CN112632868B - Filling and correcting method and system for radial flow missing value observed by high-frequency ground wave radar - Google Patents
Filling and correcting method and system for radial flow missing value observed by high-frequency ground wave radar Download PDFInfo
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Abstract
The invention discloses a filling and correcting method and a system for a high-frequency ground wave radar observation radial flow missing value, wherein the method comprises the following steps: obtaining a sea surface radial flow missing point through statistical analysis; filling the defect of the sea surface radial flow; laying unmanned boats and collecting observation data to obtain observation data; superposing the filled radial flow field diagram on the corresponding radar observation area to obtain a sea surface flow distribution diagram; revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map; and carrying out quantitative comprehensive evaluation on the revised ocean current distribution map. The system comprises: the system comprises a missing point acquisition module, a filling module, an unmanned ship observation module, a visual processing module, a revision module and an evaluation module. The method can be used for filling and correcting the missing data of the high-frequency ground wave radar. The method and the system for filling and correcting the missing value of the radial flow observed by the high-frequency ground wave radar can be widely applied to the field of data correction of the high-frequency ground wave radar.
Description
Technical Field
The invention belongs to the field of high-frequency ground wave radar data correction, and particularly relates to a filling correction method and a system for a high-frequency ground wave radar observation radial flow missing value.
Background
The sea surface flow data observed by the high-frequency ground waves has a missing condition, and a data set with missing values directly influences the research result, so that errors and losses in research and application are finally caused. In practical application, the observed deficiency value of the high-frequency ground wave radar may influence the high-frequency ground wave radar to play a key role in emergency response such as coast rescue, oil spill treatment and the like, so that the quality, density and timeliness of hydrodynamic field information provided for marine ecological environment protection and treatment are reduced. In the process of observing radial flow by a high-frequency ground wave radar, the integrity and the accuracy of regional data are guaranteed as much as possible, but the discontinuity of ocean current space-time detection caused by external factors cannot be avoided. Therefore, when the observation data of the high-frequency ground wave radar is actually applied, effective measures need to be taken to reduce the occurrence of data missing, and the missing data needs to be filled and revised.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method for filling and correcting a missing value of a radial flow observed by a high-frequency ground wave radar, which is helpful for comprehensively and comprehensively knowing observed data of the high-frequency ground wave radar.
The first technical scheme adopted by the invention is as follows: a filling and correcting method for a high-frequency ground wave radar observation radial flow missing value comprises the following steps:
acquiring historical data of the radial sea surface flow of the ground wave radar and performing statistical analysis on the historical data of the sea surface flow to obtain a missing point of the radial sea surface flow;
filling the sea surface radial flow missing points to obtain filling data and a filled radial flow field diagram;
laying unmanned boats according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
superposing and filling the radial flow field diagram in the corresponding radar observation area based on the CAD to obtain a sea surface flow distribution diagram;
revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
and carrying out quantitative comprehensive evaluation on the revised ocean current distribution map.
Further, the step of obtaining the historical data of the radial sea surface current of the ground wave radar and performing statistical analysis on the historical data of the sea surface current to obtain the missing point of the radial sea surface current specifically includes:
acquiring radial ocean current historical data detected by a high-frequency ground wave radar in a research area;
obtaining observation data and a radial flow field diagram according to the radial sea surface flow historical data, and establishing a spatial two-dimensional matrix of a radial flow field;
judging high-density points and low-density points according to a space two-dimensional matrix of the radial flow field and observation data;
and carrying out deletion detection on the high-density points and the low-density points according to a preset rule to obtain the sea surface radial flow field deletion points.
Further, the specific judgment rule for judging the high-density point and the low-density point according to the spatial two-dimensional matrix of the radial flow field and the observation data is as follows:
recording the historical observation period T of the ith observation point 0 The observation frequency in the time period is M, T 0 The number of effective observation data obtained in the time period is N i K =100% × (N) i /M), taking the point with the K of more than or equal to 85 percent as a high-density point
The rest is low density points
On a time scale, when delta T is more than or equal to 0.2T k While at the same timeThe inter-dimension is T k+1 ＝T k At + Δ t, the high and low density points should be subdivided.
Further, the preset rule is specifically as follows:
when the number of spatio-temporal adjacent sea surface radial flow missing points is N (N is more than or equal to 3), the number is recorded at the r-th j Arc of equal diameter of strip, theta j The coordinate of a point on the radial line is (r) j ，θ j ) Defined as the coordinate (r) j±1 ，θ i±1 ) The points of (1) are adjacent points of the point, and the area formed by connecting the observation points is judged as a missing area;
when the number of continuous missing values of the same observation point in the Y time period is n (n is more than or equal to 2), the observation point is judged as a sea surface radial flow continuous missing point.
Further, the step of filling the defect point of the sea surface radial flow to obtain a filled radial flow field map specifically includes:
fitting radial flow data on a high-density point time scale to obtain a smooth curve;
filling missing values of the sea surface radial flow high-density points according to the smooth curve;
carrying out interpolation filling on missing values of low-density points of the sea surface radial flow based on a preset cascade neural network;
and obtaining the filling data and the filled radial flow field diagram.
Further, the preset cascade neural network comprises a first-stage artificial neural network and a second-stage LSTM model.
Further, according to the sea surface radial flow missing point, laying of the unmanned ship and collection of observation data are carried out, and the step of obtaining the observation data specifically comprises the following steps:
and counting the sea surface radial flow missing points, respectively arranging a preset number of unmanned boats on the missing points, and performing long-period backward survey patrol or fixed-point observation on radial branch low-density points with a resolution angle as an interval of the high-frequency ground wave radar through a background remote real-time control unmanned boat to obtain observation data.
Further, the unmanned ship is provided with an ultrasonic wind measuring sensor, an ADCP flow measuring sensor, a ship-based laser wave meter and a tide level sensor.
Further, the step of performing quantitative comprehensive evaluation on the revised ocean surface current distribution map specifically includes:
obtaining five statistical values of a correlation coefficient, a negative correlation coefficient, a root mean square error, an average absolute error and an average absolute percentage error according to filling data and observation data corresponding to the revised ocean current distribution map;
and performing quantitative comprehensive evaluation according to the five statistical values.
The second technical scheme adopted by the invention is as follows: the filling and correcting system for the high-frequency ground wave radar observation radial flow missing value comprises the following modules:
the missing point acquisition module is used for acquiring the historical data of the radial sea surface flow of the ground wave radar and carrying out statistical analysis on the historical data of the sea surface flow to obtain a missing point of the radial sea surface flow;
the filling module is used for filling the sea surface radial flow missing points to obtain filling data and a filled radial flow field diagram;
the unmanned ship observation module is used for laying unmanned ships according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
the visualization processing module is used for obtaining a sea surface flow distribution map based on the radial flow field map which is overlapped and filled in the corresponding radar observation area by the CAD;
the revision module is used for revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
and the evaluation module is used for carrying out quantitative comprehensive evaluation on the revised sea surface flow distribution map.
The method and the system have the beneficial effects that: the method and the device combine the surface unmanned ship ocean current observation data to fill and revise the missing data of the high-frequency ground wave radar, solve the problems of high observation and movement cost and low precision of the filled data in the prior filling technology, and finally evaluate the filling and revising effects of the missing values, so that the filled data are more accurate.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for filling and correcting missing values of observed radial flows of a high-frequency ground wave radar according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system for filling and correcting missing values of observed radial flows of a high-frequency ground wave radar according to an embodiment of the present invention;
FIG. 3 is a distribution diagram of radial ocean current missing values of a high-frequency ground wave radar at a certain time according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a predetermined cascaded neural network test procedure according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
Referring to fig. 1, the invention provides a ratio evaluation method for observing a wind wave flow field by a high-frequency ground wave radar, which comprises the following steps:
s1, acquiring historical data of radial sea surface flow of a ground wave radar and carrying out statistical analysis on the historical data of the sea surface flow to obtain a missing point of the radial sea surface flow;
s2, filling the sea surface radial flow missing points to obtain filling data and a filled radial flow field diagram;
s3, laying unmanned boats according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
s4, superposing and filling the radial flow field diagram in the corresponding radar observation area based on the CAD to obtain a sea surface flow distribution diagram;
s5, revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
in particular, by using T a The point (x, y) of the missing value at the moment is the center of a circle, the radius R is less than or equal to R (R is the distance between two adjacent observation points on the same radial observation line of the high-frequency ground wave radar)And (4) carrying out mean value processing on the data of satellites, ships, buoys and the like in the range and the filling value obtained by the cascade network (if no relevant data source exists, the filling value is a revised value), so that low-density points of the radial flow missing value of the high-frequency ground wave radar are revised.
And S6, carrying out quantitative comprehensive evaluation on the revised sea surface flow distribution map.
Further, as a preferred embodiment of the method, the step of obtaining historical data of the radial sea surface flow of the ground wave radar and performing statistical analysis on the historical data of the sea surface flow to obtain a point of missing the radial sea surface flow specifically includes:
acquiring radial ocean current historical data detected by a high-frequency ground wave radar in a research area;
obtaining observation data and a radial flow field diagram according to the radial sea surface flow historical data, and establishing a spatial two-dimensional matrix of a radial flow field;
judging high-density points and low-density points according to a space two-dimensional matrix of the radial flow field and observation data;
and performing deletion detection on the high-density points and the low-density points according to a preset rule to obtain the sea surface radial flow field deletion points.
As a preferred embodiment of the method, the specific judgment rule for judging the high density point and the low density point according to the spatial two-dimensional matrix of the radial flow field and the observation data is as follows:
recording the historical observation period T of the ith observation point 0 The observation frequency in the time period is M, T 0 The number of effective observation data obtained in the time period is N i K =100% × (N) i (M), taking points with K being more than or equal to 85 percent as high-density points
The rest is low density points
On a time scale, when delta T is more than or equal to 0.2T k Time scale of T k+1 ＝T k At + Δ t, the high and low density points should be subdivided.
In addition, according to high frequency ground waveThe radar observation resolution angle theta determines the number a of radial observation branches in the whole detection plane (if the maximum fan-shaped included angle is 160 degrees and the high-frequency ground wave radar resolution angle is 10 degrees, a =160 degrees/10 degrees = 16) and the label of each observation point on a radial line (from 1 to b, the observable point on the longest radial line is b), so as to establish t a The spatial two-dimensional matrix of the radial flow field at the moment can be recorded as:
as a preferred embodiment of the present invention, the preset rule is specifically:
when the number of spatio-temporal adjacent sea surface radial flow missing points is N (N is more than or equal to 3), the number is recorded at the r-th j Arc of equal diameter of strip, theta j The coordinate of a point on the radial line is (r) j ，θ j ) Defining the coordinate as (r) j±1 ，θ i±1 ) The points of (1) are adjacent points of the point, and the area formed by connecting the observation points is judged as a missing area;
when the number of consecutive missing values of the same observation point in the Y time period is n (n is more than or equal to 2), the observation point is judged as a sea surface radial flow consecutive missing point, and the reference is made to FIG. 3.
Further, as a preferred embodiment of the present invention, the step of filling the defect point of the sea surface radial flow to obtain a filled radial flow field map specifically includes:
fitting radial flow data on a high-density point time scale to obtain a smooth curve;
filling missing values of sea surface radial flow high-density points according to the smooth curve;
carrying out interpolation filling on missing values of low-density points of the sea surface radial flow based on a preset cascade neural network;
and obtaining the filling data and the filled radial flow field diagram.
As a further preferred embodiment of the present invention, the preset cascade neural network includes a first-stage artificial neural network and a second-stage LSTM model.
Specifically, since the dynamic process of ocean currents is influenced by multiple factors, different interaction relationships and information mechanisms may exist among the factor variables or indexes, a data sequence rule cannot be completely described by using a single model, and meanwhile, too many factors are considered by a single neural network, which results in complex network topology structure, slow learning rate and the like. Based on the limitations, the application provides a high-frequency ground wave radar observation data missing value filling model adopting a cascade neural network. The model is composed of two serially connected neural networks, the differently cascaded neural networks relatively independently strengthen processing of different types of data, the output of the first-stage network is used as partial input of the next-stage network, the multi-stage neural networks cooperate with each other to form complementary advantages, the generalization level is higher, and a better filling effect can be achieved.
The first-stage artificial neural network modeling specifically comprises the following procedures: (1) data cleaning: and (4) reexamining and checking the data, deleting repeated information and abnormal values, correcting existing errors and providing data consistency. (2) normalization treatment: in order to make the optimization process of the optimal solution more gradual and make convergence easier to obtain the optimal solution, the method adopts a (0,1) standardization method to traverse each data, records Max and Min, and performs normalization processing on the data by using Max-Min as a base number (making Max =1,min = 0). (3) feature selection: irrelevant features are removed, important features are selected to reduce the difficulty of learning tasks, the dimension problem is relieved, and the model efficiency is improved. (4) model structure: the timeliness and the accuracy of filling missing values by using the model are comprehensively considered, the common 3-layer artificial neural network is adopted as a model structure of a first-layer network, and the 3 layers are divided into an input layer, a hidden layer and an output layer. (5) adjusting the parameters: the neural network threshold value is determined to select the excitation function, based on the existing research, the S-shaped function has a good effect as the excitation function, and the S-shaped function is selected as the excitation function. The number of nodes of the hidden layer, the maximum number of iterations, the connection weight of the first layer and the second layer of the neural network, the learning rate of the neural network, the number of input nodes of the input layer and the number of output nodes of the output layer are all parameters needing to be considered and adjusted in the modeling process, through sensitivity testing, the correlation coefficient and the root mean square error are calculated, and a model with the maximum correlation coefficient and the minimum root mean square error is selected as an optimal model through comparison. And (6) checking: and comparing the obtained preliminary data with the observation value of the unmanned ship, and selecting a model with a large correlation coefficient and a small root mean square error for the next prediction.
In addition, the second-level network in the preset cascade neural network model adopts ocean current data within a time length range of delta T before the observation time point T of the missing value (delta T = T) 1 ) And tides strongly correlated with ocean currents (Δ t = t) 2 ) Wave (Δ t = t) 3 ) Wind (Δ t = t) 4 ) The data of the equal parameters and the initial filling data of the missing values obtained through the first-level network are used as input variables, and the data sources of wind, waves, flow, tides and the like are the same as those of the first-level network. The output value obtained by inputting the data through the second-level network is the missing filling value considering the space-time continuity of the ocean current and the interaction of the ocean current with wind, waves and tides.
Referring to fig. 4, the inspection of the entire shim model: and (3) checking the finally obtained model of the observation point by using the data observed at the point by the unmanned ship, if the check meets the requirement, the model can be directly used, and if the check does not meet the requirement, the parameter adjusting step is required to be carried out again, and the check is carried out again until the model meets the requirement.
Further, as a preferred embodiment of the method, the step of laying unmanned ships according to the sea surface radial flow missing points and collecting observation data to obtain the observation data specifically includes:
and counting the sea surface radial flow missing points, respectively arranging a preset number of unmanned boats on the missing points, and performing long-period backward survey patrol or fixed-point observation on radial branch low-density points with a resolution angle as an interval of the high-frequency ground wave radar through a background remote real-time control unmanned boat to obtain observation data.
Further as a preferred embodiment of the method, the unmanned boat is equipped with an ultrasonic anemometer sensor, an ADCP flow sensor, a ship-based laser wave meter and a tide level sensor.
Specifically, unmanned vessels are equipped with wind, wave, flow, and tide sensors: the ultrasonic wind measuring sensor is fixedly arranged right above the hull of the unmanned ship; the ADCP flow measuring sensor is arranged at the water depth of about 0.5m to 1.0m in the center of the ship bottom; the ship-based laser wave meter and the tide level sensor are arranged at the vertical water surface of the ship head, the 4 sensors are debugged respectively before being arranged on the spot, test data are collected, processing and comprehensive evaluation are carried out, and the sensors are continuously debugged according to an evaluation result until the precision required by an observation standard is reached. The obtained observation data of the synchronous wind element, the wave element, the flow element and the tide level element can be formatted and output through a wind sensor, a wave sensor, a flow sensor and a tide sensor which are arranged on the unmanned ship.
Further, as a preferred embodiment of the method, the step of performing quantitative comprehensive evaluation on the revised ocean surface current distribution map specifically includes:
obtaining five statistical values of a correlation coefficient, a negative correlation coefficient, a root mean square error, an average absolute error and an average absolute percentage error according to filling data and observation data corresponding to the revised ocean current distribution map;
and carrying out quantitative comprehensive evaluation according to the five statistical values.
Specifically, the formula is as follows:
correlation coefficient r:
complex correlation coefficient R:
root mean square error RMSE:
mean absolute error MAE:
mean absolute percent error MAPE:
in the formula, x and y are respectively ocean current data obtained by actual measurement of an unmanned ship and data obtained by filling a model based on a high-frequency ground wave radar observation ocean current data missing value of a cascade neural network,
respectively, the data mean values of two corresponding groups of data, m represents the data quantity of each group of data,
expressing the predicted value, x, of the regression equation i The value of the ith item in the ocean current data obtained by actual measurement of the unmanned ship is expressed, y i And the value of the ith item in the data obtained by the model for filling the missing value of the high-frequency ground wave radar observation ocean current data based on the cascade neural network is represented.
The larger the correlation coefficient R and the complex correlation coefficient R is, the smaller the root mean square error RMSE, the smaller the average absolute error MAE and the average absolute percentage error MAPE are, and the better the missing value filling and revising effects of the high-frequency ground wave radar are.
As shown in FIG. 2, the system for filling and correcting missing values of radial flows observed by a high-frequency ground wave radar comprises the following modules:
the missing point acquisition module is used for acquiring historical data of the radial sea surface flow of the ground wave radar and performing statistical analysis on the historical data of the sea surface flow to obtain a missing point of the radial sea surface flow;
the filling module is used for filling the sea surface radial flow missing points to obtain filling data and a filled radial flow field diagram;
the unmanned ship observation module is used for laying unmanned ships according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
the visualization processing module is used for obtaining a sea surface flow distribution map based on the radial flow field map which is overlapped and filled in the corresponding radar observation area by the CAD;
the revising module is used for revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
and the evaluation module is used for carrying out quantitative comprehensive evaluation on the revised sea surface flow distribution map.
The contents in the system embodiments are all applicable to the method embodiments, the functions specifically realized by the method embodiments are the same as the system embodiments, and the beneficial effects achieved by the method embodiments are also the same as the beneficial effects achieved by the system embodiments.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The filling and correcting method for the high-frequency ground wave radar observation radial flow missing value is characterized by comprising the following steps of:
acquiring radial ocean current historical data detected by a high-frequency ground wave radar in a research area;
obtaining observation data and a radial flow field diagram according to the radial sea surface flow historical data, and establishing a spatial two-dimensional matrix of a radial flow field;
judging high-density points and low-density points according to a space two-dimensional matrix of the radial flow field and observation data;
performing deletion detection on the high-density points and the low-density points according to a preset rule to obtain sea surface radial flow deletion points;
the preset rule is that;
when the number of spatio-temporal adjacent sea surface radial flow missing points is N (N is more than or equal to 3), the number is recorded at the r-th j Arc of equal diameter of strip, theta j The coordinate of a point on the radial line is (r) j ，θ j ) Defined as the coordinate (r) j±1 ,θ i±1 ) Is the phase of the pointAdjacent points, namely judging the region formed by connecting the observation points as a missing region;
when the number of continuous missing values of the same observation point in the Y time period is n (n is more than or equal to 2), judging the observation point as a sea surface radial flow continuous missing point;
filling the missing point of the sea surface radial flow to obtain filling data and a filled radial flow field diagram;
laying unmanned boats according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
superposing and filling the radial flow field diagram in the corresponding radar observation area based on the CAD to obtain a sea surface flow distribution diagram;
revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
and carrying out quantitative comprehensive evaluation on the revised ocean current distribution map.
2. The method for filling and correcting the missing value of the high-frequency ground wave radar observation radial flow according to claim 1, wherein the specific judgment rule for judging the high-density points and the low-density points according to the spatial two-dimensional matrix of the radial flow field and the observation data is as follows:
recording the historical observation period T of the ith observation point 0 The observation frequency in the time period is M, T 0 The number of effective observation data obtained in the time period is N i K =100% × (N) i /M), taking the point with the K of more than or equal to 85 percent as a high-density point
The rest are low density points
On a time scale, when delta T is more than or equal to 0.2T k Time scale of T k+1 ＝T k At + Δ t, the high and low density points should be subdivided.
3. The method for filling and correcting the observed radial flow missing value of the high-frequency ground wave radar according to claim 2, wherein the step of filling the sea surface radial flow missing point to obtain filling data and a filled radial flow field map specifically comprises:
fitting the radial flow data on the time scale of the high-density points to obtain a smooth curve;
filling missing values of the sea surface radial flow high-density points according to the smooth curve;
carrying out interpolation filling on missing values of low-density points of the sea surface radial flow based on a preset cascade neural network;
and obtaining the filling data and the filled radial flow field diagram.
4. The method for filling and correcting the observed radial flow missing value of the high-frequency ground wave radar according to claim 3, wherein the preset cascade neural network comprises a first-stage artificial neural network and a second-stage long-short term memory model.
5. The method for filling and correcting the observed radial flow missing value of the high-frequency ground wave radar according to claim 4, wherein the step of laying unmanned boats according to sea surface radial flow missing points and collecting observation data to obtain observation data specifically comprises:
and counting the sea surface radial flow missing points, respectively arranging a preset number of unmanned boats on the missing points, and performing long-period backward survey patrol or fixed-point observation on radial branch low-density points with a resolution angle as an interval of the high-frequency ground wave radar through a background remote real-time control unmanned boat to obtain observation data.
6. The method for filling and correcting the high-frequency ground wave radar observation radial flow missing value according to claim 5, wherein the unmanned ship is provided with an ultrasonic wind measuring sensor, a Doppler current profiler, a ship-based laser wave measuring instrument and a tide level sensor.
7. The method for filling and correcting the missing value of the radial flow observed by the high-frequency ground wave radar according to claim 6, wherein the step of quantitatively and comprehensively evaluating the revised sea surface flow distribution map specifically comprises the following steps:
obtaining five statistical values of a correlation coefficient, a negative correlation coefficient, a root mean square error, an average absolute error and an average absolute percentage error according to filling data and observation data corresponding to the revised ocean current distribution map;
and performing quantitative comprehensive evaluation according to the five statistical values.
8. The system for filling and correcting the radial flow missing value observed by the high-frequency ground wave radar is characterized by comprising the following modules:
the missing point acquisition module is used for acquiring the radial sea surface flow historical data detected by the high-frequency ground wave radar in the research area; obtaining observation data and a radial flow field diagram according to the radial sea surface flow historical data, and establishing a spatial two-dimensional matrix of a radial flow field; judging high-density points and low-density points according to a space two-dimensional matrix of the radial flow field and observation data; performing deletion detection on the high-density points and the low-density points according to a preset rule to obtain sea surface radial flow field deletion points; the preset rule is; when the number of the spatio-temporal adjacent sea surface radial flow missing points is N (N is more than or equal to 3), the number is recorded at the r-th j Arc of equal diameter of strip, theta j The coordinate of a point on the radial line is (r) j ，θ j ) Defining the coordinate as (r) j±1 ,θ i±1 ) The points of (1) are adjacent points of the point, and the area formed by connecting the observation points is judged as a missing area; when the number of continuous missing values of the same observation point in the Y time period is n (n is more than or equal to 2), judging the observation point as a sea surface radial flow continuous missing point;
the filling module is used for filling the sea surface radial flow missing points to obtain filling data and a filled radial flow field diagram;
the unmanned ship observation module is used for laying unmanned ships according to the sea surface radial flow missing points and collecting observation data to obtain observation data;
the visualization processing module is used for obtaining a sea surface flow distribution map based on the radial flow field map which is overlapped and filled in the corresponding radar observation area by the CAD;
the revising module is used for revising the sea surface current distribution map according to the filling data and the observation data to obtain a revised sea surface current distribution map;
and the evaluation module is used for carrying out quantitative comprehensive evaluation on the revised sea surface flow distribution map.
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