A method and system for monitoring wind speed and direction on a marine buoy

Abstract

The application belongs to the technical field of wind speed and direction monitoring, and particularly relates to a marine buoy wind speed and direction monitoring method and system, comprising the following steps: S1. Setting a sampling period, and acquiring data information by using a sensor; S2. Preprocessing the data to obtain processed wind speed and direction data; S3. Calculating the confidence of each sensor and checking fault data; S4. Data weighted fusion, and outputting final wind direction and final wind speed; The method has the advantages that in combination with a real-time confidence evaluation mechanism, when any single sensor is invalid due to salt spray corrosion, mechanical failure or electromagnetic interference, the continuous output of wind speed and direction data can still be ensured, and the system availability is improved; IMU-environment joint compensation is used for motion noise suppression; an adaptive decision engine and a self-diagnosis engine are constructed, the weight of a distorted sensor is reduced in an extreme working condition, and the measurement accuracy is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wind speed and direction monitoring, and particularly relates to a marine buoy wind speed and direction monitoring method and system. BACKGROUND

[0002] As an unattended automatic observation platform, the marine buoy plays an irreplaceable role in marine weather monitoring, disaster warning and wind energy development. One of its core functions is the accurate measurement of wind speed and direction, which not only relates to the accuracy of weather forecasting, but also directly affects the safety of offshore operations, the effectiveness of typhoon warning, and the selection and operation efficiency of offshore wind farms. The buoy wind measurement technology has experienced an evolution from mechanical to electronic and then to remote sensing. In the early stage, it mainly relied on mechanical anemometers, which converted wind speed through the rotation speed of wind cups or propellers, but had problems such as mechanical wear and inertia lag. The six-degree-of-freedom motion (roll, pitch, yaw, heave, sway, surge) of the buoy under the action of waves is the core problem affecting the accuracy of wind measurement. Especially in extreme sea conditions (such as during a typhoon), the violent motion of the buoy will cause a high deviation of up to 30% in the transverse wind speed measurement.

[0003] A single sensor cannot distinguish between real wind speed fluctuations and noise caused by the buoy's own shaking, especially under extreme conditions such as typhoons and high sea states, where the error is significant. Existing data fusion methods mostly use simple weighted averaging, without fully considering the dynamic correlation between sensor real-time confidence and environmental interference. SUMMARY

[0004] The application proposes a marine buoy wind speed and direction monitoring system and method based on multi-sensor data fusion and adaptive decision-making, which solves the above problems through multi-sensor redundancy detection, data fusion and motion compensation. The technical solution is as follows:

[0005] A marine buoy wind speed and direction monitoring method, comprising the following steps:

[0006] S1. Set the sampling period and use the sensor to obtain data information;

[0007] S2. Preprocess the data to obtain processed wind speed and direction data;

[0008] S3. Calculate the confidence of each sensor and check for faulty data;

[0009] S4. Data weighted fusion, output final wind direction and final wind speed.

[0010] Preferably, the data preprocessing step is as follows:

[0011] S21. Sensor data input and time synchronization;

[0012] S22. Adopting Kalman filter based on error state to fuse IMU and Beidou satellite data, estimating the buoy attitude, position and velocity, and estimating and correcting the sensor error of IMU at the same time, Beidou positioning data is used for error covariance calculation, combining prediction and observation information, completing state update in Kalman filter update module, and outputting the attitude quaternion of the carrier;

[0013] S23. Using the calculated attitude quaternion, converting the wind speed data measured by the wind speed and direction sensor in the carrier coordinate system to the geographic coordinate system to obtain the time-domain wind speed signal after preliminary motion compensation;

[0014] S24. Frequency domain filtering processing: after windowing, FFT transformation, band-stop filtering based on wave energy dynamic setting, inverse FFT, overlap addition and zero phase processing, finally outputting the motion-compensated wind speed and direction.

[0015] Preferably, the window function in the frequency domain filtering processing is:

[0016] ;

[0017] The Fourier transform FFT is:

[0018] ;

[0019] is the complex result of the nth frequency component, n is the index of the time domain sampling point, ranging from 0 to 1023; k

[0020] Calculate whether the wave energy exceeds the preset threshold:

[0021] ;

[0022] ;

[0023] N is the number of FFT points, is the frequency resolution, is the sampling rate, is the nth time domain sampling value after windowing.

[0024] Preferably, the frequency domain wind speed vector obtained by FFT transformation is multiplied by an 8th order band-stop filter to filter out the frequency components in the stop band;

[0025] Inverse FFT transformation and zero phase processing: the filtered frequency domain signal is converted back to the time domain by inverse FFT transformation, and zero phase processing is realized by filtering twice in the forward and reverse directions, and the same 8th order Butterworth band-stop filter is used for the filter;

[0026] Overlap addition reconstruction: ​

[0027] ;

[0028] Zero-phase processing:

[0029] ;

[0030] processed signal for the mth frame, original input signal, H{} filtering operation, reverse() signal inversion operation, frame shift time, (t ) the position of the segment signal moving on the time axis;

[0031] obtain wind speed and direction data after motion compensation and frequency domain filtering;

[0032] ;

[0033] true heading angle.

[0034] Preferably, including:

[0035] S31. Collect raw data from each sensor, use the time synchronization vector generated in step S2 to generate an anomaly detection matrix;

[0036] The input data are: ultrasonic anemometer data, mechanical anemometer data, barometric gradient sensor data, IMU attitude data;

[0037] S32. The confidence evaluation model is:

[0038] ;

[0039] wherein:

[0040] : the output of the variance detection term, 1 when there is no fault, 0 when there is a fault;

[0041] : the output of the gradient detection term; defined as , is the total number of samples;

[0042] : attitude overrun term; 1 when there is no overrun, 0 when there is an overrun;

[0043] : sensor reliability factor;

[0044] is a weight coefficient, satisfying ; ​

[0045] S33. Fault isolation: based on the confidence evaluation results, the detected fault sensor data is excluded and does not participate in subsequent fusion calculation.

[0046] Preferably, it comprises:

[0047] S41. Data preprocessing and standardization stage: the motion compensated data is first subjected to data standardization processing;

[0048] S42. The normalized data enters the weight calculation model;

[0049] The calculation model is as follows:

[0050] ;

[0051] Wherein:

[0052] : sensor health confidence ;

[0053] : signal-to-noise ratio factor of sensor r;

[0054] : environmental fitness factor:

[0055] ;

[0056] : sensor reliability factor;

[0057] M: the number of effective sensors currently participating in fusion;

[0058] S43. Fusion decision: based on these weights, data fusion decision is made, and two different types of measurement data, wind speed and wind direction, are processed respectively;

[0059] S44. Data fusion:

[0060] Wind speed fusion: wind speed is a scalar and adopts a weighted average algorithm;

[0061] Wind direction fusion: wind direction is an angle and has periodicity, which is converted into a vector for synthesis;

[0062] S45. Confidence rating and feedback: the reliability of the fusion results is evaluated, and the rating results are fed back to the weight calculation core to form a closed-loop feedback mechanism, which is used to evaluate the priority of data transmission or trigger system maintenance alarms;

[0063] S46. After weighted fusion processing, two final results are output:

[0064] Final wind speed: wind speed measurement value after wind speed fusion ;

[0065] Final wind direction: the wind direction measurement after wind direction fusion .

[0066] Preferably, the wind direction fusion step S44 is as follows:

[0067] a. Convert the wind direction angle to unit vector:

[0068] For wind direction angle , the unit vector in the northeast celestial coordinate system is:

[0069] ;

[0070] The X component represents the east direction;

[0071] The Y component represents the north direction;

[0072] b. Perform weighted vector synthesis:

[0073] ;

[0074] is the eastward weighted wind direction vector sum, is the northward weighted wind direction vector sum;

[0075] c. Calculate the final wind direction from the synthesized vector:

[0076] ;

[0077] Using the atan2 function can correctly handle all quadrants to get the final wind direction between 0~360 degrees, and the modulus of the synthesized vector can reflect the consistency of the wind direction data. The shorter the modulus, the greater the difference between the wind directions of the sensors.

[0078] Preferably, the final confidence S45 is defined as the weighted sum of all participating fusion sensors:

[0079] ;

[0080] Where the closer to 1, the higher the overall confidence of the final result determined by high-confidence sensors.

[0081] A marine buoy wind speed and direction monitoring system, comprising an information acquisition unit, a motion compensation unit, a self-diagnosis processing unit, a weighted fusion unit and an adaptive decision unit;

[0082] Information acquisition unit: collect the raw data of wind speed and direction sensor;

[0083] Motion compensation unit: time synchronization of multi-source sensor data, unify the data of IMU, Beidou positioning and wind direction and speed sensor to 50Hz time sequence, fuse IMU and Beidou positioning data through Kalman filter, wherein IMU is used for state prediction, Beidou positioning is used for state update, and error suppression is carried out in the update process; The Kalman filter outputs the attitude quaternion of the buoy, which is used to convert the wind speed vector measured by the wind direction and speed sensor from the buoy coordinate system to the earth coordinate system; The converted wind speed signal is further processed in the frequency domain, including windowing, FFT transformation, band-stop filtering based on wave energy dynamic setting of stop band, inverse FFT, overlap addition and zero phase processing, and finally the motion compensated wind speed and direction are output;

[0084] Self-diagnosis processing unit: identify typical fault modes through fault feature recognition, and quantify the health status of each sensor to provide decision basis for subsequent data fusion;

[0085] Weighted fusion unit: multiple wind speed and direction data streams and sensor confidence output by the self-diagnosis processing unit are output through an adaptive weighting algorithm to output a set of optimal and final wind speed and direction values;

[0086] Adaptive decision unit: evaluate the credibility of the fusion result and feed back the rating result to the adaptive weighting algorithm to form a closed-loop feedback mechanism for evaluating the priority of data transmission or triggering system maintenance alarm.

[0087] Preferably, the information acquisition unit: the raw data includes ultrasonic anemometer, mechanical anemometer, air pressure gradient value; IMU 9-axis attitude sensor value is collected, temperature and humidity value is collected, Beidou satellite positioning data is collected;

[0088] Self-diagnosis processing unit:

[0089] Data input stage: collect raw data from various sensors, generate an abnormality detection matrix using the time synchronization vector generated by the motion compensation unit; The input data are: ultrasonic anemometer data, mechanical anemometer data, air pressure gradient sensor data, IMU attitude data;

[0090] Fault type judgment: sensor type, fault is divided into three types:

[0091] a. The mechanical anemometer fault type is mechanical jamming, the sequence sample variance of the mechanical anemometer data in the time window is calculated, and compared with the threshold value, if the sample variance value of three consecutive time windows is lower than the threshold value, it can be judged that mechanical jamming fault occurs;

[0092] b. Pitch angle and roll angle calculated by IMU sensor exceed threshold, threshold from buoy design data;

[0093] c. Fault judgment of ultrasonic, mechanical anemometer, barometric pressure gradient sensor, temperature and humidity sensor: calculate the first-order difference of each point in the data sequence, and compare it with the threshold value. If it exceeds the threshold value in the continuous p time window, a jump fault occurs; the threshold value is set according to the physical characteristics of the sensor.

[0094] Compared with the prior art, the application has the following advantages:

[0095] Breakthrough the reliability limit of a single sensor: through the redundant architecture of ultrasonic + mechanical + barometric pressure gradient three-mode sensors, combined with real-time confidence evaluation mechanism, the continuous output of wind speed and direction data can still be guaranteed when any single sensor fails or fails, and the availability of the system is improved.

[0096] Eliminate measurement distortion caused by buoy motion: based on 9-axis IMU and north satellite positioning, a motion compensation algorithm is designed, which upgrades the traditional scalar compensation to vector coordinate transformation, effectively suppressing the interference of buoy motion on wind speed and direction measurement, and improving the accuracy of wind speed and direction measurement;

[0097] Realize adaptive monitoring in extreme environment: build an adaptive decision model to automatically switch the optimal data source in extreme conditions (such as shielding the mechanical sensor in high humidity and enabling the barometric pressure gradient calculation in large inclination), improve the system availability;

[0098] System self-diagnosis capability:

[0099] Realize real-time identification and dynamic shielding of sensor-level faults, set confidence evaluation mechanism, design data weighted fusion algorithm, and improve the measurement accuracy of the system. BRIEF DESCRIPTION OF DRAWINGS

[0100] Figure 1 The flowchart of the present application;

[0101] Figure 2 The flowchart of the self-diagnosis processing unit;

[0102] Figure 3 The flowchart of the weighted fusion unit;

[0103] Figure 4 The comparison chart of multi-sensor fusion effect;

[0104] Figure 5 Fault type judgment effect chart. DETAILED DESCRIPTION

[0105] The technical scheme of the present application is described in detail below through specific embodiments and drawings. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations thereof, and the specific technical features can be combined with each other.

[0106] A marine buoy wind speed and direction monitoring system comprises an information acquisition unit, a motion compensation unit, a self-diagnosis processing unit, a weighted fusion unit and an adaptive decision unit.

[0107] The information acquisition unit acquires raw data of wind speed and direction sensors. The sensor installation positions are as follows: ultrasonic sensors and anemometers are installed on a mast; air pressure gradient sensors and temperature and humidity sensors are installed on a mast pedestal; and an IMU and a Beidou module are installed on a buoy cabin body.

[0108] The motion compensation unit synchronizes the data of multiple source sensors in time, unifies the data of the IMU, Beidou positioning and wind direction and speed sensors to a time sequence of 50 Hz, fuses the IMU and Beidou positioning data through a Kalman filter, wherein the IMU is used for state prediction, the Beidou positioning is used for state updating, and error suppression is performed in the updating process; the Kalman filter outputs the attitude quaternion of the buoy, which is used to convert the wind speed vector measured by the wind direction and speed sensors from the buoy coordinate system to the earth coordinate system; the converted wind speed signal is further processed in the frequency domain, including windowing, FFT transformation, band-stop filtering based on wave energy dynamic setting of stop band, inverse FFT, overlap addition and zero-phase processing, and finally outputs the motion-compensated wind speed and direction.

[0109] The self-diagnosis processing unit identifies typical fault modes through fault feature recognition and quantifies the health status of each sensor to provide a decision basis for subsequent data fusion.

[0110] The weighted fusion unit outputs a set of optimal and final wind speed and direction values through an adaptive weighting algorithm by using multiple wind speed and direction data streams and the confidence of each sensor output by the self-diagnosis processing unit.

[0111] The adaptive decision unit evaluates the credibility of the fusion result and feeds back the rating result to the adaptive weighting algorithm to form a closed-loop feedback mechanism for evaluating the priority of data transmission or triggering a system maintenance alarm.

[0112] The final calculation result of the system is sent to a satellite through a satellite module, and then forwarded to a customer.

[0113] A marine buoy wind speed and direction monitoring method comprises the following steps:

[0114] S1. Set a sampling period and acquire data information by using sensors.

[0115] This stage is to collect the original data of wind speed and direction sensor, including ultrasonic anemometer, mechanical wind speed and direction meter, air pressure gradient value; collect IMU 9-axis attitude sensor value, collect temperature and humidity value, collect Beidou satellite positioning data; but because the data transmission period of each sensor is inconsistent, time synchronization is needed; in this invention, 50HZ data sampling period is adopted; Beidou positioning data is used to calibrate IMU data, and eliminate the accumulated error of IMU.

[0116] S2. Preprocess the data to obtain processed wind speed and direction data;

[0117] The first stage of the system is to synchronize the time of multi-source sensor data, and unify the data of IMU, Beidou positioning and wind direction and speed sensor to 50Hz time sequence. Then the Kalman filter is used to fuse the IMU and Beidou positioning data, in which the IMU is used for state prediction, and the Beidou positioning is used for state update, and error suppression is performed in the update process (including HDOP adaptive adjustment of observation noise, zero offset correction and accumulation error zeroing). The Kalman filter outputs the attitude quaternion of the buoy, which is used to convert the wind speed vector measured by the wind direction and speed sensor from the buoy coordinate system to the earth coordinate system. The converted wind speed signal is then processed by frequency domain filtering, including windowing, FFT transformation, band-stop filtering based on wave energy dynamic setting of stop band, inverse FFT, overlap addition and zero phase processing, and finally the motion compensated wind speed and direction are output.

[0118] 1) Sensor data input and time synchronization:

[0119] The algorithm first receives three types of sensor data, which need to be time synchronized to ensure that the data is aligned on the time axis and generates a vector with a time variable.

[0120] Linear interpolation is used for time synchronization.

[0121] RTC time is used inside the system; when the Beidou satellite time is received, the Beidou satellite time is used to calibrate the system RTC time, eliminating the system error caused by the RTC crystal oscillator frequency deviation.

[0122] 2) Kalman filtering and motion compensation:

[0123] a. Kalman filtering core processing:

[0124] The system uses a Kalman filter based on error state to fuse IMU and Beidou satellite data, estimates the buoy attitude, position and velocity, and estimates and corrects the sensor errors of the IMU. The IMU data is input to the Kalman prediction module at a frequency of 50Hz;

[0125] The prediction module model is:

[0126] ;

[0127] : The predicted attitude quaternion at the current time instant. This is a 4-dimensional vector, usually in the form of [q w , q x , q y , q z ] T , where q w is the real part. It represents the rotational attitude of the object in 3D space. k

[0128] Sampling time interval, usually in seconds.

[0129] , in this application , is the sampling frequency.

[0130] ： The raw angular velocity measured by the gyroscope at the current time instant. This is a 3-dimensional vector [ k , , , ] T , representing the angular velocity of the object rotating around the X, Y, Z axes (usually in rad / s).

[0131] ： Bias of the gyroscope. This is also a 3-dimensional vector. The gyroscope will have a tiny output even when there is no rotation, and this inherent error is the Bias. It is one of the state quantities that the Kalman filter needs to estimate. Subtracting the Bias from the measured value is to get a more accurate angular velocity.

[0132] Skew-symmetric matrix composed of the corrected angular velocity.

[0133] : Skew-symmetric matrix, this 4x4 matrix is the core part of the quaternion derivative equation. Its role is to convert the angular velocity vector into a matrix form that can be multiplied by the quaternion, and its physical meaning represents the instantaneous rotation rate.

[0134] Beidou positioning data is used for error covariance calculation, combined with prediction and observation information, to complete state update in the Kalman filter update module, outputting the attitude quaternion q of the carrier.

[0135] ​​According to satellite geometry distribution quality factor (HDOP) to adaptively adjust filtering parameters, real-time correction of sensor zero drift, according to Beidou satellite positioning data to remove accumulated error in filter regularly, improve system stability and accuracy.

[0136] c. Coordinate system conversion:

[0137] Using the calculated attitude quaternion, the wind speed data measured by the wind speed and direction sensor in the carrier coordinate system is converted to the geographic coordinate system to obtain the time-domain wind speed signal after preliminary motion compensation.

[0138] The quaternion calculation formula is:

[0139] ;

[0140] The time-domain wind speed signal after motion compensation is:

[0141] ;

[0142] Northeast celestial coordinate wind speed and direction value; Buoy coordinate system wind speed and direction value.

[0143] 3) Frequency domain filtering processing:

[0144] a. Pretreatment stage: Hamming window is used for window processing of time-domain wind speed signal, window is N=1024 points, FFT transform is adopted, time-domain signal is converted to frequency domain, which is convenient for feature extraction and analysis in frequency domain.

[0145] The window function is:

[0146] ;

[0147] FFT transform is:

[0148] ;

[0149] Calculate whether the wave energy exceeds the preset threshold:

[0150] ;

[0151] ;

[0152] The first k The complex result of the frequency component is the complex frequency spectrum calculated by FFT; n The index of the time-domain sampling point is from 0 to 1023, and N is the number of FFT points, The frequency resolution is is the sampling rate (50HZ is used in this application), is the n-th time domain sample value after windowing.

[0153] If the wave energy exceeds the threshold, set the upper limit of the stop band to 0.4Hz; if the wave energy does not exceed the threshold, set the upper limit of the stop band to 0.3Hz.

[0154] c. 8-order Butterworth band-stop filter for filtering, the stop band range is 0.05Hz to the dynamically set upper limit of the stop band 0.4Hz / 0.3Hz, used to filter out the wind speed noise caused by waves;

[0155] 8-order Butterworth band-stop filter is:

[0156] ;

[0157] is the filter frequency of the filter, indicating the frequency of the signal after passing through the filter Gain (attenuation or amplification) of the frequency component.

[0158] The frequency domain wind speed vector obtained by FFT transformation is multiplied by the 8-order Butterworth band-stop filter to filter out the frequency components in the stop band.

[0159] d. Inverse FFT transformation and zero-phase processing: the filtered frequency domain signal is converted back to the time domain by inverse FFT transformation, and since 50% overlap is used in the windowing and framing processing, the adjacent frame overlap parts need to be added in the time domain to reconstruct the continuous signal; the signal continuously converted between time domain and frequency domain will have phase distortion problem, therefore, the same 8-order Butterworth band-stop filter is used for the forward and reverse filtering to realize zero-phase processing;

[0160] Overlap and addition reconstruction:

[0161] ;

[0162] Zero-phase processing:

[0163] ;

[0164] is the m-th processed signal, Original input signal, H{} filtering operation, reverse() signal inversion operation, Frame shift time, ( ) The position of the m-th segment signal on the time axis.

[0165] ​wind speed and direction data after motion compensation and frequency domain filtering are obtained;

[0166] ;

[0167] true heading angle; east direction velocity component, the velocity of the carrier in the east direction, usually in m / s; north direction velocity component, the velocity of the carrier in the north direction.

[0168] S3. Calculate the confidence of each sensor, troubleshoot faulty data;

[0169] In this phase, according to the sensor data, the typical fault mode is identified by fault feature, and the health status (confidence) of each sensor is quantified, which provides the basis for decision-making for subsequent data fusion.

[0170] 1) Data input stage: the system collects raw data from various sensors, uses the time synchronization vector generated in stage 2 to generate an anomaly detection matrix. The input data are: ultrasonic anemometer data, mechanical anemometer data, barometric gradient sensor data, IMU attitude data.

[0171] 2) Fault type judgment: the sensor type, the fault is divided into 3 types:

[0172] a. The mechanical anemometer fault type is mechanical jamming, and the data shows that the wind speed sensor data does not change or changes very little with time, resulting in a significant decrease in the variance of the data sequence.

[0173] Calculate the sample variance of the mechanical anemometer data in the time window (sample period value), and compare it with the threshold value. If the sample variance value of the continuous three time windows is lower than the threshold value, it can be judged that the mechanical jamming fault occurs; the threshold value can be determined by experiment.

[0174] b. The pitch angle and roll angle calculated by the IMU sensor exceed the threshold value, and the threshold value comes from the design data of the buoy.

[0175] c. The data collected by ultrasonic, mechanical anemometer, barometric gradient sensor, and temperature and humidity sensor jump in a large range with time; calculate the forward first-order difference of each point in the data sequence, and compare it with the threshold value. If it exceeds the threshold value in the continuous 10 time windows, it occurs jump fault; the threshold value is set by the physical characteristics of the sensor, which can be determined by the manual. The first-order difference model is as follows:

[0176] ;

[0177] 3) Confidence evaluation: This module outputs a confidence value between 0 and 1 for each sensor, which quantifies its health status. A confidence value of 1 indicates complete reliability, while a confidence value of 0 indicates complete failure, making its data unusable and requiring isolation. The confidence evaluation model is:

[0178] ;

[0179] where:

[0180] : Output of the variance detection term, which is 1 when there is no fault and 0 when there is a fault. It can also be designed as a continuous function, such as ;

[0181] : Output of the gradient detection term; defined as ; is the total number of samples;

[0182] : Attitude overrun term; 1 when there is no overrun, 0 when there is an overrun;

[0183] : Sensor reliability factor (e.g., the proportion of time without faults in the past 24 hours);

[0184] is the weight coefficient, which satisfies .

[0185] Fault isolation: Based on the confidence evaluation results, the system performs fault isolation operations to separate the detected faulty sensors or functional modules from the normal system, preventing the spread of faults and affecting the overall system performance.

[0186] Figure 5 As shown in the figure, the adaptive weight dynamic strategy is adopted, which improves the accuracy by 36% under normal working conditions, 78% when the mechanical sensor is stuck, 79% when the ultrasonic data jumps, and 57% in severe sea conditions.

[0187] Figure 4 As shown in the figure, the fault detection accuracy is improved by 98.2%, the system availability is improved by 99.8%, and the wind speed and direction measurement accuracy is improved by 42.1%.

[0188] S4. Data weighted fusion, output final wind direction and final wind speed.

[0189] The data weighting fusion core task is to receive multiple wind speed and direction data streams from the motion compensation unit and sensor confidence outputs from the diagnostic processing unit. Through an adaptive weighting algorithm, a set of optimal and final wind speed and direction values are output. The core idea is to give more reliable sensor data a greater weight in the final result.

[0190] Data preprocessing and standardization phase: The motion-compensated data is first standardized by the mean-variance normalization method to eliminate the dimensional differences between different sensor data and use the Z-score standardization method.

[0191] 2) Weight calculation model: The normalized data enters the weight calculation core module, which considers the following factors to determine the weight of each sensor data:

[0192] a. Sensor health status: Evaluate the current working state and performance of each sensor;

[0193] b. Environmental parameters: Adjust the applicability weight of each sensor according to the current environmental conditions;

[0194] c. Weight arbitrator: Arbitrate and optimize different weight calculation results;

[0195] d. Historical reliability;

[0196] The calculation model is as follows:

[0197] ;

[0198] Where:

[0199] : Sensor health confidence ;

[0200] : Signal-to-noise ratio factor of sensor r; the higher the signal-to-noise ratio, the greater the weight. It can be approximately calculated as (mean and standard deviation ratio), and the result is normalized;

[0201] : Environmental adaptability factor:

[0202] ;

[0203] : Sensor reliability factor;

[0204] M: The number of effective sensors currently participating in fusion.

[0205] 3) Fusion decision: The fusion decision unit receives the calculated weight information and makes data fusion decisions based on these weights, separately processing two different types of measurement data, wind speed and wind direction.

[0206] 4) Data fusion: The fusion decision unit divides the processing into two parallel paths:

[0207] Wind speed fusion: Wind speed is a scalar and uses a weighted average algorithm;

[0208] Wind direction fusion: Wind direction is an angle and has periodicity. After converting it to a vector, it is synthesized.

[0209] a. Convert the wind direction angle to a unit vector:

[0210] For the wind direction angle , the unit vector in the northeast celestial coordinate system is:

[0211] ;

[0212] The X component represents the east direction;

[0213] The Y component represents the north direction;

[0214] b. Perform weighted vector synthesis:

[0215] ;

[0216] is the eastward weighted wind direction vector sum, is the northward weighted wind direction vector sum.

[0217] c. Calculate the final wind direction from the synthesized vector:

[0218] ;

[0219] Using the atan2 function can correctly handle all quadrants to obtain the final wind direction between 0 and 360 degrees. The magnitude of the synthesized vector can reflect the consistency of the wind direction data. The shorter the magnitude, the greater the difference in wind direction between sensors.

[0220] 5) Confidence rating and feedback: Evaluate the credibility of the fusion results and feed the rating results back to the weight calculation core to form a closed-loop feedback mechanism for evaluating data transmission priorities or triggering system maintenance alerts.

[0221] Final confidence: Define the weighted sum of all participating fusion sensor weights.

[0222] ;

[0223] wherein The closer to 1, the final result is determined by the high confidence sensor, the overall confidence is high.

[0224] Consistency index: the length of the vector Consistency index representing the wind direction; weighted standard deviation of wind speed represents the consistency of wind speed.

[0225] 6) After the weighted fusion processing, the system outputs two final results:

[0226] Final wind speed: wind speed measurement after wind speed fusion .

[0227] Final wind direction: wind direction measurement after wind direction fusion .

[0228] The whole data weighted fusion engine realizes intelligent fusion of multi-source sensor data through multi-dimensional weight calculation and closed-loop feedback mechanism, and improves the accuracy and reliability of wind speed and direction measurement.

[0229] The above only describes the preferred embodiments of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should be considered as the protection scope of the present application.

Claims

1. A method of monitoring wind speed and direction for an ocean buoy, the method comprising: Comprising the following steps: S1. Set the sampling period, use the sensor to obtain data information; S2. Preprocess the data to obtain the processed wind speed and wind direction data; The window function in the frequency domain filtering process is: ； The Fourier transform FFT transform is: ； For the first The complex result of each frequency component, where n is the index of the time-domain sampling point, ranging from 0 to 1023; Calculate whether the wave energy exceeds the preset threshold: ； ; N is the number of points of FFT, is the frequency resolution, is the sampling rate, is the n-th time-domain sample value after windowing; S3. Calculate the confidence of each sensor to troubleshoot faulty data; S4. Data weighted fusion, output the final wind direction and final wind speed.

2. A method of monitoring wind speed and direction for a marine buoy according to claim 1, wherein, The data preprocessing step is as follows: S21. Sensor data input and time synchronization; S22. Fuse IMU and Beidou satellite data based on error state Kalman filter to estimate the attitude, position and speed of the buoy, and estimate and correct the sensor error of the IMU, the Beidou positioning data is used for error covariance calculation, combined with prediction and observation information, the state update is completed in the Kalman filter update module, and the attitude quaternion of the carrier is output; S23. Convert the wind speed data measured by the wind speed and direction sensor in the carrier coordinate system to the geographic coordinate system using the calculated attitude quaternion, to obtain the time domain wind speed signal after preliminary motion compensation; S24. Frequency domain filtering process: after windowing, FFT transform, band-stop filtering based on wave energy dynamic setting, inverse FFT, overlap addition and zero phase processing, finally output the motion compensated wind speed and direction.

3. A method of monitoring wind speed and direction for a marine buoy according to claim 2, wherein, The frequency domain wind speed vector obtained by FFT transform is multiplied by an 8th order band-stop filter to filter out the frequency components in the stop band; Inverse FFT transform and zero phase processing: the filtered frequency domain signal is converted back to the time domain through inverse FFT transform, and zero phase processing is realized through forward and reverse filtering twice, and the same 8th order Butterworth band-stop filter is used; Overlap addition reconstruction: ； Zero phase processing: ； processed signal for the mth frame, original input signal, H{} filtering operation, reverse() signal inversion operation, frame shift time, (tfs) ) the mth position of the segment signal on the time axis; Obtain the wind speed and direction data after motion compensation and frequency domain filtering; ； True heading angle.

4. A method of monitoring wind speed and direction for a marine buoy according to claim 2, wherein, Comprising: S31. Collect raw data from each sensor, and use the time synchronization vector generated in step S2 to generate an anomaly detection matrix; The input data are: ultrasonic anemometer data, mechanical anemometer data, barometric pressure gradient sensor data, and IMU attitude data; The confidence evaluation model is: ； Wherein: : output of variance detection term, 1 for no fault, 0 for fault; : output of the gradient detection term; defined as , N is the total number of samples; : attitude over-limit item; 1 if no over-limit, 0 if over-limit; : sensor reliability factor; are weight coefficients satisfying ; S33. Fault isolation: based on the confidence evaluation result, the detected faulty sensor data is excluded and does not participate in the subsequent fusion calculation.

5. A method of monitoring wind speed and direction for a marine buoy according to claim 2, wherein, Comprising: S41. Data preprocessing and standardization stage: the motion compensated data is first subjected to data standardization processing; S42. The normalized data enters the weight calculation model; The calculation model is as follows: ； Wherein: : sensor health confidence ; : signal-to-noise ratio factor of sensor r; : environmental fitness factor: ; : sensor reliability factor; M: the number of effective sensors currently participating in fusion; S43. Fusion decision: based on these weights, make data fusion decisions for wind speed and wind direction, two different types of measurement data; S44. Data fusion: Wind speed fusion: wind speed is a scalar, using weighted average algorithm; Wind direction fusion: wind direction is an angle, which is converted into a vector for synthesis; S45. Confidence rating and feedback: evaluate the credibility of the fusion result, and feed back the rating result to the weight calculation core to form a closed-loop feedback mechanism, which is used to evaluate the priority of data transmission or trigger system maintenance alarm; S46. After the weighted fusion processing, output two final results: Final wind speed: wind speed measurement after fusion of wind speeds ; Final wind direction: the wind direction measurement after fusion of the wind direction measurements .

6. A method of monitoring wind speed and direction for a marine buoy according to claim 5, characterised in that, S44 wind direction fusion steps as follows: a. Convert the wind direction angle to unit vector: For the wind direction angle its unit vector in the northeast celestial coordinate system is: ； x component representing an east direction; Y component representing the north direction; b. Perform weighted vector synthesis: ; is the eastward weighted wind direction vector sum, is the northward weighted wind direction vector sum; c. Calculate the final wind direction from the synthesized vector: ; Using the atan2 function can correctly handle all quadrants, resulting in a final wind direction between 0 and 360 degrees, and the magnitude of the resultant vector The consistency of the wind direction data can be reflected, and the shorter the magnitude, the greater the difference in wind direction between the sensors.

7. A method of monitoring wind speed and direction for a marine buoy according to claim 5, wherein, S45 Final confidence: define the weighted sum of all participating fusion sensor weights: ； wherein The closer to 1, the more the final result is determined by the high-confidence sensor, and the overall trust is high.

8. A system for monitoring wind speed and direction at a marine buoy, employing the method for monitoring wind speed and direction at a marine buoy according to any one of claims 1 to 7, characterised in that, It includes information acquisition unit, motion compensation unit, self-diagnosis processing unit, weighted fusion unit and adaptive decision unit; Information acquisition unit: collect the original data of wind speed and direction sensor; Motion compensation unit: time synchronization of multi-source sensor data, unify IMU, Beidou positioning and wind direction and speed sensor data to 50Hz time sequence, fuse IMU and Beidou positioning data through Kalman filter, wherein IMU is used for state prediction, Beidou positioning is used for state update, and error suppression is carried out in the update process; The Kalman filter outputs the attitude quaternion of the buoy, which is used to convert the wind speed vector measured by the wind direction and speed sensor from the buoy coordinate system to the earth coordinate system; The converted wind speed signal is further processed in the frequency domain, including windowing, FFT transformation, band-stop filtering based on wave energy dynamic setting of stop band, inverse FFT, overlap addition and zero phase processing, finally outputting the motion compensated wind speed and direction; Self-diagnosis processing unit: identify typical fault modes through fault feature recognition, and quantify the health status of each sensor to provide decision basis for subsequent data fusion; Weighted fusion unit: through an adaptive weighting algorithm, a set of optimal and final wind speed and direction values are output from multiple wind speed and direction data streams and the confidence of each sensor output by the self-diagnosis processing unit; Adaptive decision unit: evaluate the credibility of the fusion result, and feed back the rating result to the adaptive weighting algorithm to form a closed-loop feedback mechanism for evaluating the priority of data transmission or triggering system maintenance alarm.

9. A system for monitoring wind speed and direction for a marine buoy according to claim 8, wherein, Information acquisition unit: original data includes ultrasonic anemometer, mechanical wind speed and direction meter, air pressure gradient value; Collect IMU 9-axis attitude sensor value, collect temperature and humidity value, collect Beidou satellite positioning data; Self-diagnosis processing unit: Data input stage: collect original data from each sensor, generate an abnormal detection matrix using the time synchronization vector generated by the motion compensation unit; Input data: ultrasonic anemometer data, mechanical anemometer data, air pressure gradient sensor data, IMU attitude data; Fault type judgment: sensor type, fault is divided into 3 types: a. The mechanical anemometer fault type is mechanical jamming, calculate the sample variance of the mechanical anemometer data in the time window, and compare it with the threshold value, if the sample variance value of three consecutive time windows is lower than the threshold value, it can be judged that mechanical jamming fault occurs; b. The pitch angle and roll angle calculated by IMU sensor exceed the threshold value, the threshold value comes from the design data of the buoy; c. Ultrasonic, mechanical anemometer, air pressure gradient sensor, temperature and humidity sensor fault judgment: calculate the first-order difference of each point in the data sequence, and compare it with the threshold value, if it exceeds the threshold value in p consecutive time windows, it occurs jump fault; The threshold value is set according to the physical characteristics of the sensor.

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