A fishing boat rescue method based on a Beidou intelligent navigation positioning system

Abstract

The present application belongs to the technical field of ship rescue, and provides a fishing boat rescue method based on a Beidou intelligent navigation positioning system, which comprises the following steps: comparing and analyzing the Beidou terminal calculated position and the real position of the rescued fishing boat in historical rescue, dividing the historical rescue into positioning error rescue and normal rescue, then analyzing the spatial consistency and periodic consistency of the positioning error vector, identifying target rescue caused by the multi-path effect, fitting the wave height-positioning error linear relationship according to the satellite elevation angle grouping based on the target rescue data, constructing a multi-path error compensation model combined with the wave direction-azimuth angle difference, and constructing a multi-path effect risk prediction model using the sea wave parameters and the fishing boat position parameters of normal / target rescue, if there is a risk, correcting the Beidou positioning result using the compensation model to solve the positioning error problem caused by the sea wave multi-path effect, improving the positioning accuracy to within the nominal accuracy range of Beidou, and being suitable for offshore fishing boat emergency rescue.

Description

Technical Field

[0001] This invention belongs to the field of ship rescue technology, specifically a fishing boat rescue method based on the Beidou intelligent navigation positioning system. Background Technology

[0002] The BeiDou Navigation Satellite System has been widely used in maritime fishing vessel rescue, and its positioning accuracy directly determines the efficiency and success rate of the rescue. However, in maritime rescue scenarios, satellite signals are susceptible to multipath effects caused by reflections from ocean waves: direct waves and reflected waves superimpose and interfere at the antenna, leading to pseudorange measurement errors, which in turn cause positioning errors. In severe cases, this can cause the rescue vessel to miss the target and prolong the rescue time.

[0003] Existing fishing vessel rescue positioning methods mostly rely on raw output data from Beidou terminals and lack targeted risk prediction and error correction mechanisms for multipath effects. Firstly, they lack in-depth analysis of historical rescue data, making it impossible to quantify the correlation between factors such as ocean waves and satellite geometry and multipath errors. Secondly, they lack real-time risk prediction models, making it difficult to identify potential multipath hazards during rescues in advance. Thirdly, positioning corrections often employ fixed filtering algorithms without dynamically adjusting based on real-time ocean wave and satellite parameters, resulting in limited correction accuracy.

[0004] Therefore, this invention provides a fishing boat rescue method based on the BeiDou intelligent navigation positioning system. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a fishing boat rescue method based on the Beidou intelligent navigation positioning system, comprising:

[0007] Step 1: Compare and analyze the BeiDou terminal-calculated positions of the rescued fishing boats in multiple historical rescues with their actual positions, and classify historical rescues into rescues with positioning errors and normal rescues;

[0008] Step 2: For positioning error rescue, conduct fluctuation cycle overlap analysis and error consistency analysis on the positioning error of the fishing vessel being rescued in the target sea area and the wave cycle to determine whether the positioning error is caused by multipath effect. If so, mark the positioning error rescue as the target rescue.

[0009] Step 3: Conduct correlation analysis on wave height and positioning error in different satellite elevation angle ranges during target rescue, and construct a multipath error compensation model;

[0010] Step 4: Based on the wave parameters of normal rescue and target rescue and the location of the fishing vessel to be rescued, construct a multipath effect risk prediction model, and combine the current wave parameters with the location of the fishing vessel to be rescued to determine whether there is a multipath effect risk in the current rescue.

[0011] Step 5: If present, input the current wave parameters and the location of the fishing vessel to be rescued into the multipath error compensation model to dynamically correct the BeiDou positioning results and obtain the corrected positioning coordinates of the fishing vessel to be rescued.

[0012] Furthermore, historical rescue operations can be categorized into rescues due to positioning errors and normal rescues as follows:

[0013] For every fishing boat rescued in any given historical rescue operation;

[0014] The horizontal error of the BeiDou terminal's calculated position is determined using the geographic coordinate distance formula.

[0015] The horizontal error is compared with the error threshold. If there is a fishing vessel that is being rescued with a horizontal error greater than the error threshold, the historical rescue is marked as a positioning error rescue.

[0016] Conversely, if there are no fishing vessels rescued with a horizontal error greater than the error threshold in the historical rescues, then it is considered a normal rescue.

[0017] Furthermore, the method for analyzing the coincidence of the positioning error of fishing vessels awaiting rescue within the target sea area and the wave cycle is as follows:

[0018] Extract the wave cycle, i.e. the wave crest interval, during the rescue period from the historical meteorological database, and record the wave crest time;

[0019] The continuous positioning points of the rescued fishing boat were extracted from the Beidou terminal logs, and the horizontal error was calculated with the actual position and integrated into an error sequence.

[0020] For any fishing vessel rescued in each positioning error rescue operation:

[0021] The error sequence is segmented according to the wave cycle, and the Pearson correlation coefficient between the error peak and the wave peak time is calculated.

[0022] The percentage of rescued fishing vessels with a Pearson correlation coefficient greater than the preset correlation coefficient is counted and compared with the preset percentage. If the Pearson correlation coefficient is greater than the preset percentage, then the error rescue has periodic consistency.

[0023] Furthermore, the process of analyzing the consistency between the positioning error of the fishing vessel awaiting rescue and the wave cycle within the target sea area is as follows:

[0024] For each fishing vessel rescued within the target sea area;

[0025] Starting from the BeiDou positioning point of the rescued fishing vessel and ending at the actual position, the positioning error vector is calculated, including the error direction and the error magnitude. The error direction is the horizontal angle from the BeiDou terminal's calculated position to the actual position.

[0026] The error direction of the rescued fishing boat is decomposed into x and y components on the horizontal plane, and the arithmetic mean of the x and y components of all the rescued fishing boats is taken to obtain the average direction.

[0027] Calculate the error between the error direction and the average direction, take the absolute value and then average it to obtain the directional concentration.

[0028] If the directional concentration is less than or equal to the threshold, then the error direction is concentrated.

[0029] Calculate the deviation between the error direction of the rescued fishing vessel and the satellite reflection direction;

[0030] The absolute mean of the error is obtained by taking the absolute values ​​of the deviations between the error directions of all rescued fishing boats and the satellite reflection directions.

[0031] If the absolute mean of the error is less than or equal to the preset absolute mean, then the error direction is consistent with the satellite reflection direction;

[0032] If the error directions are concentrated and coincide with the satellite reflection direction, then spatial consistency exists.

[0033] Furthermore, the method for determining whether the positioning error is due to multipath effect is as follows:

[0034] For any single location error rescue:

[0035] If there is periodic consistency and spatial consistency, the positioning error is determined to be caused by the multipath effect, and the error rescue is marked as the target rescue.

[0036] Furthermore, the multipath error compensation model is constructed as follows:

[0037] Real-time wave height, wave direction-azimuth difference, and satellite elevation angle are obtained from historical target rescue data as input parameters, and the magnitude and direction of positioning error are used as output parameters. Among them, the satellite elevation angle is the horizontal angle between the satellite and the line connecting the antenna of the fishing boat.

[0038] Based on the sliding window method, the wave height and positioning error under different satellite elevation angles are linearly fitted, and the satellite elevation angle grouping points are identified by comparing and analyzing the slope of the fitted equations of wave height and positioning error in each sliding window.

[0039] Using the satellite elevation angle grouping point as the boundary, the satellite elevation angle is divided into two elevation angle groups according to the satellite elevation angle grouping point. Linear regression is performed on the data of each group, and the wave height range of each satellite elevation angle group is recorded.

[0040] The error direction is consistent with the satellite reflection direction, and the reflection direction equals the satellite azimuth angle. , When the time is negative, When taking the positive value, among which, Wave direction-azimuth difference;

[0041] By integrating the relationship between positioning error, wave height, error direction, and satellite elevation angle, a multipath error compensation model is formed that combines error magnitude and error direction.

[0042] Furthermore, the method for identifying the satellite elevation angle groups is as follows:

[0043] Wave height and error were fitted within a continuous satellite elevation angle range using a sliding window;

[0044] The regression coefficients are solved by the least squares method to obtain the linear regression equations within each sliding window;

[0045] Record the center satellite elevation angle, wave height coefficient, and constant term for each sliding window, where the wave height coefficient is the slope of the linear regression equation;

[0046] Calculate the difference between adjacent sliding windows k, and calculate the mean μ and standard deviation σ of all differences, and set the sudden change threshold to μ+2σ;

[0047] Iterate through the differences in k values ​​between all adjacent sliding windows and compare them with the sudden change threshold. The upper limit of the previous sliding window corresponding to the first difference that exceeds the sudden change threshold is the satellite elevation angle grouping point.

[0048] Furthermore, the construction process of the multipath effect risk prediction model is as follows:

[0049] Extract continuous wave data during the period of positioning error in normal rescue and target rescue;

[0050] The peak is identified by the extreme value method, which is the highest point between two adjacent troughs. The height of each wave is recorded, which is the vertical distance between the peak and the previous trough.

[0051] Arrange the wave heights in descending order of magnitude, and calculate the average of the first third of the wave heights to obtain the effective wave height.

[0052] Construct a multipath effect risk prediction model, input the current wave parameters and the position parameters of the fishing vessel to be rescued, and output the probability of positioning error caused by multipath effect;

[0053] The dependent variable Y represents whether a multipath effect occurs, with Y=1 representing target rescue and Y=0 representing normal rescue.

[0054] Ocean wave parameters and fishing vessel position parameters are extracted from historical data. The ocean wave parameters include effective wave height, wave period, and wave direction-azimuth difference, while the fishing vessel position parameters include satellite elevation angle.

[0055] Construct an interaction term between wave parameters and fishing vessel position parameters. The interaction term is a wave height-elevation angle co-term: X = H × (30° - θ) 卫 ), where H is the wave height, θ 卫 This refers to the satellite's elevation angle;

[0056] Ocean wave parameters, fishing vessel position parameters, and wave height-elevation angle co-term are used as input features.

[0057] Calculate the Pearson correlation coefficient between the input features and the dependent variable Y, retain features whose Pearson correlation coefficient is greater than or equal to a preset correlation coefficient, and obtain the feature set [x1, x2, ..., x]. i ], where x i For the i-th feature;

[0058] With feature set [x1, x2, ..., x i Using ] as the independent variable, construct the probability prediction formula:

[0059] ;

[0060] Where β0 is a constant term, and β1~β4 are regression coefficients;

[0061] The regression coefficients are obtained by fitting the training set, and finally the multipath effect risk prediction model is obtained.

[0062] Furthermore, the method for determining whether the current rescue operation carries a risk of multipath effects is as follows:

[0063] Input the current wave parameters and the location of the fishing vessel to be rescued into the risk prediction model of multipath effect, and output the probability of multipath effect occurring in the current location of the fishing vessel to be rescued.

[0064] If the probability is greater than or equal to the preset probability, then the current rescue operation carries the risk of a multipath effect.

[0065] Furthermore, the method for dynamically correcting the BeiDou positioning results is as follows:

[0066] Based on the current location of the fishing vessel awaiting rescue using BeiDou positioning, the satellite elevation angle is determined, the satellite elevation angles are grouped, and the wave height is substituted into the multipath error compensation model to obtain the positioning error magnitude.

[0067] Calculate the wave direction-azimuth angle, substitute it into the multipath error compensation model, and obtain the error direction;

[0068] After converting the error direction to radians, the magnitude and direction of the positioning error are substituted into the azimuth-distance to latitude and longitude formula to obtain the corrected positioning coordinates of the fishing vessel to be rescued.

[0069] The beneficial effects of this invention are as follows: Through spatiotemporal consistency analysis, deviations caused by multipath effects are accurately screened from positioning errors, eliminating other interference factors such as equipment failure, and improving attribution accuracy. The error compensation model based on satellite elevation angle grouping can output a quantified error vector according to real-time wave height and satellite parameters, realizing dynamic correction of positioning results and improving positioning accuracy. The multipath effect risk prediction model can predict the risk probability of rescue scenarios in advance, providing a basis for rescue decisions and avoiding the lag of passive correction. Combined with environmental parameters such as wave period and wave direction, the model can adapt to the positioning error patterns under different sea conditions and has a wide range of applications. Attached Figure Description

[0070] The invention will now be further described with reference to the accompanying drawings.

[0071] Figure 1 This is a flowchart of the steps of a fishing boat rescue method based on the Beidou intelligent navigation positioning system described in this invention;

[0072] Figure 2 This is a logic diagram of a fishing boat rescue method based on the Beidou intelligent navigation positioning system described in this invention. Detailed Implementation

[0073] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0074] Please see Figure 1 As shown in the embodiment of the present invention, a fishing boat rescue method based on the Beidou intelligent navigation positioning system includes the following steps:

[0075] Step 1: Compare and analyze the BeiDou terminal-calculated positions of the rescued fishing boats in multiple historical rescues with their actual positions, and classify historical rescues into rescues with positioning errors and normal rescues;

[0076] In step one, the process of dividing historical rescues into error-prone rescues and normal rescues includes:

[0077] Obtain BeiDou positioning data of the rescued fishing vessel at key historical rescue nodes from BeiDou terminal historical logs or rescue platform archived data, including latitude and longitude, positioning timestamp, and positioning mode;

[0078] For each fishing boat rescued in any given historical rescue operation:

[0079] The location calculated by the BeiDou terminal is denoted as P. 测 :(Lon测 Lat 测 H 测 The actual location is denoted as P. 真 :

[0080] (Lon) 真 Lat 真 H 真 (), where Lon is longitude and Lat is latitude;

[0081] The horizontal error, which is the difference in distance in a planar coordinate system, needs to take into account the effect of the Earth's curvature. It is calculated using the geographic coordinate distance formula.

[0082] ;

[0083] in, (Difference in longitude, unit: radians) (Latitude difference, unit: radians), R is the average radius of the Earth;

[0084] Based on the nominal accuracy of the BeiDou system and the actual needs of rescue scenarios, an error threshold is set as follows:

[0085] Obtain the horizontal nominal accuracy of the corresponding positioning mode from the technical manual of the Beidou terminal, including:

[0086] Single-point positioning: The nominal horizontal accuracy of Beidou-3 terminals is typically 5-10 meters;

[0087] Differential positioning: After correction using differential signals from base stations, the nominal horizontal accuracy is typically 1-3 meters;

[0088] Based on the actual accuracy requirements of the rescue scenario, determine the maximum acceptable normal error, including:

[0089] Offshore rescue: The radar accuracy of the rescue vessel is greater than or equal to 5 meters, and the density of fishing boats is high. Precise positioning is required to avoid missing the target. The maximum acceptable error is 10 meters (exceeding 10 meters may double the search range of the rescue vessel).

[0090] For offshore rescue: due to the low density of fishing vessels, the search range of rescue vessels can be appropriately expanded, with a maximum acceptable error of 15 meters (exceeding 15 meters will result in a search time extension of more than 30%).

[0091] Special scenarios (such as nighttime or inclement weather): Visual search is difficult and requires stricter thresholds. The maximum acceptable error is 8 meters.

[0092] The error threshold T0 is set by the intersection of the upper limit of the horizontal nominal accuracy and the maximum acceptable normal error, i.e., T0 = max (1.5 times the horizontal nominal accuracy and 0.8 times the maximum acceptable normal error).

[0093] It is understandable that the logic for setting the error threshold is as follows:

[0094] 1.5 times the nominal horizontal accuracy: to avoid misjudgment during normal rescue due to individual differences in equipment (such as some terminals having slightly lower accuracy than the nominal value);

[0095] 0.8 times the maximum acceptable normal error: reserve identification space for multipath deviation (multipath deviation is usually 10-50 meters, much larger than this value);

[0096] For any given historical rescue

[0097] For each rescued fishing vessel, the horizontal error is compared with the error threshold. If there is a rescued fishing vessel with a horizontal error greater than the error threshold, the historical rescue is marked as a positioning error rescue.

[0098] Conversely, if there are no fishing vessels rescued with a horizontal error greater than the error threshold in the history of rescue efforts, then it is considered a normal rescue.

[0099] The purpose of dividing historical rescues into location error rescues and normal rescues is to: filter out location error rescues from historical rescue data, eliminate irrelevant interference for subsequent multipath effect analysis, and serve as the data preprocessing and classification entry point for the entire process;

[0100] Step 2: For positioning error rescue, conduct fluctuation cycle overlap analysis and error consistency analysis on the positioning error of the fishing vessel to be rescued in the target sea area and the wave cycle to determine whether the positioning error is caused by multipath effect. If so, mark the positioning error rescue as the target rescue.

[0101] Please see Figure 2 As shown, in step two, the process of performing error consistency analysis on the fishing vessels awaiting rescue in the target sea area includes:

[0102] For any single location error rescue:

[0103] Collect location data, real location data, satellite parameters, and wave parameters of all fishing vessels being rescued within the target sea area;

[0104] Among them, the positioning data is the location P calculated by the Beidou terminal for each fishing boat. 测 = (Lon 测 Lat 测 The actual location data is the actual location P of each fishing boat. 真 = (Lon 真 Lat 真 The satellite parameters are the azimuth of the primary satellite (the horizontal direction of the satellite relative to the fishing boat), and the wave parameters are the wave direction (the direction of wave propagation) of the target sea area.

[0105] For each rescued fishing vessel, the positioning error vector is calculated, taking the BeiDou positioning point as the starting point and the actual location as the ending point, including the error direction and error magnitude;

[0106] The location P of the BeiDou terminal is calculated using the geographic coordinate distance formula. 测 = (Lon 测 Lat 测 ) and the actual location P of each fishing boat 真 = (Lon 真 Lat 真 The horizontal error is used to determine the magnitude of the error.

[0107] The error direction is the horizontal angle from the calculated position of the BeiDou terminal to the actual position, calculated using a geographic coordinate system with true north as 0° and increasing clockwise. Specifically:

[0108] Calculate the difference between latitude and longitude (unit: radians):

[0109] ;

[0110] ;

[0111] Based on a mathematical coordinate system: x-axis east, y-axis north, counterclockwise is positive, the initial direction angle is calculated using the arctangent function:

[0112] ;

[0113] Among them, cos(Lat) 真,rad This is used to correct for the variation of longitude difference with latitude (near the equator, a 1° longitude difference corresponds to a distance of approximately 111 kilometers, while it approaches 0 near the poles).

[0114] The formula for converting the initial direction angle from the mathematical coordinate system to the geographic coordinate system is:

[0115] ;

[0116] The final output error vector for each rescued fishing vessel is ( );

[0117] For each rescued fishing vessel, the error direction is decomposed into x (eastward) and y (northward) components on the horizontal plane:

[0118] , ;

[0119] Take the arithmetic mean of the x and y components of all the rescued fishing boats:

[0120] , ;

[0121] Where n is the number of fishing boats rescued;

[0122] Convert the average vector to geographic coordinate system direction:

[0123] For each rescued fishing vessel, calculate the error δ between the error direction and the average direction. i :

[0124] δ i =min(|θ error,i -θ avg |,360°-|θ error,i -θ avg |), take the absolute value and then average to obtain the directional concentration. ;

[0125] The directional concentration is compared with a threshold. If the directional concentration is less than or equal to the threshold, the error direction is determined to be concentrated.

[0126] It should be noted that the threshold is set as follows: filter historical samples of multi-path and non-multi-path scenarios, calculate the orientation concentration of each group, and find the point where the orientation concentration distributions of the two types of samples overlap the least, which is the threshold.

[0127] According to the formula The wave direction-azimuth difference was calculated. , where θ 波 It is the direction of wave propagation, θ 卫 It is the satellite azimuth angle;

[0128] Satellite reflection direction θ 反 The direction from which the satellite signal reaches the fishing boat after being reflected by the waves is equal to the satellite azimuth angle + 180°, i.e.: θ 反 =(θ 卫 +180°) mod 360°;

[0129] For each rescued fishing vessel, calculate the error direction θ of the rescued fishing vessel. error With respect to satellite reflection direction θ 反 deviation γ i γ i =min(|θ error,i -θ 反 |,360°-|θ error,i -θ 反 |);

[0130] The error direction θ of all rescued fishing boats error With respect to satellite reflection direction θ 反 The absolute value of the deviation is taken and the mean is calculated to obtain the absolute mean of the error.

[0131] The absolute mean of the error is compared with the preset absolute mean. If the absolute mean of the error is less than or equal to the preset absolute mean, the error direction is determined to be consistent with the satellite reflection direction.

[0132] It should be noted that the preset absolute mean is set as follows: the deviation between the error direction of the multipath effect and the satellite reflection direction is essentially the offset of the reflected wave direction caused by the tilt of the sea wave surface. When the satellite signal is reflected by the sea wave, the reflection angle equals the incident angle. If there is a slope α on the sea wave surface, the reflection direction will be offset by 2α, which leads to the upper limit of the deviation between the error direction and the ideal reflection direction being 2α, that is, the preset absolute mean is 2α.

[0133] If the error directions are concentrated and the error directions are consistent with the satellite reflection direction, then spatial consistency is determined to exist;

[0134] In step two, the process of analyzing the coincidence of the positioning error of the fishing vessel to be rescued in the target sea area with the wave period includes:

[0135] For any fishing vessel rescued in each positioning error rescue operation:

[0136] Extract the wave cycle, i.e. the wave crest interval, during the rescue period from the historical meteorological database, and record the wave crest time;

[0137] Extracting continuous positioning points (latitude and longitude) of the rescued fishing vessel from the BeiDou terminal logs and calculating the horizontal error with the actual location. t is the timestamp, forming an error sequence. ;

[0138] The error sequence is segmented according to the wave cycle. The Pearson correlation coefficient r between the error peak and the wave peak time is calculated. r is compared with the preset correlation coefficient. The proportion of rescued fishing boats with r greater than the preset correlation coefficient is counted and compared with the preset proportion. If it is greater than the preset proportion, the error rescue has periodic consistency.

[0139] It should be noted that the preset correlation coefficient is the correlation threshold between the peak error of a single vessel and the peak time, and the preset proportion is the threshold of the proportion of fishing vessels that meet the correlation, which is set by those skilled in the art.

[0140] In step two, the process of determining whether the positioning error is caused by multipath effects includes:

[0141] For any single location error rescue:

[0142] If both periodic and spatial consistency are present, the positioning error is determined to be caused by the multipath effect, and the error rescue is marked as the target rescue.

[0143] Understandably, the logic for determining whether the positioning error is due to the multipath effect is as follows:

[0144] The essence of the multipath effect is the superposition interference between direct satellite waves and reflected ocean waves. Ocean waves have periodic motion characteristics (time dimension), and the reflection direction and intensity of ocean waves in the same sea area have spatial similarity (spatial dimension). This directly leads to the positioning error exhibiting a corresponding spatiotemporal pattern:

[0145] Time dimension: Ocean waves move in a fixed cycle (wave crest → wave trough → wave crest), and the propagation path and phase difference of the reflected waves also change with this cycle, ultimately causing the positioning error to fluctuate synchronously with the wave cycle;

[0146] Spatial dimension: Within the same sea area, the wave direction and wave height distribution are relatively uniform. If multiple fishing boats are in the same satellite signal reflection path coverage area, they will be affected by similar reflected waves, resulting in consistent error direction and magnitude.

[0147] For rescue operations involving positioning errors, determining whether the positioning error is due to the multipath effect involves:

[0148] Multipath effect attribution is performed on positioning error rescue, and error rescue (i.e. target rescue) caused by multipath effect is accurately screened from all positioning errors, eliminating interference from other error causes (such as equipment failure);

[0149] Step 3: Conduct correlation analysis on wave height and positioning error in different satellite elevation angle ranges during target rescue, and construct a multipath error compensation model;

[0150] In step three, the construction process of the multipath error compensation model includes:

[0151] Real-time wave height, wave direction-azimuth difference, and satellite elevation angle are obtained from historical target rescue data as input parameters, and the magnitude and direction of positioning error are used as output parameters. Among them, the satellite elevation angle is the horizontal angle between the satellite and the line connecting the fishing boat antenna, which is used to measure the position of the fishing boat.

[0152] Define a sliding window that uses the satellite elevation angle as the axis to extract samples within a continuous interval of satellite elevation angles for fitting. The window size is W and the sliding step size is 1.

[0153] It should be noted that, based on the attenuation law of reflected wave energy with elevation angle, when the satellite elevation angle is below the threshold, the signal propagates close to the sea surface, the sea surface is approximately mirror-like, the reflected wave energy is strong, and the interference after superposition is significant. When the satellite elevation angle is in a certain range above the threshold, the sea surface exhibits mixed reflection, the reflected wave energy attenuates rapidly as the satellite elevation angle increases, and the interference weakens. When the satellite elevation angle is above the upper limit of the range, the signal propagation path is far from the sea surface, the sea surface exhibits diffuse reflection, the reflected wave energy is dispersed, and it cannot form effective superposition interference. At this time, the multipath effect can be ignored.

[0154] For each sample within a sliding window, a linear regression is performed on the wave height and error. The linear regression equation for each sliding window is as follows: , where k i Let b be the wave height coefficient within the i-th sliding window. i This refers to the constant term within the i-th sliding window;

[0155] The regression coefficients are solved by the least squares method to obtain the linear regression equations within each sliding window;

[0156] It is understandable that the physical meaning of the wave height coefficient k is: the number of meters the positioning error increases by for every 1 meter increase in wave height within the satellite elevation angle range, and the physical meaning of the constant term b is: the basic error when the wave height is 0 within the satellite elevation angle range, reflecting the basic error of the inherent error of the equipment and the interference of non-wave height factors.

[0157] Each sliding window was fitted sequentially, and the center satellite elevation angle (the average of the elevation angles of the first and last satellites in the window) and wave height coefficient k were recorded for each window. i constant term b i ;

[0158] Using the center satellite elevation angle of each sliding window as the x-axis, k i and b i Using the y-axis, plot the wave height coefficient-satellite elevation angle curve and the constant term-satellite elevation angle curve to directly observe the variation of parameters with satellite elevation angle;

[0159] It should be noted that the analysis of curve characteristics is based on the expectation of physical mechanisms:

[0160] kE curve: At low satellite elevation angles, the signal propagates close to the sea surface, and the wave height has a strong influence on the reflected wave → the k value is large and stable; as the satellite elevation angle increases, the signal propagation path moves away from the sea surface, and the influence of wave height weakens → the k value gradually decreases; when the satellite elevation angle exceeds a certain threshold, the k value will suddenly drop sharply (abrupt change), and then the downward trend will slow down (in the medium elevation angle region, the influence of wave height stabilizes at a low level).

[0161] bE curve: The constant term b mainly reflects non-wave height factors (such as equipment error), and its change with the elevation angle is relatively gradual, with no obvious sudden changes (therefore, the grouping is based on the sudden change of the k value).

[0162] The abrupt change point is the satellite elevation angle position where the change in k-value suddenly increases. It is identified by calculating the difference in k-values ​​between adjacent windows.

[0163] Calculate the difference between adjacent windows k: That is, the absolute value of the difference between the (i+1)th window and the k value of the ith window;

[0164] Calculate all Given the mean μ and standard deviation σ, a sudden change threshold is set as μ+2σ. Values ​​exceeding this threshold are considered normal. This is considered a significant difference, corresponding to a sudden change in the k-value;

[0165] Iterate through the differences of k values ​​between all adjacent sliding windows. And compare it with the sudden change threshold, the first one to exceed the sudden change threshold The upper limit of the previous sliding window is the satellite elevation angle grouping point;

[0166] Using the satellite elevation angle grouping point as the boundary, the historical samples were divided into two elevation angle groups. Linear regression was performed on the data of each group, and the linear regression equation was: The regression coefficients were solved using the least squares method to obtain the linear regression equation, and the wave height range of each satellite elevation angle group was recorded.

[0167] The error direction needs to be considered in conjunction with the wave direction-azimuth difference. Based on the established patterns of satellite reflection, historical data analysis revealed that:

[0168] The error direction is consistent with the satellite reflection direction, and the reflection direction equals the satellite azimuth angle. , Time take -, Take + when;

[0169] By integrating the relationship between error magnitude, wave height, wave direction, and satellite elevation angle, a dual-output model of error magnitude + error direction is formed:

[0170] ;

[0171] Wherein, k1×H+b1 is the linear regression equation of error and wave height within the satellite elevation angle interval [H0,H1], and k2×H+b2 is the linear regression equation of error and wave height within the satellite elevation angle interval [H1,H2], where [H0,H1] and [H1,H2] are two elevation angle groups divided by the satellite elevation angle grouping point;

[0172] The purpose of constructing a multipath error compensation model is to establish a quantitative relationship between input parameters and positioning error based on historical data of target rescue and normal rescue, and to provide a feasible mathematical model for subsequent real-time correction of positioning error. This is the core link in solving how to compensate for multipath errors.

[0173] Step 4: Based on the wave parameters of normal rescue and target rescue and the location of the fishing vessel to be rescued, construct a multipath effect risk prediction model, and combine the current wave parameters with the location of the fishing vessel to be rescued to determine whether there is a multipath effect risk in the current rescue.

[0174] In step four, the construction process of the multipath effect risk prediction model includes:

[0175] Extract continuous wave data during the period of positioning error in normal rescue and target rescue;

[0176] Extreme wave heights were removed using the 3σ principle: wave height data greater than three times the mean were removed, as this may be due to sensor interference.

[0177] Wave peaks, i.e., the highest points between two adjacent troughs, are identified using the extreme value method. The wave height H of each wave is recorded. i That is, the vertical distance between the crest and the previous trough;

[0178] Arrange the wave heights in descending order of magnitude: H1≥H2≥...≥H N Where N is the number of wave heights;

[0179] Extract the first third of the wave height: take k = N / 3 wave heights (if N is not an integer multiple of 3, take the integer part, such as N = 152, then k = 50);

[0180] The effective wave height is obtained by calculating the average of the first third of the wave height: H s =(H1+H2+...+H k ) / k;

[0181] Construct a predictive model, inputting current wave parameters and the location parameters of the fishing vessel awaiting rescue, and outputting the probability of positioning error caused by multipath effects, specifically:

[0182] The dependent variable is whether a multipath effect occurs (Y);

[0183] Y=1 (Target Rescue): The positioning error is greater than or equal to the threshold, where the threshold is set based on the nominal accuracy of the Beidou system and the actual needs of the rescue scenario.

[0184] Y=0 (Normal Rescue): Positioning error is within the normal range;

[0185] Based on the core causes of the multipath effect, ocean wave parameters and fishing vessel position parameters are extracted from historical data;

[0186] Among them, the wave parameters include effective wave height, wave period, and wave direction-azimuth difference, and the fishing vessel position parameters include satellite elevation angle;

[0187] Z-score standardization is applied to wave parameters and fishing vessel position parameters: Standardized eigenvalue = (Original value - Mean eigenvalue) / Standard deviation eigenvalue;

[0188] Construct an interaction term between wave parameters and fishing vessel position parameters. The interaction feature is a wave height-elevation angle co-term: X = H × (30° - θ) 卫 ), where H is the wave height, θ 卫 This refers to the satellite's elevation angle;

[0189] Calculate the Pearson correlation coefficient between the independent variable and Y, retain the feature set [x1, x2, ..., x] whose Pearson correlation coefficient is greater than or equal to the preset correlation coefficient, and obtain the feature set [x1, x2, ..., x]. i ], where x i For the i-th feature;

[0190] The cleaned samples were divided into training and test sets according to the proportion. The training set was used to fit the model parameters, and the test set was used to verify the model's generalization ability.

[0191] With feature set [x1, x2, ..., x i Using ] as the independent variable, construct the probability prediction formula:

[0192] ;

[0193] Where β0 is a constant term, and β1~β4 are regression coefficients (reflecting the relationship between independent variables and regression coefficients). (Influence intensity and direction)

[0194] The regression coefficients are obtained by fitting the training set, and finally a risk prediction model for multipath effects is obtained.

[0195] Input the current wave parameters and the location of the fishing vessel to be rescued into the risk prediction model of multipath effect, and output the probability of multipath effect occurring at the current location of the fishing vessel.

[0196] Compare the probability with the preset probability. If the probability is greater than or equal to the preset probability, then the current rescue operation is at risk of multipath effect.

[0197] It should be noted that the preset probability is the threshold for judging the risk of multipath effects, which is set by those skilled in the art;

[0198] The purpose of constructing a multi-path effect risk prediction model is:

[0199] Predicting the probability of multipath effects before real-time rescue, and avoiding the passive situation of correcting positioning errors after they have occurred, is part of the pre-event risk prevention and control process.

[0200] Step 5: If present, input the current wave parameters and the location of the fishing vessel to be rescued into the multipath error compensation model to dynamically correct the BeiDou positioning results and obtain the corrected positioning coordinates of the fishing vessel to be rescued.

[0201] In step five, the process of dynamically correcting the BeiDou positioning results to obtain the corrected positioning coordinates includes:

[0202] Based on the current location of the fishing vessel awaiting rescue using BeiDou positioning, the satellite elevation angle is determined. The satellite elevation angles are then grouped, and the wave height is substituted into the corresponding formula to obtain the magnitude of the positioning error. ;

[0203] Calculate wave direction-azimuth difference Substituting into the direction formula, we obtain the error direction θ. 误 ;

[0204] The error vector is: towards θ 误 Direction offset rice;

[0205] Let the original coordinates be (Lon 原 ,Lat 原 The corrected coordinates are (Lon) 修 Lat 修 The offset coordinates of the Earth's surface are obtained using the azimuth-distance to latitude / longitude formula:

[0206] ;

[0207] ;

[0208] Where R is the average radius of the Earth;

[0209] Obtain the BeiDou positioning data to be corrected from the rescue platform: original coordinates (Lon 原 ,Lat 原 Location timestamp (aligned with real-time meteorological data timestamp);

[0210] θ 误 After converting to radians, the magnitude and direction of the positioning error are substituted into the azimuth-distance to latitude and longitude formula to obtain the corrected positioning coordinates;

[0211] The role of dynamic correction of BeiDou positioning results is as follows: when the risk of multipath effect is predicted, the original positioning data is corrected by using the multipath effect compensation model, and finally the accurate location of the fishing boat is output. This is the implementation link of the whole process.

[0212] The technical solution and advantages of this application embodiment are as follows: A comparative analysis is performed on the calculated position of the rescued fishing vessel using the BeiDou terminal and its actual position in multiple historical rescues. Historical rescues are divided into positioning error rescues and normal rescues. For positioning error rescues, a fluctuation period overlap analysis and error consistency analysis are conducted on the positioning error of the rescued fishing vessel within the target sea area and the wave period to determine whether the positioning error is caused by multipath effects. If so, the positioning error rescue is marked as a target rescue. A correlation analysis is performed on the wave height and positioning error in different satellite elevation angle intervals during the target rescue to construct a multipath error compensation model. Based on the wave parameters of normal and target rescues and the position of the rescued fishing vessel, a multipath effect risk prediction model is constructed. Combining the current wave parameters and the position of the fishing vessel to be rescued, it is determined whether the current rescue has a multipath effect risk. If so, the current wave parameters and the position of the fishing vessel to be rescued are input into the multipath error compensation model to dynamically correct the BeiDou positioning results, obtaining the corrected positioning coordinates. This invention analyzes the comparison between the calculated position of the rescued fishing vessel's BeiDou terminal and its actual position in historical rescues, classifying historical rescues into positioning error rescues and normal rescues. For positioning error rescues, it analyzes the spatial and periodic consistency of the positioning error vector, identifies target rescues caused by multipath effects, and, based on target rescue data, fits the linear relationship between wave height and positioning error by grouping according to satellite elevation angle. It then constructs a multipath error compensation model by combining wave direction and azimuth difference. Simultaneously, it constructs a multipath effect risk prediction model using wave parameters and fishing vessel position parameters from normal / target rescues. If a risk is predicted, the compensation model corrects the BeiDou positioning result, solving the positioning error problem caused by wave multipath effects and improving the positioning accuracy to within the nominal accuracy range of BeiDou. This invention is suitable for emergency rescue of fishing vessels at sea.

[0213] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fishing boat rescue method based on a Beidou intelligent navigation positioning system, characterized in that: Comprise: Step one: compare and analyze the position calculated by the Beidou terminal of the rescued fishing boat in historical rescues with the real position, divide the historical rescues into positioning error rescue and normal rescue; Step two: for positioning error rescue, analyze the fluctuation period coincidence of the positioning error of the target sea area and the wave period, and the error consistency, to determine whether the positioning error is caused by multipath effect, if so, mark the positioning error rescue as target rescue; Step three: correlate the wave height and positioning error in different satellite elevation angle intervals in target rescue to build a multipath error compensation model; Step four: build a multipath effect risk prediction model according to the sea wave parameters and the position of the rescued fishing boat in normal rescue and target rescue, combine the current sea wave parameters and the position of the fishing boat to be rescued to determine whether there is a risk of multipath effect in the current rescue; Step five: if so, input the current sea wave parameters and the position of the fishing boat to be rescued into the multipath error compensation model to dynamically correct the Beidou positioning result and get the corrected positioning coordinates of the fishing boat to be rescued; The construction method of the multipath error compensation model is: From the historical target rescue data, get real-time wave height, wave direction-azimuth angle difference, satellite elevation angle as input parameters, and positioning error size and error direction as output parameters, where the satellite elevation angle is the horizontal angle between the satellite and the fishing boat antenna; Based on the sliding window method, linearly fit the wave height and positioning error under different satellite elevation angles, and identify the satellite elevation angle grouping point by comparing the slopes of the fitting equations of the wave height and positioning error in each sliding window; Divide the satellite elevation angle into two elevation angle groups according to the satellite elevation angle grouping point, linearly regress each group of data, and record the wave height interval of each satellite elevation angle group; Error direction is the same as satellite reflection direction, reflection direction = satellite azimuth , negative when, positive when, is the wave direction-azimuth angle difference; Integrate the relationship between positioning error and wave height, and error direction and satellite elevation angle to form a multipath error compensation model of error size + error direction; The construction process of the multipath effect risk prediction model is: Extract the continuous wave data of the positioning error period of normal rescue and target rescue; Identify the wave peak by the extreme value method, that is, the highest point between two adjacent troughs, and record the wave height of each wave, that is, the vertical distance between the wave peak and the previous trough; Arrange the wave height in descending order, calculate the average of the first third wave height, and get the effective wave height; Build a multipath effect risk prediction model, input the current sea wave parameters and the position of the fishing boat to be rescued, and output the probability of positioning error caused by multipath effect; Dependent variable Y is whether multipath effect occurs, Y=1 represents target rescue, and Y=0 represents normal rescue; Extract the sea wave parameters and fishing boat position parameters from the historical data, where the sea wave parameters include effective wave height, sea wave period, and wave direction-azimuth angle difference, and the fishing boat position parameters include satellite elevation angle; An interaction term between the sea wave parameter and the fishing vessel position parameter is constructed, which is the wave height-elevation angle interaction term: X = H × (30° - θ 卫 ), where H is the wave height, θ 卫 is the satellite elevation angle; Take the sea wave parameters, fishing boat position parameters, and wave height-elevation angle coordination as input features Calculate the Pearson correlation coefficient between the input features and the dependent variable Y, retain features whose Pearson correlation coefficient is greater than or equal to a preset correlation coefficient, and obtain the feature set [x1, x2, ..., x]. i ], where x i For the i-th feature; Taking the feature set [x1, x2, …, x i ] as the independent variable, a probability prediction formula is constructed: where β0 is a constant term, and β1~β4 are regression coefficients. Use the training set to fit the regression coefficients and finally get the multipath effect risk prediction model.

2. The fishing boat rescue method based on the Beidou intelligent navigation positioning system according to claim 1, characterized in that: The way to divide the historical rescue into positioning error rescue and normal rescue is: For each of the rescued fishing vessels in any historical rescue; The horizontal error of the Beidou terminal position solution is calculated using the geographic coordinate distance formula; The horizontal error is compared with the error threshold. If there is a rescued fishing vessel with a horizontal error greater than the error threshold, the historical rescue is marked as a positioning error rescue. Conversely, if there is no rescued fishing vessel with a horizontal error greater than the error threshold in the historical rescue, it is a normal rescue.

3. The fishing vessel rescue method based on the Beidou intelligent navigation positioning system according to claim 1, characterized in that: The wave period fluctuation coincidence analysis method for the positioning error of the fishing vessels to be rescued in the target sea area is as follows: Extract the wave period of the rescue period, i.e. the wave peak interval time, from the historical meteorological database and record the wave peak time; Extract the continuous positioning points of the rescued fishing vessels from the Beidou terminal log, calculate the horizontal error with the true position, and integrate them into an error sequence; For any one of the rescued fishing vessels in each positioning error rescue: Divide the error sequence into segments according to the wave period, calculate the error peak value and the Pearson correlation coefficient of the wave peak time; Statistically analyze the proportion of the rescued fishing vessels with a Pearson correlation coefficient greater than the preset correlation coefficient in all rescued fishing vessels, and compare it with the preset proportion. If it is greater than the preset proportion, the error rescue has periodic consistency.

4. The fishing vessel rescue method based on the Beidou intelligent navigation positioning system according to claim 3, characterized in that: The error consistency analysis process for the positioning error of the fishing vessels to be rescued in the target sea area is as follows: For each of the rescued fishing vessels in the target sea area; Take the Beidou positioning point of the rescued fishing vessel as the starting point and the true position as the ending point to calculate the positioning error vector, which includes the error direction and the error size. The error direction is the horizontal angle from the Beidou terminal position solution to the true position; Decompose the error direction of the rescued fishing vessel into x and y components in the horizontal plane, and take the arithmetic mean of the x and y components of all rescued fishing vessels to obtain the average direction; Calculate the error of the error direction and the average direction, and then take the absolute value to obtain the direction concentration degree; If the direction concentration degree is less than or equal to the threshold, the error direction is concentrated; Calculate the deviation of the error direction of the rescued fishing vessel from the satellite reflection direction; Take the absolute value of the deviation of the error direction of all rescued fishing vessels from the satellite reflection direction, calculate the mean value, and obtain the error absolute mean value; If the error absolute mean value is less than or equal to the preset absolute mean value, the error direction is consistent with the satellite reflection direction. If the error direction is concentrated and the error direction is consistent with the satellite reflection direction, there is spatial consistency.

5. The fishing vessel rescue method based on the Beidou intelligent navigation positioning system according to claim 4, characterized in that: The method for determining whether the positioning error is caused by multipath effect is as follows: For any positioning error rescue: If there is periodic consistency and spatial consistency, it is determined that the positioning error is caused by multipath effect, and the error rescue is marked as a target rescue.

6. The fishing vessel rescue method based on the Beidou intelligent navigation positioning system according to claim 1, characterized in that: The identification method for the satellite elevation angle grouping is as follows: The wave height and error in the continuous satellite elevation interval are fitted by a sliding window; The regression coefficient is solved by the least square method to obtain the linear regression equation in each sliding window; The central satellite elevation, wave height coefficient and constant term of each sliding window are recorded, wherein the wave height coefficient is the slope of the linear regression equation; The difference value of adjacent sliding window k is calculated, and the mean value μ and standard deviation σ of all difference values are calculated, and the sudden change threshold is set as μ+2σ; All the difference values of adjacent sliding window k are traversed and compared with the sudden change threshold, and the upper limit of the previous sliding window corresponding to the first difference value exceeding the sudden change threshold is the satellite elevation grouping point.

7. The fishing boat rescue method based on the Beidou intelligent navigation positioning system according to claim 1, characterized in that: The judgment method of whether the current rescue exists the risk of multipath effect is: The sea wave parameters of the current rescue and the position of the fishing boat to be rescued are input into the risk prediction model of multipath effect, and the probability of the current fishing boat positioning to be rescued appearing multipath effect is output; If the probability is greater than or equal to the preset probability, the current rescue exists the risk of multipath effect.

8. The fishing boat rescue method based on the Beidou intelligent navigation positioning system according to claim 1, characterized in that: The dynamic correction method of the Beidou positioning result is: The satellite elevation is determined according to the position of the fishing boat to be rescued in the current Beidou positioning, the satellite elevation grouping is judged, the wave height is substituted into the multipath error compensation model to obtain the positioning error size; The wave direction-azimuth angle is calculated and substituted into the multipath error compensation model to obtain the error direction; After the error direction is converted into radian, the positioning error size and the error direction are substituted into the azimuth-distance conversion latitude and longitude formula to obtain the corrected positioning coordinates of the fishing boat to be rescued.

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