CN117235658B - Method, device and equipment for fusing wind field data and readable storage medium - Google Patents
Method, device and equipment for fusing wind field data and readable storage medium Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a readable storage medium for fusing wind field data, wherein the method comprises the steps of establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field and a background wind field respectively; performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function; and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data. The method can achieve the effect of improving the precision of the fusion wind field data.
Description
Technical Field
The present application relates to the field of data fusion, and in particular, to a method, apparatus, device, and readable storage medium for fusing wind farm data.
Background
The sea surface wind field is one of basic climate variables determined by a global climate observation system, and relates to various aspects of sea-air interaction, and plays an important role in sea and atmosphere science and application. Sea surface wind field data observed by a satellite microwave remote sensing instrument plays an increasingly important role in sea and atmosphere business monitoring and forecasting.
However, the single satellite remote sensing instrument has limited observation data in one day, for example, the single satellite remote sensing instrument can only observe the same area twice, and cannot be directly applied to a service system with high data space-time resolution requirement. The accuracy of fusing wind field data in a particular environment may also be degraded.
Therefore, how to improve the accuracy of the fusion wind field data is a technical problem to be solved.
Disclosure of Invention
The embodiment of the application aims to provide a method for fusing wind field data, and the effect of improving the precision of the fused wind field data can be achieved through the technical scheme of the embodiment of the application.
In a first aspect, an embodiment of the present application provides a method for fusing wind field data, including establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field with a background wind field, respectively; performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function; and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
In the embodiment of the application, the initial wind field data is subjected to deviation correction through the newly built wind field deviation function, then each subset in the wind field set to be fused is subjected to weighting fusion according to the weight obtained by calculating the newly built wind field error function, the wind field data to be fused can be subjected to preliminary correction before fusion, and then the weighting fusion is completed according to the error magnitude of different wind field data, so that the effect of improving the precision of the fused wind field data can be achieved.
In some embodiments, establishing a wind field deviation function of the scatterometer-detected wind field and the radiometer-detected wind field, respectively, from the background wind field, comprises: and establishing an empirical relation between the wind field detected by the scatterometer and the wind speed change of the background wind field detected by the radiometer, and obtaining a wind field deviation function.
In the embodiment of the application, an empirical relation can be established according to the change relation of the wind field and the background wind field along with the wind speed, which is measured by the scatterometer and the radiometer, and the obtained wind field deviation function can accurately calculate the deviation of wind field data measured by the scatterometer and the radiometer.
In some embodiments, prior to establishing the wind field deviation function of the scatterometer-detected wind field and the radiometer-detected wind field, respectively, from the background wind field, further comprising: acquiring satellite remote sensing data and predicted background wind field data of a target sea area; and performing space-time conversion on the satellite remote sensing data and the predicted background wind field data to obtain an initial wind field set to be fused.
In the embodiment of the application, the remote sensing data measured by the satellite and the predicted background wind field data can be subjected to space-time conversion, so that the data of the same type can be obtained, and the subsequent data fusion is more convenient.
In some embodiments, performing bias correction on the initial wind field set to be fused through a wind field bias function to obtain the wind field set to be fused, including: determining the deviation of each remote sensing data along with the sea surface wind speed in the initial wind field set to be fused through a wind field deviation function; and updating the numerical value of each remote sensing data in the initial wind field set to be fused according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, so as to obtain the wind field set to be fused.
In the embodiment of the application, the deviation of each remote sensing data can be calculated through the wind field deviation function, so that the deviation of the remote sensing data is modified, the wind field data to be fused for data fusion is obtained, and the accuracy of wind field data fusion is indirectly improved.
In some embodiments, determining the wind farm error function based on the scaling factor, the bias factor, and the inherent error of each remote sensing wind farm in the set of wind farms to be fused relative to the buoy wind farm comprises: determining the spatial resolution difference, the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, and determining a dynamic observation error function; and determining a scaling factor, a deviation coefficient and an inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused and the difference between the background field and the observation field in each remote sensing wind field, and determining a background wind field error function.
In the embodiment of the application, the difference between the data in the wind field set to be fused and the related information of the buoy wind field can be determined, and further the wind field error function constructed according to the difference between the data and the related information of the buoy wind field can accurately reflect the remote sensing data and the error information of the buoy wind field.
In some embodiments, fusing the set of wind fields to be fused according to a background wind field error function to obtain target wind field data, including: according to the background wind field error function, determining the weight of each remote sensing wind field in the wind field set to be fused; and weighting and fusing the wind field set to be fused according to the weight of each remote sensing wind field in the wind field set to be fused to obtain target wind field data.
In the embodiment of the application, the weight of the data in the wind field set to be fused can be determined according to the error information of the data in the wind field set to be fused, and then the data in the wind field set to be fused is fused according to the weight, so that the precision of wind field data fusion can be improved.
In some embodiments, after fusing the to-be-fused wind field set according to the background wind field error function to obtain the target wind field data, the method further includes: and analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data, and optimizing the target wind field data.
In the embodiment of the application, the difference of the precision of the target wind field data, the buoy wind field and the satellite observation data can be further optimized to achieve the effect of improving the precision of the target wind field data.
In a second aspect, an embodiment of the present application provides an apparatus for fusing wind farm data, including:
The construction module is used for establishing wind field deviation functions of the scatterometer detection wind field and the radiometer detection wind field and the background wind field respectively;
The correction module is used for carrying out deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused;
The determining module is used for determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises: a dynamic observation error function and a background wind field error function;
and the fusion module is used for fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
Optionally, the construction module is specifically configured to:
and establishing an empirical relation between the wind field detected by the scatterometer and the wind speed change of the background wind field detected by the radiometer, and obtaining a wind field deviation function.
Optionally, the apparatus further includes:
The conversion module is used for acquiring satellite remote sensing data and predicted background wind field data of a target sea area before establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field and a background wind field respectively;
And performing space-time conversion on the satellite remote sensing data and the predicted background wind field data to obtain an initial wind field set to be fused.
Optionally, the correction module is specifically configured to:
Determining the deviation of each remote sensing data along with the sea surface wind speed in the initial wind field set to be fused through a wind field deviation function;
and updating the numerical value of each remote sensing data in the initial wind field set to be fused according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, so as to obtain the wind field set to be fused.
Optionally, the determining module is specifically configured to:
Determining the spatial resolution difference, the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, and determining a dynamic observation error function;
And determining a scaling factor, a deviation coefficient and an inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused and the difference between the background field and the observation field in each remote sensing wind field, and determining a background wind field error function.
Optionally, the fusion module is specifically configured to:
According to the background wind field error function, determining the weight of each remote sensing wind field in the wind field set to be fused;
and weighting and fusing the wind field set to be fused according to the weight of each remote sensing wind field in the wind field set to be fused to obtain target wind field data.
Optionally, the apparatus further includes:
and the optimizing module is used for analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data and optimizing the target wind field data after the fusion module fuses the wind field set to be fused according to the background wind field error function to obtain the target wind field data.
In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method as provided in the first aspect above.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method as provided in the first aspect above.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for fusing wind farm data according to an embodiment of the present application;
FIG. 2 is a schematic diagram of curves before and after correction of sea surface wind speed and deviation by different satellites according to an embodiment of the present application;
FIG. 3 is a flowchart of an implementation method for fusing wind farm data according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of an apparatus for fusing wind farm data according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for fusing wind field data according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Some of the terms involved in the embodiments of the present application will be described first to facilitate understanding by those skilled in the art.
Terminal equipment: the mobile terminal, stationary terminal or portable terminal may be, for example, a mobile handset, a site, a unit, a device, a multimedia computer, a multimedia tablet, an internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a personal communications system device, a personal navigation device, a personal digital assistant, an audio/video player, a digital camera/camcorder, a positioning device, a television receiver, a radio broadcast receiver, an electronic book device, a game device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that the terminal device can support any type of interface (e.g., wearable device) for the user, etc.
And (3) a server: the cloud server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, basic cloud computing services such as big data and artificial intelligent platforms and the like.
Scatterometer: the active microwave detection device also called strabismus observation is a non-imaging satellite radar sensor. The scatterometer obtains sea surface roughness information by measuring a sea line surface backscattering coefficient, and then obtains a sea surface wind vector through inversion. Scatterometry data can provide accurate information of the wind speed and direction at the ocean surface. The scatterometer data covers about 70% of the area of the global sea surface, can penetrate through cloud layers, and can monitor all-weather and all-day wind fields. The characteristics of high resolution, high timeliness and wide coverage of the scatterometer effectively make up for the defects of the conventional marine observation data, and become a main means for detecting the wind field on the ocean surface.
Radiometer: also known as a "radiometer" is a device that measures the radiant flux of electromagnetic radiation. The term "radiometer" sometimes refers specifically to an infrared radiation detector, but may also refer to a detector that detects electromagnetic radiation of various other wavelengths. The more common radiometer is the kruex radiometer, which is an early model in half vacuum with a rotor (with blades of different shades) that rotates when illuminated.
The method is applied to a scene of data fusion, and the specific scene is to correct the deviation of the wind field data before the wind field data obtained by measurement of a scatterometer and a radiometer are fused, and then calculate the weight according to an error function to complete the data fusion.
The sea surface wind field is one of basic climate variables determined by a global climate observation system, and relates to various aspects of sea-air interaction, and plays an important role in sea and atmosphere science and application. Sea surface wind field data observed by a satellite microwave remote sensing instrument plays an increasingly important role in sea and atmosphere business monitoring and forecasting. However, the single satellite remote sensing instrument has limited observation data in one day, for example, the single satellite remote sensing instrument can only observe the same area twice, and cannot be directly applied to a service system with high data space-time resolution requirement. The accuracy of fusing wind field data in a particular environment may also be degraded.
Therefore, wind field deviation functions of the scatterometer detection wind field and the radiometer detection wind field and the background wind field are respectively established; performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function; and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data. The initial wind field data is subjected to deviation correction through the newly built wind field deviation function, then each subset in the wind field set to be fused is subjected to weighting fusion according to the weight obtained by calculating the newly built wind field error function, the wind field data to be fused can be subjected to preliminary correction before fusion, and then the weighting fusion is completed according to the error magnitude of different wind field data, so that the effect of improving the precision of the fused wind field data can be achieved.
In the embodiment of the present application, the execution subject may be a fusion wind field data device in a fusion wind field data system, and in practical application, the fusion wind field data device may be electronic devices such as a terminal device and a server, which are not limited herein.
The method for fusing wind field data according to the embodiment of the present application will be described in detail with reference to fig. 1.
Referring to fig. 1, fig. 1 is a flowchart of a method for fusing wind farm data according to an embodiment of the present application, where the method for fusing wind farm data shown in fig. 1 includes:
step 110: and establishing wind field deviation functions of the scatterometer detection wind field and the radiometer detection wind field and the background wind field respectively.
Wherein the scatterometer detection wind field represents data obtained by detecting a marine surface wind field of the target sea area with the scatterometer. The radiometer detection wind field represents data obtained by radiometer emission electromagnetic radiation to detect the ocean surface wind field of the target ocean area. The background wind field represents standard wind field data for wind fields as a function of wind speed, which may be standard buoy wind field data. The wind field data represents wind speed and wind speed variation data of the ocean surface of the target sea area.
In some embodiments of the application, the method shown in fig. 1 further comprises, prior to establishing the wind field deviation function of the scatterometer-detected wind field and the radiometer-detected wind field, respectively, from the background wind field: acquiring satellite remote sensing data and predicted background wind field data of a target sea area; and performing space-time conversion on the satellite remote sensing data and the predicted background wind field data to obtain an initial wind field set to be fused.
In the process, the application can perform space-time conversion on the remote sensing data measured by the satellite and the predicted background wind field data, and the data of the same type can be obtained, so that the subsequent data fusion is more convenient.
The target sea area can be any sea area which is wanted to be detected, and the satellite remote sensing data represent satellite remote sensing image data of the target sea area. The predicted background wind field data represents wind field change related data of a predicted target sea area through satellite remote sensing images. Space-time conversion means that satellite remote sensing data and prediction background wind field data are converted into two-dimensional grid data and displayed in an imaging mode. The initial wind field data to be fused comprises a plurality of initial wind field subsets to be fused, each subset comprising a plurality of wind field data for the target sea area.
In some embodiments of the application, establishing a wind field deviation function of a scatterometer-detected wind field and a radiometer-detected wind field, respectively, from a background wind field, comprises: and establishing an empirical relation between the wind field detected by the scatterometer and the wind speed change of the background wind field detected by the radiometer, and obtaining a wind field deviation function.
In the process, an empirical relation can be established according to the change relation of the wind field and the background wind field along with the wind speed, which is measured by the scatterometer and the radiometer, and the obtained wind field deviation function can accurately calculate the deviation of wind field data measured by the scatterometer and the radiometer.
The method comprises the steps of performing offline analysis on wind speed deviation of satellite remote sensing data as a function of wind speed and wind vector unit number, establishing a lookup table, and using the lookup table in a data assimilation system. The wind field deviation function is as follows:
Where w is the difference in wind speed between the scatterometer or radiometer and the background wind field and i and j are the wind speed and wind vector number, respectively. The superscript A is expressed as a wind field deviation function of a scatterometer detection wind field and a background wind field, the superscript E is expressed as a wind field deviation function of a radiometer detection wind field and a background wind field, and N is a natural number;
In addition, according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, the numerical value of each remote sensing data in the initial wind field set to be fused can be updated to obtain the wind field set to be fused, the wind field set to be fused comprises a plurality of updated remote sensing wind speed data, and the updated and corrected remote sensing partial speed is calculated as follows:
w represents remote sensing wind speed data in the initial wind field set to be fused, and w represents remote sensing wind speed data in the wind field set to be fused.
Step 120: and carrying out deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused.
The wind field set to be fused comprises a plurality of wind field subsets to be fused, and each subset comprises a plurality of wind field data of a target sea area.
In some embodiments of the present application, performing bias correction on an initial wind field set to be fused by a wind field bias function to obtain a wind field set to be fused, including: determining the deviation of each remote sensing data along with the sea surface wind speed in the initial wind field set to be fused through a wind field deviation function; and updating the numerical value of each remote sensing data in the initial wind field set to be fused according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, so as to obtain the wind field set to be fused.
In the process, the deviation of each remote sensing data can be calculated through the wind field deviation function, so that the deviation of the remote sensing data is modified, the wind field data to be fused for data fusion is obtained, and the accuracy of wind field data fusion is indirectly improved.
The images before and after the wind field data correction according to the embodiment of the present application will be described with reference to fig. 2.
Referring to fig. 2, fig. 2 is a schematic diagram of a curve before and after correction deviation of sea surface wind speed detected by different satellites according to an embodiment of the present application, where the curve before and after correction deviation of sea surface wind speed detected by different satellites shown in fig. 2 includes:
Fig. 2 (a) before the correction deviation of the sea surface wind speed detected by different satellites and fig. 2 (b) after the correction deviation of the sea surface wind speed detected by different satellites, wherein H2B, H2C, H2D, ASCAT-A, ASCAT-B, ASCAT-C, F, F17, F18 and AMSR2 represent different satellites, and corresponding curves represent the change condition of a wind field obtained by detecting the sea surface wind speed by different satellites along with the wind speed. The ordinate (WIND SPEED bias (m/s)) represents the wind speed deviation and the abscissa (MEAN WIND SPEED (m/s)) represents the average wind speed.
Step 130: and determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused.
Wherein the wind field error function comprises: a dynamic observation error function and a background wind field error function. The buoy wind field represents standard sea surface wind fields and related data that varies with wind speed. The scale factor, the deviation factor and the inherent error can be set according to the result data obtained by multiple experiments.
In some embodiments of the present application, determining a wind field error function based on a scaling factor, a deviation coefficient, and an inherent error of each remote sensing wind field in a set of wind fields to be fused relative to a buoy wind field, comprises: determining the spatial resolution difference, the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, and determining a dynamic observation error function; and determining a scaling factor, a deviation coefficient and an inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused and the difference between the background field and the observation field in each remote sensing wind field, and determining a background wind field error function.
In the process, the difference between the data in the wind field set to be fused and the related information of the buoy wind field can be determined, and further the wind field error function built according to the difference between the data and the related information of the buoy wind field can accurately reflect the remote sensing data and the error information of the buoy wind field.
The method for determining the dynamic observation error function comprises the following steps of:
The error estimation needs to consider not only the error of the observation system, but also the difference between the spatial resolution of the remote sensing wind field and the spatial resolution of the reference wind field. The set of wind fields to be fused { W i }, where W represents the warp (u) component, the weft (v) component, or the transverse (l) and tangential (t) components of the wind fields, and i=1, 2,3, … represents the ith wind field data. The relationship between the wind field of each data source and the real wind field W is typically linear, then W i is expressed as follows:
Wi＝aiW+bi+δi；
Wherein a i、bi and delta i respectively represent the scale factor, the deviation coefficient and the random error of the wind field of the ith data source in the wind field set to be fused relative to the real wind field. For example, assume that three data sources are provided, the true empty resolutions of which are arranged in the order of i=1, 2,3 from high to low; delta i is incoherent with W, delta 3 is incoherent with the random errors of the other two data sources, and r 2＝<δ1δ2 > represents the wind field variability (also called the characterization error) that can be resolved by the higher resolution system, but not by the lowest resolution system, M represents the matrix, then:
the method for determining the error by the dynamic observation error function is as follows:
bi＝Mi-aiM1(b1＝0；i＝2,3)；
The above equation M ij represents the mixed second moment of the ith and jth data samples, and M i represents the first moment of the ith data, i.e., the average value. It is often necessary to solve by iterative methods. Equations (16) and (17) represent the calibration coefficients of the wind field components, Representing errors reflecting the remote sensing data wind field components and the float wind field components.
In one embodiment, determining a scaling factor, a coefficient of deviation, and an inherent error for each remote sensing wind field in the set of wind fields to be fused relative to the buoy wind field, and differences in the background field and the observation field in each remote sensing wind field, determining a background wind field error function, includes:
considering the conditions of the currently available data, project studies are intended to estimate the spatial correlation function of the background wind field using one approach. Because the current Ku-band scatterometer wind field inversion mostly adopts a multi-fuzzy solution method, the inversion wind field has strong dependence on a background wind field used when the wind field is deblurred, and therefore, a C-band ASCAT observation wind field can be used for analyzing a spatial correlation function of a background wind field error.
The relation between the flow function ρ χχ and the velocity potential function ρ ψψ of the background wind field error and the wind field longitudinal and transverse component autocorrelation functions ρll and ρtt is as follows:
Furthermore:
Where r represents the detection distance, and when the distance r approaches infinity, the flow function and the velocity potential function of the background wind field error approach 0. The spatial correlation functions ρχχ and ρψ ψ of the background wind field velocity potential χ and the wind field longitudinal and transverse component autocorrelation functions ρ ll、ρtt, S represent the spatial distances, I (R), R (R) and J (R), S (R) are shorthand expressions of partial integral of the sum of the differences between the wind field longitudinal and transverse component autocorrelation functions ρll, ρtt, respectively; alpha χ and alpha ψ are a set of constants, p χχ respectively (≡) =0 sum the equation ρ ψψ (+_0) holds.
Step 140: and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
The target wind field data represent wind field data obtained by fusing a plurality of remote sensing data of one satellite or a plurality of remote sensing data of a plurality of satellites.
In some embodiments of the present application, fusing a set of wind fields to be fused according to a background wind field error function to obtain target wind field data, including: according to the background wind field error function, determining the weight of each remote sensing wind field in the wind field set to be fused; and weighting and fusing the wind field set to be fused according to the weight of each remote sensing wind field in the wind field set to be fused to obtain target wind field data.
In the process, the method and the device can determine the weight of the data according to the error information of the data in the wind field set to be fused, further fuse the data in the wind field set to be fused according to the weight, and improve the precision of wind field data fusion.
The larger the error is, the smaller the weight of the corresponding remote sensing wind field is.
In some embodiments of the present application, after fusing the to-be-fused wind farm set according to the background wind farm error function to obtain the target wind farm data, the method shown in fig. 1 further includes: and analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data, and optimizing the target wind field data.
In the process, the method can further optimize the target wind field data according to the precision difference of the target wind field data, the buoy wind field and the satellite observation data, so as to achieve the effect of improving the precision of the target wind field data.
The satellite observation data represent real standard data of sea surface wind fields of a sea area of a target through satellite observation.
In the process shown in the above figure 1, the wind field deviation functions of the scatterometer detection wind field and the radiometer detection wind field and the background wind field are respectively established; performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function; and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data. The initial wind field data is subjected to deviation correction through the newly built wind field deviation function, then each subset in the wind field set to be fused is subjected to weighting fusion according to the weight obtained by calculating the newly built wind field error function, the wind field data to be fused can be subjected to preliminary correction before fusion, and then the weighting fusion is completed according to the error magnitude of different wind field data, so that the effect of improving the precision of the fused wind field data can be achieved.
The following describes in detail a method for implementing fusion of wind field data according to an embodiment of the present application with reference to fig. 3.
Referring to fig. 3, fig. 3 is a flowchart of an implementation method of fusing wind field data according to an embodiment of the present application, where the implementation method of fusing wind field data shown in fig. 3 includes:
Step 310: wind field data of different satellites are acquired.
Specific: and acquiring different satellite remote sensing data and predicted background wind field data of the target sea area.
Step 320: and fusing the wind field data to obtain an initial wind field set to be fused.
Specific: and fusing different satellite remote sensing data and predicted background wind field data to obtain an initial wind field set to be fused.
Step 330: and (5) space-time matching of the wind fields to be fused is initiated.
Specific: and converting the wind field data in the initial wind field set to be fused into two-dimensional wind field data.
Step 340: and estimating a background wind field error correlation function.
Specific: performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; and determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused.
Step 350: and (5) fusing the wind fields.
Specific: and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
Step 360: and analyzing the precision of the fusion wind field relative to the buoy wind field and independent satellite observation data.
Specific: and analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data, and optimizing the target wind field data.
The specific method and steps shown in fig. 3 may refer to the method shown in fig. 1, and will not be described in detail herein.
The method of fusing wind farm data is described above with reference to fig. 1, and the apparatus for fusing wind farm data is described below with reference to fig. 4 to 5.
Referring to fig. 4, a schematic block diagram of an apparatus 400 for fusing wind farm data according to an embodiment of the present application is provided, where the apparatus 400 may be a module, a program segment, or a code on an electronic device. The apparatus 400 corresponds to the embodiment of the method of fig. 1 described above, and is capable of performing the steps involved in the embodiment of the method of fig. 1. The specific functions of the apparatus 400 will be described below, and detailed descriptions thereof will be omitted herein as appropriate to avoid redundancy.
Optionally, the apparatus 400 includes:
A construction module 410, configured to establish wind field deviation functions of the scatterometer-detected wind field and the radiometer-detected wind field from the background wind field, respectively;
The correction module 420 is configured to perform offset correction on the initial wind field set to be fused by using a wind field offset function, so as to obtain a wind field set to be fused;
the determining module 430 is configured to determine a wind field error function according to a scaling factor, a deviation coefficient and an inherent error of each remote sensing wind field in the wind field set to be fused with respect to the buoy wind field, where the wind field error function includes: a dynamic observation error function and a background wind field error function;
And the fusion module 440 is configured to fuse the wind field set to be fused according to the background wind field error function, and obtain target wind field data.
Optionally, the construction module is specifically configured to:
and establishing an empirical relation between the wind field detected by the scatterometer and the wind speed change of the background wind field detected by the radiometer, and obtaining a wind field deviation function.
Optionally, the apparatus further includes:
The conversion module is used for acquiring satellite remote sensing data and predicted background wind field data of a target sea area before establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field and a background wind field respectively; and performing space-time conversion on the satellite remote sensing data and the predicted background wind field data to obtain an initial wind field set to be fused.
Optionally, the correction module is specifically configured to:
Determining the deviation of each remote sensing data along with the sea surface wind speed in the initial wind field set to be fused through a wind field deviation function; and updating the numerical value of each remote sensing data in the initial wind field set to be fused according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, so as to obtain the wind field set to be fused.
Optionally, the determining module is specifically configured to:
Determining the spatial resolution difference, the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, and determining a dynamic observation error function; and determining a scaling factor, a deviation coefficient and an inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused and the difference between the background field and the observation field in each remote sensing wind field, and determining a background wind field error function.
Optionally, the fusion module is specifically configured to:
According to the background wind field error function, determining the weight of each remote sensing wind field in the wind field set to be fused; and weighting and fusing the wind field set to be fused according to the weight of each remote sensing wind field in the wind field set to be fused to obtain target wind field data.
Optionally, the apparatus further includes:
and the optimizing module is used for analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data and optimizing the target wind field data after the fusion module fuses the wind field set to be fused according to the background wind field error function to obtain the target wind field data.
Referring to fig. 5, a schematic structural diagram of an apparatus for fusing wind farm data according to an embodiment of the present application may include a memory 510 and a processor 520. Optionally, the apparatus may further include: a communication interface 530 and a communication bus 540. The apparatus corresponds to the embodiment of the method of fig. 1 described above, and is capable of performing the steps involved in the embodiment of the method of fig. 1, and specific functions of the apparatus may be found in the following description.
In particular, the memory 510 is used to store computer readable instructions.
Processor 520, for processing the memory-stored readable instructions, is capable of performing the various steps in the method of fig. 1.
A communication interface 530 for communicating signaling or data with other node devices. For example: for communication with a server or terminal, or with other device nodes, although embodiments of the application are not limited in this regard.
A communication bus 540 for implementing direct connection communication of the above components.
The communication interface 530 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The memory 510 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. Memory 510 may also optionally be at least one storage device located remotely from the aforementioned processor. The memory 510 has stored therein computer readable instructions which, when executed by the processor 520, perform the method process described above in fig. 1. Processor 520 may be used on apparatus 400 and to perform functions in the present application. By way of example, the Processor 520 described above may be a general purpose Processor, a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), an off-the-shelf programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and embodiments of the application are not limited in this regard.
Embodiments of the present application also provide a readable storage medium, which when executed by a processor, performs a method process performed by an electronic device in the method embodiment shown in fig. 1.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding procedure in the foregoing method for the specific working procedure of the apparatus described above, and this will not be repeated here.
In summary, the embodiments of the present application provide a method, an apparatus, an electronic device, and a readable storage medium for fusing wind field data, where the method includes establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field with a background wind field, respectively; performing deviation correction on the initial wind field set to be fused through a wind field deviation function to obtain the wind field set to be fused; determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function; and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data. The method can achieve the effect of improving the precision of the fusion wind field data.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (9)
1. A method of fusing wind farm data, comprising:
Establishing wind field deviation functions of a scatterometer detection wind field and a radiometer detection wind field and a background wind field respectively;
Performing deviation correction on an initial wind field set to be fused through the wind field deviation function to obtain the wind field set to be fused, wherein the initial wind field set to be fused is obtained by performing space-time conversion on satellite remote sensing data of a target sea area and predicted background wind field data, and the predicted background wind field data represents related data for predicting the wind field change of the target sea area through satellite remote sensing images;
Determining a wind field error function according to the scale factors, the deviation coefficients and the inherent errors of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises the following steps: a dynamic observation error function and a background wind field error function;
and fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
2. The method of claim 1, wherein establishing a wind field deviation function of the scatterometer-detected wind field and the radiometer-detected wind field, respectively, from the background wind field comprises:
and establishing an empirical relation between the wind field detected by the scatterometer and the wind speed change of the background wind field detected by the radiometer, and obtaining the wind field deviation function.
3. The method according to claim 1 or 2, wherein performing offset correction on the initial set of wind fields to be fused by the wind field offset function to obtain the set of wind fields to be fused comprises:
Determining the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed through the wind field deviation function;
And updating the numerical value of each remote sensing data in the initial wind field set to be fused according to the deviation of each remote sensing data in the initial wind field set to be fused along with the sea surface wind speed, so as to obtain the wind field set to be fused.
4. The method according to claim 1 or 2, wherein said determining a wind field error function based on scaling factors, deviation coefficients and inherent errors of each remote sensing wind field in the set of wind fields to be fused relative to the buoy wind field comprises:
Determining the spatial resolution difference, the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, and determining the dynamic observation error function;
And determining a scale factor, a deviation coefficient and an inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused and the difference between a background field and an observation field in each remote sensing wind field, and determining the error function of the background wind field.
5. The method according to claim 1 or 2, wherein the fusing the to-be-fused wind farm set according to the background wind farm error function to obtain target wind farm data includes:
According to the background wind field error function, determining the weight of each remote sensing wind field in the wind field set to be fused;
and weighting and fusing the wind field set to be fused according to the weight of each remote sensing wind field in the wind field set to be fused to obtain the target wind field data.
6. The method according to claim 1 or 2, wherein after said fusing the set of wind fields to be fused according to the background wind field error function to obtain target wind field data, the method further comprises:
and analyzing the accuracy of the target wind field data relative to the buoy wind field and satellite observation data, and optimizing the target wind field data.
7. An apparatus for fusing wind farm data, comprising:
The construction module is used for establishing wind field deviation functions of the scatterometer detection wind field and the radiometer detection wind field and the background wind field respectively;
The correction module is used for carrying out deviation correction on an initial wind field set to be fused through the wind field deviation function to obtain the wind field set to be fused, wherein the initial wind field set to be fused is obtained by carrying out space-time conversion on satellite remote sensing data of a target sea area and predicted background wind field data, and the predicted background wind field data represents related data for predicting the wind field change of the target sea area through satellite remote sensing images;
The determining module is used for determining a wind field error function according to the scale factor, the deviation coefficient and the inherent error of each remote sensing wind field relative to the buoy wind field in the wind field set to be fused, wherein the wind field error function comprises: a dynamic observation error function and a background wind field error function;
and the fusion module is used for fusing the wind field set to be fused according to the background wind field error function to obtain target wind field data.
8. An electronic device, comprising:
a memory and a processor, the memory storing computer readable instructions that, when executed by the processor, perform the steps in the method of any of claims 1-6.
9. A computer-readable storage medium, comprising:
computer program which, when run on a computer, causes the computer to perform the method according to any one of claims 1-6.
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| CN105975763A (en) * | 2016-04-29 | 2016-09-28 | 国家卫星海洋应用中心 | Fusion method and device of multisource sea surface wind field |
| CN109886354A (en) * | 2019-03-06 | 2019-06-14 | 国家卫星海洋应用中心 | Wind field fusion method and device |
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| CN109886354A (en) * | 2019-03-06 | 2019-06-14 | 国家卫星海洋应用中心 | Wind field fusion method and device |
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