Multi-source observation data fusion method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-source observation data fusion method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining original L0 data of satellite observation, carrying out the positioning and calibration preprocessing of the original L0 data, so as to generate L1 data, and carrying out the inversion processing according to the generated L1 data, so as to obtain L2 data; performing space-time matching on the L2 data subjected to quality control through a preset space-time interpolation method so as to correct mismatching of different data sources in time and space; and performing weighted average calculation on the data after time-space matching according to a weight distribution algorithm to obtain fused data, verifying the fused data through verification data, and performing data evaluation on the fused data by calculating a statistical index between the fused data and the verification data. According to the method, the problem of space-time mismatching of the multi-source observation data can be effectively solved, so that the fused data can more accurately reflect the real state of the atmosphere, and the availability and value of the data are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of satellite occultation technology, and particularly relates to a multi-source observation data fusion method and device, an electronic device and a storage medium. BACKGROUND

[0002] In the field of atmospheric science research and weather forecasting, the fusion of multi-source observation data is of great significance to improve the accuracy and reliability of data. At present, common observation data sources include radio occultation, microwave radiometer, medium-resolution multi-spectral imager, ground station and sounding data, etc. These data sources each have unique advantages, for example, radio occultation can provide atmospheric refractive index information, microwave radiometer can measure atmospheric water vapor content, medium-resolution multi-spectral imager can obtain multi-spectral images of the atmosphere, ground station can provide real-time observation of ground meteorological elements, and sounding data can reflect the vertical structure of the atmosphere. However, these data sources often have time and space mismatches, that is, the data acquisition time and spatial position of different data sources are inconsistent, which brings great challenges to data fusion.

[0003] Traditional data fusion methods are usually difficult to effectively handle such temporal and spatial mismatches, resulting in low precision of the fused data, which cannot fully exert the advantages of each data source, thereby limiting its application effect in related fields. SUMMARY

[0004] Therefore, the present application aims to provide a multi-source observation data fusion method, device, electronic device and storage medium to solve the problem of low precision of fused data caused by temporal and spatial mismatches of multi-source observation data.

[0005] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0006] In a first aspect, the present application provides a multi-source observation data fusion method, comprising:

[0007] obtaining original L0 data observed by a satellite, and performing positioning and scaling preprocessing on the original L0 data to generate L1 data, and performing inversion processing according to the generated L1 data to obtain L2 data about atmospheric temperature and atmospheric specific humidity profile;

[0008] performing temporal and spatial matching on the L2 data subjected to quality control by a preset temporal and spatial interpolation method to correct the temporal and spatial mismatch of different data sources;

[0009] The data after the spatio-temporal matching is weighted and averaged according to a weight distribution algorithm to obtain fused data, and the fused data is verified by verification data, and statistical indicators between the fused data and the verification data are calculated to evaluate the fused data.

[0010] In a second aspect, based on the same inventive concept, the application further provides a multi-source observation data fusion device, comprising:

[0011] The data processing module is configured to obtain original L0 data observed by a satellite, and perform positioning and scaling preprocessing on the original L0 data to generate L1 data, and perform inversion processing according to the generated L1 data to obtain L2 data about atmospheric temperature and atmospheric specific humidity profile;

[0012] The spatio-temporal matching module is configured to perform spatio-temporal matching on the L2 data after the quality control by a preset spatio-temporal interpolation method to correct the mismatch of different data sources in time and space;

[0013] The data fusion module is configured to perform weighted average calculation on the data after the spatio-temporal matching according to a weight distribution algorithm to obtain fused data, and verify the fused data by verification data, and evaluate the fused data by calculating statistical indicators between the fused data and the verification data.

[0014] In a third aspect, based on the same inventive concept, the application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of the first aspect when executing the program.

[0015] In a fourth aspect, based on the same inventive concept, the application further provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing the computer to execute the method of the first aspect.

[0016] Compared with the prior art, the multi-source observation data fusion method, device, electronic device and storage medium provided by the application have the following beneficial effects:

[0017] The multi-source observation data fusion method provided by the application can effectively solve the spatio-temporal mismatch problem of multi-source observation data, significantly improve the precision of the fused data, organically combine the advantages of different data sources, realize complementary advantages, improve the overall quality and information content of the data, and effectively improve the discontinuity of multi-source observation data in time and space, generate fused data with higher spatio-temporal continuity, and help better analyze the dynamic change process of the atmosphere. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The illustrations, together with their description, serve to explain the application without unduly limiting it.

[0019] Figure 1 A multi-source observation data fusion method flow chart described in the embodiments of the present application;

[0020] Figure 2 A multi-source observation data fusion device structure schematic diagram described in the embodiments of the present application;

[0021] Figure 3 An electronic device hardware structure schematic diagram described in the embodiments of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below with specific embodiments and with reference to the drawings.

[0023] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood as the usual meaning understood by those skilled in the art to which the present application belongs. The terms "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar words mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right" and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0024] As described in the above background section, the embodiments aim to solve the problem of low fusion accuracy caused by the spatio-temporal mismatch of multi-source observation data in the prior art. Specifically, a data fusion method capable of effectively handling the spatio-temporal mismatch of multi-source observation data such as radio occultation, microwave radiometer, medium-resolution multi-spectral imager, ground station and sounding data is needed, so that the fused data can more accurately reflect the true state of the atmosphere, improve the usability and value of the data, and provide more reliable data support for atmospheric science research and weather forecasting.

[0025] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0026] Please refer to Figure 1As shown, the embodiment provides a multi-source observation data fusion method, which specifically includes the following steps:

[0027] In step S101, raw L0 data observed by a satellite is acquired, and the raw L0 data is preprocessed for positioning and scaling to generate L1 data, and inversion processing is performed according to the generated L1 data to obtain L2 data about atmospheric temperature and atmospheric specific humidity profile.

[0028] Specifically, in the embodiment, when the raw L0 data of the radio (GNSS) occultation, the microwave radiometer, and the medium-resolution multi-spectral imager are processed, positioning and scaling processing are first required to generate L1 data.

[0029] Scaling: for the raw L0 data of the GNSS occultation, a single difference technique (difference between LEO satellite and GNSS occultation observation and reference satellite GNSS occultation observation) is adopted to eliminate the clock error of the LEO satellite-borne receiver; the additional phase of the occultation observation is obtained by using the precise orbit of the LEO and GNSS satellites and the GNSS precise clock difference data.

[0030] For the raw L0 data of the microwave radiometer, through scaling procedures such as blackbody radiation correction, cold space radiation correction, non-linear correction, antenna reflector emission correction, and antenna pattern correction, the scaled radiance brightness temperature is finally obtained.

[0031] For the raw L0 data (infrared channel) of the medium-resolution multi-spectral imager, the radiance and the instrument count are fitted as a quadratic function, the radiometric scaling coefficient is obtained by using the least square method according to the blackbody and cold space two-point scaling, and the scaled radiance brightness temperature is finally obtained.

[0032] Positioning: for the raw L0 data of the GNSS occultation, the precise orbit determination procedure includes GNSS observation value cycle slip detection preprocessing, observation model linearization, observation equation differentiation, linear transformation and covariance propagation, precise orbit determination numerical integration, and precise orbit determination comprehensive algorithm.

[0033] For the raw L0 data of the microwave radiometer and the medium-resolution multi-spectral imager, the positioning procedure starts from the sensor exit vector calculation, including antenna beam misalignment correction, instrument and spacecraft alignment, spacecraft to orbit coordinate transformation, orbit to geocentric inertial coordinate system transformation, geocentric inertial to geocentric and fixed coordinate system transformation, intersection algorithm with WGS84 earth ellipsoid, and topographic intersection algorithm processing.

[0034] It should be noted that each algorithm for scaling and positioning processing of the raw L0 data is a conventional technical means adopted by those skilled in the art, and the present application does not make specific improvements, so further elaboration is not made.

[0035] The satellite L1 data needs to be processed by inversion to obtain the L2 data of atmospheric temperature and atmospheric specific humidity profile.

[0036] Specifically, for the radio occultation L1 data, under the assumption of local spherical symmetry, the inversion of the signal received by the low-orbit satellite can obtain various parameters of the earth's atmosphere. The signal received by the low-orbit satellite is calculated by the geometric optics and radio holographic method to obtain the bending angle and collision parameter, the refractive index profile is obtained by using the Abel integral transformation, and the temperature and humidity profiles of the atmosphere are obtained by inversion through the ideal gas state equation and the Smith-Weintraub equation.

[0037] For the L1 data of the microwave radiometer and the medium-resolution multi-spectral imager, first, the outlier rejection is performed on the brightness temperature data, then the simulated brightness temperature is calculated by using the RTTOV radiation transfer model, and finally the inversion is realized by minimizing the objective function through the one-dimensional variation algorithm. Among them, the objective function includes the root mean square error of the observed brightness temperature and the simulated brightness temperature and the background constraint term, and the background field uses the ECMWF prediction data. In the iteration process, the algorithm adjusts the initial temperature and humidity profile to minimize the objective function, and finally obtains the optimal atmospheric temperature and humidity profile.

[0038] In step S102, the L2 data subjected to the quality control is matched in time and space by using a preset time-space interpolation method, so as to correct the mismatch in time and space of different data sources.

[0039] Specifically, in the embodiment, the inversion data of satellite observation (GNSS occultation, microwave radiometer, medium-resolution multi-spectral imager) needs to be further subjected to quality control.

[0040] Among them, the quality control of the ground station observation data is relatively direct, mainly depends on the periodic calibration and maintenance of the instrument, checks the observation record of the ground station, and ensures that the instrument is normally operated during observation and is not disturbed by the environment. For the sounding observation data, since it can provide high vertical resolution temperature and humidity profile, the key of the quality control lies in checking whether the ascending process of the sounding instrument is smooth and whether the data transmission is complete. Any abnormal ascending of the sounding instrument or data loss will affect the accuracy of the temperature and humidity profile, and the reliability of the sounding data can be further verified by comparing with the synchronous observation data of the ground station.

[0041] The inversion data of satellite observation can be compared with the ground station and sounding data in the adjacent area to eliminate abnormal data points, and compared with the climate average value of ECMWF (European Centre for Medium-Range Weather Forecasts) to eliminate data points obviously deviating from the climate state.

[0042] Step S103, the spatio-temporal matched data is weighted and averaged according to a weight distribution algorithm to obtain fused data, and the fused data is verified by verification data, and the statistical indicators between the fused data and the verification data are calculated to evaluate the fused data.

[0043] Specifically, in the embodiment, a spatio-temporal interpolation method is adopted to correct the time and space mismatch of different data sources.

[0044] For the time mismatch, according to the time sequence characteristics of each data source, a linear interpolation method is adopted to interpolate the data to a unified time point; for the space mismatch, according to the spatial distribution characteristics of each data source, a Kriging interpolation method is adopted to interpolate the data to a unified spatial position.

[0045] First, for the time mismatch, the target time is taken as the reference, and the GNSS occultation data provides observation values at the time point , and other data provides observation values at the time point , then the observation values of other data at the time point are:

[0046] ;

[0047] wherein, represents different observation data types, represents the target time of time interpolation, .

[0048] In this way, the time sequence data of all data sources is interpolated to a unified time point, thereby solving the problem of time mismatch.

[0049] For the space mismatch, the Kriging interpolation method is adopted. Since the observation data comes from different sources, there are multiple spatial observation points, each of which provides observation data, and the observation value of an unknown spatial point (the spatial point is a point in the self-defined resolution grid) needs to be estimated. The key of Kriging interpolation lies in calculating the semi-variance function between spatial points, which describes the spatial correlation between spatial points. The semi-variance function can be expressed as:

[0050] ;

[0051] wherein, represents the observation value at the spatial point , and represents the spatial lag distance, and N(h) represents the number of observation points within the lag distance . Actual observation value of spatial point position By fitting the semi-variance function, a correlation model between spatial points can be obtained. With this model, the Kriging interpolation can estimate the observation value of unknown spatial point by the following formula:

[0052]

[0053] where is the weight coefficient, is the number of observation points within the lag distance of . The calculation of the weight coefficient needs to consider the distance between spatial points and the semi-variance function, which is realized by solving the following linear equations:

[0054]

[0055] where represents the spatial position of the th known point, represents the semi-variance between spatial point positions and , which is used to quantify the spatial correlation between two points.

[0056] In order to assign weights according to the accuracy, reliability and sensitivity to atmospheric state of each data source, the method of calculating the average variance of historical statistics of different data sources is adopted. This method quantifies the stability and accuracy of data sources to provide objective basis for weight allocation.

[0057] First, collect the historical observation data of each data source, which covers a long enough time period to ensure that the statistical results are representative. For each data source, calculate the variance of its observation data, which is a statistical measure of the dispersion of data, and a data source with smaller variance usually has higher accuracy and reliability. Specifically, for the th data source, its variance can be calculated by the following formula:

[0058]

[0059] where represents the total number of observation data, represents the value of the th data source in the th observation, represents the average value of the observation data of the th data source, represents the number of data source types.

[0060] Next, calculate the average value of all data source methods: ​​​

[0061] .

[0062] Then, the weight of each data source is determined according to the ratio of the variance of each data source to the average variance. The weight is calculated by the following formula:

[0063] ;

[0064] In the formula, represents the serial number of different types of data sources.

[0065] The spatio-temporally matched data is weighted and averaged according to the weight distribution algorithm to obtain the fused data. The fusion calculation formula is:

[0066] ;

[0067] In the formula, represents the fused data, represents the weight of the i th data source, represents the data of the i th data source at the spatial point x and the temporal point t.

[0068] In this embodiment, independent observation data or ERA5 reanalysis data are used as verification data to verify the fused data. The accuracy of the fused data is evaluated by calculating the root mean square error (RMSE) and the correlation coefficient between the fused data and the verification data.

[0069] The multi-source observation data fusion method described in the application can effectively handle the spatio-temporal mismatch problem of multi-source observation data, significantly improve the precision of the fused data, organically combine the advantages of different data sources, realize complementary advantages, improve the overall quality and information content of the data, effectively improve the discontinuity of multi-source observation data in time and space, generate fused data with higher spatio-temporal continuity, and help better analyze the dynamic change process of the atmosphere.

[0070] It should be noted that some embodiments of the application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than described above and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or required.

[0071] ​​​​Based on the same inventive concept, embodiments of the present application also provide a multi-source observation data fusion device corresponding to the method of any of the above embodiments.

[0072] As shown in Figure 2 The multi-source observation data fusion device comprises:

[0073] The data processing module 11 is configured to acquire satellite observation raw L0 data, and perform positioning and scaling preprocessing on the raw L0 data to generate L1 data, and perform inversion processing according to the generated L1 data to obtain L2 data about atmospheric temperature and atmospheric specific humidity profile;

[0074] The space-time matching module 12 is configured to perform space-time matching on the L2 data subjected to quality control through a preset space-time interpolation method, so as to correct the mismatch of different data sources in time and space;

[0075] The data fusion module 13 is configured to perform weighted average calculation on the data subjected to space-time matching according to a weight distribution algorithm, so as to obtain fused data, and perform verification on the fused data through verification data, and perform data evaluation on the fused data by calculating statistical indicators between the fused data and the verification data.

[0076] For the convenience of description, the above device is described in various modules according to functions. Of course, the functions of the modules can be implemented in one or more software and / or hardware in the implementation of the embodiments of the present application.

[0077] The device of the above embodiments is used to implement the corresponding method of any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here.

[0078] Based on the same inventive concept, embodiments of the present application also provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of the above embodiments when executing the program.

[0079] Figure 3 A more specific hardware structure of an electronic device provided by the present embodiment is shown, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040 and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030 and the communication interface 1040 are connected to each other through the bus 1050 for communication within the device.

[0080] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing relevant programs to implement the technical solutions provided by the embodiments of the present specification.

[0081] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the relevant program codes are saved in the memory 1020 and called and executed by the processor 1010.

[0082] The input / output interface 1030 is configured to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input devices can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices can include a display, a speaker, a vibrator, an indicator light, etc.

[0083] The communication interface 1040 is configured to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as USB, network cable, etc.) or through a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0084] The bus 1050 includes a channel for transmitting information between various components (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[0085] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only include the components necessary to implement the solutions of the embodiments of the present specification, and does not have to include all the components shown in the figure.

[0086] The electronic device of the above embodiments is used to implement the corresponding method in any of the preceding embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

[0087] Based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any of the above embodiments.

[0088] The computer readable medium of the embodiments can include permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0089] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to perform the method of any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described here.

[0090] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope (including claims) of the present application is limited to these examples; the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of the different aspects of the embodiments of the present application as described above. In order to be brief, they are not provided in detail.

[0091] Additionally, to simplify the description and discussion, and so as not to obscure the embodiments of the application being presented, the well-known functions or constructions of integrated circuit (IC) chips and other components can or can not be shown in the figures and will be omitted as not to unnecessarily obscure the embodiments of the application being presented. Moreover, the devices can be shown in block diagram form in order to avoid obscuring the embodiments of the application, and this also acknowledges the fact that the details in regard to the implementation of the block diagram devices are highly dependent on the platform within which the embodiments of the application are to be implemented (i.e., these details should be well within the purview of one of ordinary skill in the art). Where specific details are set forth in order to describe an illustrative embodiment of the application, it will be apparent to one of ordinary skill in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Thus, the description is to be considered as illustrative and not restrictive, and the scope of the application should be determined not with reference to the above description, but should be given to the appended claims.

[0092] While the application has been described in connection with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0093] Embodiments of the application are intended to cover all such alternatives, modifications and variations as falling within the scope of the appended claims. Accordingly, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principle of the embodiments of the application should be included in the scope of protection of the application.

Claims

1. A method for fusing multi-source observation data, characterized in that, include: The raw L0 data from satellite observations is acquired, and the raw L0 data is preprocessed for positioning and calibration to generate L1 data. The generated L1 data is then inverted to obtain L2 data on atmospheric temperature and atmospheric specific humidity profiles. The L2 data, which has undergone quality control, is spatiotemporally matched using a preset spatiotemporal interpolation method to correct for time and space mismatches between different data sources. The spatiotemporally matched data are weighted and averaged according to a weighting algorithm to obtain fused data. The fused data is then validated using verification data. Finally, the fused data is evaluated by calculating statistical indicators between the fused data and the verification data.

2. The method according to claim 1, characterized in that: The raw L0 data includes radio occultation data, microwave radiometer data, and medium-resolution multispectral imager data.

3. The method according to claim 2, characterized in that, The quality control includes: The L2 data after inversion processing is compared with ground station data and radiosonde data from adjacent areas to remove outlier data points, and compared with the climatic average of ECMWF to remove data points that deviate significantly from the climatic state.

4. The method according to claim 1, characterized in that, The spatiotemporal interpolation method includes: In response to time mismatch, the data is interpolated to a unified time point using a linear interpolation method based on the time series characteristics of each data source. In response to spatial mismatch, the data is interpolated to a uniform spatial location using the Kriging interpolation method, based on the spatial distribution characteristics of each data source.

5. The method according to claim 4, characterized in that, The weight allocation algorithm includes: Based on the historical observation data collected from each data source, the variance and mean deviation of each observation data are calculated, and the weight of each data source is determined according to the ratio of its variance to its mean variance.

6. The method according to claim 5, characterized in that, The weight allocation algorithm formula is as follows: ； in, , ; In the formula, This represents the total number of observed data. Indicates the first The data source in the first The value in this observation Indicates the first The average value of the observed data from each data source. Indicates the number of data source types. Indicates the sequence number of different types of data sources. Indicates the first The weight of each data source.

7. The method according to claim 6, characterized in that, The fusion calculation formula is as follows: ； In the formula, This represents the merged data. Indicates the first The weight of each data source, Indicates the first A data source at a spatial point Time point The data.

8. A multi-source observation data fusion device, characterized in that, include: The data processing module is configured to acquire the raw L0 data from satellite observations, perform positioning and calibration preprocessing on the raw L0 data to generate L1 data, and perform inversion processing on the generated L1 data to obtain L2 data on atmospheric temperature and atmospheric specific humidity profiles. The spatiotemporal matching module is configured to perform spatiotemporal matching on the quality-controlled L2 data using a preset spatiotemporal interpolation method to correct for mismatches in time and space between different data sources. The data fusion module is configured to perform a weighted average calculation on the spatiotemporally matched data according to a weight allocation algorithm to obtain fused data, and to verify the fused data with verification data. The fused data is then evaluated by calculating statistical indicators between the fused data and the verification data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, in, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method described in any one of claims 1-7.

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