Near space temperature calculation method

Abstract

The application provides a near space temperature calculation method, and relates to the technical field of near space, and the method comprises the following steps: acquiring satellite observation data sets of TIMED / SABER, using an information entropy sampling method to perform hierarchical sampling on the observation data sets to obtain a sampling data set; the sampling data set comprises a turning point temperature data set, a temperature layer temperature data set and environmental parameters related to the turning point temperature data set; performing influence factor analysis on the environmental parameters related to the turning point temperature data set to obtain element information; fitting a turning point temperature function and a turning point height function based on the turning point temperature data set and the element information; establishing a near space temperature model of different temperature layers based on the temperature layer temperature data set, the element information, the turning point temperature function and the turning point height function; inputting the acquired current element information into the near space temperature model to obtain the current near space temperature. The application solves the problems of poor limitation, complex calculation and low accuracy in the existing near space temperature calculation method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of near space, in particular to a near space temperature calculation method. BACKGROUND

[0002] Near space refers to the airspace 20-100 km away from the ground, between the highest flight altitude of ordinary aircraft and the lowest orbit altitude of space-based satellites, which is an important part of the middle and high atmosphere of the earth, and is affected by both the convection layer and solar radiation and cosmic rays. Due to the coupling between the low ionosphere and the convection layer, the atmospheric environmental factors in the near space are complex and changeable. The complex state changes and dynamic disturbances of the near space atmospheric environment will directly affect the safety of near space vehicles, aerospace activities and wireless information transmission, etc. Therefore, the analysis and research of the atmospheric characteristics of the near space not only have important significance in the field of scientific research, but also have important value for the development and utilization of the near space.

[0003] At present, the global, regional and different height atmospheric models established in the prior art include: standard atmospheric model (USSA atmospheric model), international reference CIRA atmospheric model (CIRA, COSPAR International Reference Atmosphere), Jacchia atmospheric model, MSIS atmospheric model (MSIS, Mass Spectrometer and Incoherent Scatter) and JB atmospheric model (JB, Jacchia-Bowman), etc. used to simulate the variation characteristics of the atmospheric temperature, density and other environmental parameters in the near space.

[0004] However, these atmospheric models have certain use limitations in atmospheric temperature profile fitting. For example, the USSA atmospheric model can only roughly represent the annual average state of the atmospheric environmental factors in the middle latitude region; the environmental parameters of the CIRA atmospheric model are fixed data list at 0-120 km, with low resolution, and do not include the effects of annual variation and geomagnetic activity; the calculation height of the Jacchia atmospheric model and the JB atmospheric model does not cover the near space. In addition, these atmospheric models are not only extremely complex, but also have low accuracy in calculating the near space temperature. SUMMARY

[0005] The present application provides a near space temperature calculation method to solve the problems of poor limitation, complex calculation and low accuracy in the prior art, which comprises:

[0006] Obtain a satellite observation data set of TIMED / SABER, sample the observation data set in layers by using an information entropy sampling method to obtain a sampling data set; wherein the sampling data set includes a temperature inversion point data set, a temperature layer temperature data set, and environmental parameters related to the temperature inversion point data set;

[0007] Perform an influence factor analysis on the environmental parameters related to the temperature inversion point data set to obtain element information;

[0008] Fit a temperature inversion point function and a temperature inversion point height function based on the temperature inversion point data set and the element information;

[0009] Establish a near space temperature model of different temperature layers based on the temperature layer temperature data set, the element information, the temperature inversion point function, and the temperature inversion point height function;

[0010] Input the obtained current element information into the near space temperature model to obtain a current near space temperature.

[0011] Further, the obtaining of the sampling data set includes:

[0012] Randomly divide the observation data set by rows to obtain a plurality of different layers;

[0013] For each layer, randomly divide the observation data in each layer into a plurality of observation subsets, calculate the temperature information entropy of each observation subset, and select the sub-sample data corresponding to the temperature information entropy satisfying the preset condition;

[0014] Merge the selected sub-sample data from each layer to obtain the sampling data set.

[0015] Further, the obtaining of the element information includes: obtaining environmental parameters related to the temperature inversion point data set, performing orthogonal function decomposition on different environmental parameters in combination with temperature inversion points to obtain orthogonal function modes, calculating the correlation of each environmental parameter with the orthogonal function modes, and selecting element information based on the correlation.

[0016] Further, the element information includes: height, latitude, season, and local time.

[0017] Further, the temperature inversion point function and the temperature inversion point height function of the near space are fitted by using Fourier series and Gaussian functions.

[0018] Further, the fitting of the temperature inversion point function includes: fitting the function relationship between the temperature inversion point and the latitude, season, and local time based on the temperature inversion point data set, latitude, season, and local time.

[0019] Further, the inflection point temperature function includes a temperature function at the bottom of the near space, a temperature function at the top of the stratosphere, and a temperature function at the top of the mesosphere.

[0020] Further, the establishing of the near space temperature model includes:

[0021] fitting the stratosphere temperature function based on the temperature function at the bottom of the near space, the temperature function at the top of the stratosphere, the inflection point height function, the latitude, the first height, and the temperature layer temperature dataset;

[0022] fitting the mesosphere temperature function based on the temperature function at the top of the stratosphere, the temperature function at the top of the mesosphere, the inflection point height function, the latitude, the second height, and the temperature layer temperature dataset.

[0023] Further, the stratosphere temperature function is:

[0024] ;

[0025] wherein, Tstrat represents the temperature function at the top of the stratosphere; Tnear represents the temperature function at the bottom of the near space; Tinf represents the inflection point height function, a polynomial function of the first height within the stratosphere range and the latitude.

[0026] Further, the mesosphere temperature function is:

[0027] ;

[0028] wherein, Tmes represents the temperature function at the top of the mesosphere; Tstrat represents the temperature function at the top of the stratosphere; Tinf represents the inflection point height function, a polynomial function of the second height within the mesosphere range and the latitude.

[0029] Overall, the present application provides a near space temperature calculation method, and compared with the prior art, the present application can achieve the following beneficial effects:

[0030] Firstly, the present application introduces information entropy sampling, which can quickly filter out a subset of data with significant information and representativeness from massive data, which helps to reduce data dimension and computational complexity. At the same time, the influence factor analysis of environmental parameters related to the inflection point temperature dataset can extract the main spatial mode from the original observation data to help obtain the spatial structure and variation of the data, further mine the environmental parameter information with high correlation, and the use range is more extensive. Through the combination of the two technical features, not only can the correlation between data be efficiently captured while ensuring the representativeness of the data, but also a more reliable and effective foundation is provided for subsequent analysis and modeling; moreover, the calculation process is simpler and the accuracy of the calculation result of the near space temperature is higher. The present application solves the problems of poor limitation, complex calculation and low accuracy in the existing method.

[0031] Secondly, the present application uses Fourier method and Gaussian function to fit the inflection point temperature function and the inflection point height function, and fits the temperature profile based on the inflection point temperature function, the inflection point height function, the latitude and the height, which not only ensures high accuracy, but also avoids excessive calculation. The present application has the characteristics of high efficiency and accuracy, simple operation, and can quickly and accurately capture the key features of temperature data, providing a reliable tool for temperature analysis and prediction. In addition, the present application integrates various factors into the temperature profile fitting, which can more comprehensively consider the influence of geographical and environmental factors, and improves the prediction accuracy and interpretability of the model.

[0032] Thirdly, the present application has high spatial and temporal resolution, and contains the influence of various element information such as latitude, season, local time, etc., which has good effect on the simulation of atmospheric temperature in the near space, and provides a more reliable and effective foundation for subsequent analysis and modeling. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0034] Figure 1 is a method flow diagram of a near space temperature calculation method provided by the present application. DETAILED DESCRIPTION

[0035] In order to make the objects, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the present application with reference to the drawings and embodiments in the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0036] It should be noted that in the description of the embodiments of the present application, the terms "comprise", "contain" or any other variants thereof are intended to cover the non-exclusive inclusion, so that the method and the steps comprising a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such method and steps. Without more limitation, the element defined by the statement "comprises a" does not exclude the presence of another identical element in the method and the steps comprising the element.

[0037] The present application provides a near space temperature calculation method, as shown in Figure 1 The method comprises the following steps:

[0038] Step S101: Obtain a TIMED / SABER satellite observation data set, sample the observation data set by using an information entropy sampling method, and obtain a sampling data set.

[0039] It should be noted that the TIMED / SABER satellite observation data set is a two-dimensional array, which includes a plurality of observation data, and each observation data includes a plurality of attribute data. The attribute data can include temperature data and environment parameters related to the temperature data, and the temperature data includes inflection point temperature data and temperature layer temperature data.

[0040] Specifically, the two-dimensional array is as follows: Wherein, represents the number of attribute data of the observation data; represents the number of observation data; represents an observation data; , , , All of them represent attribute data, which can represent temperature or environment parameters or any other attribute. That is to say, the two-dimensional array includes a plurality of rows and a plurality of columns, one row represents one observation data, and one column represents one attribute.

[0041] As an embodiment, the obtaining of the sampling data set comprises the following steps:

[0042] Firstly, the observation data set is randomly divided by row number to obtain multiple different layers. Among them, the layering method can be random layering. For example, a two-dimensional array includes 10000 rows and 10 columns; if the number of layers is set to 1000, the row number is randomly divided into 1000 layers based on the number of layers, and each layer includes 10 rows; if the number of layers is set to 2000 layers, the row number is randomly divided into 2000 layers based on the number of layers, and each layer includes 5 rows.

[0043] It should be noted that the attributes of each observation data can include temperature and related environmental parameters. Among them, the environmental parameters can be extracted in each profile of the SABER observation data set, and can include parameters such as date, local time, longitude, latitude, height, atmospheric temperature, season, F10.7 index, Ap index, etc. Preferably, the environmental parameters can include height, latitude, local time, season, F10.7 index, Ap index, etc.

[0044] Then, for each layer, the observation data in each layer is randomly divided into multiple observation subsets, and the temperature information entropy of each observation subset is calculated, and the subsample data corresponding to the temperature information entropy satisfying the preset condition is selected.

[0045] For example, each layer includes 10 rows, and all observation data included in the 10 rows is randomly divided into multiple observation subsets. For example, the first layer includes 1000 observation data, and if it is randomly divided into 10 observation subsets, each observation subset includes 100 observation data, and the temperature information entropy of the 10 observation subsets is calculated to obtain 10 temperature information entropies; if it is randomly divided into 5 observation subsets, each observation subset includes 200 observation data, and the temperature information entropy of the 5 observation subsets is calculated to obtain 5 temperature information entropies.

[0046] Selecting the temperature information entropy satisfying the preset condition can be: for a layer, the corresponding temperature information entropies calculated from multiple observation subsets are arranged in descending order or ascending order, and one or more subsample data with the highest information amount is selected to quickly filter out data subsets with significant information amount and representativeness from massive data, which helps to reduce data dimension and computational complexity. For example, there are 10 observation subsets in a layer, and 10 temperature information entropies obtained from the 10 observation subsets are arranged, and the observation subset with the highest information amount is selected as the subsample data.

[0047] Finally, the selected subsample data from each layer is combined to obtain a sampling data set. For example, there are 1000 layers in total, each layer has 10 observation subsets, and one observation subset is selected from the 10 observation subsets. Then, 1000 observation subsets are selected from the 1000 layers, and the 1000 observation subsets are combined to form the final sampling data set.

[0048] The sampling data set includes a turning point temperature data set, a temperature layer temperature data set, and an environmental parameter related to the turning point temperature data set.

[0049] The turning point temperature is the temperature at which the temperature trend of each layer top in the near space atmosphere changes. The near space has an atmospheric stratosphere region (referring to the airspace 18 to 55 kilometers from the ground), an atmospheric intermediate layer region (referring to the airspace 55 to 85 kilometers from the ground), and a small part of the warming layer region (referring to the airspace 85 to 800 kilometers from the ground). The near space atmosphere has obvious layering characteristics, and the analysis of the turning point temperature is an important part of the establishment of the near space atmospheric environment model. The fitting of the turning point temperature is an important process in the modeling of the near space environment.

[0050] The turning point temperature in the application includes, from bottom to top, the near space bottom temperature (20km temperature), the stratosphere top temperature, and the intermediate layer top temperature. The temperature layer temperature includes, from bottom to top, the stratosphere temperature and the intermediate layer temperature.

[0051] Step S102: performing factor analysis on the environmental parameters related to the turning point temperature data set to obtain element information.

[0052] That is, selecting the elements with greater correlation with the turning point temperature change from the environmental parameters related to the turning point temperature.

[0053] As an example, the acquisition of the element information includes: acquiring the environmental parameters related to the turning point temperature data set, performing orthogonal function decomposition on different environmental parameters in combination with the turning point temperature to obtain orthogonal function modes, calculating the correlation between each environmental parameter and the orthogonal function mode, and selecting the element information based on the correlation.

[0054] For example, the environmental parameters that may be related to the turning point temperature are selected, including height, latitude, longitude, local time, season, F10.7 index value, F10.7 index 81-day average, Ap index value, Ap index daily average, orthogonal function decomposition is performed on the environmental parameters in combination with the turning point temperature to obtain orthogonal function modes, the correlation between each environmental parameter and the orthogonal function mode is calculated, and the environmental parameters with higher correlation are selected as independent variables for subsequent fitting, so that the element information is: height, latitude, season, and local time.

[0055] Step S103: fitting inflection temperature function and inflection height function based on inflection temperature dataset and element information.

[0056] Preferably, Fourier series and Gaussian function are used to fit inflection temperature function and inflection height function in the near space.

[0057] For each selected environmental parameter with high correlation with inflection temperature, Fourier series or Gaussian function is used to fit the functional relationship between element information and inflection temperature, and inflection temperature is linearly fitted using these relationships.

[0058] Specifically, Fourier series is characterized by: ; wherein, represents the average value of the periodic function in one period; and represents the coefficient in the Fourier series, respectively representing the amplitude of the sine wave and the cosine wave of different frequencies in f(t); is a positive integer.

[0059] Gaussian function is characterized by: ; wherein, the coefficient reflects the peak value of the function; the coefficient represents the center position of the function; and the coefficient represents the degree of dispersion of the function.

[0060] The fitting of inflection temperature function includes fitting the functional relationship between inflection temperature dataset and element information based on Fourier series and Gaussian function, and linearly fitting the inflection temperature function using the functional relationship.

[0061] As a specific embodiment, the inflection temperature function is fitted based on inflection temperature dataset, latitude, season, and local time.

[0062] The fitting of inflection temperature function includes fitting the functional relationship between inflection temperature dataset, latitude, season, and local time.

[0063] Preferably, the inflection temperature function is: ; wherein, , and are fitting coefficients; represents a polynomial function of latitude and season; represents a polynomial function of local time.

[0064] Furthermore, the inflection temperature function includes temperature function at the bottom of the near space, temperature function at the top of the stratosphere, and temperature function at the top of the intermediate layer.

[0065] The function relationship between the temperature at the bottom of the near space and the latitude, season, and local time is fitted based on the inflection point temperature dataset, latitude, season, and local time. Preferably, ; wherein, , and are fitting coefficients; represents a polynomial function of latitude and season; represents a polynomial function of local time.

[0066] As a specific embodiment, the temperature function at the bottom of the near space is Specifically,

[0067] ;

[0068] wherein, ; ; represents latitude; represents season, i.e., the day of the year; represents local time.

[0069] The function relationship between the temperature at the top of the stratosphere and the latitude, season, and local time is fitted based on the inflection point temperature dataset, latitude, season, and local time. Preferably, ; wherein, , and are fitting coefficients; represents a polynomial function of latitude and season; represents a polynomial function of local time.

[0070] As a specific embodiment, the temperature function at the top of the stratosphere is Specifically,

[0071] ;

[0072] wherein, ; ; represents latitude; represents season, i.e., the day of the year; represents local time.

[0073] The function relationship between the temperature at the top of the intermediate layer and the latitude, season, and local time is fitted based on the inflection point temperature dataset, latitude, season, and local time. Preferably, ; wherein, , and are fitting coefficients; represents a polynomial function of latitude and season; a polynomial function of local time.

[0074] As a specific embodiment, the temperature function at the top of the intermediate layer Specifically,

[0075] ;

[0076] wherein, ; ; denotes the latitude; denotes the season, i.e. the day of the year; denotes the local time.

[0077] The inflection point temperature function fitting comprises: fitting the functional relationship between the inflection point height and the element information based on the Fourier series and the Gaussian function, and linearly fitting the inflection point height function by using the functional relationship.

[0078] As an embodiment, the inflection point height function is a functional relationship between the height at the top of the stratosphere and the latitude and the season, in other words, the inflection point height function is also the height function at the top of the stratosphere , specifically,

[0079] ;

[0080] wherein, denotes the latitude; denotes the season, i.e. the day of the year.

[0081] Step S104: establishing the near space temperature model of different temperature layers based on the temperature layer temperature data set, the element information, the inflection point temperature function and the inflection point height function. That is to say, on the basis of the upper and lower inflection point temperatures and the related element information, the near space temperature of different temperature layers is fitted by using the temperature layer temperature data set, the upper and lower inflection point temperatures, the height and the latitude.

[0082] As an embodiment, the establishment of the near space temperature model comprises fitting the stratosphere temperature function and fitting the intermediate layer temperature function.

[0083] For the stratosphere temperature, the fitting is a function of the stratosphere top temperature, the near space bottom temperature, the inflection point height function, the latitude and the first height. More specifically, the stratosphere temperature function is fitted based on the near space bottom temperature function, the stratosphere top temperature function, the inflection point height function, the latitude, the first height and the temperature layer temperature data set.

[0084] As a specific embodiment, the stratosphere temperature function is:

[0085] ;

[0086] wherein, represents a stratosphere top temperature function; represents a near space bottom temperature function; represents a inflection point height function, a polynomial function of the first height and the latitude within the stratosphere range, since the inflection point height function is a polynomial function of the latitude and the season of the stratosphere top height function; thus, a polynomial function of the first height, the latitude and the season within the stratosphere range can be represented.

[0087] In the present embodiment, ; wherein, represents the first height within the stratosphere range, i.e. the current height; represents the stratosphere top height; represents the latitude.

[0088] For the mesosphere temperature, it is fitted as a function of the mesosphere top temperature, the stratosphere top temperature, the inflection point height function, the latitude and the second height. More specifically, the mesosphere temperature function is fitted based on the stratosphere top temperature function, the mesosphere top temperature function, the inflection point height function, the latitude, the second height and the temperature layer temperature dataset.

[0089] As a specific embodiment, the mesosphere temperature function is:

[0090] ;

[0091] wherein, represents a mesosphere top temperature function; represents a stratosphere top temperature function; represents a polynomial function of the second height and the latitude within the mesosphere range, since the inflection point height function is a polynomial function of the latitude and the season of the stratosphere top height function; thus a polynomial function of the second height, the latitude and the season within the mesosphere range can be represented.

[0092] In the present embodiment, ; wherein, wherein, represents the second height within the mesosphere range, i.e. the current height; represents the stratosphere top height; represents the latitude.

[0093] Step S105: input the acquired current element information into the near space temperature model to obtain the current near space temperature.

[0094] wherein, the current element information can include the current height, the latitude, the season, the local time.

[0095] As an embodiment, inputting the acquired current element information into the near space temperature model can include: judging whether the current height is in the stratosphere range or the mesosphere range of the critical space; if in the stratosphere range, inputting the current element information into the stratosphere temperature function; if in the mesosphere range, inputting the current element information into the mesosphere temperature function; and if in the inflection point height, inputting into the corresponding inflection point temperature function.

[0096] It should be noted that the inflection point temperature fitted in the present application is the temperature at the top height of the atmospheric stratosphere and the temperature at the height of 20 km. At the top of the stratosphere, the temperature changes from the trend of increasing with height of the stratosphere temperature to the trend of decreasing with height of the mesosphere temperature, so the temperature at the top of the stratosphere is the maximum temperature. At the height of 20 km, it is the interface that the temperature of the atmospheric troposphere decreases with height and the temperature of the stratosphere increases with height, so the temperature at the height of 20 km is the minimum temperature.

[0097] In summary, the near space temperature calculation method based on information entropy sampling, orthogonal function decomposition, Fourier method and Gaussian method can obtain the near space temperature of the global range without resolution limit with less calculation, has higher accuracy and stronger adaptability.

[0098] It should be noted that for the foregoing various embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0099] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0100] In the several embodiments provided by the present application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are only schematic, and the division of units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0101] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0102] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0103] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0104] Those of ordinary skill in the art can understand that all or part of the circuits in the above embodiments can be instructed by programs to complete the related hardware, and the programs can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0105] The above is only an exemplary embodiment of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art that are not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0106] The technical features of the above embodiments can be combined arbitrarily, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present disclosure.

[0107] It is to be understood that the above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading the above description. The scope of the application should therefore, be determined not with reference to the above description, but should instead be determined with reference to the appended claims, along with their full scope of equivalents. The disclosure of all articles and references referred to herein are incorporated by reference in their entirety.

Claims

1. A method for calculating near-space temperature, characterized in that: The method comprises: Obtain a TIMED / SABER satellite observation dataset, and perform stratified sampling on the observation dataset using an information entropy sampling method to obtain a sampled dataset; wherein the sampled dataset includes an inflection point temperature dataset, a temperature layer temperature dataset, and environmental parameters related to the inflection point temperature dataset; Performing an influencing factor analysis on environmental parameters related to the inflection point temperature dataset to obtain factor information; An inflection point temperature function and an inflection point height function are obtained by fitting based on the inflection point temperature data set and the element information; the inflection point temperature function includes a temperature function at the bottom of the near space, a temperature function at the top of the stratosphere, and a temperature function at the top of the mesosphere; Establishing near-space temperature models of different temperature layers based on the temperature layer temperature dataset, the element information, the inflection point temperature function, and the inflection point height function; establishing the near-space temperature model includes: fitting the stratospheric temperature function based on the near-space bottom temperature function, the stratosphere top temperature function, the inflection point height function, latitude, a first height, and the temperature layer temperature dataset; and fitting the mesosphere temperature function based on the stratosphere top temperature function, the mesosphere top temperature function, the inflection point height function, latitude, a second height, and the temperature layer temperature dataset; The stratospheric temperature function for: ;in, represents the temperature function of the stratosphere; represents the temperature function near the bottom of the space; a polynomial function representing the function of the inflection point height, the first altitude within the stratosphere, and latitude; The middle layer temperature function for: ;in, represents the temperature function at the top of the mesosphere; represents the temperature function at the top of the stratosphere; a polynomial function representing the function of the inflection point height, the second height within the mesosphere, and latitude; The acquired current element information is input into the adjacent space temperature model to obtain the current adjacent space temperature.

2. A near-space temperature calculation method according to claim 1, characterized in that: The obtaining of the sample data set includes: Randomly dividing the observation data set according to the number of rows to obtain multiple different layers; For each layer, the observation data in each layer is randomly divided into multiple observation subsets, and the temperature information entropy of each observation subset is calculated, and the subsample data corresponding to the temperature information entropy that meets the preset conditions is selected; The subsample data selected from each stratum are merged to obtain the sampling data set.

3. The method for calculating near-space temperature according to claim 1, wherein: The acquisition of the element information includes: acquiring environmental parameters related to the inflection point temperature data set, performing orthogonal function decomposition on different environmental parameters in combination with the inflection point temperature to obtain orthogonal function modes, calculating the correlation between each environmental parameter and the orthogonal function mode, and selecting element information based on the correlation.

4. The method for calculating near-space temperature according to claim 1, wherein: The element information includes: altitude, latitude, season, and local time.

5. The method for calculating near-space temperature according to claim 1, wherein: The inflection point temperature function and the inflection point height function in the near space are fitted using Fourier series and Gaussian function.

6. A method for calculating near-space temperature according to claim 4, characterized in that: The fitting of the inflection point temperature function includes: obtaining a functional relationship between the inflection point temperature and the latitude, season, and local time by fitting based on the inflection point temperature data set, latitude, season, and local time.

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