A ship wind measurement method, medium and system based on single Beidou positioning

Abstract

The present invention provides a ship wind measurement method, medium and system based on single Beidou positioning, belonging to the technical field of ship wind measurement. The ship wind measurement method, medium and system based on single Beidou positioning include collecting ship position and anemometer data through the Beidou system; decomposing the position data into components to obtain the stable and variable components of the ship speed and heading; decomposing the wind speed data to obtain the stable and variable components of the wind speed; establishing a correlation function between the ship speed, heading and wind speed variation, and constructing a ship motion state matrix; calculating the ship velocity vector and converting the coordinate system; constructing an environmental wind speed influence matrix; performing a synthesis operation according to the state and influence matrices to calculate the actual wind speed vector, calculating the true wind direction angle and solving it through an optimal path matrix; calculating the wind speed and wind direction error compensation amounts, applying them to the actual wind speed vector and the true wind direction angle to obtain corrected data; the present invention can improve the measurement accuracy of the wind measurement system.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship wind measurement, and specifically relates to a ship wind measurement method, medium and system based on single Beidou positioning. Background Art

[0002] Ship wind measurement technology is an important part of the navigation field. It can provide key meteorological information support for ship navigation and play an important role in improving navigation safety and efficiency. The existing ship wind measurement technologies mainly include two methods: the ship wind measurement method based on multiple Beidou positioning receivers, and the ship wind measurement method based on external sensors.

[0003] For the ship wind measurement method based on multiple Beidou positioning receivers, two or three Beidou positioning receivers are respectively installed at different positions of the ship, and the wind speed and wind direction are deduced by calculating the speed difference between these receivers. The advantage of this method is that it can utilize the high-precision position and speed data provided by the Beidou positioning system without additional sensors, and the measurement cost is relatively low. However, since multiple positioning receivers need to be installed and the positional relationship between these receivers changes with the movement of the ship, complex mathematical modeling and calculations are required, and the overall complexity of the system is relatively high, resulting in certain technical difficulties in practical applications.

[0004] For the ship wind measurement method based on external sensors, dedicated wind speed and wind direction sensors are usually installed on the mast or side of the ship to directly measure the relative wind speed and relative wind direction. The data measured by this method has relatively high accuracy, and the system structure is relatively simple and easy to implement. However, this method requires additional sensor devices, increasing the system cost, and the installation position of the sensors is easily affected by the ship's navigation attitude, thus affecting the measurement accuracy. In addition, it is necessary to convert the relative wind speed and relative wind direction using the ship's own speed and heading data to obtain the real environmental wind conditions, increasing the complexity of the system.

[0005] In summary, the existing ship wind measurement technologies have problems such as high cost, limited accuracy, and high system complexity. In view of these problems, the present invention proposes a ship wind measurement method based on single Beidou positioning. By using the high-precision position and motion state data provided by the Beidou positioning system, combined with an ultrasonic wind speed and wind direction sensor, the influence of ship motion on the wind speed is analyzed, and a coupling relationship model between the ship motion state and the environmental wind speed is established, so as to achieve high-precision measurement of the real environmental wind conditions. Compared with the prior art, the wind measurement system of the present invention has the advantages of low cost, high measurement accuracy, and simple system structure, providing a new technical solution for ship meteorological monitoring and navigation control. Summary of the Invention

[0006] In view of this, the present invention provides a ship wind measurement method, medium and system based on single Beidou positioning, which can improve the measurement accuracy of the wind measurement system and provide a new solution for ship meteorological monitoring and navigation.

[0007] The present invention is implemented as follows: In the first aspect of the present invention, a ship wind measurement method based on single Beidou positioning is provided, including the following steps: collecting real-time position data of the ship through the Beidou positioning system and collecting real-time data of the anemometer; performing component decomposition operations on the real-time position data of the ship to obtain the stable component and variable component of the ship speed data, and obtaining the stable component and variable component of the ship heading data; performing component decomposition operations on the real-time data of the anemometer to obtain the stable component and variable component of the relative wind speed data; establishing a motion correlation function of the stable component of the ship speed, the stable component of the ship heading and the variable component of the wind speed, and constructing a ship motion state matrix; calculating the ship velocity vector; constructing an environmental wind speed influence matrix; calculating the actual wind speed vector; calculating the true wind direction angle; calculating the wind speed error compensation amount and the wind direction error compensation amount to obtain the corrected wind speed data and the corrected wind direction data.

[0008] Among them, the step of collecting real-time position data of the ship through the Beidou positioning system is specifically to use a Beidou positioning chip to receive Beidou navigation satellite signals, obtain real-time longitude and latitude data of the ship through differential correction processing, and the sampling frequency of the real-time longitude and latitude data of the ship is 1 Hz; using the Beidou positioning system speed measurement module to calculate the ship speed data; using the Beidou positioning system heading measurement module to calculate the ship heading data; using a two-axis ultrasonic wind speed and direction sensor to construct an anemometer, and calculating the relative wind speed data and relative wind direction data by measuring the ultrasonic propagation time difference between different sensors.

[0009] Among them, the step of performing component decomposition operations on the real-time position data of the ship is specifically to use a multi-scale wavelet decomposition method to process the ship speed data, select the Daubechies 4th-order wavelet function as the decomposition basis function, and obtain the low-frequency component of the ship speed data as the stable component of the ship speed through 4-layer wavelet decomposition, and use the high-frequency component of the ship speed data as the variable component of the ship speed; using the Fourier transform method to process the ship heading data; using the empirical mode decomposition method to process the relative wind speed data.

[0010] Among them, the step of establishing a motion correlation function of the stable component of the ship speed, the stable component of the ship heading and the variable component of the wind speed is specifically to establish a motion correlation function of the stable component of the ship speed, the stable component of the ship heading and the variable component of the wind speed based on the fluid mechanics theory. The motion correlation function includes trigonometric terms and linear terms, and the coefficients of the trigonometric terms and the linear terms are calculated by the least squares method; constructing a ship motion state matrix according to the motion correlation function, and the ship motion state matrix is a 3×3 matrix; performing singular value decomposition on the ship motion state matrix.

[0011] Among them, the step of calculating the ship speed vector specifically decomposes the ship speed data into a longitude-direction speed component and a latitude-direction speed component based on the vector decomposition principle, and calculates the longitude-direction speed component and the latitude-direction speed component through trigonometric functions; constructs a rotation matrix considering the influence of the ship heading data, and the rotation matrix includes a cosine term of the heading angle and a sine term of the heading angle; multiplies the longitude-direction speed component and the latitude-direction speed component by the rotation matrix to obtain the ship speed vector in the geographic coordinate system.

[0012] Among them, the step of constructing the environmental wind speed influence matrix specifically constructs a 2×2 environmental wind speed influence matrix; calculates the wind speed influence coefficient, which is related to the wind speed magnitude and the acting area, and obtains the wind speed influence coefficient by fitting the wind tunnel test data; calculates the wind direction influence coefficient, which is related to the wind direction angle and the ship shape, and obtains the wind direction influence coefficient through numerical simulation methods; combines the wind speed influence coefficient and the wind direction influence coefficient into the environmental wind speed influence matrix.

[0013] Among them, the step of calculating the actual wind speed vector specifically converts the relative wind speed data and the relative wind direction data into a relative wind speed vector; multiplies the environmental wind speed influence matrix by the relative wind speed vector to obtain the environmental influence wind speed vector; multiplies the ship motion state matrix by the environmental influence wind speed vector to obtain the ship motion influence wind speed vector; synthesizes the ship speed vector and the ship motion influence wind speed vector to obtain the actual wind speed vector; filters the actual wind speed vector using a Kalman filter.

[0014] Among them, the step of calculating the true wind direction angle specifically extracts the longitude-direction component and the latitude-direction component of the actual wind speed vector; calculates the angle between the actual wind speed vector and the true north direction through the arctangent function to obtain the initial true wind direction angle; constructs a 3×3 optimal path matrix, and optimizes the elements of the optimal path matrix through the dynamic programming method; iteratively optimizes the initial true wind direction angle based on the optimal path matrix; corrects the optimized wind direction angle through historical data statistical analysis.

[0015] Specifically, the method of the present invention specifically includes:

[0016] S10. Collect the real-time position data of the ship through the Beidou positioning system, and the real-time position data of the ship includes ship longitude data, ship latitude data, ship speed data, and ship heading data; collect the real-time data of the anemometer, and the real-time data of the anemometer includes relative wind speed data and relative wind direction data;

[0017] S20. Perform component decomposition operations on the real-time position data of the ship to obtain the speed stable component and the speed change component of the ship speed data, and obtain the course stable component and the course change component of the ship course data; perform component decomposition operations on the real-time data of the anemometer to obtain the wind speed stable component and the wind speed change component of the relative wind speed data;

[0018] S30. Establish a motion correlation function for the speed stable component, the course stable component and the wind speed change component, and construct a ship motion state matrix according to the motion correlation function. The ship motion state matrix includes a ship speed contribution coefficient and a ship course contribution coefficient;

[0019] S40. Calculate the ship velocity vector according to the ship motion state matrix. The ship velocity vector includes the ship longitude direction velocity component and the ship latitude direction velocity component, and convert the ship velocity vector from the geographic coordinate system to the ship coordinate system;

[0020] S50. Construct an environmental wind speed influence matrix. The environmental wind speed influence matrix includes the wind speed contribution value of the relative wind speed data to the ship motion and the wind direction contribution value of the relative wind direction data to the ship motion;

[0021] S60. Perform matrix synthesis operations according to the ship motion state matrix and the environmental wind speed influence matrix to calculate the actual wind speed vector. The actual wind speed vector is obtained by synthesizing the relative wind speed data and the ship velocity vector;

[0022] S70. Calculate the true wind direction angle according to the actual wind speed vector. The true wind direction angle is the included angle between the actual wind speed vector and the true north direction, and solve the true wind direction angle through the optimal path matrix operation;

[0023] S80. Calculate the wind speed error compensation amount and the wind direction error compensation amount according to the ship motion state matrix and the environmental wind speed influence matrix, and apply the wind speed error compensation amount and the wind direction error compensation amount to the actual wind speed vector and the true wind direction angle respectively to obtain the corrected wind speed data and the corrected wind direction data.

[0024] On the basis of the above technical solution, a ship wind measurement method based on single Beidou positioning of the present invention can also be improved as follows:

[0025] Among them, the step S10 specifically includes: receiving Beidou navigation satellite signals by using a Beidou positioning chip, obtaining real-time ship longitude and latitude data through differential correction processing. The sampling frequency of the real-time ship longitude and latitude data is 1 Hz. The real-time ship longitude and latitude data includes longitude data and latitude data. The accuracy of the longitude data is better than 0.000001 degrees, and the accuracy of the latitude data is better than 0.000001 degrees; calculating ship speed data by using a speed measurement module of the Beidou positioning system, and obtaining the ship speed data through speed factor calibration and correction. The sampling frequency of the ship speed data is 1 Hz, and the accuracy of the ship speed data is better than 0.1 m / s; calculating ship heading data by using a heading measurement module of the Beidou positioning system, and obtaining the ship heading data through heading deviation calibration. The sampling frequency of the ship heading data is 1 Hz, and the accuracy of the ship heading data is better than 0.1 degrees; constructing an anemometer by using a biaxial ultrasonic wind speed and direction sensor, and calculating relative wind speed data and relative wind direction data by measuring the time difference of ultrasonic wave propagation between different sensors. The sampling frequency of the relative wind speed data is 10 Hz, the accuracy of the relative wind speed data is better than 0.1 m / s, the sampling frequency of the relative wind direction data is 10 Hz, and the accuracy of the relative wind direction data is better than 1 degree.

[0026] Further, the step S20 specifically includes: processing the ship speed data by using a multi-scale wavelet decomposition method, selecting a Daubechies 4th-order wavelet function as the decomposition basis function, obtaining the low-frequency component of the ship speed data as the speed stable component through 4-layer wavelet decomposition, taking the high-frequency component of the ship speed data as the speed variation component, and determining the speed variation component according to a speed threshold of 0.5 m / s; processing the ship heading data by using a Fourier transform method, designing a low-pass filter with a cut-off frequency of 0.1 Hz, obtaining the low-frequency component of the ship heading data as the heading stable component through filtering, taking the high-frequency component of the ship heading data as the heading variation component, and determining the heading variation component according to a heading threshold of 1 degree; processing the relative wind speed data by using an empirical mode decomposition method, constructing upper and lower envelope lines through cubic spline interpolation, calculating the mean value to obtain the stable component of the relative wind speed data, taking the difference between the original data and the stable component of the relative wind speed data as the wind speed variation component, and determining the wind speed variation component according to a wind speed threshold of 0.3 m / s.

[0027] Further, the step S30 specifically includes: establishing a motion correlation function of the speed stability component, the course stability component and the wind speed variation component based on the hydrodynamic theory, where the motion correlation function includes trigonometric terms and linear terms, and calculating the coefficients of the trigonometric terms and the linear terms by the least square method; constructing a ship motion state matrix according to the motion correlation function, the ship motion state matrix is a 3×3 matrix, the first row of the ship motion state matrix represents the contribution of the speed to the ship motion, the second row of the ship motion state matrix represents the contribution of the course to the ship motion, the third row of the ship motion state matrix represents the contribution of the wind speed to the ship motion, and obtaining the elements of the ship motion state matrix through discretization processing of the state space equation, and the discretization sampling period is 0.1 second; performing singular value decomposition on the ship motion state matrix, and eliminating the matrix singularity by setting a singular value threshold of 0.1.

[0028] Further, the step S40 specifically includes: decomposing the ship speed data into a longitude direction speed component and a latitude direction speed component based on the vector decomposition principle, and calculating the longitude direction speed component and the latitude direction speed component by trigonometric functions; constructing a rotation matrix considering the influence of the ship course data, the rotation matrix includes a cosine term of the course angle and a sine term of the course angle; multiplying the longitude direction speed component and the latitude direction speed component by the rotation matrix to obtain a ship speed vector in the geographic coordinate system; converting the ship speed vector in the geographic coordinate system to the ship coordinate system through coordinate transformation, and calibrating the coordinate transformation parameters by the least square method.

[0029] Further, the step S50 specifically includes: constructing a 2×2 environmental wind speed influence matrix; calculating a wind speed influence coefficient, the wind speed influence coefficient is related to the wind speed magnitude and the acting area, and obtaining the wind speed influence coefficient by fitting the wind tunnel test data, and the value range of the wind speed influence coefficient is 0.5 to 1.5; calculating a wind direction influence coefficient, the wind direction influence coefficient is related to the wind direction angle and the ship shape, and obtaining the wind direction influence coefficient through a numerical simulation method, and the value range of the wind direction influence coefficient is -1 to 1; combining the wind speed influence coefficient and the wind direction influence coefficient into the environmental wind speed influence matrix, and the diagonal elements of the environmental wind speed influence matrix represent the main influence, and the non-diagonal elements of the environmental wind speed influence matrix represent the cross influence.

[0030] Further, the step S60 specifically includes: converting the relative wind speed data and the relative wind direction data into a relative wind speed vector; multiplying the environmental wind speed influence matrix by the relative wind speed vector to obtain an environmental influence wind speed vector; multiplying the ship motion state matrix by the environmental influence wind speed vector to obtain a ship motion influence wind speed vector; synthesizing the ship speed vector and the ship motion influence wind speed vector to obtain an actual wind speed vector; filtering the actual wind speed vector by using a Kalman filter, where the process noise variance of the Kalman filter is 0.1 and the measurement noise variance of the Kalman filter is 0.05.

[0031] Further, the step S70 specifically includes: extracting the longitude direction component and the latitude direction component of the actual wind speed vector; calculating the angle between the actual wind speed vector and the true north direction through the arctangent function to obtain an initial true wind direction angle; constructing a 3×3 optimal path matrix, and optimizing the elements of the optimal path matrix through a dynamic programming method; iteratively optimizing the initial true wind direction angle based on the optimal path matrix, where the optimization objective function is the mean square error between the measured value and the theoretical value, and the optimization termination condition is that the error is less than 0.1 degree or the number of iterations exceeds 10 times; correcting the optimized wind direction angle through historical data statistical analysis; the step S80 specifically includes: calculating the error between the actual wind speed vector and the reference wind speed vector to obtain a wind speed error compensation amount; calculating the error between the true wind direction angle and the reference wind direction angle to obtain a wind direction error compensation amount; designing a wind speed compensator and a wind direction compensator, and calibrating the parameters of the wind speed compensator and the wind direction compensator by using the least squares method, where the value range of the coefficient of the wind speed compensator is from 0.1 to 0.5, and the value range of the coefficient of the wind direction compensator is from 0.2 to 0.8; applying the wind speed error compensation amount to the actual wind speed vector to obtain corrected wind speed data; applying the wind direction error compensation amount to the true wind direction angle to obtain corrected wind direction data; performing a limiting process on the wind speed error compensation amount and the wind direction error compensation amount according to a wind speed error compensation threshold of 0.3 m / s and a wind direction error compensation threshold of 3 degrees.

[0032] The second aspect of the present invention provides a computer-readable storage medium, where program instructions are stored in the computer-readable storage medium, and when the program instructions run on a computer, they are used to execute the above-mentioned ship wind measurement method based on single Beidou positioning.

[0033] The third aspect of the present invention provides a ship wind measurement system based on single Beidou positioning, which includes the above-mentioned computer-readable storage medium.

[0034] The ship wind measurement method based on single Beidou positioning proposed by the present invention realizes high-precision measurement of the ship's motion state and environmental wind conditions by comprehensively utilizing the Beidou positioning system and ultrasonic wind speed and direction sensors. Compared with the prior art, this method has the following main technical effects:

[0035] (1) Low measurement cost. The method of the present invention only requires a single Beidou positioning receiver and an ultrasonic wind speed and direction sensor, without the need to install multiple positioning receivers or targeted external sensor devices, greatly reducing the overall cost of the system;

[0036] (2) High measurement accuracy. The method of the present invention utilizes the high-precision position and motion state data provided by the Beidou positioning system. By establishing a coupling relationship model between the ship's motion state and environmental wind speed, it can accurately calculate the real environmental wind conditions, overcoming the measurement errors existing in the prior art based on relative wind speed and heading data. At the same time, optimization algorithms such as Kalman filtering and dynamic programming are adopted to further improve the measurement accuracy of wind speed and direction;

[0037] (3) Simple system structure. The method of the present invention does not require the installation of additional data acquisition equipment, only using a single Beidou positioning receiver and an ultrasonic wind speed and direction sensor. The system structure is simple and compact, and is easy to deploy and integrate on ships. Compared with the prior wind measurement methods that require multiple sensors and complex circuits, the system of the present invention has better reliability and maintainability.

[0038] In summary, the ship wind measurement method based on single Beidou positioning proposed by the present invention, by innovatively establishing a coupling relationship model between the ship's motion state and environmental wind speed and adopting advanced data processing algorithms, solves the problems of high cost, limited accuracy, and high system complexity existing in the prior wind measurement technology, and provides a more economical, efficient, and reliable technical solution for ship meteorological monitoring and navigation control. Description of the Drawings

[0039] Figure 1 It is a flowchart of a ship wind measurement method based on single Beidou positioning. Detailed Embodiments

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0041] As Figure 1 shown, it is a flowchart of a ship wind measurement method based on single Beidou positioning provided in the first aspect of the present invention. In this embodiment, the following steps are included: The specific implementation of each step will be described in detail below:

[0042] The specific implementation method of step S10 is based on the data acquisition principle of the high-precision positioning function of the Beidou positioning system and the ultrasonic wind speed and direction sensor. First, the Beidou positioning chip is used to receive the Beidou navigation satellite signal, and the real-time longitude and latitude data of the ship are obtained through differential correction processing. The sampling frequency of the real-time position data of the ship is 1 Hz. The collected data includes longitude data and latitude data, wherein the accuracy of the longitude data is better than 0.000001 degrees, and the accuracy of the latitude data is better than 0.000001 degrees; then the speed measurement module of the Beidou positioning system is used to calculate the ship's speed data, and the speed data is obtained through speed factor calibration correction. The sampling frequency of speed data is 1 Hz, and the accuracy is better than 0.1 meters per second. At the same time, the heading measurement module of the Beidou positioning system is used to calculate the ship heading data, and the heading data is obtained through heading deviation calibration. The sampling frequency of heading data is 1 Hz, and the accuracy is better than 0.1 degrees. Secondly, based on the principle of ultrasonic time difference method, a dual-axis ultrasonic wind speed and direction sensor is used to construct an anemometer. The relative wind speed and wind direction data are calculated by measuring the ultrasonic propagation time difference between different sensors. The sampling frequency of relative wind speed data is 10 Hz, and the accuracy is better than 0.1 meters per second. The sampling frequency of relative wind direction data is 10 Hz, and the accuracy is better than 1 degree.

[0043] The specific implementation method of step S20 is to first decompose the speed data of the ship into components, and based on the wavelet analysis theory, use the multi-scale wavelet decomposition method to process the speed data, select the Doppler 4th-order wavelet function as the decomposition basis function, and obtain the low-frequency component of the speed as the speed stability component through four-layer wavelet decomposition, and use the high-frequency component as the speed variation component, wherein the threshold of the speed data is selected as 0.5 meters per second, and when the speed change exceeds the threshold, it is determined as a variation component; then decompose the heading data of the ship into components, and use the Fourier transform method to first convert the heading data into the frequency domain, and design the cutoff A low-pass filter with a stop frequency of 0.1 Hz is used. The low-frequency component of the heading is obtained by filtering as the heading stable component, and the high-frequency component is taken as the heading changing component. The threshold of the heading data is selected as 1 degree. When the heading change exceeds the threshold, it is determined to be a changing component. Finally, the relative wind speed data is decomposed into components. Based on the empirical mode decomposition theory, the upper and lower envelopes are constructed by cubic spline interpolation. The mean value is calculated to obtain the stable component of the wind speed. The difference between the original data and the stable component is taken as the wind speed changing component. The threshold of the wind speed data is selected as 0.3 meters per second. When the wind speed change exceeds the threshold, it is determined to be a changing component.

[0044] The specific implementation of step S30 is as follows: First, based on the hydrodynamic theory, a motion correlation function between the stable components of the ship's speed, the stable components of the heading, and the wind speed variation components is established. This correlation function includes trigonometric terms and linear terms. The trigonometric terms reflect the periodic influence of the heading angle on the wind speed, and the linear terms represent the direct relationship between the ship's speed and the wind speed. The coefficients of each term are calculated by the least squares method. Then, a ship motion state matrix is constructed according to the motion correlation function. This matrix is a 3×3 matrix. The first row of the matrix represents the contribution of the ship's speed to the ship's motion, the second row represents the contribution of the heading to the ship's motion, and the third row represents the contribution of the wind speed to the ship's motion. Each element of the matrix is obtained through the discretization of the state space equation, and the discretization sampling period is 0.1 second. Finally, the singular value decomposition is performed on the ship motion state matrix. By setting the singular value threshold to 0.1, the singularity in the matrix is eliminated, and the stability of the matrix is improved.

[0045] The specific implementation of step S40 is as follows: First, based on the vector decomposition principle, the ship's speed is decomposed into velocity components in the longitude and latitude directions, and the component values in the two directions are calculated by trigonometric functions. Then, considering the influence of the ship's heading angle, a rotation matrix is constructed, which includes the cosine term and sine term of the heading angle. Then, the velocity components are multiplied by the rotation matrix to obtain the ship's velocity vector in the geographical coordinate system. Finally, the velocity vector is transformed from the geographical coordinate system to the ship coordinate system through coordinate transformation. The construction of the transformation matrix is based on the angle between the two coordinate systems, and the coordinate transformation parameters are calibrated by the least squares method.

[0046] The specific implementation of step S50 is as follows: First, based on the wind field analysis theory, considering the influence of wind speed and wind direction on the ship's motion, an environmental wind speed influence matrix is constructed. This matrix is a 2×2 matrix. Then, the wind speed influence coefficient is calculated. This coefficient is related to the wind speed magnitude and the acting area and is obtained by fitting the wind tunnel test data. The value range of the wind speed coefficient is from 0.5 to 1.5. Then, the wind direction influence coefficient is calculated. This coefficient is related to the wind direction angle and the ship's shape and is obtained by numerical simulation methods. The value range of the wind direction coefficient is from -1 to 1. Finally, the wind speed influence coefficient and the wind direction influence coefficient are combined into a matrix form. The diagonal elements of the matrix represent the main influence, and the non-diagonal elements represent the cross influence.

[0047] The specific implementation of step S60 is as follows: First, the relative wind speed data is converted into a vector form, including the wind speed magnitude and the wind direction angle information. Then, the environmental wind speed influence matrix is multiplied by the relative wind speed vector to obtain the wind speed vector considering the environmental influence. Then, the ship motion state matrix is multiplied by the wind speed vector considering the environmental influence to obtain the wind speed vector considering the ship motion influence. Then, the ship's velocity vector and the wind speed vector are synthesized to obtain the actual wind speed vector. Finally, the actual wind speed vector is filtered by a Kalman filter. The process noise variance of the filter is 0.1, and the measurement noise variance is 0.05.

[0048] The specific implementation of step S70 is as follows: First, extract the longitude-direction component and latitude-direction component of the actual wind speed vector; then calculate the angle between the wind speed vector and the true north direction through the arctangent function to obtain the initial true wind direction angle; then construct an optimal path matrix, which is a 3×3 matrix, and the matrix elements are optimized through the dynamic programming method; then iteratively optimize the initial wind direction angle based on the optimal path matrix, and the objective function of the optimization is the mean square error between the measured value and the theoretical value, and the termination condition of the iteration is that the error is less than 0.1 degree or the number of iterations exceeds 10 times; finally, correct the optimized wind direction angle, and the correction term is obtained through statistical analysis of historical data.

[0049] The specific implementation of step S80 is as follows: First, based on the feedback control theory, calculate the error between the actual wind speed vector and the reference wind speed vector to obtain the wind speed error compensation amount; then calculate the error between the true wind direction angle and the reference wind direction angle to obtain the wind direction error compensation amount; then design a wind speed compensator and a wind direction compensator, and the parameters of the compensator are calibrated by the least squares method. The value range of the wind speed compensation coefficient is from 0.1 to 0.5, and the value range of the wind direction compensation coefficient is from 0.2 to 0.8; then apply the wind speed error compensation amount to the actual wind speed vector to obtain the corrected wind speed data; finally, apply the wind direction error compensation amount to the true wind direction angle to obtain the corrected wind direction data; where the threshold of the wind speed error compensation amount is 0.3 m / s, and the threshold of the wind direction error compensation amount is 3 degrees. When the compensation amount exceeds the threshold, the threshold is used for amplitude limiting processing.

[0050] The above steps realize the ship wind measurement method based on single Beidou positioning through the comprehensive application of various algorithms and theories. The methods mainly include wavelet analysis, Fourier transform, empirical mode decomposition, least squares method, state space theory, vector decomposition, coordinate transformation, wind field analysis, Kalman filter, dynamic programming, and feedback control. There is a strict logical relationship and data transfer relationship between each step, forming a complete wind measurement system.

[0051] The following details the calculation processes or equations involved in the present invention:

[0052] Data acquisition in step S10:

[0053] The real-time position data vector of the ship is represented as follows: ; where is the longitude at time t; is the latitude at time t; is the ship speed at time t; is the course angle at time t.

[0054] The real-time data vector of the anemometer is represented as follows: ; where is the relative wind speed at time t; is the relative wind direction angle at time t.

[0055] Component decomposition in step S20:

[0056] Ship speed decomposition formula: ; where is the stable component of the ship speed; is the variable component of the ship speed; is the ship speed error term, with a range of -0.5 to 0.5 m / s.

[0057] Course decomposition formula: ; where is the stable component of the course; is the variable component of the course; is the course error term, with a range of -1° to 1°.

[0058] Relative wind speed decomposition formula: ; where is the stable component of the wind speed; is the variable component of the wind speed; is the wind speed error term, with a range of -0.3 to 0.3 m / s.

[0059] Motion correlation function in step S30:

[0060] ; where is the undetermined coefficient; is the correlation function error term, with a range of -0.1 to 0.1.

[0061] Ship motion state matrix: ; where is the ship motion state coefficient.

[0062] Velocity vector calculation in step S40:

[0063] Ship velocity vector in the geographic coordinate system: ; where is the velocity component in the longitude direction; is the velocity component in the latitude direction; is the ship course angle; is the velocity error vector.

[0064] Coordinate transformation matrix: ; where is the coordinate system transformation angle.

[0065] Environmental wind speed influence matrix in step S50:

[0066] ; where is the wind speed influence coefficient.

[0067] Calculation of the actual wind speed vector in step S60:

[0068] ; where is the actual wind speed vector; is the ship speed vector; is the composite error vector.

[0069] Calculation of the true wind direction angle in step S70:

[0070] ; where are the x and y components of the actual wind speed vector respectively; is the angle correction term.

[0071] Optimal path matrix: ; where is the path optimization coefficient.

[0072] Error compensation in step S80:

[0073] Wind speed error compensation amount: ; where is the wind speed compensation coefficient; is the reference wind speed vector; is the compensation error.

[0074] Wind direction error compensation amount: ; where is the wind direction compensation coefficient; is the reference wind direction angle; is the compensation error.

[0075] Corrected wind speed and wind direction: ; .

[0076] The construction principles and meanings of these equations are as follows:

[0077] 1. The position data vector adopts a four-dimensional vector form, which contains the complete state information of the ship's movement;

[0078] 2. The component decomposition adopts an additive model to separate the stable component and the variable component, which is convenient for subsequent analysis;

[0079] 3. The motion correlation function adopts a trigonometric function form, which considers the periodic characteristics of the course angle;

[0080] 4. The state matrix and the influence matrix adopt the 3×3 and 2×2 forms, which can express the multi-dimensional interaction relationship;

[0081] 5. The coordinate transformation matrix adopts the form of a rotation matrix, ensuring the orthogonality of the coordinate transformation;

[0082] 6. The influence of measurement error and system error is considered in the introduction of the error term;

[0083] 7. The optimal path matrix is used to optimize the angle calculation process and improve the accuracy;

[0084] 8. The calculation of the compensation amount adopts a proportional-error model, with good self-adaptability.

[0085] The derivation process of each equation is explained in detail below.

[0086] 1. Derivation of the real-time ship position data vector :

[0087] This vector is constructed based on the basic output data of the Beidou positioning system, and the steps are as follows: Step 1: Obtain NMEA-0183 protocol data from the Beidou positioning system; Step 2: Parse the sentences in the NMEA data to extract longitude, latitude, speed, and heading information; Step 3: Integrate the scatter data into vector form: ; where the position accuracy can reach the meter level, the speed accuracy is 0.1 m / s, and the heading accuracy is 0.1°.

[0088] 2. Derivation of the real-time anemometer data vector :

[0089] This vector is constructed based on the output data of the ultrasonic anemometer, and the steps are as follows: Step 1: Collect the RS485 output data of the anemometer; Step 2: Data preprocessing, including denoising and outlier removal; Preprocessing formula: ; ; where is the sampling interval, usually taken as 0.1 s; is the median filtering operation. Step 3: Construct the anemometer data vector: .

[0090] 3. Derivation of the speed decomposition formula:

[0091] Based on the time series analysis theory, the wavelet decomposition method is adopted, and the steps are as follows: Step 1: Perform wavelet transform on the original speed sequence: ; where is the wavelet basis function, and the db4 wavelet is selected. Step 2: Extract the low-frequency part as the stable component: ; where is the decomposition level, and the empirical value is 3 - 5. Step 3: Calculate the variable component: ; Finally, the decomposition formula is obtained: 。

[0092] 4. Derivation of the course decomposition formula:

[0093] Using the Fourier analysis method, the steps are as follows: Step 1: Perform Fourier transform: ; Step 2: Design a low-pass filter: ; where is the cut-off frequency, taking 0.1 Hz. Step 3: Extract the stable component: ; Step 4: Calculate the variable component: ; Finally, we get: 。

[0094] 5. Derivation of the relative wind speed decomposition formula:

[0095] Using the empirical mode decomposition (EMD) method, the steps are as follows: Step 1: Identify the extreme points; Step 2: Construct the envelope: and using cubic spline interpolation; Step 3: Calculate the mean: ; Step 4: Extract the intrinsic mode function (IMF), and the first IMF is used as the variable component ; Step 5: The remaining part is used as the stable component ; Finally, we get: 。

[0096] 6. Derivation of the motion correlation function:

[0097] Based on the fluid mechanics theory and considering the ship-wind field coupling effect, the steps are as follows: Step 1: Establish the wind pressure equation: ; where is the air density; is the wind pressure coefficient; is the reference area. Step 2: Consider the influence of ship motion: ; where is related to the ship speed and course. Step 3: Linearize to get: 。

[0098] 7. Construction of the ship motion state matrix:

[0099] Based on the state space theory, the steps are as follows: Step 1: Define the state variables: ; Step 2: Establish the state transition equation: ; Step 3: Discretize to get: ; where is the sampling period.

[0100] 8. Derivation of the velocity vector calculation:

[0101] Based on the vector decomposition principle, the steps are as follows: Step 1: Establish the velocity components in the geographical coordinate system: ; ; Step 2: Introduce the error term to obtain: .

[0102] 9. Construction of the environmental wind speed influence matrix:

[0103] Based on the wind field analysis theory, the steps are as follows: Step 1: Establish the wind speed influence model: ; ; ; ; In the formula, is the wind speed influence coefficient; is the wind direction correction angle. Step 2: Combine it into a matrix form: .

[0104] 10. Deduction of the actual wind speed vector calculation:

[0105] Based on the vector synthesis principle, the steps are as follows: Step 1: Calculate the relative wind speed vector: ; Step 2: Consider the environmental influence: ; Step 3: Consider the ship motion: .

[0106] 11. Deduction of the true wind direction angle calculation:

[0107] Adopt the optimal estimation theory, the steps are as follows: Step 1: Calculate the initial wind direction angle: ; Step 2: Construct the optimization objective function: ; Step 3: Iteratively optimize through the optimal path matrix: ; Finally, obtain: .

[0108] 12. Deduction of the error compensation amount:

[0109] Based on the feedback control theory, the steps are as follows: Step 1: Establish the error model: ; ; Step 2: Design the compensator: ; ; In the formula, is calibrated by the least squares method.

[0110] The effects of these equations are reflected in:

[0111] 1. The decomposition algorithm can effectively separate the stable and variable components, improving the accuracy of data processing;

[0112] 2. The motion correlation function considers the nonlinear influence, improving the adaptability of the model;

[0113] 3. Matrix operations improve the calculation efficiency and facilitate real-time processing;

[0114] 4. The error compensation mechanism improves the measurement accuracy, and the overall system accuracy can reach: wind speed error < 0.3 m / s, wind direction error < 3°.

[0115] The second aspect of the present invention provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when the program instructions run on a computer, they are used to execute the above-mentioned ship wind measurement method based on single Beidou positioning.

[0116] The third aspect of the present invention provides a ship wind measurement system based on single Beidou positioning, which includes the above-mentioned computer-readable storage medium.

[0117] The reason why the wind measurement method of the present invention can solve the problems existing in the prior art mainly benefits from the following innovative aspects:

[0118] 1. Using a single Beidou positioning system to obtain high-precision ship motion state data. Different from the existing methods based on multiple positioning receivers, the present invention only uses a single Beidou positioning receiver, and through technical means such as differential correction, high-precision motion state data such as the longitude, latitude, speed, and heading of the ship are obtained. This not only reduces the system cost but also avoids the influence of the relative position change between multiple positioning receivers on the measurement accuracy.

[0119] 2. Establishing a coupling relationship model between the ship motion state and the environmental wind speed. The method of the present invention analyzes the physical relationship between the ship speed, heading, and relative wind speed, and establishes a motion correlation function including trigonometric functions and linear terms. And this correlation function is transformed into a ship motion state matrix and an environmental wind speed influence matrix, and a state space model of their coupling is established. This lays a foundation for the subsequent calculation of the actual wind speed vector.

[0120] 3. Using an optimization algorithm to improve the measurement accuracy. When calculating the actual wind speed vector and the true wind direction angle, the method of the present invention respectively uses algorithms such as Kalman filtering and dynamic programming. Kalman filtering can effectively eliminate the random noise in the measurement process and improve the accuracy of the wind speed vector; dynamic programming can find the optimal solution of the true wind direction angle through multiple iterations of optimization, thereby improving the accuracy of wind direction measurement.

[0121] 4. Introducing an error compensation mechanism. In order to further eliminate various error sources in the system, the present invention adopts an error compensation method based on feedback control theory when finally calculating and correcting the wind speed and wind direction. By analyzing the deviation between the actual measurement value and the reference value and dynamically adjusting the compensation amount, the system error caused by measurement error, modeling error, etc. can be effectively suppressed.

[0122] A specific embodiment 1 of the present invention is provided below. The specific method of each step in this embodiment 1 is described in detail as follows:

[0123] The specific implementation method of step S10 is based on the data acquisition principle of the high-precision positioning function of the Beidou positioning system and the ultrasonic wind speed and direction sensor. First, the Beidou positioning chip is used to receive the Beidou navigation satellite signal, and the real-time longitude and latitude data of the ship are obtained through differential correction processing. The sampling frequency of the real-time position data of the ship is 1 Hz, and the collected data includes longitude data and latitude data, wherein the accuracy of the longitude data is better than 0.000001 degrees, and the accuracy of the latitude data is better than 0.000001 degrees. Then the speed measurement module of the Beidou positioning system is used to calculate the ship's speed data, and the speed data is obtained by speed factor calibration correction. The sampling frequency of the speed data is 1 Hz, and the accuracy is better than 0.1 meters per second. At the same time, the heading measurement module of the Beidou positioning system is used to calculate the ship's heading data, and the heading data is obtained through heading deviation calibration. The sampling frequency of the heading data is 1 Hz, and the accuracy is better than 0.1 degrees. Secondly, based on the principle of ultrasonic time difference method, a dual-axis ultrasonic wind speed and direction sensor is used to build an anemometer. The relative wind speed and wind direction data are calculated by measuring the ultrasonic propagation time difference between different sensors. The sampling frequency of relative wind speed data is 10 Hz, and the accuracy is better than 0.1 meters per second. The sampling frequency of relative wind direction data is 10 Hz, and the accuracy is better than 1 degree.

[0124] The specific implementation method of step S20 is to first decompose the speed data of the ship into components. Based on the wavelet analysis theory, a multi-scale wavelet decomposition method is used to process the speed data, and the Doppler 4th-order wavelet function is selected as the decomposition basis function. The low-frequency component of the speed is obtained as the speed stability component through four-layer wavelet decomposition, and the high-frequency component is used as the speed change component. The threshold of the speed data is selected as 0.5 meters per second, and when the speed changes beyond the threshold, it is determined as a change component. Then the heading data of the ship is decomposed into components, and the Fourier transform method is used to first convert the heading data into the frequency domain, and a low-pass filter with a cutoff frequency of 0.1 Hz is designed. The low-frequency component of the heading is obtained as the heading stability component through filtering, and the high-frequency component is used as the heading change component. The threshold of the heading data is selected as 1 degree, and when the heading changes beyond the threshold, it is determined as a change component. Finally, the relative wind speed data is decomposed into components. Based on the empirical mode decomposition theory, the upper and lower envelopes are constructed by cubic spline interpolation. The mean value is calculated to obtain the stable component of the wind speed, and the difference between the original data and the stable component is used as the variable component of the wind speed. The threshold of the wind speed data is selected as 0.3 meters per second. When the wind speed changes beyond the threshold, it is determined as a variable component.

[0125] The specific implementation of step S30 is to first establish a motion correlation function between the stable components of the ship's speed, the stable components of the heading, and the wind speed variation components based on the theory of fluid mechanics. This correlation function contains trigonometric terms and linear terms. Among them, the trigonometric terms reflect the periodic influence of the heading angle on the wind speed, and the linear terms represent the direct relationship between the ship's speed and the wind speed. Calculate the coefficients of each term by the least squares method. Then construct a ship motion state matrix according to the motion correlation function. This matrix is a 3×3 matrix. The first row of the matrix represents the contribution of the ship's speed to the ship's motion, the second row represents the contribution of the heading to the ship's motion, and the third row represents the contribution of the wind speed to the ship's motion. Each element of the matrix is obtained through the discretization of the state space equation, and the discretization sampling period is 0.1 second. Finally, perform singular value decomposition on the ship motion state matrix. By setting the singular value threshold to 0.1, eliminate the singularity in the matrix and improve the stability of the matrix.

[0126] The specific implementation of step S40 is to first decompose the ship's speed into speed components in the longitude and latitude directions based on the principle of vector decomposition, and calculate the component values in the two directions through trigonometric functions. Then consider the influence of the ship's heading angle, and construct a rotation matrix, which contains the cosine term and sine term of the heading angle. Then multiply the speed components by the rotation matrix to obtain the ship's speed vector in the geographic coordinate system. Finally, transform the speed vector from the geographic coordinate system to the ship coordinate system through coordinate transformation. The construction of the transformation matrix is based on the angle between the two coordinate systems, and the coordinate system transformation parameters are calibrated by the least squares method.

[0127] The specific implementation of step S50 is to first construct an environmental wind speed influence matrix based on the theory of wind field analysis, considering the influence of wind speed and wind direction on the ship's motion. This matrix is a 2×2 matrix. Then calculate the wind speed influence coefficient, which is related to the wind speed magnitude and the acting area, and is obtained by fitting the wind tunnel test data. The value range of the wind speed coefficient is 0.5 to 1.5. Then calculate the wind direction influence coefficient, which is related to the wind direction angle and the ship shape, and is obtained through numerical simulation methods. The value range of the wind direction coefficient is -1 to 1. Finally, combine the wind speed influence coefficient and the wind direction influence coefficient into a matrix form. The diagonal elements of the matrix represent the main influence, and the off-diagonal elements represent the cross influence.

[0128] The specific implementation of step S60 is to first convert the relative wind speed data into a vector form, including the wind speed magnitude and wind direction angle information. Then multiply the environmental wind speed influence matrix by the relative wind speed vector to obtain the wind speed vector considering the environmental influence. Then multiply the ship motion state matrix by the wind speed vector considering the environmental influence to obtain the wind speed vector considering the ship motion influence. Then synthesize the ship speed vector and the wind speed vector to obtain the actual wind speed vector. Finally, perform filtering processing on the actual wind speed vector through a Kalman filter. The process noise variance of the filter is 0.1, and the measurement noise variance is 0.05.

[0129] The specific implementation of step S70 is as follows: First, extract the longitude-direction component and latitude-direction component of the actual wind speed vector. Then, calculate the angle between the wind speed vector and the true north direction through the arctangent function to obtain the initial true wind direction angle. Next, construct an optimal path matrix, which is a 3×3 matrix, and the matrix elements are optimized through the dynamic programming method. Then, based on the optimal path matrix, iterate and optimize the initial wind direction angle. The objective function for optimization is the mean square error between the measured value and the theoretical value, and the termination condition for iteration is that the error is less than 0.1 degree or the number of iterations exceeds 10 times. Finally, correct the optimized wind direction angle, and the correction term is obtained through statistical analysis of historical data.

[0130] The specific implementation of step S80 is as follows: First, based on the feedback control theory, calculate the error between the actual wind speed vector and the reference wind speed vector to obtain the wind speed error compensation amount. Then, calculate the error between the true wind direction angle and the reference wind direction angle to obtain the wind direction error compensation amount. Next, design a wind speed compensator and a wind direction compensator. The parameters of the compensator are calibrated through the least squares method. The value range of the wind speed compensation coefficient is from 0.1 to 0.5, and the value range of the wind direction compensation coefficient is from 0.2 to 0.8. Then, apply the wind speed error compensation amount to the actual wind speed vector to obtain the corrected wind speed data. Finally, apply the wind direction error compensation amount to the true wind direction angle to obtain the corrected wind direction data. The threshold of the wind speed error compensation amount is 0.3 m / s, and the threshold of the wind direction error compensation amount is 3 degrees. When the compensation amount exceeds the threshold, the threshold is used for amplitude limiting processing.

[0131] The following provides a specific embodiment 2 of the present invention. The specific implementation of each step in this embodiment 2 is described in detail as follows:

[0132] The specific implementation of step S10 is based on the high-precision positioning function of the Beidou positioning system and the data acquisition principle of the ultrasonic wind speed and direction sensor. First, use a Beidou positioning chip to receive Beidou navigation satellite signals, and obtain the real-time longitude and latitude data of the ship through differential correction processing. The sampling frequency of the ship's real-time position data is 1 Hz, and the collected data includes longitude data and latitude data. The accuracy of the longitude data is better than degrees, and the accuracy of the latitude data is better than degrees. Among them, the ship's real-time position data vector is expressed as follows: ; where is the longitude at time, is the latitude at time, is the ship speed at time, is the course angle at time.

[0133] Then, the speed measurement module built in the Beidou positioning system is used to calculate the ship speed data, and the speed data is obtained through calibration and correction by the speed factor. The sampling frequency of the speed data is 1 Hz, and the accuracy is better than m / s. At the same time, the heading measurement module of the Beidou positioning system is used to calculate the ship heading data, and the heading data is obtained through heading deviation calibration. The sampling frequency of the heading data is 1 Hz, and the accuracy is better than degrees.

[0134] Secondly, based on the principle of ultrasonic time difference method, a biaxial ultrasonic wind speed and direction sensor is used to construct an anemometer. The relative wind speed and direction data are calculated by measuring the ultrasonic propagation time difference between different sensors. The sampling frequency of the relative wind speed data is 10 Hz, and the accuracy is better than m / s, and the sampling frequency of the relative wind direction data is 10 Hz, and the accuracy is better than degrees. The relative wind speed and direction data vectors are expressed as follows: ; where is the relative wind speed at time, and is the relative wind direction angle at time.

[0135] The purpose of the above data acquisition is to obtain the complete state information of the ship's motion and environmental wind conditions, providing basic data support for subsequent component decomposition, state modeling, and wind speed calculation.

[0136] The specific implementation of step S20 is to decompose the ship speed data, heading data, and relative wind speed data into components.

[0137] First, decompose the ship speed data. Based on the wavelet analysis theory, the multi-scale wavelet decomposition method is used to process the ship speed data. The Daubechies 4th order wavelet function is selected as the decomposition basis function, and the low-frequency component of the ship speed is obtained as the stable ship speed component through four-layer wavelet decomposition, and the high-frequency component is used as the variable ship speed component . The ship speed decomposition formula is as follows: ; where is the ship speed error term, and its value range is m / s. When the ship speed change exceeds m / s, it is determined as the variable component.

[0138] Then, decompose the heading data. Using the Fourier transform method, first convert the heading data to the frequency domain, design a low-pass filter with a cut-off frequency of Hz, and obtain the low-frequency component of the heading as the stable heading component through filtering, and use the high-frequency component as the variable heading component . The heading decomposition formula is as follows: ; where is the course error term, and its value range is . When the course change exceeds , it is determined as the variable component.

[0139] Finally, decompose the relative wind speed data into components. Based on the empirical mode decomposition theory, construct the upper and lower envelope lines through cubic spline interpolation, and calculate the mean value to obtain the stable component of the wind speed. Take the difference between the original data and the stable component as the variable component of the wind speed. The relative wind speed decomposition formula is as follows: ; where is the wind speed error term, and its value range is m / s. When the wind speed change exceeds m / s, it is determined as the variable component.

[0140] The purpose of the above component decomposition is to separate the stable component and the variable component, providing good input data for subsequent state modeling and wind speed calculation.

[0141] The specific implementation of step S30 is to establish the motion correlation function between the stable component of the ship's speed, the stable component of the course, and the variable component of the wind speed, and construct the ship motion state matrix according to this correlation function.

[0142] First, based on the fluid mechanics theory, establish the following motion correlation function: ; where , and are undetermined coefficients, is the correlation function error term, and its value range is . This correlation function contains trigonometric terms and linear terms. The trigonometric terms reflect the periodic influence of the course angle on the wind speed, and the linear terms represent the direct relationship between the ship speed and the wind speed. Calculate each coefficient by the least squares method.

[0143] Then, construct the ship motion state matrix according to the above motion correlation function: ; where is the ship motion state coefficient. The first row of this matrix represents the contribution of the ship speed to the ship motion, the second row represents the contribution of the course to the ship motion, and the third row represents the contribution of the wind speed to the ship motion. The matrix elements are obtained through the discretization process of the state space equation, and the discretization sampling period is seconds.

[0144] Finally, perform singular value decomposition on the ship motion state matrix. By setting the singular value threshold to , eliminate the singularity in the matrix and improve the stability of the matrix.

[0145] The purpose of the above steps is to establish a coupling relationship model between ship motion and environmental wind speed, providing a basis for the subsequent calculation of the ship velocity vector and the actual wind speed vector.

[0146] The specific implementation of step S40 is to calculate the ship velocity vector according to the ship motion state matrix and transform it from the geographic coordinate system to the ship coordinate system.

[0147] First, based on the principle of vector decomposition, the ship speed is decomposed into velocity components in the longitude and latitude directions, and the component values in the two directions are calculated through the following trigonometric functions: ; where is the velocity component in the longitude direction, is the velocity component in the latitude direction, is the ship's course angle, is the velocity error vector.

[0148] Then, considering the influence of the ship's course angle, the following rotation matrix is constructed: ; where is the coordinate system transformation angle.

[0149] Next, multiply the velocity components by the rotation matrix to obtain the ship velocity vector in the geographic coordinate system .

[0150] Finally, through coordinate transformation, the velocity vector is transformed from the geographic coordinate system to the ship coordinate system. The transformation matrix is constructed based on the angle between the two coordinate systems, and the coordinate system transformation parameters are calibrated by the least squares method.

[0151] The purpose of the above steps is to convert the ship motion state data into the representation of the velocity vector in the ship coordinate system, providing a basis for the subsequent synthesis with the environmental wind speed.

[0152] The specific implementation of step S50 is to construct an environmental wind speed influence matrix, representing the influence of wind speed and wind direction on ship motion.

[0153] First, based on the wind field analysis theory, considering the influence of wind speed and wind direction on ship motion, the following environmental wind speed influence matrix is constructed : ; where is the wind speed influence coefficient.

[0154] Then, calculate the wind speed influence coefficients and . The wind speed influence coefficients are related to the wind speed magnitude and the acting area, and are obtained by fitting the wind tunnel test data, with a value range of .

[0155] Next, calculate the wind direction influence coefficient and The wind direction influence coefficient is related to the wind direction angle and the ship shape, obtained through numerical simulation methods, and the value range is .

[0156] Finally, the wind speed influence coefficient and the wind direction influence coefficient are combined into a matrix form. The diagonal elements of the matrix represent the main influence, and the non-diagonal elements represent the cross influence.

[0157] The purpose of the above steps is to establish a model for the influence of environmental wind speed on ship motion, providing a basis for the subsequent calculation of the actual wind speed vector.

[0158] The specific implementation of step S60 is to calculate the actual wind speed vector according to the ship motion state matrix and the environmental wind speed influence matrix.

[0159] First, convert the relative wind speed data into a vector form, including the wind speed magnitude and the wind direction angle information.

[0160] Then, multiply the environmental wind speed influence matrix by the relative wind speed vector to obtain the wind speed vector considering the environmental influence .

[0161] Next, multiply the ship motion state matrix by the wind speed vector considering the environmental influence to obtain the wind speed vector considering the ship motion influence .

[0162] Then, synthesize the ship speed vector with the above wind speed vector to obtain the actual wind speed vector :[[]] ; where is the synthesis error vector.

[0163] Finally, filter the actual wind speed vector through a Kalman filter. The process noise variance of the filter is , and the measurement noise variance is .

[0164] The purpose of the above steps is to comprehensively consider the influence of ship motion and environmental wind speed, calculate the actual wind speed vector, and provide a basis for the subsequent calculation of the true wind direction angle.

[0165] The specific implementation of step S70 is to calculate the true wind direction angle according to the actual wind speed vector and optimize it through the optimal path matrix.

[0166] First, extract the longitude direction component and the latitude direction component of the actual wind speed vector .

[0167] Then, calculate the angle between the wind speed vector and the true north direction through the arctangent function , and obtain the initial true wind direction angle: ; where is the angle correction term.

[0168] Next, construct the following optimal path matrix : ; where is the path optimization coefficient.

[0169] Then, based on the optimal path matrix, iteratively optimize the initial wind direction angle . The objective function of the optimization is the mean square error between the measured value and the theoretical value, and the termination condition of the iteration is that the error is less than or the number of iterations exceeds times.

[0170] Finally, correct the optimized wind direction angle, and the correction term is obtained through statistical analysis of historical data.

[0171] The purpose of the above steps is to improve the measurement accuracy of the true wind direction angle through the optimization algorithm, providing a basis for subsequent error compensation of wind speed and wind direction.

[0172] The specific implementation of step S80 is to calculate the wind speed error compensation amount and the wind direction error compensation amount according to the ship motion state matrix and the environmental wind speed influence matrix, and apply them to the actual wind speed vector and the true wind direction angle to obtain the corrected wind speed data and wind direction data.

[0173] First, based on the feedback control theory, calculate the error between the actual wind speed vector and the reference wind speed vector to obtain the wind speed error compensation amount : ; where is the wind speed compensation coefficient, and its value range is , is the compensation error.

[0174] Then, calculate the error between the true wind direction angle and the reference wind direction angle to obtain the wind direction error compensation amount : ; where is the wind direction compensation coefficient, and its value range is , is the compensation error.

[0175] Next, apply the wind speed error compensation amount to the actual wind speed vector to obtain the corrected wind speed data : ;

[0176] Finally, apply the wind direction error compensation amount to the true wind direction angle , and obtain the corrected wind direction data : ;

[0177] Among them, when the wind speed error compensation amount exceeds m / s, or the wind direction error compensation amount exceeds , a threshold is used for clipping processing.

[0178] The purpose of the above steps is to eliminate systematic errors and random errors in the measurement process through the error compensation method, improve the measurement accuracy of wind speed and wind direction, and meet the requirements of subsequent ship navigation control and meteorological monitoring.

[0179] To better understand and implement the present invention, the following provides Example 3 of a specific application scenario of the present invention:

[0180] An offshore engineering enterprise deployed a fleet of operating vessels near an oilfield in the South China Sea, responsible for tasks such as offshore drilling and platform construction. To ensure operation safety and improve operation efficiency, the enterprise urgently needs a wind measurement system that can accurately monitor the environmental wind conditions in the operation area.

[0181] After investigation by the enterprise's technical department, it was found that existing ship wind measurement technologies have problems such as high cost, limited accuracy, and high system complexity. Therefore, they decided to adopt the ship wind measurement method based on single Beidou positioning proposed by the present invention and design and deploy a wind measurement monitoring system. The specific implementation process is as follows:

[0182] First, the enterprise selected an operating ship as the carrier of the wind measurement system. At the top of the mast of the ship, a set of wind measurement equipment consisting of a Beidou positioning receiver and an ultrasonic wind speed and direction sensor was installed. The Beidou positioning receiver model is the BDS4000 series, which has a positioning accuracy of millimeter level and an update frequency of 1 Hz, and can provide high-precision real-time position, speed, and heading data of the ship. The ultrasonic wind speed and direction sensor adopts a biaxial design and can collect relative wind speed and relative wind direction data at a frequency of 10 Hz by measuring the time difference of ultrasonic wave propagation between different sensors. The measurement accuracies are better than 0.1 m / s and 1 degree respectively.

[0183] After the installation of the wind measurement equipment, the enterprise's technical personnel debugged and calibrated the entire wind measurement system. First, using professional wind tunnel experimental equipment, the performance of the ultrasonic wind speed and direction sensor was calibrated and error corrected. Through comparative tests with standard wind speed and direction instruments, the measurement error range of the sensor under different wind speed and direction conditions was determined. At the same time, the position, speed, and heading data of the Beidou positioning receiver were also calibrated and corrected to eliminate the influence of installation errors and environmental factors.

[0184] Next, the technical personnel wrote relevant data processing algorithm programs according to the specific implementation mode of the present invention. It mainly includes:

[0185] 1. Component decomposition of the real-time position data, speed data, and heading data of the ship to extract the stable component and the variable component. Taking the speed data as an example, through the wavelet analysis method, the speed data is decomposed into a low-frequency stable component and a high-frequency variable component , where the threshold of the speed data is selected as 0.5 m / s.

[0186] 2. Based on the theory of fluid mechanics, establish the motion correlation function between the stable component of the ship's speed, the stable component of the heading, and the variable component of the relative wind speed , and calculate the coefficients , , using the least squares method. The error term of the correlation function is within the range of .

[0187] 3. According to the above motion correlation function, construct a 3×3 ship motion state matrix , and the matrix element is obtained through the discretization of the state space equation, and the discretization sampling period is 0.1 s. Perform singular value decomposition on this matrix, set the singular value threshold to 0.1, and eliminate the singularity in the matrix.

[0188] 4. Establish a 2×2 environmental wind speed influence matrix , and the value range of the wind speed influence coefficient is 0.5~1.5, and the value range of the wind direction influence coefficient is -1~1.

[0189] 5. According to the ship motion state matrix and the environmental wind speed influence matrix, use the Kalman filter algorithm to calculate the actual wind speed vector , the process noise variance is 0.1, and the measurement noise variance is 0.05.

[0190] 6. Use the dynamic programming algorithm to optimize the true wind direction angle , and construct a 3×3 optimal path matrix , an optimal solution is obtained through iterative calculation, and the iteration termination condition is that the angular error is less than 0.1 degree or the number of iterations exceeds 10 times.

[0191] 7. Design a wind speed and wind direction error compensator based on feedback control, and the wind speed compensation coefficient is in the range of 0.1 - 0.5, and the wind direction compensation coefficient is in the range of 0.2 - 0.8. When the compensation amount exceeds the preset threshold (wind speed 0.3 m / s, wind direction 3 degrees), threshold limiting processing is adopted.

[0192] After a period of test operation, the wind measurement and monitoring system has shown excellent performance in the operation area. Taking the historical data from June 1 to June 30, 2022 as an example, the specific measurement results are shown in the following table:

[0193] Table 1 Monitoring Data of Wind Measurement and Monitoring System

[0194]

[0195] From the data in the above table, it can be seen that the wind measurement and monitoring system can accurately record key parameters such as the real-time position, speed, heading of the ship, and the actual wind speed and wind direction of the environment. By comparing the data of relative wind speed and relative wind direction with the actual wind speed and actual wind direction, it can be found that there are obvious differences between the two, which is mainly due to the influence of the ship's motion state on the wind speed and wind direction.

[0196] In order to further analyze this influence relationship, the technical personnel extracted all the measurement data from June 1 to June 30, 2022, and carried out detailed analysis and calculation according to steps S20 - S60 of the present invention. First, the speed, heading, and relative wind speed data of the ship were respectively decomposed into components, and the stable component and the variable component were extracted. Taking the speed as an example, after wavelet analysis, the stable component and the variable component were obtained, where the threshold of the speed data is 0.5 m / s. Similarly, the stable component and the variable component of the heading data, as well as the stable component and the variable component of the relative wind speed were also obtained successively.

[0197] Then, based on the theory of fluid mechanics, a motion correlation function between the stable component of the ship's speed, the stable component of the heading, and the variable component of the relative wind speed was established. Through least squares fitting, the coefficients were calculated as , , , and the error term of the correlation function is within within the range.

[0198] Next, according to the above motion correlation function, a 3×3 ship motion state matrix was constructed , and the matrix elements , , , , , , , , . After performing singular value decomposition on this matrix, a singular value threshold of 0.08 was set, and the singularity in the matrix was successfully eliminated.

[0199] Meanwhile, based on the wind field analysis theory, a 2×2 environmental wind speed influence matrix was constructed , where the wind speed influence coefficients , , and the wind direction influence coefficients , .

[0200] With the above ship motion state matrix and environmental wind speed influence matrix, the actual wind speed vector can be calculated according to step S60. First, the relative wind speed vector is converted into the form, and then multiplied by the two matrices to obtain the wind speed vector considering ship motion and environmental influence. Finally, the ship speed vector is synthesized with this wind speed vector, and after Kalman filtering (process noise 0.1, measurement noise 0.05), the final actual wind speed vector is obtained.

[0201] Similarly, according to step S70, the true wind direction angle can be calculated. First, the x and y components of the actual wind speed vector are extracted, and the initial wind direction angle is obtained through the arctangent function. Then, a 3×3 optimal path matrix is constructed, where the path optimization coefficients , , , , , , , , . Based on this matrix, the initial wind direction angle is iteratively optimized, and the iteration terminates when the angle error is less than 0.08 degrees or after 10 iterations, and the corrected true wind direction angle is obtained.

[0202] Finally, according to step S80, a wind speed and wind direction error compensator based on feedback control was designed. The wind speed compensation coefficient ,Wind direction compensation coefficient Apply the wind speed error compensation amount and the wind direction error compensation amount to the actual wind speed vector and the true wind direction angle respectively to obtain the corrected wind speed data and wind direction data When the compensation amount exceeds the preset threshold values (wind speed 0.25 m / s, wind direction 2.5 degrees), limit the amplitude using the threshold values

[0203] By performing the above calculations and analyses on all the measurement data from June 1st to June 30th, 2022, the technical personnel obtained the overall performance data of this wind measurement monitoring system. Comparing it with the existing traditional wind measurement method based on relative wind speed and heading data, the results are shown in the following table:

[0204] Table 2 Comparison of performance indicators between the traditional wind measurement method and the method of the present invention

[0205]

[0206] As can be seen from the comparison in the above table, compared with the traditional wind measurement method based on relative wind speed and heading data, the ship wind measurement method based on single Beidou positioning proposed by the present invention has obvious advantages in terms of measurement cost, measurement accuracy, system complexity, and reliability. This is mainly due to the innovations of the present invention in the following aspects:

[0207] 1. Make full use of the high-precision ship motion state data provided by the Beidou positioning system, avoiding the problems of installing multiple sensors and complex data calibration in the traditional method, thus greatly reducing the system cost and complexity

[0208] 2. By establishing a coupling relationship model between the ship motion state and the environmental wind speed and adopting advanced data processing algorithms, effectively compensate for the influence of ship motion on wind speed and wind direction measurement, significantly improving the overall measurement accuracy

[0209] 3. The system structure is simple and compact, without the need to install additional sensor devices, with higher overall reliability, and is convenient for deployment and maintenance on ships

[0210] It should be noted that the detailed explanations of the variables involved in the present invention are shown in Table 3 below

[0211] Table 3 Variable explanation table

[0212]

[0213] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention

Claims

1. A ship wind measurement method based on single Beidou positioning, characterized in that, Including: Collecting real-time position data of the ship through the Beidou positioning system and collecting real-time data of the anemometer; Performing component decomposition operations on the real-time position data of the ship to obtain the speed stable component and speed variation component of the ship speed data, and obtaining the heading stable component and heading variation component of the ship heading data; performing component decomposition operations on the real-time data of the anemometer to obtain the wind speed stable component and wind speed variation component of the relative wind speed data; Establishing a motion correlation function for the speed stable component, heading stable component and wind speed variation component, constructing a ship motion state matrix; calculating the ship velocity vector; constructing an environmental wind speed influence matrix; Calculating the actual wind speed vector; Calculating the true wind direction angle; Calculating the wind speed error compensation amount and the wind direction error compensation amount to obtain the corrected wind speed data and the corrected wind direction data.

2. The ship wind measurement method based on single Beidou positioning according to claim 1, wherein The step of collecting real-time position data of the ship through the Beidou positioning system is specifically to use a Beidou positioning chip to receive Beidou navigation satellite signals, obtain real-time longitude and latitude data of the ship through differential correction processing, and the sampling frequency of the real-time longitude and latitude data of the ship is 1 Hz; using the Beidou positioning system speed measurement module to calculate the ship speed data; using the Beidou positioning system heading measurement module to calculate the ship heading data; using a biaxial ultrasonic wind speed and direction sensor to construct an anemometer, and calculating the relative wind speed data and relative wind direction data by measuring the ultrasonic propagation time difference between different sensors.

3. The ship wind measurement method based on single Beidou positioning according to claim 2, characterized in that, The step of performing component decomposition operations on the real-time position data of the ship is specifically to use the multi-scale wavelet decomposition method to process the ship speed data, select the Daubechies 4th order wavelet function as the decomposition basis function, and obtain the low-frequency component of the ship speed data as the speed stable component through 4-layer wavelet decomposition, and use the high-frequency component of the ship speed data as the speed variation component; using the Fourier transform method to process the ship heading data; Using the empirical mode decomposition method to process the relative wind speed data.

4. The ship wind measurement method based on single Beidou positioning according to claim 3, wherein, The step of establishing a motion correlation function for the speed stable component, heading stable component and wind speed variation component is specifically to establish a motion correlation function for the speed stable component, heading stable component and wind speed variation component based on the fluid mechanics theory. The motion correlation function includes trigonometric terms and linear terms, and calculates the coefficients of the trigonometric terms and the linear terms by the least squares method; constructing a ship motion state matrix according to the motion correlation function, and the ship motion state matrix is a 3×3 matrix; performing singular value decomposition on the ship motion state matrix.

5. The ship wind measurement method based on single Beidou positioning according to claim 4, wherein, The step of calculating the ship velocity vector is specifically to decompose the ship speed data into the longitude direction speed component and the latitude direction speed component based on the vector decomposition principle, and calculate the longitude direction speed component and the latitude direction speed component through trigonometric functions; considering the influence of the ship heading data to construct a rotation matrix, and the rotation matrix includes the cosine term of the heading angle and the sine term of the heading angle; Multiplying the longitude direction speed component and the latitude direction speed component by the rotation matrix to obtain the ship velocity vector in the geographic coordinate system.

6. The ship wind measurement method based on single Beidou positioning according to claim 5, wherein, The step of constructing the environmental wind speed influence matrix is specifically to construct a 2×2 environmental wind speed influence matrix; Calculate the wind speed influence coefficient, which is related to the wind speed magnitude and the acting area, and obtain the wind speed influence coefficient by fitting the wind tunnel test data. Calculate the wind direction influence coefficient, which is related to the wind direction angle and the ship shape, and obtain the wind direction influence coefficient through numerical simulation methods. Combine the wind speed influence coefficient and the wind direction influence coefficient to form the environmental wind speed influence matrix.

7. The ship wind measurement method based on single Beidou positioning according to claim 6, characterized in that The steps of calculating the actual wind speed vector are specifically as follows: convert the relative wind speed data and the relative wind direction data into a relative wind speed vector; multiply the environmental wind speed influence matrix by the relative wind speed vector to obtain the environmental influence wind speed vector; multiply the ship motion state matrix by the environmental influence wind speed vector to obtain the ship motion influence wind speed vector; synthesize the ship speed vector and the ship motion influence wind speed vector to obtain the actual wind speed vector; and perform filtering processing on the actual wind speed vector using a Kalman filter.

8. The ship wind measurement method based on single Beidou positioning according to claim 7, characterized in that, The steps of calculating the true wind direction angle are specifically as follows: extract the longitude direction component and the latitude direction component of the actual wind speed vector; calculate the angle between the actual wind speed vector and the true north direction through the arctangent function to obtain the initial true wind direction angle; construct a 3×3 optimal path matrix, and optimize the elements of the optimal path matrix through dynamic programming methods. Iteratively optimize the initial true wind direction angle based on the optimal path matrix; correct the optimized wind direction angle through statistical analysis of historical data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions that, when running on a computer, are used to execute the ship wind measurement method based on single Beidou positioning according to any one of claims 1-8.

10. A ship wind measurement system based on single Beidou positioning, characterized in that, Including the computer-readable storage medium according to claim 9.

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