1. The utility model provides a meteorological remote sensing data precision inspection analytic system based on cross radiometric calibration which characterized in that includes:

the matching module is used for performing space-time cross matching on the remote sensor and a reference remote sensor of the remote sensor;

the standardization module is used for carrying out data standardization on the remote sensing observation data after the space-time cross matching so as to obtain standardized data;

the receiving module is used for receiving the screening parameters set by the client through the standardized interface;

the classification module is used for classifying the standardized data according to the screening parameters;

the screening module is used for screening the classified standardized data according to the screening threshold value set by the client;

and the sending module is used for sending the screened standardized data to the client so as to enable the client to perform visual analysis processing on the received standardized data.

2. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration of claim 1,

the visualization analysis processing includes at least one of spatial analysis, temporal-spatial analysis, matching value distribution analysis, time series analysis, and correlation analysis of the remote sensing data.

3. The system for precision testing and analyzing meteorological remote sensing data based on cross radiometric calibration of claim 1 or 2, wherein the classification module comprises:

the dividing submodule is used for dividing according to the standardized data and the source attribute of the remote sensing data;

the naming submodule is used for naming the standardized data according to the data source attribute dividing result and a preset naming format;

and the archiving submodule is used for classifying and archiving the named standardized data according to the data content, the data channel and the central wavelength.

4. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration of claim 3,

the remote sensing data source attribute comprises at least one of satellite name, reference satellite name, load name, reference load name and data version information.

5. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross-radiometric calibration of claim 3, wherein the screening module comprises:

the receiving submodule is used for receiving the starting time and the ending time set by the client;

the determining submodule is used for determining a time period range according to the starting time and the ending time and determining the standardized data of the classified archives in the time period range;

the receiving submodule is also used for receiving the screening threshold value set by the client;

and the screening module is also used for screening the standardized data in the time period range according to the screening threshold.

6. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration of claim 5,

the screening threshold comprises at least one of a latitude and longitude range threshold, a time threshold, a data angle threshold and a surface terrain type threshold of meteorological data.

7. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration of claim 5 or 6, further comprising:

and the analysis module is used for carrying out data analysis on the standard data subjected to the screening processing, and the data analysis comprises at least one of deviation analysis, standard deviation analysis, root mean square error analysis and matching point analysis.

8. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross-radiometric calibration of claim 7, further comprising:

and the statistical module is used for determining the data content, the data type and the latitude and longitude information of the standardized data after the data analysis, and performing statistical analysis according to the preset time granularity or space granularity.

9. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross-radiometric calibration of claim 8, further comprising:

and the visualization module is used for performing visualization analysis on the standardized data subjected to the statistical analysis so as to generate a visualization result of the standardized data.

10. The system for verifying and analyzing accuracy of meteorological remote sensing data based on cross-radiometric calibration of claim 9,

the visualization results include at least one of results of a deviation analysis, results of a standard deviation analysis, results of a root mean square error analysis, and results of a match point analysis.

Background

Because the influence of environmental meteorological factors is great in the processes of production, construction and equipment operation, the application of meteorological data and satellite remote sensing data is more and more extensive at present. Meanwhile, with the continuous progress of society and the continuous development of computer technology, the quality requirement of people on weather service is continuously improved.

In the related art, because the real-time satellite observation data is influenced by the environment and the performance of a remote sensor, a ground application system is required to perform a large amount of preprocessing and precision analysis on the original observation data to obtain a required customized meteorological product, so that the response delay of a client to a meteorological data request is large, and the client experience is seriously influenced.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The invention aims to provide a meteorological remote sensing data precision inspection and analysis system based on cross radiometric calibration, which at least solves the problems of poor reliability of meteorological data and the like in the related technology to a certain extent.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to one aspect of the present disclosure, there is provided a system for precision testing and analyzing meteorological remote sensing data based on cross radiometric calibration, comprising: the matching module is used for performing space-time cross matching on the remote sensor and the reference remote sensor; the standardization module is used for carrying out data standardization on the remote sensing observation data subjected to space-time cross matching to generate standardized data; the receiving module is used for receiving the screening parameters set by the client through the standardized interface; the classification module is used for classifying the standardized data according to the screening parameters; the screening module is used for screening the classified standardized data according to a screening threshold value set by the client; and the sending module is used for sending the screened standardized data to the client for visual analysis so that the client can perform visual analysis processing on the received standardized data.

In one embodiment of the present disclosure, the visualization analysis process includes at least one of spatial analysis, spatio-temporal analysis, matching value distribution analysis, time series analysis, and correlation analysis of the remote sensing data.

In one embodiment of the disclosure, the classification module includes: the dividing submodule is used for dividing according to the standardized data and the source attribute of the remote sensing data; the naming submodule is used for naming the standardized data according to the result of data source attribute division and a preset naming format; and the archiving submodule is used for classifying and archiving the named standardized data according to the data content, the data channel and the central wavelength.

In one embodiment of the disclosure, the remote sensing data source attribute includes at least one of a satellite name, a reference satellite name, a payload name, a reference payload name, and data version information.

In one embodiment of the disclosure, the screening module includes: the receiving submodule is used for receiving the starting time and the ending time set by the client; the determining submodule is used for determining a time period range according to the starting time and the ending time and determining standardized data which belong to classified archives in the time period range; the receiving submodule is also used for receiving a screening threshold value set by the client; the screening module is further used for screening the standardized data within the time period range according to the screening threshold value.

In one embodiment of the disclosure, the screening threshold comprises at least one of a latitude and longitude range threshold, a time threshold, a data angle threshold, and a surface terrain type threshold of the remote sensing data.

In one embodiment of the present disclosure, the screening threshold includes at least one of a latitude and longitude threshold, a solar zenith angle threshold, a satellite zenith angle threshold, a deviation threshold of two loading satellite zenith angles, a matching point time deviation threshold, a sea-land template parameter, and a matching block standard deviation threshold.

In one embodiment of the present disclosure, further comprising: and the analysis module is used for carrying out data analysis on the screened standardized data, and the data analysis comprises at least one of deviation analysis, standard deviation analysis, root mean square error analysis and matching point analysis.

In one embodiment of the present disclosure, further comprising: and the statistical module is used for determining the data content, the data type and the latitude and longitude information of the standardized data after data analysis and performing statistical analysis according to the preset time granularity or space granularity.

In one embodiment of the present disclosure, further comprising: and the visualization module is used for performing visualization analysis on the standardized data subjected to the statistical analysis so as to generate a visualization result of the standardized data.

In one embodiment of the present disclosure, the visualization results include at least one of results of a deviation analysis, results of a standard deviation analysis, results of a root mean square error analysis, and results of a matching point analysis.

In one embodiment of the present disclosure, the visualization analysis includes at least one of a spatial distribution visualization analysis, a spatio-temporal distribution visualization analysis, a value distribution visualization analysis, a time series visualization analysis, a data correlation visualization analysis.

According to a second aspect of the present disclosure, there is provided a cross radiometric calibration-based meteorological remote sensing data precision testing and analyzing method, including: performing space-time cross matching on the remote sensor and the reference remote sensor; carrying out data standardization on the remote sensing observation data subjected to space-time cross matching to generate standardized data; receiving screening parameters set by a client through a standardized interface; classifying the standardized data according to the screening parameters; screening the classified standardized data according to a screening threshold set by the client; and sending the screened standardized data to a client for visual analysis so that the client can perform visual analysis processing on the received standardized data, wherein the analysis processing comprises at least one of space analysis, space-time analysis, matching value distribution analysis, time sequence analysis and correlation analysis.

In one embodiment of the present disclosure, classifying the normalized data according to the screening parameters includes: dividing according to the standardized data and the source attribute of the remote sensing data; naming the standardized data according to the result of data source attribute division and a preset naming format; and classifying and archiving the named standardized data according to the data content, the data channel and the central wavelength.

In one embodiment of the present disclosure, the screening the classified standardized data according to the screening threshold set by the client includes: receiving a start time and an end time set by a client; determining a time period range according to the starting time and the ending time, and determining standardized data which belong to classified archives in the time period range; receiving a screening threshold value set by a client; and screening the standardized data in the time period range according to the screening threshold value.

In one embodiment of the present disclosure, the screening threshold includes at least one of a latitude and longitude threshold, a solar zenith angle threshold, a satellite zenith angle threshold, a deviation threshold of two loading satellite zenith angles, a matching point time deviation threshold, a sea-land template parameter, and a matching block standard deviation threshold.

In one embodiment of the present disclosure, further comprising: and performing data analysis on the standard data subjected to the screening treatment, wherein the data analysis comprises at least one of deviation analysis, standard deviation analysis, root mean square error analysis and matching point analysis.

In one embodiment of the present disclosure, further comprising: and determining the data content, the data type and the latitude and longitude information of the standardized data after data analysis, and performing statistical analysis according to the preset time granularity or space granularity.

In one embodiment of the present disclosure, further comprising: and performing visual analysis on the standardized data subjected to the statistical analysis to generate a visual result of the standardized data.

In one embodiment of the present disclosure, the visualization results include at least one of results of a deviation analysis, results of a standard deviation analysis, results of a root mean square error analysis, and results of a matching point analysis.

In one embodiment of the present disclosure, the visualization analysis includes at least one of a spatial distribution visualization analysis, a spatio-temporal distribution visualization analysis, a value distribution visualization analysis, a time series visualization analysis, a data correlation visualization analysis.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to execute the above-mentioned cross-radiometric-calibration-based meteorological remote sensing data accuracy verification analysis method via executing executable instructions.

According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, and a computer program is executed by a processor to implement the above-mentioned cross-radiometric-calibration-based meteorological remote sensing data accuracy verification analysis method.

According to the meteorological remote sensing data precision inspection and analysis scheme based on cross radiometric calibration, cross rough matching processing is conducted on the meteorological data of the remote sensors, and space-time calibration is conducted on the meteorological data acquired by the remote sensors, so that the reliability of the meteorological data is improved.

Furthermore, the calibrated meteorological data are standardized, the standardized meteorological data are divided into meteorological data of different types or versions, unified storage, management and access are performed, the meteorological data are customized rapidly, the quality and the precision of inversion meteorological data are improved, the response delay of acquiring the meteorological data is shortened, and the user experience is improved.

In addition, by adopting the technical scheme disclosed by the invention, the inquiry is carried out in a webpage self-adaptive mode, so that the processing efficiency of unified storage, management, access and analysis of the remote sensing observation data is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 is a schematic diagram illustrating a system for precision verification and analysis of meteorological remote sensing data based on cross radiometric calibration in an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating a method for precision inspection and analysis of meteorological remote sensing data based on cross radiometric calibration according to an embodiment of the present disclosure;

FIG. 3 is a flow chart of another method for accuracy testing and analysis of meteorological remote sensing data based on cross radiometric calibration in the embodiment of the present disclosure;

FIG. 4 is a flow chart of a method for precision verification and analysis of meteorological remote sensing data based on cross radiometric calibration according to another embodiment of the present disclosure;

FIG. 5 is a flow chart of a method for accuracy testing and analyzing meteorological remote sensing data based on cross radiometric calibration according to another embodiment of the present disclosure;

FIG. 6 is a flow chart of a method for precision verification and analysis of meteorological remote sensing data based on cross radiometric calibration according to another embodiment of the present disclosure;

FIG. 7 is a flowchart illustrating a method for accuracy testing and analyzing meteorological remote sensing data based on cross-radiometric calibration according to another embodiment of the present disclosure;

FIG. 8 is a flow chart of a method for accuracy testing and analyzing meteorological remote sensing data based on cross radiometric calibration according to another embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a meteorological remote sensing data precision testing and analyzing device based on cross radiometric calibration in the embodiment of the present disclosure;

FIG. 10 is a schematic diagram of another meteorological remote sensing data precision test analysis device based on cross radiometric calibration in the embodiment of the present disclosure;

fig. 11 shows a schematic diagram of an electronic device in an embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

According to the scheme, cross coarse matching processing is carried out on the meteorological data of the remote sensors, space-time calibration is carried out on the meteorological data acquired by the remote sensors, and reliability of the meteorological data is improved. Furthermore, the calibrated meteorological data are standardized, the standardized meteorological data are divided into meteorological data of different types or versions, unified storage, management and access are performed, the meteorological data are customized rapidly, the quality and the precision of inversion meteorological data are improved, the response delay of acquiring the meteorological data is shortened, and the user experience is improved. In addition, by adopting the technical scheme disclosed by the invention, the inquiry is carried out in a webpage self-adaptive mode, so that the processing efficiency of unified storage, management, access and analysis of the remote sensing observation data is improved.

The scheme provided by the embodiment of the application relates to technologies such as meteorological data standardization, meteorological data analysis and meteorological data screening, and is specifically explained by the following embodiment.

As shown in FIG. 1, the platform for precision inspection and analysis of meteorological remote sensing data based on the multi-instrument radiometric cross-calibration method comprises: a data layer 102, a service layer 104, and an application layer 106.

The data sources of the data layer 102 include: the method includes but is not limited to, crude extraction data of wind and cloud satellite wide channel load cross matching, crude extraction data of wind and cloud satellite hyperspectral load cross matching and other satellite load matching methods.

The data layer 102 provides a data interface management (file system) for receiving longitude and latitude analysis ranges after the client sets the time-space matching through the standardized interface, setting a matching time threshold, setting a matching data angle threshold, screening a matching data marine land type, screening a ground surface type, and performing regional and type-based precise matching data extraction and downloading, but is not limited thereto.

The service layer 104 provides a data analysis service, a spatial distribution analysis service, a spatio-temporal distribution analysis service, a value distribution analysis service, a data acquisition service, a data screening service, a data statistics service, and a data correlation analysis service, but is not limited thereto.

Wherein the data analysis service comprises: a deviation analysis service, a standard deviation analysis service, a root mean square error analysis service, and a data volume analysis service, but is not limited thereto.

The application layer 106 provides an analysis report generation service, an analysis graph batch download service, an analysis graph batch deletion service, and the like, but is not limited thereto.

The data processing system architecture diagram is cross-analyzed by combining the time-space matching of the remote sensor, and the embodiment is mainly divided into three parts: the system comprises a data standardization process, a data storage service and an interactive data analysis display program based on parameter customization of a browser.

The input data of the meteorological remote sensing data precision inspection and analysis platform based on the multi-instrument radiation cross calibration method comprises wind cloud satellites (such as, but not limited to, wind cloud 1A, wind cloud 1B, wind cloud 1C, wind cloud 1D, wind cloud 2A, wind cloud 2B, wind cloud 2C, wind cloud 2D, wind cloud 2E, wind cloud 2F, wind cloud 2G, wind cloud 2H, wind cloud 3A, wind cloud 3B, wind cloud 3C, wind cloud 3D, wind cloud 4A and the like), wide-channel remote sensors cross matching crude extraction data, hyperspectral sensors cross matching crude extraction data and other satellite remote sensors matching data.

The cross matching coarse data related to the meteorological data product supports multiple data types such as multi-star, multi-remote sensor, multi-channel, multi-resolution, multi-version and the like. The construction of the system fully considers the diversity and complexity of data and the huge data volume of high-resolution data, and meanwhile, the efficiency of data processing, data storage, data retrieval, data loading, data analysis and browsing is improved through various optimization means. The customized analysis and evaluation report generation of multi-satellite, multi-remote sensor, multi-channel, multi-resolution and multi-version data is realized.

The specific execution steps of the meteorological remote sensing data precision inspection analysis platform based on the multi-instrument radiation cross calibration method are as follows:

(1) according to a data standardization link, long-time sequence data generation is carried out on data after time-space rough matching of the remote sensor and a reference remote sensor according to a satellite, a load and a version, observation data (brightness temperature/reflectivity), calibration data (longitude and latitude), angle data (sun zenith angle, sun azimuth angle, satellite zenith angle and satellite azimuth angle), surface data (sea-land mask, surface coverage), matching time and the like after rough matching are stored, file names of output data are subjected to standardized design, and naming rules are unified.

For example:

FY3B_MWRI_GPM_GMI_V0-1.h5。

FY3B_MWHS_METOPA_MHS_V0-1.h5。

satellite name _ remote sensor name _ reference satellite name _ reference remote sensor name _ version number h 5.

(2) Managing the generated products through a unified directory structure, for example:

“～/web/data/dview/。

the general entry of the-/web page code path/data storage directory/cross coarse matching data storage directory/".

For example, as shown in FIG. 3, the long-time sequence datｃA product content after the coarse space-time matching of the remote sensor "MWHS" of the "FY 3B" satellite and the remote sensor "MHS" of the "METOP-A" satellite is realized.

(3) And (3) for the generated long-time sequence data products after the coarse time-space matching of each remote sensor, self-adaptively identifying the data list in a mode of mapping a webpage client and a file system directory. After a user selects a long-time sequence Data product file after rough matching, a client adaptively displays all channels and remote sensor names in the file, selects a corresponding channel and remote sensor Data1 to be analyzed (Data1), refers to remote sensor Data 2(Data2), and then can COUNT all full-set rough matching Data in the file, wherein the full-set rough matching Data comprises deviation (BIAS), standard deviation (STD), Root Mean Square Error (RMSE) and rough matching point number (DATA _ COUNT) of Data1-Data 2.

(4) The analysis and display supporting the statistical results comprises the following steps: the method comprises the following steps of spatial distribution analysis display, space-time distribution analysis display, value distribution analysis display, time series analysis display and data correlation analysis display.

(5) And the method supports the generation of standardized analysis reports of all analysis display results, supports the export and storage of all analysis display results, and supports the emptying of all analysis display results.

(6) In the aspect of data management, all the meteorological satellite remote sensor crossed rough matching products are abstracted into a product object with the attributes of data type, data resource path, data time axis resource path and the like, and management is carried out through mapping of a client and a data directory.

By means of reasonable comprehensive application of the multiple means, efficient organization, management and analysis of multi-satellite, multi-remote sensor, multi-reference satellite reference remote sensor and multi-version remote sensing data can be well achieved. The whole system has the advantages of openness, easy expansion, easy maintenance and the like.

The steps of the method for verifying and analyzing the accuracy of the meteorological remote sensing data based on cross radiometric calibration according to the present exemplary embodiment will be described in more detail with reference to the accompanying drawings and examples.

FIG. 2 shows a flowchart of a method for precision inspection and analysis of meteorological remote sensing data based on cross radiometric calibration in the embodiment of the present disclosure.

As shown in fig. 2, the method for performing the accuracy test and analysis of the meteorological remote sensing data based on cross radiometric calibration by the terminal includes the following steps:

and step S202, performing space-time cross matching on the remote sensor and a reference remote sensor of the remote sensor.

In the embodiment, the meteorological data of a plurality of remote sensors are subjected to cross coarse matching processing, and the meteorological data acquired by the remote sensors are subjected to space-time calibration, so that the reliability of the meteorological data is improved.

In one embodiment of the disclosure, the condition of the matching module for performing spatio-temporal cross matching on the remote sensor and the reference remote sensor comprises:

1. and (3) performing time matching, wherein the pixel observation time of the target remote sensor and the reference remote sensor meets the formula (1) as follows:

|t₁-t₂|＜δ_{max_sec}equation (1)

In the formula, t₁Is the target remote sensor pixel observation time, t₂Observing the actual time, delta, for reference to remote sensor pixels_{max_sec}Is a time matching threshold.

2. And (3) carrying out spatial matching: the pixel longitude and latitude of the target remote sensor and the reference remote sensor meet the following formulas (2) - (6):

x₁＝Lat₁x pi/180, formula (2)

x₂＝Lat₂X pi/180, formula (3)

y₁＝Lon₁X pi/180, formula (4)

y₂＝Lon₂X pi/180, formula (5)

acos(sin(x₁)×sin(x₂)+cos(x₁)×cos(x₂)×cos(y₁-y₂))＜d_maxEquation (6)

In the formula, Lat₁、Lon₁Respectively the longitude and latitude of the pixel of the target remote sensor, x1 and x2 respectively correspond to the radian value, Lat₂、Lon₂Longitude and latitude of the reference remote sensor pixel, y1, y2 corresponding to camber value, d_maxIs the spatial matching threshold (in radians).

3. Carrying out observation angle matching: the pixel observation angles of the target remote sensor and the reference remote sensor satisfy the following formula (7):

wherein cos θ₁Observation of zenith angle, cos θ, for target remote sensor pixels₂Observing the actual zenith angle, delta, for reference to remote sensor pixels_{max_zen}Is an angle matching threshold.

And S204, carrying out data standardization on the remote sensing observation data subjected to the space-time cross matching to obtain standardized data.

In the embodiment, the cross coarse matching data of the remote sensors are standardized, divided into different types and versions of data, stored, managed and accessed in a unified manner, and queried in a webpage self-adaptive manner, so that the cross coarse matching data of the remote sensors can be effectively managed and accessed.

The data standardization comprises the aspects of file names, file attributes, file formats, storage modes, data management and the like of data, and a uniform data format defined in the system is generated.

And step S206, receiving the screening parameters set by the client through the standardized interface.

In the above embodiment, the receiving client performs the regional and type-based fine matching data extraction and download by using the screening parameters set by the standardized interface, such as the latitude and longitude analysis range after space-time matching, the matching time threshold, the matching data angle threshold, the matching data marine land type and the ground surface type.

And S208, classifying the standardized data according to the screening parameters.

In the embodiment, after the partition and classification type fine matching data is extracted and downloaded, the various types of data are uniformly stored, managed and accessed through the file system sub-directory, and the storage, management and access efficiency of the system is ensured through the optimization design in multiple aspects such as storage, middleware, application systems and the like.

And step S210, screening the classified standardized data according to the screening threshold set by the client.

In the above embodiment, the filtering threshold includes at least one of a latitude and longitude range threshold of the meteorological data, a time threshold, a data angle threshold, and a surface terrain type threshold.

In the above embodiment, the surface terrain type threshold comprises at least one of a sea-land template threshold and a matching block standard deviation threshold.

In the above embodiment, the data angle threshold includes at least one of a threshold of a solar zenith angle, a threshold of a satellite zenith angle, and a threshold of a deviation of two loaded satellite zenith angles.

In the above embodiment, the time threshold includes a threshold value of a time deviation of the matching point.

Step S212, the standard data after screening processing is sent to the client side, so that the client side can perform visual analysis processing on the received standard data.

In the above embodiment, after the client confirms the analysis result, the relevant configuration parameters, the threshold values and the relevant instructions may be saved to the database.

Furthermore, each day of timing task obtains the adjusted threshold parameter and the relevant instruction by reading the latest static parameter file or the relevant configuration parameter information of the database, and the static analysis result can be output at regular time every day for visual display and release, so that the client can automatically update and release the customized picture as required.

In the above embodiment, the visualization analysis process includes at least one of spatial analysis, spatio-temporal analysis, matching value distribution analysis, time series analysis, and correlation analysis of the remote sensing data.

Based on the steps shown in fig. 2, classifying the normalized data according to the screening parameters, as shown in fig. 3, includes:

and S3082, dividing the data according to the standardized data according to the source attribute of the remote sensing data, wherein the source attribute of the remote sensing data comprises at least one of satellite names, reference satellite names, load names, reference load names and data version information.

Step S3084, naming the standardized data according to the result of the data source attribute division and a preset naming format.

In the embodiment, the cross analysis data after the coarse time-space matching of the remote sensors is subjected to standardization processing, then unified naming is performed according to naming rules, and the analysis data list is adaptively identified in a client and file system directory mapping mode, so that the cross analysis data of the remote sensors can be effectively managed and accessed.

Further, the client accesses the data file storage path, lists all data which can be analyzed under the path through a matching principle, and distinguishes all data through file names. And inputting the screening conditions of the data files at the client, automatically listing the data of the matching conditions, and uniformly storing, managing and accessing the product files by adopting a file system.

Specifically, in data management, all remote sensor space-time matching crude extraction data are converted into a product object with remote sensor reference remote sensor description, channel observation data, calibration data, angle parameters, time and the like, and each product object is managed through a path and a file name, so that more effective management can be performed.

And S3086, classifying and archiving the named standardized data according to the data content, the data channel and the central wavelength.

In the above embodiments, the data content may be, but is not limited to, a temperature distribution, a humidity distribution, an infrared distribution, a cloud map, and the like.

In the above embodiment, the remote sensing data source attribute includes at least one of a satellite name, a reference satellite name, a payload name, a reference payload name, and data version information.

Based on the steps shown in fig. 2, as shown in fig. 4, the screening process of the classified normalized data according to the screening threshold set by the client includes:

in step S4102, the start time and the end time set by the client are received.

In the above embodiment, the start time and the end time set by the client may be the time length of the observation data in the whole file by screening the data in the corresponding time period range in the file.

Step S4104 determines a time period range from the start time and the end time, and determines the standardized data of the classified archives belonging to the time period range.

Step S4106, receives a filtering threshold set by the client.

Step S4108, screening the standardized data in the time period range according to the screening threshold.

In the above embodiment, the screening threshold includes at least one of a latitude and longitude range threshold, a time threshold, a data angle threshold, and a surface topography type threshold.

Based on the steps shown in fig. 2, as shown in fig. 5, the method for detecting and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration further includes:

step S502, performing data analysis on the screened standardized data, wherein the data analysis comprises at least one of deviation analysis, standard deviation analysis, root mean square error analysis and matching point analysis.

In an embodiment of the disclosure, after the client selects the long-time sequence Data product file after rough matching, the interactive interface of the client can adaptively display all channels and remote sensor names in the file, and after selecting the corresponding channel and the remote sensor Data1 to be analyzed (Data1) and referring to the remote sensor Data 2(Data2), statistics can be performed on all the full-set rough matching Data in the file, including the deviation (BIAS), standard deviation (STD), Root Mean Square Error (RMSE) and rough matching point number (Data _ COUNT) of Data1-Data 2.

Based on the steps shown in fig. 2 and fig. 5, as shown in fig. 6, the method for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration further includes:

step S602, determining data content, data type, and latitude and longitude information of the standardized data after data analysis, and performing statistical analysis according to a preset time granularity or a preset space granularity.

In the above embodiments, the temporal granularity may include 1 day, 2 days, 3 days, 5 days, 10 days, 15 days, 30 days, natural months, and the like, and the spatial granularity may include latitude 1 degree, 2 degrees, 3 degrees, and the like, but is not limited thereto.

Based on the steps shown in fig. 2 and fig. 5, as shown in fig. 7, the method for verifying and analyzing accuracy of meteorological remote sensing data based on cross radiometric calibration further includes:

step S702, performing visualization analysis on the standardized data subjected to the statistical analysis to generate a visualization result of the standardized data.

In the above embodiment, statistical analysis data and time and spatial information are acquired, deviation, standard deviation, root mean square error and the number of matching points are performed, and visualization analysis, such as spatial distribution visualization analysis, temporal and spatial distribution visualization analysis, value distribution visualization analysis, time series visualization analysis, and data correlation visualization analysis, is performed.

In one embodiment of the present disclosure, the visualization results include at least one of results of a deviation analysis, results of a standard deviation analysis, results of a root mean square error analysis, and results of a matching point analysis.

In one embodiment of the present disclosure, the visualization analysis includes at least one of a spatial distribution visualization analysis, a spatio-temporal distribution visualization analysis, a value distribution visualization analysis, a time series visualization analysis, a data correlation visualization analysis.

In one embodiment of the present disclosure, the filtering threshold includes at least one of a latitude and longitude range threshold of the meteorological data, a time threshold, a data angle threshold, a surface terrain type threshold.

FIG. 8 is a flow chart of another method for precision verification and analysis of meteorological remote sensing data based on cross radiometric calibration in the embodiment of the present disclosure.

As shown in fig. 8, another method for precision inspection and analysis of meteorological remote sensing data based on cross radiometric calibration in the embodiment of the present disclosure includes:

and S802, carrying out data standardization on the observation data of the remote sensor and the reference remote sensor after the time and space rough matching, extracting the observation data and the relevant parameter data after the rough matching, and outputting a standardization result.

The standardized data content comprises remote sensor and remote sensor reference channel observation data, calibration data (longitude and latitude, latitude), sea and land template data, solar satellite angle data (zenith angle and azimuth angle), time data, cloud detection data and the like.

The preset data format is a pre-specified data format.

Specifically, on a data input interface, firstly, in order to ensure the expandability and consistency of the system, standardization processing is performed on input remote sensor cross coarse matching data, including generating a uniform data format defined inside the system on the aspects of file names, file attributes, file formats, storage modes, data management and the like of the data.

Step S804, the client adaptively displays the internal data list according to the standardized data file.

Step S806, the receiving client sets a longitude and latitude analysis range after space-time matching through a standardized interface, sets a matching time threshold, sets a matching data angle threshold, screens a matching data marine land type and a ground surface type, and performs regional and classified data extraction, statistics and analysis.

Specifically, after a user selects one piece of standardized data, the client adaptively displays a cross matching channel, a remote sensor and a reference remote sensor name in the data. At present, the analysis of the space-time matching cross data of the remote sensor is supported but not limited, and the comparison analysis of the remote sensor observation data by other methods, such as simulation comparison analysis, stable target comparison analysis and the like, is also included. In the aspect of data management, a unified management mode of a file system is adopted, various types of data are uniformly stored, managed and accessed through sub-directories of the file system, and the storage, management and access efficiency of the system is guaranteed through optimization design in multiple aspects of storage, middleware, application systems and the like.

And step S808, supporting spatial analysis, space-time analysis, matching value distribution analysis, time sequence analysis and correlation analysis of the data after statistical screening, and supporting generation of analysis reports and batch downloading and emptying of visual analysis results.

Specifically, after the user confirms the analysis result, the relevant configuration parameters, threshold values and relevant instructions may be saved to the database. The timing task of each day obtains the adjusted threshold parameter and the related instruction by reading the latest static parameter file or the related configuration parameter information of the database, and the static analysis result can be output at regular time each day for visual display and release. And the automatic updating and publishing of the customized pictures by the user according to the needs are realized.

According to the method, the cross analysis data after the remote sensor space-time rough matching is subjected to standardization processing, then is named uniformly according to the naming rule, and the analysis data list is identified in a self-adaptive mode in a client and file system directory mapping mode, so that the cross analysis data of the remote sensor can be effectively managed and accessed.

Further, on the basis of the above method embodiment, the method specifically includes:

the client accesses the data file storage path, lists all data which can be analyzed under the path according to a matching principle, and distinguishes all the data through file names. And inputting the screening conditions of the data files at the client, automatically listing the data of the matching conditions, and uniformly storing, managing and accessing the product files by adopting a file system.

Specifically, in data management, all remote sensor space-time matching crude extraction data are converted into a product object with remote sensor reference remote sensor description, channel observation data, calibration data, angle parameters, time and the like, and each product object is managed through a path and a file name, so that more effective management can be performed.

Further, on the basis of the above method embodiment, receiving a data screening statistical analysis request sent by a client through a standardized interface, and performing statistical analysis on the screened cross matching data specifically includes:

and screening the data in the corresponding time period range in the file according to the set starting time parameter and the set ending time parameter, wherein the default is the time length of the observed data in the whole file. And finely screening the observation data after the rough extraction according to the set parameter threshold value, and supporting the downloading of the finely extracted data. The fine screening parameters comprise latitude threshold setting, sun zenith angle threshold setting, satellite zenith angle threshold setting, two loading satellite zenith angle deviation threshold setting, matching point time deviation threshold setting, sea-land template parameter setting, matching block standard deviation threshold setting and the like.

And calculating a freely selected data analysis value according to the refined screening result, which specifically comprises the following steps:

according to the data analysis type supported by the webpage: the deviation, standard deviation, root mean square error, and match point, may be selected freely from one or more data analysis types.

Counting according to the selected statistical parameters according to the data analysis type, which specifically comprises the following steps:

acquiring fine extraction data, data analysis types, time, longitude and latitude, and according to statistical parameters: the time granularity (but not limited to 1 day, 2 days, 3 days, 5 days, 10 days, 15 days, 30 days, natural months, etc.), the space granularity (but not limited to 1 degree, 2 degrees, 3 degrees, etc.) were subjected to statistical analysis.

Carrying out free selection visual analysis according to the data after statistical analysis, which specifically comprises the following steps:

and acquiring statistical analysis data and time and space information, and performing space distribution visualization analysis, space-time distribution visualization analysis, value distribution visualization analysis, time sequence visualization analysis and data correlation visualization analysis on the deviation, the standard deviation, the root-mean-square error and the number of matching points.

Through customized screening statistics on the data extracted roughly, the method can meet various condition combination customized analysis and improve the data analysis timeliness.

Further, on the basis of the embodiment of the method, the generation of the batch downloading and emptying of the visual analysis results and the analysis reports is supported, and the method specifically comprises the following steps:

and the client splices the file names of the visual analysis results according to the picture naming specifications, clears the output visual picture list and generates an analysis report document according to a standard format.

The meteorological remote sensing data precision verification analysis apparatus 900 based on cross radiometric calibration according to this embodiment of the present invention is described below with reference to fig. 9. The meteorological remote sensing data precision test analysis device 900 based on cross radiometric calibration shown in fig. 9 is only an example, and should not bring any limitation to the function and the application scope of the embodiment of the present invention.

The cross radiometric calibration based meteorological remote sensing data precision verification analysis device 900 represents the components of the cross radiometric calibration based meteorological remote sensing data precision verification analysis device 900 in the form of hardware modules, which may include but is not limited to: a matching module 902, a normalization module 904, a receiving module 906, a classification module 908, a screening module 910, a sending module 912, an analysis module 914, a statistics module 916, and a visualization module 918.

The matching module 902 is used for performing spatio-temporal cross matching on the remote sensor and the reference remote sensor.

In the embodiment, the meteorological data of a plurality of remote sensors are subjected to cross coarse matching processing, and the meteorological data acquired by the remote sensors are subjected to space-time calibration, so that the reliability of the meteorological data is improved.

The standardization module 904 is configured to standardize data of the remote sensing observation data after the time-space cross matching to obtain standardized data.

In the embodiment, the cross coarse matching data of the remote sensors are standardized, divided into different types and versions of data, stored, managed and accessed in a unified manner, and queried in a webpage self-adaptive manner, so that the cross coarse matching data of the remote sensors can be effectively managed and accessed.

The data standardization comprises the aspects of file names, file attributes, file formats, storage modes, data management and the like of data, and a uniform data format defined in the system is generated.

The receiving module 906 is configured to receive the filtering parameters set by the client through the standardized interface.

In the above embodiment, the receiving client performs the regional and type-based fine matching data extraction and download by using the screening parameters set by the standardized interface, such as the latitude and longitude analysis range after space-time matching, the matching time threshold, the matching data angle threshold, the matching data marine land type and the ground surface type.

The classification module 908 is configured to classify the normalized data according to the screening parameters.

In the embodiment, after the partition and classification type fine matching data is extracted and downloaded, the various types of data are uniformly stored, managed and accessed through the file system sub-directory, and the storage, management and access efficiency of the system is ensured through the optimization design in multiple aspects such as storage, middleware, application systems and the like.

The screening module 910 is configured to perform screening processing on the classified standardized data according to a screening threshold set by the client.

The sending module 912 is configured to send the screened normalized data to the client, so that the client performs visual analysis on the received normalized data, where the analysis includes at least one of spatial analysis, temporal-spatial analysis, matching value distribution analysis, time series analysis, and correlation analysis.

In the above embodiment, after the client confirms the analysis result, the relevant configuration parameters, the threshold values and the relevant instructions may be saved to the database.

Furthermore, each day of timing task obtains the adjusted threshold parameter and the relevant instruction by reading the latest static parameter file or the relevant configuration parameter information of the database, and the static analysis result can be output at regular time every day for visual display and release, so that the client can automatically update and release the customized picture as required.

In one embodiment of the present disclosure, the classification module 908 comprises:

the dividing submodule 9082 is used for dividing the standardized data according to the source attribute of the remote sensing data, wherein the source attribute of the remote sensing data comprises at least one of satellite names, reference satellite names, load names, reference load names and data version information.

And the naming submodule 9084 is used for naming the standardized data according to the result of the data source attribute division and a preset naming format.

In the embodiment, the cross analysis data after the coarse time-space matching of the remote sensors is subjected to standardization processing, then unified naming is performed according to naming rules, and the analysis data list is adaptively identified in a client and file system directory mapping mode, so that the cross analysis data of the remote sensors can be effectively managed and accessed.

Further, the client accesses the data file storage path, lists all data which can be analyzed under the path through a matching principle, and distinguishes all data through file names. And inputting the screening conditions of the data files at the client, automatically listing the data of the matching conditions, and uniformly storing, managing and accessing the product files by adopting a file system.

Specifically, in data management, all remote sensor space-time matching crude extraction data are converted into a product object with remote sensor reference remote sensor description, channel observation data, calibration data, angle parameters, time and the like, and each product object is managed through a path and a file name, so that more effective management can be performed.

And the archiving submodule 9086 is used for classifying and archiving the named standardized data according to the data content, the data channel and the central wavelength.

In the above embodiments, the data content may be, but is not limited to, a temperature distribution, a humidity distribution, an infrared distribution, a cloud map, and the like.

In one embodiment of the present disclosure, the screening module 910 includes:

the receiving submodule 9102 is configured to receive a start time and an end time set by the client.

In the above embodiment, the start time and the end time set by the client may be the time length of the observation data in the whole file by screening the data in the corresponding time period range in the file.

The determining submodule 9104 is configured to determine a time period range according to the start time and the end time, and determine standardized data of the classified archives belonging to the time period range.

The receiving submodule 9102 is further configured to receive a filtering threshold set by the client.

The screening module 910 is further configured to perform a screening process on the normalized data within the time period range according to a screening threshold.

In one embodiment of the present disclosure, the filtering threshold includes at least one of a latitude and longitude range threshold of the meteorological data, a time threshold, a data angle threshold, a surface terrain type threshold.

In one embodiment of the present disclosure, the data angle threshold includes one of a threshold for a solar zenith angle, a threshold for a satellite zenith angle, and a threshold for a deviation of two loaded satellite zenith angles.

In one embodiment of the present disclosure, the time threshold comprises a threshold of a match point time offset.

In one embodiment of the present disclosure, the surface terrain type threshold comprises at least one of a parameter of a sea-land template and a matching block standard deviation threshold.

In one embodiment of the present disclosure, further comprising: an analysis module 914 for performing data analysis on the normalized data after the screening process, wherein the data analysis includes at least one of deviation analysis, standard deviation analysis, root mean square error analysis, and matching point analysis.

In an embodiment of the disclosure, after the client selects the long-time sequence Data product file after rough matching, the interactive interface of the client can adaptively display all channels and remote sensor names in the file, and after selecting the corresponding channel and the remote sensor Data1 to be analyzed (Data1) and referring to the remote sensor Data 2(Data2), statistics can be performed on all the full-set rough matching Data in the file, including the deviation (BIAS), standard deviation (STD), Root Mean Square Error (RMSE) and rough matching point number (Data _ COUNT) of Data1-Data 2.

In one embodiment of the present disclosure, further comprising: the statistical module 916 is configured to determine data content, data type, and latitude and longitude information of the standardized data after data analysis, and perform statistical analysis according to a preset time granularity or a preset space granularity.

In the above embodiments, the temporal granularity may include 1 day, 2 days, 3 days, 5 days, 10 days, 15 days, 30 days, natural months, and the like, and the spatial granularity may include latitude 1 degree, 2 degrees, 3 degrees, and the like, but is not limited thereto.

In one embodiment of the present disclosure, further comprising: a visualization module 918 for performing a visualization analysis on the statistically analyzed normalized data to generate a visualization of the normalized data.

In the above embodiment, statistical analysis data and time and spatial information are acquired, deviation, standard deviation, root mean square error and the number of matching points are performed, and visualization analysis, such as spatial distribution visualization analysis, temporal and spatial distribution visualization analysis, value distribution visualization analysis, time series visualization analysis, and data correlation visualization analysis, is performed.

In one embodiment of the present disclosure, the visualization results include at least one of results of a deviation analysis, results of a standard deviation analysis, results of a root mean square error analysis, and results of a matching point analysis.

In one embodiment of the present disclosure, the visualization analysis includes at least one of a spatial distribution visualization analysis, a spatio-temporal distribution visualization analysis, a value distribution visualization analysis, a time series visualization analysis, a data correlation visualization analysis.

In one embodiment of the present disclosure, the screening threshold includes at least one of a latitude and longitude analysis range of the meteorological data, a matching time threshold, a matching data angle threshold, a marine land type, and a surface type.

The meteorological remote sensing data precision verification analysis apparatus 1000 based on cross radiometric calibration according to this embodiment of the present invention is described below with reference to fig. 10. The meteorological remote sensing data precision test analysis device 1000 based on cross radiometric calibration shown in fig. 10 is only an example, and should not bring any limitation to the function and the application scope of the embodiment of the present invention.

The cross radiometric calibration based meteorological remote sensing data precision inspection analysis device 1000 represents the components of the cross radiometric calibration based meteorological remote sensing data precision inspection analysis device 1000 in the form of hardware modules, which may include but are not limited to: a data normalization module 1002, a data storage module 1004, and a data analysis module 1006.

The data standardization module 1002 is configured to standardize the data extracted by the remote sensor in the time-space matching manner according to a preset data format, and generate standardized data.

The data storage module 1004 is configured to divide the standardized data into data after coarse matching of different data versions of different reference loads of different reference satellites with different loads of different satellites according to a standardized data file name, identify a data channel, a center wavelength, and the like according to data content and attributes, and perform unified storage, management, and access on each product object by using a combination of a file system and a web page.

The data analysis module 1006 is configured to receive an access request sent by a client through a standardized interface, perform data fine extraction according to selection of a user on data and parameter setting, perform visual analysis according to the fine extraction data, and support clearing of a visual result, batch downloading, and generation of an analysis report.

Specifically, the data normalization module 1002 is configured to perform normalization processing on the load remote sensing data after the space-time coarse matching and the remote sensing data of the reference load according to a webpage requirement format, and generate normalized data.

The data storage module 1004 is configured to generate related files according to a unified file name specification from the space-time coarse matching results of different data versions of the load and the reference load, divide the standardized data into load and reference load observation data, calibration data, sea-land template data, solar satellite angle data, cloud detection data, and other auxiliary data, and perform unified storage, management, and access on the data files after coarse matching by using a file system.

The data analysis module 1006 is specifically configured to perform fine screening on the original crude extracted data according to a user requirement, output a customized analysis result, and support clearing of a visualization result, batch downloading, and generation of an analysis report.

In the embodiment, the remote sensor time-space matching crude extraction data is standardized and named as different remote sensor analysis data, and unified storage, management and access are performed. The client side displays a standardized data file list needing to be analyzed in a self-adaptive mode according to the associated directory, and can effectively identify, manage and access remote sensor space-time matching cross analysis data.

Further, on the basis of the above device embodiment, the data storage module 1004 specifically generates a relevant file according to the unified file name specification for the coarsely matched data according to different data versions of different reference loads of different reference satellites with different loads of different satellites, divides the standardized data into auxiliary data such as coarsely matched load observation data to be analyzed, reference load observation data, load calibration data to be analyzed, reference load calibration data, sea-land template data, sun satellite angle data, cloud detection data, and the like, and uniformly stores, manages and accesses the coarsely matched data file by using a file system.

Further, on the basis of the above device embodiment, the data analysis module 1006 is specifically configured to perform customized analysis on the matched data according to the data screening condition, and perform spatial-temporal distribution analysis, spatial distribution analysis, value distribution analysis, time sequence analysis, and correlation analysis on the screening result according to the currently set temporal statistical parameter, spatial statistical parameter, and maximum and minimum value.

An electronic device 1100 according to this embodiment of the invention is described below with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not bring any limitations to the function and the scope of use of the embodiments of the present invention.

As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, and a bus 1130 that couples various system components including the memory unit 1120 and the processing unit 1110.

Where the memory unit stores program code, which may be executed by the processing unit 1110 to cause the processing unit 1110 to perform steps according to various exemplary embodiments of the present invention as described in the above-mentioned "exemplary methods" section of this specification. For example, the processing unit 1110 may perform the steps as shown in fig. 2-8, as well as other steps defined in the cross-radiometric-calibration-based meteorological remote sensing data accuracy verification analysis method of the present disclosure.

The storage unit 1120 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)11201 and/or a cache memory unit 11202, and may further include a read only memory unit (ROM) 11203.

Storage unit 1120 may also include a program/utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1130 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. The network adapter 1160 communicates with the other modules of the electronic device 1100 over the bus 1130. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when the program product is run on the terminal device.