CN109902877B - Gradual calibration method for marine distress target drift prediction model parameters - Google Patents
Gradual calibration method for marine distress target drift prediction model parameters Download PDFInfo
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Abstract
The invention provides a gradual calibration method for a marine distress target drift prediction model parameter, which comprises the following steps: constructing a marine meteorological three-dimensional monitoring network for researching a sea area, and acquiring observation data of wind, wave and flow fields; acquiring the distress target drift velocity of a plurality of observation samples based on an offshore experiment; constructing an offshore distress target drift prediction model considering wind, wave and flow fields, and obtaining the influence degree sequence of the wind, the wave and the flow fields through correlation analysis; respectively aiming at each observation sample, based on the obtained wind, wave and flow field observation data and the distress target drift velocity, utilizing a vector regression analysis method to carry out gradual rating on model parameters according to the influence degree sequencing result, and obtaining the rate fixed value of each parameter in each observation sample; and obtaining the rating result of the drift prediction model parameter with statistical significance according to the rating value of each parameter in a plurality of observation samples.
Description
Technical Field
The invention relates to the technical field of maritime search and rescue, in particular to a gradual calibration method for a maritime distress target drift prediction model parameter.
Background
In recent years, with the expansion of the ocean development scale, a series of ocean-related activities such as foreign trade, tourism industry, aquaculture industry, resource exploitation and exploration, scientific research and the like in China develop unprecedentedly, and the number of ocean-related personnel also increases rapidly. The frequency of marine accidents is greatly increased by the increasingly frequent activities at sea, and the accidents in danger at sea are more concerned by people.
When a maritime accident occurs, the drift track of personnel falling into water at the maritime can be quickly and accurately predicted, the search and rescue range can be greatly reduced, the search and rescue efficiency is improved, the search and rescue cost is saved, and the method has important significance for maritime search and rescue. However, most of the conventional distress target drift prediction models only consider the influence of wind and current, neglect the influence of waves, and when the length of a drift object is close to the wavelength of the waves or the composition of the swell is more, the influence of the waves on the drift velocity cannot be neglected. In addition, different values of parameters of the distress target drift prediction model are different due to different marine environmental characteristics of different sea areas, and a mode of directly referring to model parameter values in other sea area researches is not feasible. Meanwhile, the prior art cannot rate the model parameters considering wind, flow and waves, and obtain a parameter rating result with statistical significance.
Disclosure of Invention
In view of the above, the present invention provides a gradual rating method for a marine distress target drift prediction model parameter, which solves at least one of the above problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a gradual calibration method for marine distress target drift prediction model parameters comprises the following steps:
constructing a marine meteorological three-dimensional monitoring network for researching a sea area, and acquiring observation data of wind, wave and flow fields;
acquiring the distress target drift velocity of a plurality of observation samples based on an offshore experiment;
constructing an offshore distress target drift prediction model considering wind, wave and flow fields, and obtaining the influence degree sequence of the wind, the wave and the flow fields through correlation analysis;
respectively aiming at each observation sample, based on the obtained wind, wave and flow field observation data and the distress target drift velocity, utilizing a vector regression analysis method to carry out gradual rating on model parameters according to the influence degree sequencing result, and obtaining the rate fixed value of each parameter in each observation sample;
and obtaining the rating result of the drift prediction model parameter with statistical significance according to the rating value of each parameter in a plurality of observation samples.
Optionally, constructing a marine meteorological stereo monitoring network for researching a sea area, and acquiring observation data of wind, wave and flow fields, including:
the ocean weather three-dimensional monitoring network comprises a ground wave radar observation station, an ocean observation station and a weather observation station, and can realize the observation of researching an inner surface flow field, a wind field and a wave field in a sea area.
Optionally, based on the marine experiment, obtaining a drift velocity of the target in distress of the plurality of observation samples includes:
the method comprises the steps of carrying out multi-voyage and multi-voyage offshore experiments in a research sea area, taking preset time as a sample selection window, dividing each voyage section in each voyage, taking each divided observation data section as a data sample, obtaining a plurality of observation samples, and further calculating the drifting speed of the target in danger in each observation sample.
Optionally, a marine distress target drift prediction model considering wind, wave and flow fields is constructed, and the order of the influence degrees of the wind, wave and flow fields is obtained through correlation analysis, and includes:
the marine distress target drift prediction model comprises the following steps:
wherein the content of the first and second substances,
is the position of the target, and is,
is an initial position, t0For the purpose of the initial time, the time of the start,
in order to make the walking distance random,is the drift velocity of the distress target;
drift velocity of distress target
The expression of (a) is:
wherein the content of the first and second substances,
and epsilon is the drift velocity, the flow-induced drift velocity, the wind-induced drift velocity, the wave-induced drift velocity and the error term of the distress target respectively.
Optionally, in the marine distress target drift prediction model, the relationship between the flow-induced drift velocity and the flow velocity in both the flow direction and the side flow direction is:
Vcurrent-F-D＝kcurrent_D×Vcurrent+bcurrent_D；
Vcurrent-F-C＝kcurrent_C×Vcurrent+bcurrent_C；
wherein, VcurrentIs the superficial flow velocity of the position of the distress target, Vcurrent-F-D、Vcurrent-F-CThe components of the flow-induced drift velocity in both the flow direction and the side flow direction, kcurrent_D、bcurrent_DTo follow the flow coefficient, kcurrent_C、bcurrent_CIs the side flow coefficient.
Optionally, the wind-induced drift velocity is related to the wind speed in both the wind direction and the crosswind direction by:
Vwind-F-D＝kwind_D×Vwind+bwind_D；
Vwind-F-C＝kwind_C×Vwind+bwind_C；
wherein, VwindThe wind speed is the wind speed V at the height of 10 m of the position of the distress targetwind-F-D、Vwind-F-CThe components of the wind-induced drift velocity in both the wind direction and the cross-wind direction, kwind_D、bwind_DIs a coefficient in the wind direction, kwind_C、bwind_CIs the lateral wind direction coefficient.
Optionally, in the marine distress target drift prediction model, the wave-induced drift velocity and the wave steepness of the wave form a unitary quadratic function form, and the relationship between the wave propagation direction and the two directions perpendicular to the wave propagation direction is as follows:
wherein, Vwave-F-D、Vwave-F-CRespectively wave-induced along and perpendicular to the wave propagation directionComponent of drift velocity, H is wave height, lambda is wave wavelength, c0_D、c1_D、c2_DIs a coefficient of direction of wave propagation, c0_C、c1_C、c2_CIs the coefficient perpendicular to the direction of wave propagation.
Optionally, a marine distress target drift prediction model considering wind, wave and flow fields is constructed, and the order of the influence degrees of the wind, wave and flow fields is obtained through correlation analysis, and includes:
respectively carrying out correlation analysis on components of the drift velocity of the distress target in the flow direction, the wind direction and the wave propagation direction and corresponding flow velocity, wind speed and wave steepness, wherein the formula is as follows:
wherein R is a correlation coefficient; x, X,Respectively representing the components of the drift velocity of the distress target in the flow direction, the wind direction and the wave direction and the mean value thereof; y, Y,
Respectively representing the observed flow velocity, wind speed, wave steepness and mean value thereof;
and determining the influence degree sequence of the wind, the wave and the flow field on the target drift speed in danger according to the magnitude sequence of the correlation coefficient R.
Optionally, the step-by-step calibration of the model parameters is performed by using a vector regression analysis method according to the influence degree ranking result on the basis of the obtained wind, wave and flow field observation data and the distress target drift velocity for each observation sample, and the calibration value of each parameter in each observation sample is obtained, including:
based on the marine distress target drift prediction model and the influence degree sequencing result, taking the observed distress target drift velocity as an approximate value of the drift velocity caused by the variable with the largest influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the largest influence degree to obtain a parameter calibration result corresponding to the variable with the largest influence degree and an estimated value of the drift velocity caused by the variable;
taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the largest influence degree as the approximate value of the drift velocity caused by the variable with the second influence degree ranking in wind, wave and flow, and carrying out vector regression analysis on the variable with the second influence degree ranking to obtain a parameter calibration result corresponding to the variable with the second influence degree ranking and the estimated value of the drift velocity caused by the variable;
taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the second influence degree sequence as the approximate value of the drift velocity caused by the variable with the minimum influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the minimum influence degree to obtain a parameter calibration result corresponding to the variable with the minimum influence degree and the estimated value of the drift velocity caused by the variable;
taking the finally obtained rating result of each parameter in each observation sample as M1×14：
Wherein the content of the first and second substances,
respectively, flow induced drift coefficient kcurrent_D、bcurrent_D、 kcurrent_C、bcurrent_CThe value of (a) is determined,
respectively the wind-induced drift coefficient kwind_D、 bwind_D、kwind_C、bwind_CThe value of (a) is determined,
respectively wave-induced drift coefficient c0_D、c1_D、c2_D、c0_C、c1_C、c2_CThe rate of (c) is fixed.
Alternatively, the vector regression analysis is used to decompose the variables and their resulting drift velocities into components along and perpendicular to the direction of the variables, with regression analysis being performed in both directions separately.
Optionally, obtaining a rating result of the drift prediction model parameter with statistical significance according to the rating value of each parameter in the plurality of observation samples includes:
generating a multi-sample model parameter calibration result set N according to the calibration values of all parameters in a plurality of observation samples14×n：
Wherein, the superscript i represents the ith sample;
utilizing the Grabbs method to calibrate the generated model parameters to a result set N14×nAnd removing abnormal values, and then calculating the mean value of each parameter to obtain the calibration result of the drift prediction model parameter with statistical significance.
Compared with the prior art, the invention has the advantages that: different from the traditional distress target drift prediction model which only considers the effects of wind and flow, the invention comprehensively considers the comprehensive influence of wind, wave and flow; aiming at the characteristic that wind, wave, flow field data and the drifting speed of the distress target are vectors, the method utilizes vector regression analysis to carry out parameter gradual calibration, and then obtains a parameter calibration result with statistical significance based on the observation data of a plurality of observation samples.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of one embodiment of the present invention;
FIG. 2 is a flow chart of one embodiment of the present invention;
fig. 3 is a flowchart of the grubbs method.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Example 1
As shown in fig. 1, a method for gradual calibration of parameters of a marine distress target drift prediction model includes the following steps:
s100: and constructing a marine meteorological three-dimensional monitoring network for researching the sea area, and acquiring observation data of wind, wave and flow fields.
Specifically, the oceanographic three-dimensional monitoring network comprises a ground wave radar observation station, a marine observation station, a meteorological observation station and the like, and can realize the observation of relevant data such as a flow field, a wind field, a wave field and the like in an inner surface layer of a research area.
S200: and acquiring the drifting speed of the distress target of a plurality of observation samples based on the offshore experiment.
Specifically, a multi-voyage and multi-voyage marine experiment is carried out in a research sea area, a preset time (which can be 13 hours) is taken as a sample selection window, each voyage segment in each voyage is divided, each divided observation data segment is taken as a data sample, a plurality of observation samples are obtained, and then the drift velocity of the distress target in each observation sample is calculated.
S300: constructing an offshore distress target drift prediction model considering wind, wave and flow fields, and obtaining the influence degree sequence of the wind, the wave and the flow fields through correlation analysis;
specifically, considering the combined action of wind, waves and a flow field, the method for constructing the marine distress target drift prediction model comprises the following steps:
wherein the content of the first and second substances,is the position of the target, and is,
is an initial position, t0For the purpose of the initial time, the time of the start,
in order to make the walking distance random,
is the drift velocity of the distress target.
The drift velocity of the distress target is the vector sum of the flow-induced drift velocity, the wind-induced drift velocity and the wave-induced drift velocity, and the relation is as follows:
wherein the content of the first and second substances,and epsilon is flow-induced drift velocity, wind-induced drift velocity, wave-induced drift velocity and error term respectively.
In the constructed marine distress target drift prediction model, the relationship between the flow-induced drift velocity and the flow velocity in two directions of the flow direction and the side flow direction is respectively as follows:
Vcurrent-F-D＝kcurrent_D×Vcurrent+bcurrent_D(3)；
Vcurrent-F-C＝kcurrent_C×Vcurrent+bcurrent_C(4)；
wherein, VcurrentIs the superficial flow velocity of the position of the distress target, Vcurrent-F-D、Vcurrent-F-CThe component of the flow-induced drift velocity in both the flow direction and the side flow direction, kcurrent_D、bcurrent_DTo follow the flow coefficient, kcurrent_C、bcurrent_CIs the side flow coefficient;
in the constructed offshore distress target drift prediction model, the relationship between the wind-induced drift velocity and the wind speed in two directions of the wind direction and the side wind direction is respectively as follows:
Vwind-F-D＝kwind_D×Vwind+bwind_D(5)；
Vwind-F-C＝kwind_C×Vwind+bwind_C(6)；
wherein, VwindThe wind speed is the wind speed V at the height of 10 m of the position of the distress targetwind-F-D、Vwind-F-CThe components of the wind-induced drift velocity in the wind direction and the side wind direction, kwind_D、bwind_DIs a coefficient in the wind direction, kwind_C、bwind_CIs the lateral wind direction coefficient.
In the constructed marine distress target drift prediction model, the wave-induced drift velocity and the wave steepness S (namely the ratio of the wave height H to the wavelength lambda) of the waves form a unitary quadratic function form, and the relationship between the wave propagation direction and the two directions vertical to the wave propagation direction is as follows:
wherein, Vwave-F-D、Vwave-F-CWave-induced drift velocity components along and perpendicular to the wave propagation direction, H being the wave height, lambda being the wave wavelength, c0_D、c1_D、c2_DIs a coefficient of direction of wave propagation, c0_C、c1_C、c2_CIs the coefficient perpendicular to the direction of wave propagation.
Specifically, the correlation analysis is to perform the correlation analysis on the components of the drift velocity of the distress target in the flow direction, the wind direction and the wave propagation direction and the corresponding flow velocity, the wind speed and the wave steepness of the waves respectively:
wherein R is a correlation coefficient; x, X,
Respectively representing the components of the drift velocity of the target to be in distress in the flow direction, the wind direction and the wave direction and the mean value thereof; y, Y,
Respectively representing the observed flow velocity, wind speed, wave steepness and mean value thereof;
and determining the influence degree sequence of the wind, the wave and the flow field on the target drift speed in danger according to the magnitude sequence of the correlation coefficient R.
S400: and respectively aiming at each observation sample, based on the obtained wind, wave and flow field observation data and the distress target drift velocity, utilizing a vector regression analysis method to carry out gradual rating on the model parameters according to the influence degree sequencing result, and obtaining the rate fixed value of each parameter in each observation sample.
Specifically, the method comprises the following steps:
s401: based on the marine distress target drift prediction model and the influence degree sequencing result, taking the observed distress target drift velocity as an approximate value of the drift velocity caused by the variable with the largest influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the largest influence degree to obtain a parameter calibration result corresponding to the variable with the largest influence degree and an estimated value of the drift velocity caused by the variable;
s402: taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the largest influence degree as the approximate value of the drift velocity caused by the variable with the second influence degree ranking in wind, wave and flow, and carrying out vector regression analysis on the variable with the second influence degree ranking to obtain a parameter calibration result corresponding to the second influence degree ranking and the estimated value of the drift velocity caused by the parameter calibration result;
s403: taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the second influence degree sequence as the approximate value of the drift velocity caused by the variable with the minimum influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the minimum influence degree to obtain a parameter calibration result corresponding to the variable with the minimum influence degree and the estimated value of the drift velocity caused by the variable;
s404: taking the finally obtained rating result of each parameter in each observation sample as M1×14。
In one embodiment, as shown in fig. 2, it is assumed that the influence degrees of wind, wave and flow field on the drift velocity of the target in danger are ranked as follows: the flow field is larger than the wind field and larger than the wave field, so that parameters of the flow field, the wind field and the wave field are gradually calibrated in sequence, and the method comprises the following steps:
(1) using the observed drift velocity of the distress target obtained in the step S200 as an approximate value of the flow-induced drift velocity, and using the flow direction component Vobject-DAnd a side flow component Vobject-CThe equations (3) and (4) are respectively carried, and the regression equation is obtained as follows:
using linear regression analysis, a fixed value for the flow-induced drift coefficient in both the flow and side flows can be obtained
And flow induced drift velocity estimateUsing drift velocity of distress targets
Subtracting flow induced drift velocity estimates
The vector difference can be obtained
(2) The obtained vector difference is used
As an approximation of the wind induced drift velocity, the component E in the wind direction will be1-DAnd a side wind direction component E1-CThe equations (5) and (6) are respectively carried, and the regression equation is obtained as follows:
by linear regression analysis, the calibration values of the wind-induced drift coefficients in both the wind direction and the crosswind direction can be obtained
And wind induced drift velocity estimate
Using vector difference
Subtracting the wind induced drift velocity estimate
The vector difference can be obtained
(3) Subjecting the above to
As an approximation of the wave-induced drift velocity, its component E in the direction of wave propagation and perpendicular to the wave propagation direction2-D、E2-CThe equations (7) and (8) are respectively carried, and the regression equation is obtained as follows:
by utilizing polynomial regression analysis, the calibration values of the wave-induced drift coefficients in the wave propagation direction and the direction perpendicular to the wave propagation direction can be obtained
And wave-induced drift velocity estimate
Based on the stepwise regression analysis, the rating values of each parameter in each observation sample can be obtained and recorded as a matrix M1×14：
S500: and obtaining the rating result of the drift prediction model parameter with statistical significance according to the rating value of each parameter in a plurality of observation samples.
Specifically, it comprises the following steps:
s501: generating a multi-sample model parameter calibration result set N according to the calibration values of all parameters in a plurality of observation samples14×n。
Specifically, the step-by-step calibration method described in S400 is repeated for each observation sample to obtain the rate-fixed value of each parameter in a plurality of observation samples, thereby generating a multi-sample model parameter calibration result set, which is recorded as a matrix N14×n：
Wherein the content of the first and second substances,the rate constant value of each parameter obtained by the ith sample is shown,
is composed of
The transposing of (1).
Further, the matrix N14×nCan be unfolded into the following forms:
wherein the superscript i represents the ith sample.
S502: utilizing the Grabbs method to calibrate the generated model parameters to a result set N14×nAnd removing abnormal values, and then calculating the mean value of each parameter to obtain the calibration result of the drift prediction model parameter with statistical significance.
In particular, the model parameter calibration result set N14×nEach column in the method represents 14 parameter values obtained by one sample data rate, and each parameter in different samples has a possibility of generating an abnormal value, so the abnormal value is removed by adopting a Grabbs method.
The Grabbs method can judge suspicious values (data which are far away from the average value) in each parameter calibration result set, and eliminates the suspicious values without participating in the calculation of the average value, so that a more accurate and reasonable parameter calibration result is obtained. By parameters
For example, as shown in fig. 3, the grubbs method is divided into the following steps:
(1) a confidence probability P of the process is determined.
Specifically, the detection level α is determined first, if the requirement is strict, the detection level α can be determined to be smaller, for example, α is determined to be 0.01, the confidence probability P is 1- α is 0.99, if the requirement is not strict, α can be determined to be larger, for example, α is determined to be 0.10, that is, P is 0.90, usually α is determined to be 0.05, and P is 0.95;
(2) determining a data set to be processed, calculating a mean value of the data
Standard deviation SK。
Specifically, n samples are calculatedData set (i.e., N)14×nFirst row of (d) of the image data
Standard deviation SK；
(3) Determining and averaging
Data with maximum deviation
Calculating the degree of deviation G from the meani。
Specifically, find out
N data and mean values in the result set
The most different dataAs a suspicious value, the degree of its deviation from the mean is calculated
(4) According to the selected confidence probability P and the number n of samples, looking up a Grabbs table to obtain a critical value GP(n)；
(5) Through GiAnd GP(n) comparing the magnitudes to determine whether the suspect value is an outlier. If G isi＞GP(n) then kiIs an abnormal value, so the column corresponding to the sample is selected from the matrix N14×nDeleting;
(6) for the remaining n-1
Repeating the steps (2) - (5) until all
All satisfy Gi≤GP；
Likewise, for matrix M1×14And removing abnormal values of the other 13 parameters by using a Grabas method in sequence, and recording a calibration result set L of the model parameters after all the abnormal values are removed14×mObviously, n.gtoreq.m.
Finally, for L14×mThe average value of each parameter in the method is calculated, and the final rating result of each parameter with certain statistical significance is obtained.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A gradual calibration method for a marine distress target drift prediction model parameter is characterized by comprising the following steps:
constructing a marine meteorological three-dimensional monitoring network for researching a sea area, and acquiring observation data of wind, wave and flow fields;
acquiring the distress target drift velocity of a plurality of observation samples based on an offshore experiment;
constructing an offshore distress target drift prediction model considering wind, waves and flow fields, and respectively carrying out correlation analysis on components of the drift velocity of a distress target in the flow direction, the wind direction and the wave propagation direction and corresponding flow velocity, wind speed and wave steepness; and through correlation analysis, the influence degrees of wind, wave and flow field are sequenced; determining the influence degree of wind, wave and flow field on the drift velocity of the distress target according to the magnitude sequence of the correlation coefficient R;
respectively aiming at each observation sample, based on the obtained wind, wave and flow field observation data and the distress target drift velocity, utilizing a vector regression analysis method to carry out gradual rating on model parameters according to the influence degree sequencing result, and obtaining the rate fixed value of each parameter in each observation sample; based on a marine distress target drift prediction model and an influence degree sequencing result, taking the observed distress target drift velocity as an approximate value of the drift velocity caused by the variable with the largest influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the largest influence degree to obtain a parameter calibration result corresponding to the variable with the largest influence degree and an estimated value of the drift velocity caused by the variable; taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the largest influence degree as the approximate value of the drift velocity caused by the variable with the second influence degree ranking in wind, wave and flow, and carrying out vector regression analysis on the variable with the second influence degree ranking to obtain a parameter calibration result corresponding to the variable with the second influence degree ranking and the estimated value of the drift velocity caused by the variable; taking the difference value between the approximate value and the estimated value of the drift velocity caused by the variable with the second influence degree sequence as the approximate value of the drift velocity caused by the variable with the minimum influence degree in wind, wave and flow, and carrying out vector regression analysis on the variable with the minimum influence degree to obtain a parameter calibration result corresponding to the variable with the minimum influence degree and the estimated value of the drift velocity caused by the variable;
and according to the rating values of all parameters in a plurality of observation samples, removing abnormal values of the generated model parameter rating result set by using a Grabbs method, and then averaging each parameter to obtain the rating result of the drift prediction model parameter with statistical significance.
2. The step-by-step calibration method according to claim 1, wherein the step-by-step calibration method comprises the steps of constructing a marine meteorological stereo monitoring network for researching sea areas, and acquiring observation data of wind, wave and flow fields, wherein the step-by-step calibration method comprises the following steps:
the ocean weather three-dimensional monitoring network comprises a ground wave radar observation station, an ocean observation station and a weather observation station, and can realize the observation of researching an inner surface flow field, a wind field and a wave field in a sea area.
3. The step-by-step calibration method according to claim 1, wherein obtaining the target drift velocity in distress for a plurality of observation samples based on an offshore experiment comprises:
the method comprises the steps of carrying out multi-voyage and multi-voyage offshore experiments in a research sea area, taking preset time as a sample selection window, dividing each voyage section in each voyage, taking each divided observation data section as a data sample, obtaining a plurality of observation samples, and further calculating the drifting speed of the target in danger in each observation sample.
4. The step-by-step calibration method according to claim 1, wherein a marine distress target drift prediction model considering wind, wave and flow field is constructed, and the influence degree sequence of the wind, wave and flow field is obtained through correlation analysis, comprising:
the marine distress target drift prediction model comprises the following steps:
wherein the content of the first and second substances,
is the position of the target, and is,is an initial position, t0For the purpose of the initial time, the time of the start,
in order to make the walking distance random,is the drift velocity of the distress target;
drift velocity of distress target
The expression of (a) is:
wherein the content of the first and second substances,
and epsilon is the drift velocity, the flow-induced drift velocity, the wind-induced drift velocity, the wave-induced drift velocity and the error term of the distress target respectively.
5. The step-by-step calibration method according to claim 4, wherein in the marine distress target drift prediction model, the flow induced drift velocity is related to the flow velocity in both the down-flow and the side-flow directions by:
Vcurrent-F-D＝kcurrent_D×Vcurrent+bcurrent_D；
Vcurrent-F-C＝kcurrent_C×Vcurrent+bcurrent_C；
wherein, VcurrentIs the superficial flow velocity of the position of the distress target, Vcurrent-F-D、Vcurrent-F-CThe components of the flow-induced drift velocity in both the flow direction and the side flow direction, kcurrent_D、bcurrent_DTo follow the flow coefficient, kcurrent_C、bcurrent_CIs the side flow coefficient.
6. The gradual rating method of claim 4, wherein the wind-induced drift velocity is related to the wind speed in both the wind direction and the crosswind direction by:
Vwind-F-D＝kwind_D×Vwind+bwind_D；
Vwind-F-C＝kwind_C×Vwind+bwind_C；
wherein, VwindThe wind speed is the wind speed V at the height of 10 m of the position of the distress targetwind-F-D、Vwind-F-CRespectively in the wind direction and the side wind directionComponent in two directions, kwind_D、bwind_DIs a coefficient in the wind direction, kwind_C、bwind_CIs the lateral wind direction coefficient.
7. The step-by-step calibration method according to claim 4, wherein in the marine distress target drift prediction model, the wave-induced drift velocity and the wave steepness of the wave are in the form of a unitary quadratic function, and the relationship between the wave propagation direction and the direction perpendicular to the wave propagation direction is as follows:
wherein, Vwave-F-D、Vwave-F-CWave-induced drift velocity components along and perpendicular to the wave propagation direction, H being the wave height, lambda being the wave wavelength, c0_D、c1_D、c2_DIs a coefficient of direction of wave propagation, c0_C、c1_C、c2_CIs the coefficient perpendicular to the direction of wave propagation.
8. The step-by-step calibration method according to claim 1, wherein a marine distress target drift prediction model considering wind, wave and flow field is constructed, the components of the drift velocity of the distress target in the flow direction, wind direction and wave propagation direction and the corresponding flow velocity, wind speed and wave steepness of the wave are respectively subjected to correlation analysis, and the influence degree sequence of the wind, wave and flow field is obtained through the correlation analysis, and the method further comprises the steps of
The components of the drift velocity of the distress target in the flow direction, the wind direction and the wave propagation direction and the corresponding flow velocity, wind speed and wave steepness are respectively subjected to correlation analysis, and the formula is as follows:
wherein R is a correlation coefficient; x, X,
Respectively representing the components of the drift velocity of the distress target in the flow direction, the wind direction and the wave direction and the mean value thereof; y, Y,
Respectively representing observed flow velocity, wind velocity, wave steepness and mean values thereof.
9. The gradual rating method of claim 1, wherein the gradual rating of the model parameters is performed by using a vector regression analysis method according to the ranking result of the degree of influence based on the obtained observation data of the wind, the wave and the flow field and the drift velocity of the distress target for each observation sample, and the rating values of the parameters in each observation sample are obtained, further comprising:
taking the finally obtained rating result of each parameter in each observation sample as M1×14：
Wherein the content of the first and second substances,
respectively, flow induced drift coefficient kcurrent_D、bcurrent_D、kcurrent_C、bcurrent_CThe value of (a) is determined,
respectively the wind-induced drift coefficient kwind_D、bwind_D、kwind_C、bwind_CThe value of (a) is determined,
respectively wave-induced drift coefficient c0_D、c1_D、c2_D、c0_C、c1_C、c2_CA rating value of (d);
the vector regression analysis is to decompose the variable and the drift velocity caused by the variable into components along the direction of the variable and components perpendicular to the direction of the variable, and perform regression analysis in the two directions respectively.
10. The gradual rating method of claim 9, wherein the obtaining the rating result of the statistically significant drift prediction model parameters by removing abnormal values from the generated rating result set of model parameters by using the grassbs method according to the rating values of the parameters in the plurality of observation samples and then averaging each parameter comprises:
generating a multi-sample model parameter calibration result set N according to the calibration values of all parameters in a plurality of observation samples14×n:
Wherein, the superscript i represents the ith sample;
utilizing the Grabbs method to calibrate the generated model parameters to a result set N14×nAnd removing abnormal values, and then calculating the mean value of each parameter to obtain the calibration result of the drift prediction model parameter with statistical significance.
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