CN105653826A - Improved maritime search and rescue region predicting method and system - Google Patents

Abstract

The invention discloses an improved maritime search and rescue region predicting method and an improved maritime search and rescue region predicting system. A dynamic drifting trajectory of a maritime search and rescue target object can be predicted according to existing meteorological and environmental data, a three-dimensional flow field, an improved AP98 air pressure model and a maritime search and rescue target drifting trajectory calculation model. The improved air pressure drifting model fully considers the uncertainty that motion in the side wind direction at each moment of a drifting target, so that a search and rescue region is more precise.

Description

Improved maritime search and rescue area prediction method and system

Technical Field

The invention relates to a maritime search and rescue technology, in particular to an improved maritime search and rescue area prediction method and system.

Background

In recent years, with the expansion of the ocean development scale, a series of ocean-related activities such as the overseas trade, the tourism industry, the aquaculture industry, the resource exploitation and exploration, the scientific research and the like in China prosperity, and the number of ocean-related personnel is also rapidly increased. The frequent offshore activities greatly increase the frequency of marine accidents caused by human factors, and the marine accidents are more concerned by people. Whether the maritime search and rescue operation can quickly and effectively search for the search and rescue target and implement rescue after an accident occurs or not has important significance for guaranteeing the safety of lives and properties of people.

The research on search and rescue theory can be traced back to the second war period at the earliest, and further research is carried out on a wind pressure drift model by a research and development center of a united states coast guard, the wind pressure model assumes that wind pressure drift distributions of positive and negative vertical wind directions are the same, uncertain factors of changes of drift directions of side winds in the same movement are ignored, and further an error exists when a search and rescue area is determined by the method.

Disclosure of Invention

The invention provides a maritime search and rescue area prediction model aiming at different drowning targets, and adds uncertain factors of direction change of a drifting target in a drifting process so as to overcome the defects of a method for determining a maritime search and rescue area in the prior art and improve the success rate of search and rescue.

The specific scheme is as follows: an improved maritime search and rescue area prediction method comprises the following steps:

s1: collecting historical meteorological data and establishing a meteorological database;

s2: inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

s3: calling the meteorological database in the S1 according to the occurrence position and the occurrence time provided by the S2 to obtain a wind field of a sea area around the time and the place corresponding to the maritime distress accident;

s4: determining a wind pressure characteristic parameter of the search and rescue object according to the type of the search and rescue object by using an improved wind pressure drift model;

s5: generating an initial position sample set of the search and rescue object according to the occurrence position and the occurrence time provided by the S2;

s6: forecasting a three-dimensional flow field of the tidal current by using the ocean current numerical model;

s7: aiming at each sample, the dynamic drift trajectory prediction of the maritime search and rescue object is realized by utilizing an improved wind pressure drift model, a three-dimensional flow field and a maritime search and rescue target drift trajectory calculation model;

s8: and visually displaying the search and rescue target drift path prediction result by using a visualization technology.

The improved wind pressure drift model is an improved AP98 wind pressure drift model and is used for calculating the wind pressure drift velocity of the maritime search and rescue target, and the model is as follows:

DWL＝a_dnW₁₀+b_dn

CWL＝a_cnW₁₀+b_cn

wherein, W₁₀A high wind speed of 10 meters from the sea surface, DWL is a wind pressure vector in the downwind direction, a_dn、b_dnRespectively an estimated value of a slope coefficient of the wind speed in the downwind direction and an estimated value of an intercept in the downwind direction, CWL is a wind pressure vector in the crosswind direction, a_cn、b_cnRespectively obtaining a side wind direction wind speed slope coefficient estimation value and a side wind direction intercept estimation value;

the estimated value of the downwind direction wind speed slope coefficient, the downwind direction wind speed slope coefficient and the wind pressure disturbance coefficient satisfy the relationship a_dn＝a_d+_dn/20 wherein a_dIs the wind speed slope coefficient in the downwind direction,_dnis a downwind wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_dRandomly extracting in the normal distribution;

the estimated value of the downwind direction intercept and the wind pressure disturbance coefficient satisfy the relation b_dn＝b_d+_dn/2 wherein b_dIs the wind speed slope coefficient in the downwind direction;

the wind speed slope coefficient estimated value in the crosswind direction and the wind speed slope coefficient and the wind pressure disturbance coefficient in the crosswind directionSatisfies the relation a_cn＝a_c+_cn/20 wherein a_cIs the wind speed slope coefficient in the crosswind direction,_cnis a side wind direction wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_cRandomly extracting in the normal distribution;

the estimated value of the intercept in the crosswind direction, the intercept in the downwind direction and the disturbance coefficient of the wind pressure satisfy the relation b_cn＝b_c+_cn/2 wherein b_dThe wind speed slope coefficient in the crosswind direction;

the probability of the lateral wind direction deviating from the left or the right is randomly determined at each moment, a probability value of the probability of the change is given, the probability of the lateral wind motion of the sample in each time step being independent, the probability of the deflection of each particle at each moment is judged according to the probability value of the probability of the change, if the probability value of the lateral wind motion of the sample in each time step is independent, the zone bit is a first zone bit, and if the probability value of the lateral wind motion of the sample in each time step is independent, otherwise, the zone bit is a second zone;

and presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right according to the zone bit.

The specific steps of predicting the three-dimensional flow field by S6 include:

s61: 8 tide-dividing M provided based on TPXO global tide satellite altimeter inversion data set₂、S₂、K₂、N₂、K₁、O₁、P₁、Q₁The harmonic constant of the tidal wave is given out as an open boundary condition of the tide, and a water level boundary condition of each node of the open boundary is automatically generated by utilizing a TMD toolkit;

s62: and operating the ocean current numerical model FVCOM, and forecasting a three-dimensional flow field in a future certain time in the sea area around the accident occurrence place.

Wherein, the calculation model of the maritime search and rescue target drift trajectory of S7 is as follows:

$\stackrel{\→}{x}\left(t\right)=\stackrel{\→}{x}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{u}(x,t)dt+{\mathrm{\Δ\α}}_x$

$\stackrel{\→}{y}\left(t\right)=\stackrel{\→}{y}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{v}(y,t)dt+{\mathrm{\Δ\α}}_y$

wherein,is the initial position of the search and rescue target,as the target drift velocity, Δ α_x,Δα_yIs the random walk distance under the influence of turbulence. Search and rescue target speedDrift to after t timeThe target drift velocity mainly comprises wind pressure drift velocity and surface flow velocity, wherein the wind pressure drift velocity U and V are respectively projected in x and y directions for the wind pressure vector obtained by calculation, and the wind pressure drift velocity satisfies the relationship between the wind direction and the included angle between the x direction and the wind pressure vector as follows:

U＝DWL*cosθ+(CWL*sinθ)

V＝DWL*sinθ-(CWL*cosθ)

wherein theta is an included angle between the wind direction and the x direction;

the integral term is calculated by adopting a fourth-order Runge Kutta method, and the method comprises the following steps:

${\stackrel{\→}{\mathrm{\ξ}}}_1={\stackrel{\→}{x}}_0$

${\stackrel{\→}{\mathrm{\ξ}}}_2={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_3={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_4={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)$

${\stackrel{\→}{x}}_n={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\[\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)}{6}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_4\right)}{6}\]$ 。

an improved maritime search and rescue area prediction system, comprising:

the database establishing module is used for collecting historical meteorological data and establishing a meteorological database;

the accident information input module is used for inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

the accident wind field acquisition module calls a meteorological database according to the occurrence position and the occurrence time of the accident input by the accident information input module to acquire a wind field of a sea area around the time and the place corresponding to the maritime distress accident;

the wind pressure characteristic parameter acquisition module is used for determining the wind pressure characteristic parameter of the search and rescue object according to the type of the search and rescue object by utilizing the improved wind pressure drift model;

the initial position sample set generating module is used for generating an initial position sample set of the search and rescue object according to the occurrence position and the occurrence time provided by the accident information input module;

the three-dimensional flow field module is used for forecasting a three-dimensional flow field of the tidal current by using the ocean current numerical model;

the system comprises a drift trajectory prediction module, a data processing module and a data processing module, wherein the drift trajectory prediction module is used for realizing dynamic drift trajectory prediction of a maritime search and rescue object by utilizing an improved wind pressure drift model, a three-dimensional flow field and a maritime search and rescue target drift trajectory calculation model aiming at each sample;

and the display module is used for visually displaying the search and rescue target drift path prediction result by utilizing a visualization technology.

The improved wind pressure drift model is an improved AP98 wind pressure drift model and is used for calculating the wind pressure drift velocity of the maritime search and rescue target, and the model is as follows:

DWL＝a_dnW₁₀+b_dn

CWL＝a_cnW₁₀+b_cn

wherein, W₁₀A high wind speed of 10 meters from the sea surface, DWL is a wind pressure vector in the downwind direction, a_dn、b_dnRespectively an estimated value of a slope coefficient of the wind speed in the downwind direction and an estimated value of an intercept in the downwind direction, CWL is a wind pressure vector in the crosswind direction, a_cn、b_cnRespectively obtaining a side wind direction wind speed slope coefficient estimation value and a side wind direction intercept estimation value;

the estimated value of the downwind direction wind speed slope coefficient, the downwind direction wind speed slope coefficient and the wind pressure disturbance coefficient satisfy the relationship a_dn＝a_d＋_dn/20 wherein a_dIs the wind speed slope coefficient in the downwind direction,_dnis a downwind wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_dRandomly extracting in the normal distribution;

the estimated value of the downwind direction intercept and the wind pressure disturbance coefficient satisfy the relation b_dn＝b_d+_dn/2 wherein b_dIs the wind speed slope coefficient in the downwind direction;

the estimated value of the slope coefficient of the wind speed in the crosswind direction and the slope coefficient and the disturbance coefficient of the wind speed in the crosswind direction satisfy the relationship a_cn＝a_c+_cn/20 wherein a_cIs the wind speed slope coefficient in the crosswind direction,_cnis a side wind direction wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_cRandomly extracting in the normal distribution;

the estimated value of the intercept in the crosswind direction, the intercept in the downwind direction and the disturbance coefficient of the wind pressure satisfy the relation b_cn＝b_c+_cn/2 wherein b_dThe wind speed slope coefficient in the crosswind direction;

the probability of the lateral wind direction deviating from the left or the right is randomly determined at each moment, a probability value of the probability of the change is given, the probability of the lateral wind motion of the sample in each time step being independent, the probability of the deflection of each particle at each moment is judged according to the probability value of the probability of the change, if the probability value of the lateral wind motion of the sample in each time step is independent, the zone bit is a first zone bit, and if the probability value of the lateral wind motion of the sample in each time step is independent, otherwise, the zone bit is a second zone;

and presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right according to the zone bit.

Wherein the three-dimensional flow field module comprises:

a boundary condition module: 8 tide M provided for inversion data set based on TPXO global tide satellite altimeter₂、S₂、K₂、N₂、K₁、O₁、P₁、Q₁The harmonic constant of the tidal wave is given out as an open boundary condition of the tide, and a water level boundary condition of each node of the open boundary is automatically generated by utilizing a TMD toolkit;

a three-dimensional flow field forecasting module: the method is used for operating the ocean current numerical model FVCOM and forecasting a three-dimensional flow field in a future certain time in an ocean area around the accident occurrence place.

Wherein, the calculation model of the maritime search and rescue target drift trajectory in the drift trajectory prediction module is as follows:

$\stackrel{\→}{x}\left(t\right)=\stackrel{\→}{x}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{u}(x,t)dt+{\mathrm{\Δ\α}}_x$

$\stackrel{\→}{y}\left(t\right)=\stackrel{\→}{y}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{v}(y,t)dt+{\mathrm{\Δ\α}}_y$

wherein,is the initial position of the search and rescue target,as the target drift velocity, Δ α_x,Δα_yIs the random walk distance under the influence of turbulence. Search and rescue target speedDrift to after t timeThe target drift velocity mainly comprises wind pressure drift velocity and surface flow velocity, wherein the wind pressure drift velocity U and V are respectively projected in x and y directions of a wind pressure vector obtained by calculationAnd the wind pressure drift velocity, the included angle between the wind direction and the x direction and the wind pressure vector satisfy the following relations:

U＝DWL*cosθ+(CWL*sinθ)

V＝DWL*sinθ-(CWL*cosθ)

wherein theta is an included angle between the wind direction and the x direction;

the integral term is calculated by adopting a fourth-order Runge Kutta method, and the method comprises the following steps:

${\stackrel{\→}{\mathrm{\ξ}}}_1={\stackrel{\→}{x}}_0$

${\stackrel{\→}{\mathrm{\ξ}}}_2={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_3={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_4={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)$

${\stackrel{\→}{x}}_n={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\[\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)}{6}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_4\right)}{6}\]$ 。

compared with the prior art, the invention has the following advantages:

the improved wind pressure drift model fully considers the uncertainty of the change of the lateral wind direction motion of the drift target at each moment, so that the search and rescue area is more accurate, and the ocean current numerical model FVCOM in the three-dimensional flow field is based on the unstructured triangular grid and is an ocean numerical model of a three-dimensional original equation set by adopting a finite volume method. The finite volume method has the advantages of both the finite element method and the finite difference method, and combines the advantages of the finite element method that the complex shoreline is easy to fit, the local encryption is carried out on the gravity area, the complex submarine topography is fitted through the sigma coordinate transformation, the finite difference method is easy to disperse an equation set, and the calculation efficiency is high. The four-order Runge Kutta method is adopted for solving the drift trajectory prediction model, and compared with the traditional Eulerian method, the method has higher precision.

Drawings

FIG. 1 is a flowchart of a method routine of the present invention;

FIG. 2 is a schematic view of a wind pressure model;

FIG. 3a is a diagram illustrating a distribution of positions of particles at different times according to an embodiment of the present invention;

FIG. 3b is a diagram illustrating a distribution of positions of particles at various times according to an embodiment of the invention;

fig. 4 shows a wind pressure coefficient of a search and rescue target according to an embodiment.

Detailed Description

To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. The invention will now be further described with reference to the accompanying drawings and detailed description.

With reference to fig. 1, a specific flow of the method for predicting a maritime search and rescue area of the present invention is shown, which includes the following steps:

1. inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

2. acquiring a wind field of a sea area around a time and a place corresponding to the marine distress accident according to the accident occurrence position and the occurrence time, wherein the NCEP wind field data is provided by the American national environmental forecast center, the data accuracy can reach 0.25 degrees by 0.25 degrees, and the data is updated every six hours;

3. and generating an initial sample set of the search and rescue objects according to the accident occurrence position and the occurrence time, wherein each sample is represented by equivalent random particles, each random particle has the same attribute with the search and rescue target and drifts under the action of external marine environmental factors, and the motion of each particle is independent and does not influence each other. For each particle, in each search and rescue process, the wind pressure attribute of different search and rescue targets is provided according to different types of search and rescue targets.

4. The open boundary condition is from harmonic constants of 8 tide divisions M2, S2, K2, N2, K1, O1, P1 and Q1 provided by a TPXO global tide satellite altimeter inversion data set, a water level time sequence of each node of the open boundary is automatically generated by utilizing a TMD tool pack, a sea current numerical model FVCOM is operated, a three-dimensional flow field of a sea area around the accident occurrence place in danger in the future 24 hours is forecasted, and the model can output three-dimensional flow field information with high spatial resolution and high time resolution as required.

It should be understood by those skilled in the art that, in the present invention, the finite volume method is used to solve the ocean numerical model of the three-dimensional original equation set, but the method for solving the ocean numerical model of the three-dimensional original equation set is not limited, and may be a finite element method or a finite difference method as an alternative method for solving the ocean numerical model of the three-dimensional original equation set.

5. Flow field data obtained by calculating the NCEP wind field data and the FVCOM provide basic environmental information for the search and rescue target, and are updated in real time in the calculation process. For each random particle, combining with an improved wind pressure drift model, considering uncertain factors generated by the particles under the influence of the environment in the drift process, calculating a wind pressure vector of the wind pressure drift model, wherein the possibility that the crosswind direction is deviated to the left or the right is randomly determined at each moment, the possibility that the random particles occur in the model is considered to be 4% in one hour, and the adopted method is as follows:

the probability of the change of the sample lateral wind direction motion in each time step is independent, the probability of each particle deflection at each moment is judged according to the 4% probability, if the probability is generated, the flag bit is 1, otherwise, the probability is 0; according to the flag bit, presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right;

6. and updating the position of each particle at each moment by adopting a drift trajectory prediction model. And judging the probability of containing the search and rescue target according to the distribution of the particles until the prediction time is reached, and outputting the position distribution of the final sample.

Those skilled in the art will appreciate that the method for solving the drift trajectory prediction model is not limited, and instead of the method for solving the drift trajectory prediction model, a conventional euler method may be used for solving the drift trajectory prediction model, but the fourth-order lunger stota method is adopted, which has higher accuracy than the conventional euler method.

Fig. 2 is a schematic diagram of an existing AP98 wind pressure model, where different search and rescue targets have different wind pressure attributes and different effects of wind force. Through long-term wind pressure tests, researchers summarize the wind pressure coefficients of 8 common large-class 63-small-class search and rescue targets. According to the characteristics of the search and rescue objects, the types of the search and rescue objects are divided by referring to the wind pressure characteristic table in the international aviation and marine search and rescue manual, and the wind pressure characteristic parameters obtained through calculation are inquired. The wind pressure deflection angle is formed by the fact that the floating object deviates from the wind direction and drifts under the influence of wind force on the water surface, and the deflection angle can be on the left side of the downwind direction or on the right side of the downwind direction. The existing wind pressure model considers that the positive and negative distribution of a wind pressure deflection angle is the same and keeps unchanged in the drifting process, and researches show that the cross wind direction can be changed at different moments, namely if the tracking moment t is carried out₁The CWL value is right CWL, and at the next tracking time t₂And then, the CWL value is the left CWL.

Fig. 3a and 3b are diagrams illustrating a position distribution diagram of particles at various times using different wind pressure models in a search and rescue experiment according to an embodiment. The method comprises the following specific steps:

according to the incident position and time in the accident report, the initial position distribution of the search and rescue targets is determined, a radio distress signal is broadcast by a certain fishing boat 3, 3 and 9 in 2015, the report host stops working, the boat body is crushed due to collision, and the initial position of the boat fragment is calculated to be 119.423 degrees E and 24.986 degrees N. According to the attributes of the search and rescue targets, inquiring a wind pressure characteristic table, determining the wind pressure coefficient of the search and rescue target, and referring to fig. 4, showing the detailed wind pressure coefficient of the search and rescue target. And (4) by combining the environmental information of the wind flow field in the sea area within the time range, predicting the position distribution of the search and rescue target at different moments after falling into water by adopting a drift prediction model. Compared with the existing wind pressure model and the improved wind pressure model, the calculation results of the existing wind pressure model and the improved wind pressure model can show that the existing calculation results cause the position distribution of particles to be gradually diffused towards the two sides of the wind direction along with the increase of time as the wind pressure deflection angle is assumed to be kept unchanged in the calculation process, the diffusion range is gradually increased, the possibility of the existence of a search and rescue target between the left wind pressure angle and the right wind pressure angle is ignored, and the success probability of search and rescue is reduced; the improved model calculation result is in the initial 6 hours after the accident, the prediction result is basically overlapped with the existing model, but because the possibility that the wind pressure deflection angle changes in the downwind direction is considered, the particle position distribution is changed into strip-shaped distribution from the original two symmetrical circular distributions, although the range required by search and rescue is increased, the defect that the wind pressure drift distribution of the original model is completely symmetrical is overcome, the model is more suitable for the actual situation, and the probability of containing the search and rescue region is further improved.

Based on the method for predicting the maritime search and rescue area, the invention also provides an improved maritime search and rescue area prediction system, which comprises the following steps:

the database establishing module is used for collecting historical meteorological data and establishing a meteorological database;

the accident information input module is used for inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

the accident wind field acquisition module calls a meteorological database according to the occurrence position and the occurrence time of the accident input by the accident information input module to acquire a wind field of a sea area around the time and the place corresponding to the maritime distress accident;

the wind pressure characteristic parameter acquisition module is used for determining the wind pressure characteristic parameter of the search and rescue object according to the type of the search and rescue object by utilizing the improved wind pressure drift model;

the initial position sample set generating module is used for generating an initial position sample set of the search and rescue object according to the occurrence position and the occurrence time provided by the accident information input module;

the three-dimensional flow field module is used for forecasting a three-dimensional flow field of the tidal current by using the ocean current numerical model;

the system comprises a drift trajectory prediction module, a data processing module and a data processing module, wherein the drift trajectory prediction module is used for realizing dynamic drift trajectory prediction of a maritime search and rescue object by utilizing an improved wind pressure drift model, a three-dimensional flow field and a maritime search and rescue target drift trajectory calculation model aiming at each sample;

and the display module is used for visually displaying the search and rescue target drift path prediction result by utilizing a visualization technology.

The improved wind pressure drift model is an improved AP98 wind pressure drift model and is used for calculating the wind pressure drift velocity of the maritime search and rescue target, and the model is as follows:

DWL＝a_dnW₁₀＋b_dn

CWL=a_cnW₁₀＋b_cn

wherein, W₁₀A high wind speed of 10 meters from the sea surface, DWL is a wind pressure vector in the downwind direction, a_dn、b_dnRespectively, a downwind direction wind speed slope coefficient estimated value and a downwind direction intercept estimated value, CWL being in a crosswind directionWind pressure vector, a_cn、b_cnRespectively obtaining a side wind direction wind speed slope coefficient estimation value and a side wind direction intercept estimation value;

the estimated value of the downwind direction wind speed slope coefficient, the downwind direction wind speed slope coefficient and the wind pressure disturbance coefficient satisfy the relationship a_dn＝a_d+_dn/20 wherein a_dIs the wind speed slope coefficient in the downwind direction,_dnis a downwind wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_dRandomly extracting in the normal distribution;

the estimated value of the downwind direction intercept and the wind pressure disturbance coefficient satisfy the relation b_dn＝b_d+_dn/2 wherein b_dIs the wind speed slope coefficient in the downwind direction;

the estimated value of the slope coefficient of the wind speed in the crosswind direction and the slope coefficient and the disturbance coefficient of the wind speed in the crosswind direction satisfy the relationship a_cn＝a_c+_cn/20 wherein a_cIs the wind speed slope coefficient in the crosswind direction,_cnis a side wind direction wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_cRandomly extracting in the normal distribution;

the estimated value of the intercept in the crosswind direction, the intercept in the downwind direction and the disturbance coefficient of the wind pressure satisfy the relation b_cn＝b_c+_cn/2 wherein b_dThe wind speed slope coefficient in the crosswind direction;

the probability of the lateral wind direction deviating from the left or the right is randomly determined at each moment, a probability value of the probability of the change is given, the probability of the lateral wind motion of the sample in each time step being independent, the probability of the deflection of each particle at each moment is judged according to the probability value of the probability of the change, if the probability value of the lateral wind motion of the sample in each time step is independent, the zone bit is a first zone bit, and if the probability value of the lateral wind motion of the sample in each time step is independent, otherwise, the zone bit is a second zone;

and presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right according to the zone bit.

Wherein the three-dimensional flow field module comprises:

a boundary condition module: 8 tide M provided for inversion data set based on TPXO global tide satellite altimeter₂、S₂、K₂、N₂、K₁、O₁、P₁、Q₁The harmonic constant of the tidal wave is given out as an open boundary condition of the tide, and a water level boundary condition of each node of the open boundary is automatically generated by utilizing a TMD toolkit;

a three-dimensional flow field forecasting module: the method is used for operating the ocean current numerical model FVCOM and forecasting a three-dimensional flow field in a future certain time in an ocean area around the accident occurrence place.

Wherein, the calculation model of the maritime search and rescue target drift trajectory in the drift trajectory prediction module is as follows:

$\stackrel{\→}{x}\left(t\right)=\stackrel{\→}{x}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{u}(x,t)dt+{\mathrm{\Δ\α}}_x$

$\stackrel{\→}{y}\left(t\right)=\stackrel{\→}{y}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{v}(y,t)dt+{\mathrm{\Δ\α}}_y$

wherein,is the initial position of the search and rescue target,as the target drift velocity, Δ α_x,Δα_yIs the random walk distance under the influence of turbulence. Search and rescue target speedDrift to after t timeThe target drift velocity mainly comprises wind pressure drift velocity and surface flow velocity, wherein the wind pressure drift velocity U and V are respectively projected in x and y directions for the wind pressure vector obtained by calculation, and the wind pressure drift velocity satisfies the relationship between the wind direction and the included angle between the x direction and the wind pressure vector as follows:

U＝DWL*cosθ+(CWL*sinθ)

V＝DWL*sinθ-(CWL*cosθ)

wherein theta is an included angle between the wind direction and the x direction;

the integral term is calculated by adopting a fourth-order Runge Kutta method, and the method comprises the following steps:

${\stackrel{\→}{\mathrm{\ξ}}}_1={\stackrel{\→}{x}}_0$

${\stackrel{\→}{\mathrm{\ξ}}}_2={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_3={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_4={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)$

${\stackrel{\→}{x}}_n={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\[\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)}{6}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_4\right)}{6}\]$ 。

while the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. An improved maritime search and rescue area prediction method comprises the following steps:

s1: collecting historical meteorological data and establishing a meteorological database;

s2: inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

s3: calling the meteorological database in the S1 according to the occurrence position and the occurrence time provided by the S2 to obtain a wind field of a sea area around the time and the place corresponding to the maritime distress accident;

s4: determining a wind pressure characteristic parameter of the search and rescue object according to the type of the search and rescue object by using an improved wind pressure drift model;

s5: generating an initial position sample set of the search and rescue object according to the occurrence position and the occurrence time provided by the S2;

s6: forecasting a three-dimensional flow field of the tidal current by using the ocean current numerical model;

s7: aiming at each sample, the dynamic drift trajectory prediction of the maritime search and rescue object is realized by utilizing an improved wind pressure drift model, a three-dimensional flow field and a maritime search and rescue target drift trajectory calculation model;

s8: and visually displaying the search and rescue target drift path prediction result by using a visualization technology.

2. The method of claim 1, wherein the improved wind pressure drift model is an improved AP98 wind pressure drift model for calculating the wind pressure drift velocity of the maritime search and rescue target, and the model is as follows:

DWL＝a_dnW₁₀+b_dn

CWL＝a_cnW₁₀+b_cn

wherein, W₁₀A high wind speed of 10 meters from the sea surface, DWL is a wind pressure vector in the downwind direction, a_dn、b_dnRespectively an estimated value of a slope coefficient of the wind speed in the downwind direction and an estimated value of an intercept in the downwind direction, CWL is a wind pressure vector in the crosswind direction, a_cn、b_cnRespectively obtaining a side wind direction wind speed slope coefficient estimation value and a side wind direction intercept estimation value;

the estimated value of the downwind direction wind speed slope coefficient, the downwind direction wind speed slope coefficient and the wind pressure disturbance coefficient satisfy the relationship a_dn＝a_d+_dn/20 wherein a_dIs the wind speed slope coefficient in the downwind direction,_dnis a downwind wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_dRandomly extracting in the normal distribution;

the estimated value of the downwind direction intercept and the wind pressure disturbance coefficient satisfy the relation b_dn＝b_d+_dn/2 wherein b_dIs as followsA wind speed slope coefficient in the wind direction;

the estimated value of the slope coefficient of the wind speed in the crosswind direction and the slope coefficient and the disturbance coefficient of the wind speed in the crosswind direction satisfy the relationship a_cn＝a_c+_cn/20 wherein a_cIs the wind speed slope coefficient in the crosswind direction,_cnis a side wind direction wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_cRandomly extracting in the normal distribution;

the estimated value of the intercept in the crosswind direction, the intercept in the downwind direction and the disturbance coefficient of the wind pressure satisfy the relation b_cn＝b_c+_cn/2 wherein b_dThe wind speed slope coefficient in the crosswind direction;

the probability of the lateral wind direction deviating from the left or the right is randomly determined at each moment, a probability value of the probability of the change is given, the probability of the lateral wind motion of the sample in each time step being independent, the probability of the deflection of each particle at each moment is judged according to the probability value of the probability of the change, if the probability value of the lateral wind motion of the sample in each time step is independent, the zone bit is a first zone bit, and if the probability value of the lateral wind motion of the sample in each time step is independent, otherwise, the zone bit is a second zone;

and presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right according to the zone bit.

3. The method as claimed in claim 1, wherein the step of predicting the three-dimensional flow field in S6 comprises:

s61: 8 tide-dividing M provided based on TPXO global tide satellite altimeter inversion data set₂、S₂、K₂、N₂、K₁、O₁、P₁、Q₁The harmonic constant of the tidal wave is given out as an open boundary condition of the tide, and a water level boundary condition of each node of the open boundary is automatically generated by utilizing a TMD toolkit;

s62: and operating the ocean current numerical model FVCOM, and forecasting a three-dimensional flow field in a future certain time in the sea area around the accident occurrence place.

4. The method according to claim 1, wherein the calculation model of the maritime search and rescue target drift trajectory of S7 is:

$\stackrel{\→}{x}\left(t\right)=\stackrel{\→}{x}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{u}(x,t)dt+{\mathrm{\Δ\α}}_x$

$\stackrel{\→}{y}\left(t\right)=\stackrel{\→}{y}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{v}(y,t)dt+{\mathrm{\Δ\α}}_y$

wherein,is the initial position of the search and rescue target,as the target drift velocity, Δ α_x,Δα_yIs the random walk distance under the influence of turbulence. Search and rescue target speedDrift to after t timeThe target drift velocity mainly comprises wind pressure drift velocity and surface flow velocity, wherein the wind pressure drift velocity U and V are respectively projected in x and y directions for the wind pressure vector obtained by calculation, and the wind pressure drift velocity satisfies the relationship between the wind direction and the included angle between the x direction and the wind pressure vector as follows:

U＝DWL*cosθ+(CWL*sinθ)

V＝DWL*sinθ-(CWL*cosθ)

wherein theta is an included angle between the wind direction and the x direction;

the integral term is calculated by adopting a fourth-order Runge Kutta method, and the method comprises the following steps:

${\stackrel{\→}{\mathrm{\ξ}}}_1={\stackrel{\→}{x}}_0$

${\stackrel{\→}{\mathrm{\ξ}}}_2={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_3={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_4={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)$

${\stackrel{\→}{x}}_n={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\[\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)}{6}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_4\right)}{6}\]$ 。

5. an improved maritime search and rescue area prediction system, comprising:

the database establishing module is used for collecting historical meteorological data and establishing a meteorological database;

the accident information input module is used for inputting the accident occurrence position, the occurrence time, the prediction duration, the type of the search and rescue object and the number of samples;

the accident wind field acquisition module calls a meteorological database according to the occurrence position and the occurrence time of the accident input by the accident information input module to acquire a wind field of a sea area around the time and the place corresponding to the maritime distress accident;

the wind pressure characteristic parameter acquisition module is used for determining the wind pressure characteristic parameter of the search and rescue object according to the type of the search and rescue object by utilizing the improved wind pressure drift model;

the initial position sample set generating module is used for generating an initial position sample set of the search and rescue object according to the occurrence position and the occurrence time provided by the accident information input module;

the three-dimensional flow field module is used for forecasting a three-dimensional flow field of the tidal current by using the ocean current numerical model;

the system comprises a drift trajectory prediction module, a data processing module and a data processing module, wherein the drift trajectory prediction module is used for realizing dynamic drift trajectory prediction of a maritime search and rescue object by utilizing an improved wind pressure drift model, a three-dimensional flow field and a maritime search and rescue target drift trajectory calculation model aiming at each sample;

and the display module is used for visually displaying the search and rescue target drift path prediction result by utilizing a visualization technology.

6. The system of claim 5, wherein the improved wind pressure drift model is an improved AP98 wind pressure drift model for calculating the wind pressure drift velocity of the maritime search and rescue target, and the model is as follows:

DWL＝a_dnW₁₀+b_dn

CWL＝a_cnW₁₀+b_cn

wherein, W₁₀A high wind speed of 10 meters from the sea surface, DWL is a wind pressure vector in the downwind direction, a_dn、b_dnRespectively an estimated value of the slope coefficient of the wind speed in the downwind direction and the downwind directionIntercept estimation value CWL is wind pressure vector of side wind direction, a_cn、b_cnRespectively obtaining a side wind direction wind speed slope coefficient estimation value and a side wind direction intercept estimation value;

the estimated value of the downwind direction wind speed slope coefficient, the downwind direction wind speed slope coefficient and the wind pressure disturbance coefficient satisfy the relationship a_dn＝a_d+_dn/20 wherein a_dIs the wind speed slope coefficient in the downwind direction,_dnis a downwind wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_dRandomly extracting in the normal distribution;

the estimated value of the downwind direction intercept and the wind pressure disturbance coefficient satisfy the relation b_dn＝b_d+_dn/2 wherein b_dIs the wind speed slope coefficient in the downwind direction;

the estimated value of the slope coefficient of the wind speed in the crosswind direction and the slope coefficient and the disturbance coefficient of the wind speed in the crosswind direction satisfy the relationship a_cn＝a_c+_cn/20 wherein a_cIs the wind speed slope coefficient in the crosswind direction,_cnis a side wind direction wind pressure disturbance coefficient, the mean value is 0, and the variance of the wind pressure disturbance coefficient is sigma_cRandomly extracting in the normal distribution;

the estimated value of the intercept in the crosswind direction, the intercept in the downwind direction and the disturbance coefficient of the wind pressure satisfy the relation b_cn＝b_c+_cn/2 wherein b_dThe wind speed slope coefficient in the crosswind direction;

the probability of the lateral wind direction deviating from the left or the right is randomly determined at each moment, a probability value of the probability of the change is given, the probability of the lateral wind motion of the sample in each time step being independent, the probability of the deflection of each particle at each moment is judged according to the probability value of the probability of the change, if the probability value of the lateral wind motion of the sample in each time step is independent, the zone bit is a first zone bit, and if the probability value of the lateral wind motion of the sample in each time step is independent, otherwise, the zone bit is a second zone;

and presetting a side wind direction wind speed slope coefficient, an intercept and a side wind direction wind pressure disturbance coefficient to be left or right according to the zone bit.

7. The system of claim 5, wherein the three-dimensional flow field module comprises:

a boundary condition module: 8 tide M provided for inversion data set based on TPXO global tide satellite altimeter₂、S₂、K₂、N₂、K₁、O₁、P₁、Q₁The harmonic constant of the tidal wave is given out as an open boundary condition of the tide, and a water level boundary condition of each node of the open boundary is automatically generated by utilizing a TMD toolkit;

a three-dimensional flow field forecasting module: the method is used for operating the ocean current numerical model FVCOM and forecasting a three-dimensional flow field in a future certain time in an ocean area around the accident occurrence place.

8. The system of claim 5, wherein the calculation model of the maritime search and rescue target drift trajectory in the drift trajectory prediction module is:

$\stackrel{\→}{x}\left(t\right)=\stackrel{\→}{x}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{u}(x,t)dt+{\mathrm{\Δ\α}}_x$

$\stackrel{\→}{y}\left(t\right)=\stackrel{\→}{y}\left(t_0\right)+{\∫}_{t_0}^t\stackrel{\→}{v}(y,t)dt+{\mathrm{\Δ\α}}_y$

wherein,is the initial position of the search and rescue target,as the target drift velocity, Δ α_x,Δα_yIs the random walk distance under the influence of turbulence. Search and rescue target speedDrift to after t timeThe target drift velocity mainly comprises wind pressure drift velocity and surface flow velocity, wherein the wind pressure drift velocity U and V are respectively projected in x and y directions for the wind pressure vector obtained by calculation, and the wind pressure drift velocity satisfies the relationship between the wind direction and the included angle between the x direction and the wind pressure vector as follows:

U＝DWL*cosθ+(CWL*sinθ)

V＝DWL*sinθ-(CWL*cosθ)

wherein theta is an included angle between the wind direction and the x direction;

the integral term is calculated by adopting a fourth-order Runge Kutta method, and the method comprises the following steps:

${\stackrel{\→}{\mathrm{\ξ}}}_1={\stackrel{\→}{x}}_0$

${\stackrel{\→}{\mathrm{\ξ}}}_2={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_3={\stackrel{\→}{x}}_0+\frac{1}{2}\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)$

${\stackrel{\→}{\mathrm{\ξ}}}_4={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)$

${\stackrel{\→}{x}}_n={\stackrel{\→}{x}}_0+\mathrm{\Δ}t\[\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_1\right)}{6}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_2\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_3\right)}{3}+\frac{\stackrel{\→}{v}\left({\stackrel{\→}{\mathrm{\ξ}}}_4\right)}{6}\]$ 。