Space-time alignment method for oxygen and nitrogen concentration observation of high-rise atmosphere of multi-source satellite

Abstract

The invention discloses a multi-source satellite upper atmosphere oxygen and nitrogen concentration observation space-time alignment method. The method comprises the steps that TIMED observation data and FY3E observation data of a to-be-observed area are collected; performing coordinate transformation on the TIMED observation data and the FY3E observation data, and performing coordinate expansion on the TIMED observation data and the FY3E observation data after the coordinate transformation; setting a time window in a specified time period, screening the corresponding TIMED observation data by taking the observation time of the FY3E observation data after coordinate expansion as a matching target, setting a spatial distance window, and screening the corresponding TIMED observation data by taking the FY3E observation data as a matching target to obtain a TIMED sample; and carrying out coordinate inverse mapping and deduplication screening on the TIMED samples with the longitudes outside [-180 degrees, 180 degrees] to obtain a nitrogen concentration matching sample and an oxygen concentration matching sample. The method not only can improve the accuracy of oxygen and nitrogen concentration observation space-time alignment, but also has good interpretability, and can be directly applied to an oxygen and nitrogen concentration observation space-time alignment system.

Description

Technical Field

[0001] The present invention relates to the field of space weather, and particularly to a method for spatio-temporal alignment of multi-source satellite observations of the oxygen and nitrogen concentrations in the upper atmosphere. Background Art

[0002] At present, the comparative analysis of the atmospheric components of the vast majority of multi-source observations mainly focuses on the middle and lower atmosphere, while the research on the upper atmosphere is relatively scarce. Although there have been studies comparing the observational data of GOLD (Global-scale Observations of the Limb and Disk) and TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics), since GOLD is a geostationary satellite with limited observational range and cannot cover the whole world, there are still gaps in the research on other regions, such as the upper atmosphere of China. The evolution of the upper atmosphere is significantly affected by solar activities, which have an obvious 11-year cycle. Since 2019, the world has entered the 25th solar cycle, and currently it is in the peak period of solar activity, with frequent flares and coronal mass ejections, which may affect the state and composition of the Earth's upper atmosphere.

[0003] In recent years, extreme weather events, such as natural disasters like droughts, floods and earthquakes, have become increasingly frequent, which has triggered concerns about the relationship between them and the abnormal circulation in the upper atmosphere. Whether the increase in these disasters is related to the abnormal circulation in the upper atmosphere still needs further in-depth study. In the past, due to the relatively thin upper atmosphere, its impact on the meteorological system has not received enough attention, and relevant observations and research are relatively few. However, with the continuous in-depth study of the upper atmosphere, the scientific community has gradually recognized the important role of the upper atmosphere in the energy cycle between the lower atmosphere and the extraterrestrial.

[0004] The observational data of the upper atmosphere are becoming increasingly abundant, and more and more studies have started to utilize these data to expand and improve the theory of the atmospheric circulation mechanism. For example, by observing parameters such as the composition, temperature, and wind speed of the upper atmosphere, the interaction between the upper and lower atmospheres and its impact on the global climate can be better understood. In addition, the spatio-temporal alignment algorithm plays an important role in this process. It not only helps to accumulate long-term datasets but also reveals the vertical circulation mechanism of the atmosphere, thus providing a more accurate basis for climate models and predictions. With the development of data analysis technology, the application of the spatio-temporal alignment algorithm is continuously expanding, enabling the effective integration of different satellite observational data. Through these technologies, researchers can better analyze the dynamic changes of the upper atmosphere and its relationship with ground meteorological phenomena, and further provide a more reliable scientific basis for climate change prediction and response strategies. These studies not only promote scientific progress but also provide important data support for policymakers to address the increasingly severe climate challenges. Summary of the Invention

[0005] The object of the present invention is to provide a method for spatio-temporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations.

[0006] To achieve the above object, the present invention is implemented according to the following technical solution:

[0007] The present invention includes the following steps:

[0008] Collect TIMED observation data and FY3E observation data of the area to be observed, and preprocess the TIMED observation data and the FY3E observation data;

[0009] Perform coordinate transformation on the TIMED observation data and the FY3E observation data, and expand the coordinates of the TIMED observation data and the FY3E observation data after coordinate transformation;

[0010] Set a time window within a specified time period, use the observation time of satellite FY3E as the matching target to screen the TIMED observation data, set a spatial distance window, and use the observation time of satellite FY3E as the matching target to screen the TIMED observation data; the screening principle for the screening is:

[0011] For sample Sample FY3E , calculate the spatial distance:

[0012] D FY3E,TIMED =(P(Lat FY3E ,Lon FY3E )-P(Lat TIMED ,Lon TIMED )) 2*0.5

[0013] Among them, the spatial distance between FY3E observation data and TIMED observation data is D FY3E,TIMED , the longitude of TIMED observation data is Lat TIMED , the longitude coordinate of FY3E observation data is Lat FY3E , the latitude of TIMED observation data is Lon TIMED , the latitude coordinate of FY3E observation data is Lon FY3E , the longitude and latitude coordinates of FY3E observation data are P(Lat FY3E ,Lon FY3E ), and the longitude and latitude coordinates of TIMED observation data are P(Lat TIMED ,Lon TIMED );

[0014] On the basis of the screening in the time window, set the spatial distance window D Δ , screen all samples with the sample spatial distance D < D Δ , and then retrieve the sample Sample with the smallest D among them TIMED , to obtain the group matching samples (Sample FY3E ,Sample TIMED );

[0015] Perform coordinate inverse mapping and duplicate removal screening on the TIMED samples with longitudes outside [-180°, 180°] to obtain nitrogen concentration matching samples and oxygen concentration matching samples.

[0016] Furthermore, the method of coordinate transformation includes:

[0017] The longitude range of satellite FY3E is [-180°, 180°], and the longitude range of satellite IMED is [0, 360°]. Taking the FY3E observation data as the sample matching target, first perform longitude transformation on the TIMED observation data, and the expression is:

[0018] Lon TIMED_new = Lon TIMED_old - 180°

[0019] Among them, the longitude of TIMED observation data is Lon TIMED_old , and the longitude of the TIMED observation data after longitude transformation is Lon TIMED_new .

[0020] Furthermore, the method of coordinate expansion includes:

[0021] Map the TIMED observations in the range (0°, 180°] to the range (-360°, -180°], and map the TIMED observation data and FY3E observation data in the range (-180°, 0°] to the range (180°, 360°], finally obtaining a sample coordinate range of [-360°, 360°].

[0022] Further, the screening method includes matching and selection. Match the observation data, and select the matched observation time based on the screening principle to obtain the screening result.

[0023] Further, the matching method includes:

[0024] S1 Observation time matching based on a time window

[0025] Sort the TIMED observation data in chronological order. Input the sorted TIMED observation data into a time matching model, and use a dimensionality reduction algorithm to reduce the TIMED observation data to a one-dimensional convolutional neural network with the time series as the main axis to obtain a one-dimensional convolutional output of the time series. The expression is:

[0026]

[0027] where the size of the convolutional kernel is N, and the temporal weight of the a-th convolutional kernel is φ a , the bias term is z, and the input value of the TIMED observation data of the (u - 1)-th time window of the (a + s)-th convolutional kernel is The output of the a-th convolution of the one-dimensional time series of the (u - 1)-th time window is

[0028] Incorporate the one-dimensional convolutional output of the time series and the observation time into a matching data set to form an attention score identification coefficient. The expression is:

[0029]

[0030] where the attention score identification coefficient of the x-th observation time is The attention score function of the x-th observation time is μ x , the observation time is c, the one-dimensional convolutional output of the time series is B, and the attention score identification is w(B, c);

[0031] Calculate the attention score of the observation time:

[0032]

[0033] where the attention score is The x-th observation time is c x , and the number of TIMED observation data is M;

[0034] Improve the loss function of the time matching model with a regularization parameter, and the expression is:

[0035]

[0036] Among them, the improved loss function is The mean square error of the loss function is The regularization strength is The regularization parameter is The order of the norm is p, and the regularization term is F(φ);

[0037] Output the TIMED observation data with the highest attention score as the matching result;

[0038] S2 Observation sample matching based on the spatial distance window

[0039] Take the observation time of satellite FY3E as the matching target and screen the TIMED observation data;

[0040] Classify the TIMED observation data by region according to space to obtain the observation region data set, calculate the spatial distance between the FY3E observation data and the TIMED observation data, and output the one with the smallest distance as the matching result.

[0041] Furthermore, the selection method includes:

[0042] A Selection based on the time window to screen TIMED observation data

[0043] Extract TIMED observation data through the time window and convert the time format of the IMED observation data according to the FY3E observation data;

[0044] Screen the observation time of the corresponding satellite TIMED, and the expression is:

[0045] Datetime TIMED =Datetime FY3E -T Δ

[0046] Among them, the observation time of satellite TIMED is Datetime TIMED , the time window is T Δ , and the observation time of satellite FY3E is Datetime FY3E ;

[0047] Extract the TIMED observation data within the observation time as the time window screening result;

[0048] B Selection based on the spatial distance to screen TIMED observation data

[0049] Calculate the correlation coefficient between the FY3E observation data and the IMED observation data for the TIMED observation data filtered by selecting the time window through the spatial distance, and eliminate the IMED observation data with a correlation coefficient less than 0.216;

[0050] Calculate the information coefficient of the IMED observation data, and calculate the distance metric according to the IMED observation data with the maximum information coefficient. The expression is:

[0051]

[0052] where the longitude coordinate of the i-th FY3E observation data is Lat FY3E,i and the latitude of the j-th TIMED observation data is Lon TIMED,j The latitude coordinate of the i-th FY3E observation data is Lon FY3E,i and the longitude coordinate of the j-th TIMED observation data is Lat TIMED,j The distance metric between the FY3E observation data and the TIMED observation data is K i,j and the Harvard-Stein coefficient is ρ;

[0053] Eliminate the TIMED observation data with the smallest distance metric, and repeat the iteration until all time windows are traversed. Output the remaining TIMED observation data as the spatio-temporal distance screening result.

[0054] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0055] A processor; and a memory arranged to store computer-executable instructions, which when executed cause the processor to execute the method steps described in the first aspect.

[0056] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the method steps described in the first aspect.

[0057] The beneficial effects of the present invention are:

[0058] The present invention is a method for spatio-temporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations. Compared with the prior art, the present invention has the following technical effects:

[0059] Through preprocessing, coordinate transformation, coordinate expansion, data screening, coordinate inverse mapping, and deduplication steps, the present invention can improve the accuracy of spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations, thereby enhancing the precision of spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations. Optimizing the spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations can greatly save resources, improve work efficiency, enable intelligent spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations, perform data matching and screening on the spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations in real time, which is of great significance for the spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations, and can adapt to spatio-temporal alignments of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations with different standards and different requirements for spatio-temporal alignments of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations, having a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flowchart of the steps of the method for spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations of the present invention;

[0061] Figure 2 It is a schematic structural diagram of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] The present invention will be further described below through specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but not to limit the present invention.

[0063] The method for spatio-temporal alignment of multi-source satellite upper atmospheric oxygen and nitrogen concentration observations of the present invention includes the following steps:

[0064] As Figure 1 shown, in this embodiment, it includes the following steps:

[0065] Collect TIMED observation data and FY3E observation data of the area to be observed, and preprocess the TIMED observation data and the FY3E observation data;

[0066] Perform coordinate transformation on the TIMED observation data and the FY3E observation data, and perform coordinate expansion on the TIMED observation data and the FY3E observation data after coordinate transformation;

[0067] Set a time window within a specified time period, taking the observation time of satellite FY3E as the matching target, screen the TIMED observation data, set a spatial distance window, and take the observation time of satellite FY3E as the matching target to screen the TIMED observation data; wherein the screening principle for the screening is:

[0068] For sample Sample FY3E , calculate the spatial distance:

[0069] D FY3E,TIMED = (P(Lat FY3E , Lon FY3E ) - P(Lat TIMED , Lon TIMED )) 2*0.5

[0070] where the spatial distance between FY3E observation data and TIMED observation data is D FY3E,TIMED , the longitude of TIMED observation data is Lat TIMED , the longitude coordinate of FY3E observation data is Lat FY3E , the latitude of TIMED observation data is Lon TIMED , the latitude coordinate of FY3E observation data is Lon FY3E , the longitude and latitude coordinates of FY3E observation data are P(Lat FY3E , Lon FY3E ), and the longitude and latitude coordinates of TIMED observation data are P(Lat TIMED , Lon TIMED );

[0071] On the basis of the screening in the time window, set the spatial distance window D Δ , and screen all samples with the spatial distance D < D Δ , and then retrieve the sample Sample with the smallest D TIMED , to obtain the group matching samples (Sample FY3E , Sample TIMED );

[0072] Inverse map the coordinates and perform duplicate removal screening on the TIMED samples with longitudes outside [-180°, 180°] to obtain nitrogen concentration matching samples and oxygen concentration matching samples.

[0073] In this embodiment, the method of coordinate conversion includes:

[0074] The longitude range of satellite FY3E is [-180°, 180°], and the longitude range of satellite IMED is [0, 360°]. Taking the FY3E observation data as the sample matching target, first perform longitude conversion on the TIMED observation data, and the expression is:

[0075] Lon TIMED_new = Lon TIMED_old - 180°

[0076] where the longitude of TIMED observation data is Lon TIMED_old , and the longitude of the TIMED observation data after longitude conversion is Lon TIMED_new .

[0077] In this embodiment, the method for coordinate expansion includes:

[0078] Map the TIMED observations in the range (0°, 180°] to the range (-360°, -180°], and map the TIMED observation data and FY3E observation data in the range (-180°, 0°] to the range (180°, 360°], finally obtaining a sample coordinate range of [-360°, 360°].

[0079] In this embodiment, the screening method includes matching and selection. Match the observation data, and select the matched observation times based on the screening principle to obtain the screening result.

[0080] In this embodiment, the matching method includes:

[0081] S1 Observation time matching based on a time window

[0082] Sort the TIMED observation data in chronological order, input the sorted TIMED observation data into the time matching model, and use a dimensionality reduction algorithm to reduce the TIMED observation data to a one-dimensional convolutional neural network with the time series as the main axis to obtain a one-dimensional convolutional output of the time series. The expression is:

[0083]

[0084] where the size of the convolutional kernel is N, the temporal weight of the a-th convolutional kernel is φ a , the bias term is z, and the multi-input value of the TIMED observation data of the (u - 1)-th time window of the (a + s)-th convolutional kernel is The output of the one-dimensional convolution of the a-th convolution of the time series of the (u - 1)-th time window is

[0085] Incorporate the one-dimensional convolutional output of the time series and the observation time into the matching data set to form an attention score identification coefficient. The expression is:

[0086]

[0087] where the attention score identification coefficient of the x-th observation time is The attention score function of the x-th observation time is μ x , the observation time is c, the one-dimensional convolutional output of the time series is B, and the attention score identification is w(B, c);

[0088] Calculate the attention score of the observation time:

[0089]

[0090] where the attention score is the x-th observation time is c x , and the number of TIMED observation data is M;

[0091] The loss function of the time matching model is improved by using a regularization parameter, and the expression is:

[0092]

[0093] where the improved loss function is the mean square error of the loss function is the regularization strength is the regularization parameter is the order of the norm is p, and the regularization term is F(φ);

[0094] Output the TIMED observation data with the highest attention score as the matching result;

[0095] S2 Observation sample matching based on the spatial distance window

[0096] Taking the observation time of satellite FY3E as the matching target, screen the TIMED observation data;

[0097] Classify the TIMED observation data by region according to space to obtain an observation region data set, calculate the spatial distance between the FY3E observation data and the TIMED observation data, and output the one with the smallest distance as the matching result.

[0098] In this embodiment, the selected method includes:

[0099] A Selection based on the time window Screen the TIMED observation data

[0100] Extract the TIMED observation data through the time window, and convert the time format of the IMED observation data according to the FY3E observation data;

[0101] Screen the observation time of the corresponding satellite TIMED, and the expression is:

[0102] Datetime TIMED = Datetime FY3E - T Δ

[0103] where the observation time of satellite TIMED is Datetime TIMED , the time window is T Δ , and the observation time of satellite FY3E is Datetime FY3E ;

[0104] Extract the TIMED observation data within the observation time as the time window screening result;

[0105] B. Selection and screening of TIMED observation data based on spatial distance

[0106] The TIMED observation data after selecting the time window through spatial distance was used to calculate the correlation coefficient between the FY3E observation data and the IMED observation data, and the IMED observation data with a correlation coefficient less than 0.216 were eliminated;

[0107] Calculate the information coefficient of IMED observation data, and calculate the distance metric based on the IMED observation data with the maximum information coefficient. The expression is:

[0108]

[0109] The longitude coordinate of the i-th FY3E observation data is Lat FY3E,i , the latitude of the jth TIMED observation data is Lon TIMED,j , the latitude coordinate of the i-th FY3E observation data is Lon FY3E,i , the longitude coordinate of the jth TIMED observation data is Lat TIMED,j , the distance measure between FY3E observation data and TIMED observation data is K i,j , the Harvard-Sain coefficient is ρ;

[0110] The TIMED observation data with the smallest distance metric is eliminated, and the iteration is repeated until all time windows are traversed, and the remaining TIMED observation data are output as the spatiotemporal distance screening results.

[0111] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0112] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0113] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0114] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations at the logical level. The processor executes the program stored in the memory and is specifically used to execute the aforementioned method for spatiotemporal alignment of any multi-source satellite upper atmosphere oxygen and nitrogen concentration observations.

[0115] The above application Figure 1The method for spatio-temporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by the hardware decoding processor, or executed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0116] The electronic device can also execute Figure 1 the method for spatio-temporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations in Figure 1 and implement the functions of the illustrated embodiments, which are not elaborated herein in the embodiments of the present application.

[0117] The embodiments of the present application also propose a computer-readable storage medium that stores one or more programs, and the one or more programs include instructions that, when executed by an electronic device including a plurality of application programs, execute any of the foregoing methods for spatio-temporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations.

[0118] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0120] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realize the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0122] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0123] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0124] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. 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 tapes, magnetic tape magnetic disk storage, or other magnetic storage devices, or any other non-transitory media that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0126] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0127] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations, characterized in that: The following steps are involved: Collecting TIMED observation data and FY3E observation data of the area to be observed, and preprocessing the TIMED observation data and the FY3E observation data; Performing coordinate conversion on the TIMED observation data and the FY3E observation data, and performing coordinate expansion on the TIMED observation data and the FY3E observation data after the coordinate conversion; A time window is set within a specified time period, and the observation time of satellite FY3E is used as a matching target to filter the TIMED observation data; a spatial distance window is set, and the observation time of satellite FY3E is used as a matching target to filter the TIMED observation data; wherein the filtering principle is: For sample FY3E , calculate the spatial distance: D FY3E,TIMED =(P(Lat FY3E ,Lon FY3E )-P(Lat TIMED ,Lon TIMED )) 2*0.5 The spatial distance between the FY3E observation data and the TIMED observation data is D FY3E,TIMED , the longitude of TIMED observation data is Lat TIMED , the longitude coordinate of the FY3E observation data is Lat FY3E , the latitude of TIMED observation data is Lon TIMED , the latitude coordinate of FY3E observation data is Lon FY3E The latitude and longitude coordinates of the FY3E observation data are P(Lat FY3E ,Lon FY3E ), the latitude and longitude coordinates of TIMED observation data are P(Lat TIMED ,Lon TIMED ); Based on the time window screening, set the spatial distance window D Δ , filter sample space distance D <D Δ All samples of , and then retrieve the sample Sample with the smallest D TIMED , get the group matching sample (Sample FY3E ,Sample TIMED ); The TIMED samples with longitudes outside [-180°, 180°] are subjected to coordinate inverse mapping and de-duplication screening to obtain nitrogen concentration matching samples and oxygen concentration matching samples.

2. The method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations according to claim 1, characterized in that: The coordinate conversion method comprises: The longitude range of satellite FY3E is [-180°, 180°], and the longitude range of satellite IMED is [0, 360°]. Taking FY3E observation data as the sample matching target, first perform longitude conversion on TIMED observation data, the expression is: Lon TIMED_new =Lon TIMED_old -180° The longitude of TIMED observation data is Lon TIMED_old , the longitude of the TIMED observation data after longitude conversion is Lon TIMED_new .

3. The method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations according to claim 1, characterized in that: The coordinate expansion method comprises: Map the TIMED observation at (0°, 180°] to (-360°, -180°], and map the TIMED observation data and FY3E observation data at (-180°, 0°] to (180°, 360°], and finally obtain the sample coordinate range of [-360°, 360°].

4. The method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations according to claim 1, characterized in that: The screening method includes matching and selecting, matching the observation data, selecting the matched observation time based on the screening principle, and obtaining the screening result.

5. The method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations according to claim 4, characterized in that: The matching method comprises: S1 Observation time matching based on time window The TIMED observation data are sorted in chronological order, and the sorted TIMED observation data are input into the time matching model. The TIMED observation data are reduced to a one-dimensional convolutional neural network with time series as the main axis using the dimensionality reduction algorithm to obtain the one-dimensional convolution output of the time series. The expression is: The size of the convolution kernel is N, and the time series weight of the ath convolution kernel is φ a , the bias term is z, and the input value of the TIMED observation data of the a+sth convolution kernel u-1th time window is The output of the a-th one-dimensional convolution of the u-1-th time window time series is The one-dimensional convolution output of the time series and the observation time are incorporated into the matching dataset to form the attention score identification coefficient, which is expressed as: The attention score identification coefficient at the xth observation time is θ x , the attention score function at the x-th observation time is μ x , the observation time is c, the one-dimensional convolution output of the time series is B, and the attention score is marked as w(B,c); Calculate the attention score for the observation time: The attention score is The xth observation time is c x , the number of TIMED observation data is M; The regularization parameter is used to improve the loss function of the time matching model, and the expression is: The improved loss function is The mean square error of the loss function is The regularization strength is The regularization parameter is The order of the norm is p, and the regularization term is F(φ); Output the TIMED observation data with the highest attention score as the matching result; S2 Observation sample matching based on spatial distance window The observation time of satellite FY3E was used as the matching target to filter the TIMED observation data; The TIMED observation data are regionally classified according to space to obtain the observation area data set, and the spatial distance between the FY3E observation data and the TIMED observation data is calculated, and the one with the smallest distance is output as the matching result.

6. The method for spatiotemporal alignment of multi-source satellite upper atmosphere oxygen and nitrogen concentration observations according to claim 4, characterized in that: The selection method comprises: A. Filtering TIMED observation data based on time window selection Extract TIMED observation data through time window, and convert the time format of IMED observation data according to FY3E observation data; Filter the observation time of the corresponding satellite TIMED. The expression is: Datetime TIMED =Datetime FY3E -T Δ The observation time of satellite TIMED is Datetime TIMED , the time window is T Δ , the observation time of satellite FY3E is Datetime FY3E ; Extract the TIMED observation data within the observation time as the time window screening result; B. Selection and screening of TIMED observation data based on spatial distance The TIMED observation data after selecting the time window through spatial distance was used to calculate the correlation coefficient between the FY3E observation data and the IMED observation data, and the IMED observation data with a correlation coefficient less than 0.216 were eliminated; Calculate the information coefficient of IMED observation data, and calculate the distance metric based on the IMED observation data with the maximum information coefficient. The expression is: The longitude coordinate of the i-th FY3E observation data is Lat FY3E,i , the latitude of the jth TIMED observation data is Lon TIMED,j , the latitude coordinate of the i-th FY3E observation data is Lon FY3E,i , the longitude coordinate of the jth TIMED observation data is Lat TIMED,j , the distance measure between FY3E observation data and TIMED observation data is K i,j , the Harvard-Sain coefficient is ρ; The TIMED observation data with the smallest distance metric is eliminated, and the iteration is repeated until all time windows are traversed, and the remaining TIMED observation data are output as the spatiotemporal distance screening results.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 5.

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