Multi-source wind profile data fusion method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a multi-source wind profile data fusion method and device, equipment and a storage medium, and is applied to the technical field of atmospheric exploration. The method comprises the following steps: acquiring Beidou sounding ascending section data, Beidou sounding descending section data and wind profile radar data; based on a preset data space-time matching algorithm, the Beidou sounding ascending section data and the Beidou sounding descending section data are matched with the wind profile data, and a Beidou sounding ascending section and wind profile matching data set and a Beidou sounding descending section and wind profile matching data set are obtained; and based on a preset data evaluation algorithm and a preset data fusion algorithm, according to the same-address L-band sounding data and the ERA5 reanalysis data, respectively evaluating and fusing the Beidou sounding ascending section and wind profile matching data set and the Beidou sounding descending section and wind profile matching data set to obtain a continuous wind profile fusion product data set. In this way, the consistency of the wind speed and the wind direction can be improved, and then the accuracy of wind field data is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, in particular to the technical field of atmospheric sounding, and specifically relates to a multi-source wind profile data fusion method, device, equipment, and storage medium. Background Art

[0002] To date, based on the Beidou navigation sounding technology, a round-trip drifting sounding system with three-stage observations of "ascending - drifting horizontally - descending" has been developed. On the basis of the existing single ascending sounding, the horizontal drifting segment detection and the descending segment detection are expanded, that is, through one sounding release, three-stage sounding observations are completed, realizing the innovation of the sounding detection mode.

[0003] However, currently, basically single devices are used to detect the boundary layer and upper-air winds. Limited by factors such as insufficient site layout and equipment performance, it cannot meet the requirements of refined weather forecasting and meteorological services. In particular, high-precision, high-quality, and spatio-temporally continuous wind field products that can be applied to operations are still blank. At the same time, the data accuracy of Beidou sounding data is relatively high, but currently only 2 - 3 hourly observation data can be provided daily. A wind profiler radar is a remote sensing device that detects the upper-air wind field by emitting electromagnetic wave beams in different directions into the upper air and receiving and processing the information returned by these electromagnetic wave beams due to the uneven vertical structure of the atmosphere. The wind profiler radar can detect the changes of meteorological elements such as wind direction and wind speed with height above it using the Doppler effect, and has advantages such as high detection spatio-temporal resolution. However, the echo signal of the wind profiler radar is easily interfered by various non-meteorological targets, especially the low-level wind measurement accuracy is poor. ERA5 (fifth generation ECMWF atmospheric reanalysis of the global climate) reanalysis data is a high-resolution meteorological reanalysis data on a global scale provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), including multiple meteorological elements such as wind direction and wind speed. Therefore, fully considering the performance advantages and disadvantages of different observation data, establishing a wind profile data fusion method, conducting the evaluation and fusion of multi-source wind profile data, obtaining a wind profile fusion product dataset with high spatio-temporal accuracy and high data quality, and continuously improving the accuracy of wind field data. Summary of the Invention

[0004] The present disclosure provides a multi-source wind profile data fusion method, device, equipment, and storage medium.

[0005] According to the first aspect of the present disclosure, a multi-source wind profile data fusion method is provided. The method includes:

[0006] Obtain Beidou sounding data and wind profile data; the Beidou sounding data includes Beidou sounding ascending segment data and Beidou sounding descending segment data;

[0007] Based on a preset data spatio-temporal matching algorithm, respectively match the Beidou sounding ascending segment data and the Beidou sounding descending segment data with the wind profile data to obtain a Beidou sounding ascending segment and wind profile matching data set and a Beidou sounding descending segment and wind profile matching data set;

[0008] Based on a preset data evaluation algorithm, respectively evaluate the Beidou sounding ascending segment and wind profile matching data set and the Beidou sounding descending segment and wind profile matching data set according to co-located L-band sounding data and ERA5 reanalysis data to obtain the wind speed and direction of the Beidou sounding ascending segment and wind profile and the wind speed and direction of the Beidou sounding descending segment and wind profile;

[0009] Based on a preset data fusion algorithm, respectively fuse the wind speed and direction of the Beidou sounding ascending segment and wind profile and the wind speed and direction of the Beidou sounding descending segment and wind profile to obtain a continuous wind profile fusion product data set.

[0010] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. Based on a preset data spatio-temporal matching algorithm, match the Beidou sounding ascending segment data with the wind profile data, and the obtained Beidou sounding ascending segment and wind profile matching data set includes:

[0011] Perform co-location matching, preset the nearest time and preset the nearest height matching in the distance ball release stage between the Beidou sounding ascending segment data and the wind profile data to obtain a Beidou sounding ascending segment and wind profile matching data set.

[0012] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. Based on a preset data spatio-temporal matching algorithm, match the Beidou sounding descending segment data with the wind profile data, and the obtained Beidou sounding descending segment and wind profile matching data set includes:

[0013] Perform preset azimuth distance matching, preset the nearest time and preset the nearest height matching in the distance ball release stage between the Beidou sounding descending segment data and the wind profile data to obtain a Beidou sounding descending segment and wind profile matching data set.

[0014] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. The based-on preset data fusion algorithm respectively fuses the wind speed and direction of the Beidou sounding ascending segment and wind profile and the wind speed and direction of the Beidou sounding descending segment and wind profile to obtain a continuous wind profile fusion product data set, including:

[0015] Convert the wind speed and direction of the ascending section of the Beidou radiosonde and the wind profile into UV wind vectors and calculate the corresponding ascending section linear regression model; convert the wind speed and direction of the descending section of the Beidou radiosonde and the wind profile into UV wind vectors and calculate the corresponding descending section linear regression model;

[0016] Revise the wind profile data according to the ascending section linear regression model and the descending section linear regression model to obtain a continuous wind profile fusion product data set.

[0017] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. The method further includes:

[0018] Based on the preset data evaluation algorithm, evaluate the wind profile fusion product data set according to the co-located L-band radiosonde data and ERA5 reanalysis data.

[0019] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. The evaluation indexes of the preset data evaluation algorithm include: correlation coefficient, root mean square error, mean deviation, and evaluation absolute error.

[0020] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. The evaluation dimensions of the preset data evaluation algorithm include: overall evaluation dimension, site evaluation dimension, instrument model evaluation dimension, time evaluation dimension, sounding stage evaluation dimension, height layer evaluation dimension, and wind speed range evaluation dimension.

[0021] According to the second aspect of the present disclosure, a multi-source wind profile data fusion device is provided. The device includes:

[0022] An acquisition module, configured to acquire Beidou radiosonde data and wind profile data; the Beidou radiosonde data includes Beidou radiosonde ascending section data and Beidou radiosonde descending section data;

[0023] A matching module, configured to respectively match the Beidou radiosonde ascending section data and the Beidou radiosonde descending section data with the wind profile data based on a preset data spatio-temporal matching algorithm to obtain a Beidou radiosonde ascending section and wind profile matching data set and a Beidou radiosonde descending section and wind profile matching data set;

[0024] An evaluation module, configured to respectively evaluate the Beidou radiosonde ascending section and wind profile matching data set and the Beidou radiosonde descending section and wind profile matching data set based on a preset data evaluation algorithm according to the co-located L-band radiosonde data and ERA5 reanalysis data to obtain the wind speed and direction of the Beidou radiosonde ascending section and the wind profile and the wind speed and direction of the Beidou radiosonde descending section and the wind profile;

[0025] A fusion module, configured to fuse the wind speed and wind direction of the ascending section of the Beidou radiosonde and the wind profile and the wind speed and wind direction of the descending section of the Beidou radiosonde and the wind profile respectively based on a preset data fusion algorithm, so as to obtain a continuous wind profile fusion product data set.

[0026] According to a third aspect of the present disclosure, there is provided an electronic device. The electronic device includes: a memory and a processor, where a computer program is stored on the memory, and when the processor executes the program, the method described above is implemented.

[0027] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0028] A multi-source wind profile data fusion method, device, equipment and storage medium provided by an embodiment of the present application can obtain Beidou radiosonde data and wind profile data; the Beidou radiosonde data includes Beidou radiosonde ascending section data and Beidou radiosonde descending section data; then based on a preset data spatio-temporal matching algorithm, the Beidou radiosonde ascending section data and the Beidou radiosonde descending section data are respectively matched with the wind profile data to obtain a Beidou radiosonde ascending section and wind profile matching data set and a Beidou radiosonde descending section and wind profile matching data set; then based on a preset data evaluation algorithm, the Beidou radiosonde ascending section and wind profile matching data set and the Beidou radiosonde descending section and wind profile matching data set are respectively evaluated according to co-located L-band radiosonde data and ERA5 reanalysis data to obtain the wind speed and wind direction of the Beidou radiosonde ascending section and the wind profile and the wind speed and wind direction of the Beidou radiosonde descending section and the wind profile; then based on a preset data fusion algorithm, the wind speed and wind direction of the Beidou radiosonde ascending section and the wind profile and the wind speed and wind direction of the Beidou radiosonde descending section and the wind profile are respectively fused to obtain a continuous wind profile fusion product data set; based on this, the ascending and descending sections of the Beidou radiosonde can be used to carry out quality assessment with the wind profile radar data respectively, fully considering the performance advantages and disadvantages of different observation data, proposing a data fusion method to fuse several types of wind profile data, and finally comparing and verifying with the L-band radiosonde data and ERA5 reanalysis data to improve the consistency of the wind speed and wind direction, and further improve the accuracy of the wind field data.

[0029] It should be understood that the content described in the summary of the invention is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not limit the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0031] Figure 1 shows a flowchart of a multi-source wind profile data fusion method according to an embodiment of the present disclosure;

[0032] Figure 2 shows a block diagram of a multi-source wind profile data fusion device according to an embodiment of the present disclosure;

[0033] Figure 3 shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. Detailed Embodiments

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0035] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0036] In the present disclosure, the ascending and descending segments of Beidou radiosonde can be respectively used to conduct quality assessment with the wind profile radar data. The performance advantages and disadvantages of different observation data are fully considered, and a data fusion method is proposed to fuse several types of wind profile data. Finally, it is compared and verified with the L-band radiosonde data and ERA5 reanalysis data to improve the consistency of wind speed and direction, and further improve the accuracy of the wind field data.

[0037] Figure 1 shows a flowchart of a multi-source wind profile data fusion method 100 according to an embodiment of the present disclosure.

[0038] In block 110, obtain Beidou radiosonde data and wind profile data; the Beidou radiosonde data includes Beidou radiosonde ascending segment data and Beidou radiosonde descending segment data.

[0039] In some embodiments, the Beidou radiosonde data and the wind profiler radar data, i.e., the Beidou radiosonde data and the wind profile data, can be automatically downloaded from the meteorological big data cloud platform through a script.

[0040] For example, the ground-based and space-based data sources can be obtained and parsed from the meteorological big data cloud platform, such as Tianqing, to obtain the Beidou radiosonde data and the wind profile data.

[0041] In some embodiments, based on the wind profile data, the co-located L-band radiosonde data and the ERA5 reanalysis data can be obtained according to the Beidou radiosonde data.

[0042] In some embodiments, data preprocessing can be performed on the Beidou radiosonde data, the wind profile data, the co-located L-band radiosonde data, and the ERA5 reanalysis data. Among them, the Beidou radiosonde data includes the ascending segment data and the descending segment data of the Beidou radiosonde. During the preprocessing process, the corresponding wind speed and wind direction can be determined from the ascending segment data of the Beidou radiosonde, the descending segment data of the Beidou radiosonde, the wind profile data, the co-located L-band radiosonde data, and the ERA5 reanalysis data, respectively.

[0043] In block 120, based on the preset data spatio-temporal matching algorithm, the ascending segment data of the Beidou radiosonde and the descending segment data of the Beidou radiosonde are respectively matched with the wind profile data to obtain the matching dataset of the ascending segment of the Beidou radiosonde and the wind profile and the matching dataset of the descending segment of the Beidou radiosonde and the wind profile.

[0044] In some embodiments, the preset data spatio-temporal matching algorithm can be set according to the actual needs of the user.

[0045] For example, based on the respective advantages and disadvantages of different observation data, the data spatio-temporal matching algorithm can be set according to the need to select the optimal observation data at different detection heights.

[0046] In some embodiments, based on the preset data spatio-temporal matching algorithm, when the ascending segment data of the Beidou radiosonde is matched with the wind profile data, the obtained matching dataset of the ascending segment of the Beidou radiosonde and the wind profile includes:

[0047] The ascending segment data of the Beidou radiosonde is subjected to co-location matching, preset nearest time and preset nearest height matching with the wind profile data to obtain the matching dataset of the ascending segment of the Beidou radiosonde and the wind profile.

[0048] In some embodiments, during the process of obtaining the matching dataset of the ascending segment of the Beidou radiosonde and the wind profile, the preset nearest time and the preset nearest height can be set with specific numerical ranges or thresholds according to the actual needs of the user.

[0049] In some embodiments, the Beidou radiosonde ascending phase data can be matched with the co-located wind profile data. Search for the wind profile data at the time closest to the balloon release phase to match with the Beidou radiosonde ascending segment data, and then perform the closest height data matching to obtain the Beidou radiosonde ascending segment and wind profile matching dataset.

[0050] In some embodiments, based on a preset data spatio-temporal matching algorithm, the Beidou radiosonde descending segment data is matched with the wind profile data to obtain the Beidou radiosonde descending segment and wind profile matching dataset, including:

[0051] Perform a preset azimuth distance matching, a preset closest time to the balloon release phase, and a preset closest height matching on the Beidou radiosonde descending segment data and the wind profile data to obtain the Beidou radiosonde descending segment and wind profile matching dataset.

[0052] In some embodiments, during the process of obtaining the Beidou radiosonde descending segment and wind profile matching dataset, the preset azimuth distance, the preset closest time, and the preset closest height can be set with specific numerical ranges or thresholds according to the actual needs of the user.

[0053] In some embodiments, since the distance of the Beidou radiosonde descending phase from the balloon release site is relatively far, therefore, the nearest wind profile site within a 100 km azimuth can be searched to match with the Beidou radiosonde descending segment data, and the closest time and adjacent height data are searched for matching to obtain the Beidou radiosonde descending segment and wind profile matching dataset.

[0054] In block 130, based on a preset data evaluation algorithm, the Beidou radiosonde ascending segment and wind profile matching dataset and the Beidou radiosonde descending segment and wind profile matching dataset are evaluated according to the co-located L-band radiosonde data and ERA5 reanalysis data respectively to obtain the wind speed and direction of the Beidou radiosonde ascending segment and wind profile and the wind speed and direction of the Beidou radiosonde descending segment and wind profile.

[0055] In some embodiments, the preset data evaluation algorithm can be set according to the actual needs of the user.

[0056] In some embodiments, using the L-band radiosonde data and ERA5 reanalysis data to conduct a test and evaluation on the products before fusion, namely the Beidou radiosonde ascending segment and wind profile matching dataset and the Beidou radiosonde descending segment and wind profile matching dataset, can ensure that the effect of the fused product is better, that is, the data accuracy is higher, the quality is higher, and the spatio-temporal continuity is stronger.

[0057] In some embodiments, the evaluation indicators of the above preset data evaluation algorithm include: correlation coefficient, root mean square error, mean deviation, and evaluation absolute error.

[0058] In some embodiments, the evaluation dimensions of the above-mentioned preset data evaluation algorithm include: overall evaluation dimension, site evaluation dimension, instrument model evaluation dimension, time evaluation dimension, balloon release stage evaluation dimension, altitude layer evaluation dimension, and wind speed range evaluation dimension.

[0059] For example, a multi-source fusion wind profile data evaluation process can be established based on the preset data evaluation algorithm. Based on the co-located L-band radiosonde data and ERA5 reanalysis data, the datasets of the matching between the ascending section of Beidou radiosonde and the wind profile and the datasets of the matching between the descending section of Beidou radiosonde and the wind profile are respectively evaluated and analyzed. Correlation coefficient, root mean square error, mean deviation, etc. are used as evaluation indicators to generate evaluation results for different sites, different altitude layers, different balloon release stages, different equipment models, and different months, and obtain the wind speed and direction of the ascending section of Beidou radiosonde and the wind profile and the wind speed and direction of the descending section of Beidou radiosonde and the wind profile.

[0060] In block 140, based on the preset data fusion algorithm, the wind speed and direction of the ascending section of Beidou radiosonde and the wind profile and the wind speed and direction of the descending section of Beidou radiosonde and the wind profile are respectively fused to obtain a continuous wind profile fusion product dataset.

[0061] In some embodiments, the preset data fusion algorithm can be set according to the actual needs of users.

[0062] In some embodiments, a multi-source fusion wind profile data evaluation process can be established based on the preset data evaluation algorithm.

[0063] In some embodiments, the above-mentioned fusion of the wind speed and direction of the ascending section of Beidou radiosonde and the wind profile and the wind speed and direction of the descending section of Beidou radiosonde and the wind profile based on the preset data fusion algorithm to obtain a continuous wind profile fusion product dataset includes:

[0064] Convert the wind speed and direction of the ascending section of Beidou radiosonde and the wind profile into UV wind vectors and calculate the corresponding ascending section linear regression model; convert the wind speed and direction of the descending section of Beidou radiosonde and the wind profile into UV wind vectors and calculate the corresponding descending section linear regression model;

[0065] Correct the wind profile data according to the ascending section linear regression model and the descending section linear regression model to obtain a continuous wind profile fusion product dataset.

[0066] In some embodiments, U and V respectively represent the components of the wind in the east-west direction and the north-south direction.

[0067] In some embodiments, the wind speed and direction of the ascending section of Beidou radiosonde and the wind profile can be converted into UV wind vectors, that is, the slope and slant range of the ascending section of Beidou radiosonde and the wind profile radar. The wind speed and direction of the descending section of Beidou radiosonde and the wind profile can be converted into UV wind vectors, that is, the slope and slant range of the descending section of Beidou radiosonde and the wind profile radar.

[0068] In some embodiments, a linear regression model of the ascending segment UV wind vector corresponding to the Beidou radiosonde ascending segment and the wind profile can be calculated based on the UV wind vectors; a linear regression model of the descending segment UV wind vector corresponding to the Beidou radiosonde descending segment and the wind profile can be calculated based on the UV wind vectors.

[0069] In some embodiments, a deviation correction method can be used to fuse various wind profile data. For example, the wind profile data at the same altitude layer can be corrected according to the ascending segment linear regression model and the descending segment linear regression model to obtain a continuous wind profile fusion product data set.

[0070] In summary, the above multi-source wind profile data fusion method can utilize operational wind profile radar data, etc., to carry out quality assessment of the wind speed and direction data of the round-trip Beidou radiosonde ascending and descending segments and multi-source wind profile data fusion analysis.

[0071] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0072] It is possible to obtain Beidou radiosonde data and wind profile data; the Beidou radiosonde data includes Beidou radiosonde ascending segment data and Beidou radiosonde descending segment data; then, based on a preset data spatio-temporal matching algorithm, the Beidou radiosonde ascending segment data and the Beidou radiosonde descending segment data are respectively matched with the wind profile data to obtain a Beidou radiosonde ascending segment and wind profile matching data set and a Beidou radiosonde descending segment and wind profile matching data set; then, based on a preset data evaluation algorithm, the Beidou radiosonde ascending segment and wind profile matching data set and the Beidou radiosonde descending segment and wind profile matching data set are respectively evaluated according to the co-located L-band radiosonde data and ERA5 reanalysis data to obtain the wind speed and direction of the Beidou radiosonde ascending segment and the wind profile and the wind speed and direction of the Beidou radiosonde descending segment and the wind profile; then, based on a preset data fusion algorithm, the wind speed and direction of the Beidou radiosonde ascending segment and the wind profile and the wind speed and direction of the Beidou radiosonde descending segment and the wind profile are respectively fused to obtain a continuous wind profile fusion product data set; based on this, quality assessment can be carried out using the Beidou radiosonde ascending and descending segments and the wind profile radar data respectively, fully considering the performance advantages and disadvantages of different observation data, a data fusion method is proposed to fuse several types of wind profile data, and finally, a comparison test is carried out with the L-band radiosonde data and ERA5 reanalysis data to improve the consistency of the wind speed and direction, and further improve the accuracy of the wind field data.

[0073] In some embodiments, the above method further includes:

[0074] Based on a preset data evaluation algorithm, the wind profile fusion product data set is evaluated according to the co-located L-band radiosonde data and ERA5 reanalysis data.

[0075] According to an embodiment of the present disclosure, first, the Beidou radiosonde ascending segment and descending segment data are used to conduct a verification and evaluation together with the co-located wind profile data. Considering the respective disadvantages of different observation data, the optimal observation data are selected for fusion according to different detection heights; the deviation correction method can also be used to fuse multiple data; finally, the L-band radiosonde and ERA5 reanalysis data are used to conduct a verification and evaluation on the pre-fusion and post-fusion products to determine the effect of the post-fusion products.

[0076] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0077] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0078] Figure 2 The block diagram of a multi-source wind profile data fusion device 200 according to an embodiment of the present disclosure is shown. As Figure 2 shown, the device 200 includes:

[0079] An acquisition module 210, configured to acquire Beidou radiosonde data and wind profile data; the Beidou radiosonde data includes Beidou radiosonde ascending segment data and Beidou radiosonde descending segment data;

[0080] A matching module 220, configured to respectively match the Beidou radiosonde ascending segment data and the Beidou radiosonde descending segment data with the wind profile data based on a preset data spatio-temporal matching algorithm, to obtain a Beidou radiosonde ascending segment and wind profile matching data set and a Beidou radiosonde descending segment and wind profile matching data set;

[0081] An evaluation module 230, configured to respectively evaluate the Beidou radiosonde ascending segment and wind profile matching data set and the Beidou radiosonde descending segment and wind profile matching data set based on a preset data evaluation algorithm according to the co-located L-band radiosonde data and ERA5 reanalysis data, to obtain the wind speed and wind direction of the Beidou radiosonde ascending segment and the wind profile and the wind speed and wind direction of the Beidou radiosonde descending segment and the wind profile;

[0082] A fusion module 240, configured to respectively fuse the wind speed and wind direction of the Beidou radiosonde ascending segment and the wind profile and the wind speed and wind direction of the Beidou radiosonde descending segment and the wind profile based on a preset data fusion algorithm, to obtain a continuous wind profile fusion product data set.

[0083] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0084] In the technical solutions of the present disclosure, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0085] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0086] Figure 3 A block diagram of an exemplary electronic device 300 capable of implementing the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0087] The electronic device 300 includes a computing unit 301, which can execute various appropriate actions and processes according to the computer program stored in the ROM 302 or the computer program loaded from the storage unit 308 into the RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The I / O interface 305 is also connected to the bus 304.

[0088] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0089] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308.

[0090] In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be executed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the method 100 in any other suitable manner (e.g., by means of firmware).

[0091] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0093] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0094] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0096] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0097] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0098] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A multi-source wind profile data fusion method, characterized in that: include: Acquire Beidou sounding data and wind profile data; the Beidou sounding data includes Beidou sounding ascending segment data and Beidou sounding descending segment data; Based on a preset data time-space matching algorithm, the Beidou sounding ascending segment data and the Beidou sounding descending segment data are matched with the wind profile data to obtain a Beidou sounding ascending segment and wind profile matching data set and a Beidou sounding descending segment and wind profile matching data set; Based on the preset data evaluation algorithm, the Beidou sounding ascending segment and wind profile matching data set and the Beidou sounding descending segment and wind profile matching data set are evaluated according to the co-located L-band sounding data and ERA5 reanalysis data, and the wind speed and direction of the Beidou sounding ascending segment and wind profile and the wind speed and direction of the Beidou sounding descending segment and wind profile are obtained; Based on a preset data fusion algorithm, the wind speed and direction of the Beidou sounding ascending segment and the wind profile and the wind speed and direction of the Beidou sounding descending segment and the wind profile are fused respectively to obtain a continuous wind profile fusion product data set.

2. The method according to claim 1, characterized in that Based on the preset data time-space matching algorithm, the Beidou sounding ascending segment data is matched with the wind profile data to obtain the Beidou sounding ascending segment and wind profile matching data set including: The Beidou sounding ascent segment data and the wind profile data are matched at the same location, and matched with the preset nearest time and preset nearest height of the distance release stage to obtain the Beidou sounding ascent segment and wind profile matching data set.

3. The method according to claim 1, characterized in that Based on the preset data time-space matching algorithm, the Beidou sounding descent segment data is matched with the wind profile data to obtain the Beidou sounding descent segment and wind profile matching data set including: The Beidou sounding descent segment data is matched with the wind profile data in a preset azimuth distance, a preset nearest time in the distance release phase, and a preset nearest height to obtain a Beidou sounding descent segment and wind profile matching data set.

4. The method according to claim 1, characterized in that: Based on the preset data fusion algorithm, the wind speed and direction of the Beidou sounding ascending section and the wind profile are fused, and the wind speed and direction of the Beidou sounding descending section and the wind profile are fused, respectively, to obtain a continuous wind profile fusion product data set including: The wind speed and wind direction of the Beidou sounding ascending section and the wind profile are converted into UV wind vectors and the corresponding ascending section linear regression model is calculated; the wind speed and wind direction of the Beidou sounding descending section and the wind profile are converted into UV wind vectors and the corresponding descending section linear regression model is calculated; The wind profile data is corrected according to the ascending segment linear regression model and the descending segment linear regression model to obtain a continuous wind profile fusion product data set.

5. The method according to claim 1, characterized in that The method further comprises: Based on the preset data evaluation algorithm, the wind profile fusion product dataset is evaluated according to the co-located L-band sounding data and ERA5 reanalysis data.

6. The method according to any one of claims 1 to 5, characterized in that: The evaluation indicators of the preset data evaluation algorithm include: correlation coefficient, root mean square error, average deviation and evaluation absolute error.

7. The method according to any one of claims 1 to 5, characterized in that: The evaluation dimensions of the preset data evaluation algorithm include: overall evaluation dimension, site evaluation dimension, instrument model evaluation dimension, time evaluation dimension, release phase evaluation dimension, altitude layer evaluation dimension and wind speed range evaluation dimension.

8. A multi-source wind profile data fusion device, characterized in that: include: An acquisition module is used to acquire Beidou sounding data and wind profile data; the Beidou sounding data includes Beidou sounding ascending segment data and Beidou sounding descending segment data; A matching module is used to match the Beidou sounding ascending segment data and the Beidou sounding descending segment data with the wind profile data based on a preset data time-space matching algorithm, so as to obtain a Beidou sounding ascending segment and wind profile matching data set and a Beidou sounding descending segment and wind profile matching data set; An evaluation module is used to evaluate the Beidou sounding ascending segment and wind profile matching data set and the Beidou sounding descending segment and wind profile matching data set based on a preset data evaluation algorithm and the same-site L-band sounding data and ERA5 reanalysis data, and obtain the wind speed and direction of the Beidou sounding ascending segment and wind profile and the wind speed and direction of the Beidou sounding descending segment and wind profile; The fusion module is used to fuse the wind speed and direction of the Beidou sounding ascending segment and the wind profile and the wind speed and direction of the Beidou sounding descending segment and the wind profile based on a preset data fusion algorithm to obtain a continuous wind profile fusion product data set.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

Images