the wave-induced hull response transfer function (RAO) in this embodiment and is described in S3; v k is the measurement noise, which means the error of sensor measurement. The measurement noise covariance matrix is expressed as R.

[0102] In S3, in order to establish the mapping relationship between the hull response and wave parameters, it is necessary to calculate the wave-induced hull response transfer function (RAO) as the measurement matrix H. For the wave parameter deduction device proposed in this embodiment, the corresponding measurement matrix H can be expressed as a matrix with 2N*M columns and 7 rows:

[0103] H = [H ag H as H aw H r H p H s1 H s2 (2N*M)×7

[0104] In the formula, H ag , H as , H aw respectively represent the vertical acceleration RAO at the center of gravity of the ship, the bow and stern positions; H r , H p respectively represent the roll and pitch RAO at the center of gravity position of the ship; H s1 , H s2 respectively represent the longitudinal bending stress RAO at the port and starboard positions of the midship section of the main deck.

[0105] H ag , H as , H aw , H r , H p , H s1 and H s2 are all one-dimensional matrices of 2N*M. Taking H ag as an example, it can be expressed as:

[0106] [[ID=5�]]

[0107] In the formula, A NM , respectively represent the amplitude and phase of the acceleration response of the center of gravity position of the hull induced by the Nth frequency unit regular wave from the M direction. H as , H aw , H r , H p , H s1 and H s2 can be constructed in a similar manner. A NM , respectively represent the amplitude and phase of different types of responses.

[0108] The RAO curves of the roll and pitch responses of the ship's center of gravity induced by the M-wave downward wave are as shown in Figure 5 and Figure 6 , which are divided into two parts: amplitude and phase.​

[0109] In S4, a multi-signal fusion wave deduction model based on a Kalman filter is established, and the system state matrix ξ is estimated through prior estimation and posterior estimation k Iterate in the time domain, and the main process is as Figure 7 shown, mainly including:

[0110] (a) Conduct prior estimation of the system state. The prior estimation state vector of the wave parameters is calculated by the following formula:

[0111]

[0112] In the formula, represents the prior estimated value of the system state at the current k-th moment; represents the posterior estimated value of the system state at the previous k - 1 moment; A is the state transition matrix, which describes the dynamic characteristics of the system itself. During the Kalman filter iteration process, it reflects the statistical change trend of the wave from the previous moment to this moment and is set as the identity matrix.

[0113] The prior estimation error at the current k-th moment The predicted value of the covariance matrix (uncertainty of prior estimation) is calculated as:

[0114]

[0115] In the formula, P k-1 represents the actual value of the covariance matrix of the posterior error at the k - 1 moment; the system error matrix Q takes

[0116]

[0117] (b) Conduct posterior estimation of the system state. The posterior estimation state vector of the wave parameters is calculated by the following formula:

[0118]

[0119] In the formula, z k is the measured motion or stress signal of the sensor and is input through the device proposed by the present invention; K k is the Kalman gain, whose function is to balance the weights of the measurement model and the system model, and is calculated as:

[0120]

[0121] The posterior error at the current k-th moment The covariance matrix P k (uncertainty of posterior estimation) is calculated as:

[0122]

[0123] Where I is the unit matrix.

[0124] (c) The deduction model is iterated in the time domain. Since the measurement matrix H includes two types of signals: hull motion and stress, the wave parameter deduction method proposed in this patent realizes multi-signal fusion input, which is conducive to improving the overall accuracy of the deduction.

[0125] In S5, the deduction model iteratively approximates the real wave parameter ξ in the time domain k , combined with the phase information to output the wave height h at time k k , the calculation formula is:

[0126] h k =[cos(ω1k),-sin(ω1k),...,cos(ω N k),-sin(ω N k)]ξ k T

[0127] To determine the convergence speed of the deduction model, a theoretical wave spectrum can be established based on the actual ship application environment. The simulated hull motion is generated based on the wave-induced hull motion and stress response RAO. The wave height history is input into the deduction model inversion, and the significant wave height Hs and average zero-crossing period T0 at each moment are counted and compared with the theoretical spectrum. Taking the ship used in the present invention as an example, the convergence process of the significant wave height Hs and average zero-crossing period T0 is obtained as follows: Figure 8 As shown in the figure, we can see that after 100 seconds, the above two eigenvalues tend to be stable, indicating that the wave deduction model is credible and can be continuously iterated until the end.

[0128] Furthermore, the power spectrum of the time-domain directional wave and the directional spectrum of the three-dimensional wave are calculated according to the autocorrelation function method and compared with the theoretical spectrum, such as Figures 9 - 12 As shown, the accuracy of the deduction is verified.

[0129] The above description is an explanation of the present invention, not a limitation of the present invention. The scope of the present invention is defined in the claims. Any modifications may be made within the scope of protection of the present invention.

Claims

1. A real-time deduction method for multi-signal fusion of three-dimensional wave parameters of a ship, characterized in that: It includes a deduction device, and the structure of the deduction device is as follows: It includes an attitude sensor (2) arranged at the center of gravity of the ship. Unidirectional stress sensors (3) are symmetrically arranged on the starboard and port sides at the midship section (1) of the main deck. Two vertical acceleration sensors (4) are respectively located at the bow and stern positions of the ship. The attitude sensor (2), the unidirectional stress sensors (3), and the vertical acceleration sensors (4) are connected through a splitter (5) to distribute lines and a signal transmission cable (10). The measurement signals converge at the terminal junction box (6), are connected to a signal acquisition instrument (7) through the terminal junction box (6), and the demodulation and acquisition of the sensor measurement signals are performed, and are displayed, analyzed, and stored in the main engine (8). An analysis software (9) writes a wave parameter deduction method to realize the multi-signal fusion real-time deduction of the three-dimensional wave parameters on the ship. The deduction method includes the following operation steps: S1. Introduce the linear superposition hypothesis of irregular waves, discretize the states of wave parameters, and establish the system state matrix ξ of wave parameters k , that is, the deduction target S2. Define the system model and measurement model of wave parameters. S3. Calculate the wave-induced hull response transfer function as the measurement matrix H. S4. Establish a multi-signal fusion wave deduction model based on the Kalman filter, set the initial values of the wave parameters, input the measured ship motion signals, and perform prior estimation and posterior estimation on the system state matrix ξ k Iterate in the time domain; S5. The iteration ends, and the directional spectrum, wave surface time history, and inversion results of the directional wave power spectrum are obtained.

2. The real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, wherein: The attitude sensor (2) measures the rigid body motions such as heave, roll, and pitch of the ship during navigation.

3. The multi-signal fusion real-time deduction method for three-dimensional wave parameters of a ship as claimed in claim 1, characterized in that: The unidirectional stress sensor (3) measures the longitudinal bending stress of the hull.

4. A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, characterized in that: The vertical acceleration sensor (4) is used to measure the acceleration motion of the bow and stern of the ship under the action of waves.

5. A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, characterized in that: In S1, for the inversion target, that is, wave parameters, a linear superposition assumption and discretization processing are performed. The key points include: S1.1 The wind waves on the actual sea surface are extremely irregular, and the wave height, wavelength, and period of each wave are randomly changing. Therefore, it is difficult to use the fixed expression of regular waves for expression. Therefore, based on the understanding of the stationary random process and ergodicity of the statistical characteristics of random waves, it is assumed that the irregular wave is composed of many unit regular waves with different wavelengths, different wave amplitudes, and random phases. Then, the wave surface of the irregular wave from the j direction at the center of gravity position of the ship can be expressed as: where N represents the number of unit regular waves, generally corresponding to the number of discrete wave parameters in the frequency range; A i , ω i , φ i represent the amplitude, frequency, and phase of the i-th unit regular wave, respectively; S1.2 Discretize the state of wave parameters in two dimensions of frequency and wave direction; for the waves from direction j at the position of the ship's center of gravity, in the frequency range, discretize the unknown wave spectrum into N groups, each group containing amplitude a N and phase parameter φ N , and use the above parameters to construct the system state matrix of directional waves, expressed as: ξ kj = [a1, φ1, a2, φ2,..., a N , φ N 2N ​ In the wave state space, the 360° wave direction is evenly divided into M parts. Based on ξ kj the dimension of the wave direction angle is added, and the system state matrix ξ k of the wave parameters is further expressed as: where a NM , φ NM represent the amplitude and phase of the Nth regular wave from the M direction, respectively.

6. A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, characterized in that: In S2, the system model and measurement model of wave parameters are defined to facilitate the two to complete the iterative calculation in S4. The definitions of the two models are as follows: S2.1 The system model describes the evolution of wave parameters from t k-1 to t k . The system model formula is as follows: ξ k = A·ξ k-1 + w k where, A is the state transition matrix; w k is the process noise matrix, w k ~N(0, Q); Q is the system error matrix, which describes the covariance of the noise in the system model and reflects the error introduced due to inaccurate models or inherent system uncertainties at each step of system state update; The S2.2 measurement model is used to establish the mathematical relationship between the system state ξ k and the system measurement z k which is expressed as: z k = Hξ k + v k where H is the ship motion measurement matrix; v k is the measurement noise, which means the error of the sensor measurement, and the measurement noise covariance matrix is expressed as R..

7. A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, characterized in that: In S3, in order to construct the mapping relationship between the hull response and wave parameters, it is necessary to calculate the wave-induced hull response transfer function as the measurement matrix H. The corresponding measurement matrix H can be expressed as a matrix with 2N*M columns and 7 rows: H = [H ag H as H aw H r H p H s1 H s2 (2N*M)×7 ​ Where, H ag , H as , H aw respectively represent the vertical acceleration RAO of the ship's center of gravity and the bow and stern positions; H r and H p respectively represent the roll and pitch RAOs of the ship's center of gravity position. H s1 and H s2 respectively represent the RAO of the longitudinal bending stress at the port and starboard positions of the midship section of the main deck; H ag 、H as 、H aw 、H r 、H p 、H s1 and H s2 The matrix scales of ag are all one-dimensional matrices of 2N*M. Taking where A NM and represent the amplitude and phase of the acceleration response of the ship's center of gravity position induced by the Nth frequency unit regular wave from the M direction, respectively; H as , H aw , H r , H p , H s1 and H s2 can be constructed in a similar manner, and A NM and represent the amplitudes and phases of different types of responses, respectively.

8. A real-time deduction method for multi-signal fusion of shipborne three-dimensional wave parameters according to claim 1, characterized in that: In S4, a multi-signal fusion wave deduction model based on the Kalman filter is established, and the system state matrix ξ is iterated in the time domain through prior estimation and posterior estimation. k Iterate in the time domain.

9. A multi-signal fusion real-time deduction method for three-dimensional wave parameters on a ship as described in claim 8, characterized in that: (a) Perform a priori estimation of the system state, and the a priori estimation state vector of the wave parameters Calculate through the following formula: In the formula, represents the prior estimate of the system state at the current k-th moment; represents the posterior estimate of the system state at the previous (k-1)-th moment; A is the state transition matrix, which describes the dynamic characteristics of the system itself. During the Kalman filter iteration process, it reflects the statistical change trend of the wave from the previous moment to this moment, and is set as the identity matrix; A priori estimation error at the current k-th moment Predicted value of the covariance matrix The calculation method is as follows: where P k-1 represents the actual value of the covariance matrix of the posterior error at time k - 1; the system error matrix Q takes ​ (b) Posteriorly estimate the system state, and the posteriorly estimated state vector of the wave parameters is calculated by the following formula: where z k is the actually measured motion or stress signal of the sensor, which is input through the device proposed by the present invention; K k is the Kalman gain, whose function is to balance the weights of the measurement model and the system model, and the calculation method is as follows: Posterior error after the current k moment Covariance matrix P k The calculation method is as follows: In the formula, I is the identity matrix; (c) The deduction model iterates in the time domain. Since the measurement matrix H includes two types of signals, hull motion and stress; In S5, the deduction model iteratively approximates the real wave parameter ξ in the time domain k , combined with the phase information to output the wave height h at time k k , the calculation formula is: h k = [cos(ω1k), -sin(ω1k),..., cos(ω N k), -sin(ω N k)]ξ k T To judge the convergence speed of the deduction model, a theoretical wave spectrum can be established according to the actual ship application environment, the simulated hull motion can be generated according to the wave-induced hull motion and stress response RAO, the wave height time history is input to the deduction model for inversion, and the significant wave height Hs and the average zero-crossing period T0 at each moment are statistically calculated and compared with the theoretical spectrum.

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